Noradrenergic ensemble-based modulation of cognition over multiple timescales

Accepted Manuscript Research report Noradrenergic ensemble-based modulation of cognition over multiple timescales Nelson K.B. Totah, Nikos K. Logothetis, Oxana Eschenko PII: DOI: Reference:

S0006-8993(18)30657-7 https://doi.org/10.1016/j.brainres.2018.12.031 BRES 46080

To appear in:

Brain Research

Received Date: Revised Date: Accepted Date:

6 September 2017 11 December 2018 21 December 2018

Please cite this article as: N.K.B. Totah, N.K. Logothetis, O. Eschenko, Noradrenergic ensemble-based modulation of cognition over multiple timescales, Brain Research (2018), doi: https://doi.org/10.1016/j.brainres.2018.12.031

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Title: Noradrenergic ensemble-based modulation of cognition over multiple timescales

Authors: Nelson K. B. Totah, Nikos K. Logothetis, Oxana Eschenko Affiliations: Max Planck Institute for Biological Cybernetics, Tübingen, Germany Corresponding author: Nelson Totah ([email protected]) or Oxana Eschenko ([email protected])

Abstract: Cognition fluctuates over relatively faster and slower timescales. This is enabled by dynamic interactions among cortical neurons over similarly diverse temporal and spatial scales. Fast and slow cognitive processes, such as reorienting to surprising stimuli or using experience to develop a behavioral strategy, are also sensitive to neuromodulation by the diffusely-projecting brainstem noradrenergic nucleus, Locus Coeruleus. However, while a dynamic, multi-scale cortical ensemble code influences cognition over multiple timescales, it is unknown to what extent LC neuronal activity operates in this regime. An ensemble code within the LC may permit an interface with cortical ensembles allowing noradrenergic modulation of fast and slow cognitive processes. Alternatively, given that LC neurons are thought to spike synchronously, there may be a mismatch between LC and cortical neuronal codes that constrains how the noradrenergic system can influence cognition. We review new evidence that clearly demonstrates cell type-specific ensemble activity within LC occurring over a range of behaviorally-relevant timescales. We also review recent studies demonstrating that sub-sets of LC neurons modulate specific forebrain targets to control behavior. A critical target for future research is to study the temporal dynamics of projection-specific LC ensembles, their interactions with cortical networks, and the relevance of multi-scale coerular-cortical dynamics to behaviors over various timescales.

Main text: Cognitive functions, such as attention, perception, working memory, decision making, and action selection can fluctuate and - from moment to moment - affect what we think about, what we perceive, and what we choose to do. For example, our focus of attention can shift rapidly in the case of an unexpected fire alarm interrupting an ongoing task. Our perception of a sensory stimulus can change during perceptual alternations, for example in binocular rivalry. Our ability to hold a stimulus “in mind” can fluctuate and such mind-wandering can alter the contents of working memory. Furthermore, on a choice-by-choice basis, we can explore new possibilities for actions and ignore old decision-making strategies. Dynamic behaviors over diverse timescales are supported by cortical neurons interacting over multiple spatial scales (e.g., neurons, “columns”, regions) and temporal scales (e.g., millisecond to multi-second coordination of neurons’ relative spike timings) (Buzsáki, 2010; Panzeri et al., 2015; Tognoli and Kelso, 2014). In addition to a reliance on multi-scale cortical ensemble activity, we will review evidence demonstrating that moment-to-moment cognitive and behavioral fluctuations also correlate with the activity of neurons in the brainstem nucleus locus coeruleus (LC) and with the presumably broad release of norepinephrine (NE) into the majority of the forebrain via widespread LC axonal branching (Mitchell et al., 1994; Mundorf et al., 2008; Room et al., 1981; Schwarz et al., 2015). An emerging research question concerns whether or not LC-NE neurons might have the capacity for multi-scale ensemble activity that is similar to the neuronal activity patterns in the cortex. Why is a temporally-dynamic LC ensemble code important? Consider that cortical ensemble activity occurring over multiple timescales enables the same set of cortical neurons to represent both brief sensory stimuli (milliseconds time scale), as well as slower stimulus-guided choices (approximately one second time scale) (Runyan et al.,

2017). Thus, more information (sensory and choice) is represented with fewer neurons, which may allow the brain to perform more functions with limited resources. The LC contains relatively few neurons (~10,000 in human and ~1,600 in rat, (Kebschull et al., 2016; Loughlin et al., 1986a; Sharma et al., 2010; Swanson, 1976) and may, therefore, need to use an ensemble code to fully utilize noradrenergic neuromodulation given these cell count limitations. Such a coding scheme could be a critical contributor to the multi-scale brain dynamics that underlie temporally-diverse behaviors and cognitive functions. Cognitive and behavioral fluctuations correlate with LC-NE neuronal activity Relatively brief LC-NE neuronal activation over a timescale of a 50 to 100 milliseconds occurs when an organism suddenly reorients away from its current interests and attends to something novel (Bouret and Sara, 2004; Foote et al., 1980; Grant et al., 1988). This LC population activation contributes to the reorientation of attention by releasing NE in the forebrain, which adjusts cortico-thalamic thresholds for responding to sensory stimuli and tuning to particular stimulus features (Bouret and Sara, 2002; Edeline et al., 2011; Holdefer and Jacobs, 1994; Waterhouse et al., 1998). Simultaneous NE release in brainstem regions controls head and eye movements; thus, NE may promote redirection of sensory organs to new sensory stimuli (Foote et al., 1991; Szabadi, 2013). Given the broad connectivity of the LC, its population activation can therefore effect perceptual re-orientation by simultaneously influencing cortico-thalamic activity related to attention and perception, as well as brainstem activity related to actions that re-orient sensory organs. LC activity also varies over a few seconds during the delay period of working memory tasks (Kahneman and Beatty, 1966; Unsworth and Robison, 2014) when pupil size is used as a readout of LC-NE neuronal activity (see Box 1 for discussion on using pupil size as a

readout of LC-NE modulation during cognitive task performance). Noradrenergic neuromodulation may be implicated in momentary fluctuations in mental content (i.e., mindwandering and distraction) during working memory tasks over a scale of seconds during the delay period in which the stimulus is absent and must be maintained in mind. Under conditions of distractibility, for example during stress, increased NE release stimulates low affinity alpha1 and beta noradrenergic receptors in the prefrontal cortex, which disrupts the sustained spiking in prefrontal cortex during delay period that is thought to maintain a representation of the stimulus that is resistant to distraction and mind-wandering (Arnsten, 2011; Robbins and Arnsten, 2009). Mind-wandering during working memory and sustained attention tasks is also associated with a sustained (seconds long) pupil dilation (Lenartowicz et al., 2013; Mittner et al., 2016; Unsworth and Robison, 2018; van den Brink et al., 2016). Longer duration (tens of seconds to minutes) changes in pupil size and cortical NE release correlate with fluctuations in behavioral strategy (Browning et al., 2015; Jepma and Nieuwenhuis, 2011; Nassar et al., 2012; Tervo et al., 2014; Urai et al., 2017). For instance, studies of pupil size in humans, single unit recordings in rats and monkeys, microdialysis measurements of NE in the rat forebrain, and pharmacological manipulation of NE neurotransmission have demonstrated that LC-NE activity can remain elevated for minutes or over multiple trials lasting around tens of seconds in response to environmental novelty and rule changes which lead to uncertainty and adjustment of behavioral strategy (Aston-Jones et al., 1997; Browning et al., 2015; Dalley et al., 2001; Devauges and Sara, 1990; Lapiz and Morilak, 2006; Mingote et al., 2004; Nassar et al., 2012; Pajkossy et al., 2018; Takeuchi et al., 2016; Tervo et al., 2014; Totah et al., 2015).

Activation of the LC is also associated with the fluctuations of perception that occur on the timescale of seconds during perceptual alternation tasks. In these tasks, shifts in perception occur in less than one second and the new percept can last a few seconds to tens of seconds. Pupil dilation occurs before the perceptual change is reported and correlates with the duration that the new perception will last (Einhäuser et al., 2008; Kloosterman et al., 2015). Perceptual alternations have also been shown to depend on the LC-NE system, as opposed to another neuromodulatory system (i.e., acetylcholine) using pharmacological manipulations in humans (Pfeffer et al., 2018). Learning, which occurs over minutes or hours is also dependent upon LC-NE activity. Perceptual learning, which is a gradual change in perception across trials that allows one to hear quieter sounds or discriminate visual stimuli better with practice, is affected by sensory cortex NE neurotransmission (Roelfsema et al., 2010; Seitz and Watanabe, 2005; Vinera et al., 2015). LC neuronal firing rate can also remain elevated for approximately a half-hour after learning (Eschenko and Sara, 2008). States of vigilance, arousal, and behavioral task engagement that vary over many minutes to hours are also associated with similarly timed elevations in LC firing rate in rats and monkeys (Aston-Jones and Bloom, 1981a; Foote et al., 1980; Rajkowski et al., 1994; Usher et al., 1999). LC-NE activity is also associated with circadian fluctuations in wakefulness, which occur over hours (Aston-Jones and Bloom, 1981a; Aston-Jones et al., 2001). These various timescales of LC activity, which span from less than a second and up to many hours, appear to also interact. For example, when task contingencies change, subsecond “phasic” bursts of stimulus-evoked LC spiking become more prominent over multiple trials that unfold over minutes whereas spontaneous (non-evoked) “tonic” spiking becomes

less prominent (Aston-Jones et al., 1997; Bouret and Sara, 2004; Sara and Segal, 1991). In awake, behaving rats and monkeys, tonic spiking can fluctuate from lower (0.5 to 2 Hz) to higher (3 to 8 Hz) ranges (Aston-Jones and Bloom, 1981a; 1981b; Aston-Jones et al., 1994; Foote et al., 1980; Vankov et al., 1995). Phasic firing consists of a burst of spikes at approximately 5 - 15 Hz in behaving rats and monkeys (Aston-Jones and Bloom, 1981b; Bouret and Richmond, 2009; Clayton et al., 2004; Foote et al., 1980; Rajkowski et al., 2004; Vankov et al., 1995). The overlap between the timescales of cognition and LC neuronal activity should also be considered from a post-synaptic perspective. The timing of NE clearance from the extracellular space could vary from seconds to many minutes. Recent work on the dopaminergic neuromodulatory system has shown that phasic dopamine neuron activation results in a sub-second elevation of dopamine release, which is not cleared for tens of minutes due to an impulse-induced dopamine neuron down-regulation of the dopamine reuptake transporter (Lohani et al., 2018). The authors propose that each of these two timescales of dopamine release might affect distinct aspects of cognition; in particular, sub-second duration, stimulus-evoked phasic spiking may contribute to learning stimulus-outcome associations and/or response vigor, while tonic spiking elevated for minutes to hours after a behavioral task may affect memory consolidation. Notably, in the LC-NE system, both sub-second and minutes/hours elevations of activity have also been implicated in cognitive processes similar to those involving dopamine. Extra-synaptic NE content remains elevated for a half-hour after learning and during that same period, NE neurotransmission is critical for memory consolidation (Sara, 2009; Tronel et al., 2004). On the other hand, phasic, sub-second activation of LC-NE neurons occurs during uncertainty-driven learning, presumably by

accelerating the updating of stimulus value and also by promoting exploration of new options to obtain reward, which occurs by reorienting attention to other stimuli that might be potentially reward-associated and attempting alternative actions that may provide reward (Bouret and Sara, 2004; Browning et al., 2015; Courville et al., 2006; Nasser et al., 2017). Following the paradigm of Lohani, et al. (2018) to test the molecular basis of long-lasting NE neurotransmission after learning (e.g., norepinephrine transporter internalization or increased dopamine--hydroxylase mediated NE synthesis and vesicular packaging) may be a critical step in furthering our understanding of the time course of noradrenergic neuromodulation. In summary, cognitive processes and LC activity can co-vary from milliseconds to minutes and hours and these dynamics may allow the LC-NE system to influence momentary fluctuations in what we care about, what we perceive, and what actions we choose. In Table 1, we summarize the common temporal structure between LC activity patterns and some cognitive processes commonly tested in humans, non-human primates, and rodents.

Cognitive processes Reorienting attention Detecting targets Response selection Perceptual alternations Working memory Behavioral strategy Behavioral task engagement Perceptual learning Wakefulness

Timescales < 200 milliseconds < 100 milliseconds < 200 milliseconds 1 to 10 seconds 1 to 10 seconds seconds (single trial) to minutes (multiple trials) minutes to hours minutes to hours hours (circadian)

LC activity Timescales Tonic mode Seconds, minutes, hours, circadian Phasic mode 100 – 200 milliseconds Table 1. Similarity between timescales of LC neuronal activity and those intrinsic to behavioral and cognitive processes. Time durations are general estimates.

Many fluctuations in mental state – regardless of being fast or slow – appear to be mediated by non-specific release of NE throughout the forebrain. This non-specificity arises from anatomical and neurochemical studies demonstrating that the axons of individual LC neurons branch widely to innervate distant forebrain regions where their terminals release NE, which can spread up to ~100 um in the rodent cortex (Mitchell et al., 1994; Mundorf et al., 2008; Room et al., 1981; Schwarz et al., 2015). Volume transmission of NE also occurs in primate cortex although the spread in their larger brain is unknown. Truly “global” noradrenergic neuromodulation may indeed be crucial for some NE-associated cognitive functions, such as reorienting, which involves coordinated modulation of multiple forebrain regions and brainstem regions to reallocate attention, perception, and movement of sensory organs. However, some LC activity-dependent cognitive and behavioral processes might rely on more circumscribed modulation of a sub-set of forebrain circuits by NE (Chandler et al., 2014; Hirschberg et al., 2017; Uematsu et al., 2017; Wagatsuma et al., 2018); such targeted

neuromodulation is of growing interest to researchers studying the noradrenergic system. In summary, attention, perception, working memory, and choice of action can vary over time and it is likely that NE plays a role in these moment-to-moment cognitive fluctuations. However, a nascent area of research seeks to identify the degree to which noradrenergic neuromodulation is global or targeted and the role of targeted neuromodulation in cognitive functions that fluctuate over a wide span of timescales.

Box 1. Pupil size as a readout of LC-NE modulation during cognitive task performance Human studies have used measurements of pupil size to assess LC-NE neuronal activity during cognitive task performance (Browning et al., 2015; Einhäuser et al., 2008; Jepma and Nieuwenhuis, 2011; Kahneman and Beatty, 1966; Kloosterman et al., 2015; Lenartowicz et al., 2013; Nassar et al., 2012; Pajkossy et al., 2018; Unsworth and Robison, 2018; 2014; Urai et al., 2017; van den Brink et al., 2016). LC single unit spiking and 2-photon calcium imaging of LC-NE axons in the forebrain both correlate with pupil size changes (Joshi et al., 2016; Rajkowski et al., 1994; Reimer et al., 2016; Varazzani et al., 2015). Moreover, pupil size may report NE release, given that 2-photon calcium imaging of LC-NE axons in the forebrain correlates with pupil size (Reimer et al., 2016). It is important to note that pupil size may also be a correlate of activity in other neuromodulatory systems, such as the ventral tegmental area and basal forebrain (de Gee et al., 2017; Reimer et al., 2016). While pupil size may be correlated with the activity of other neuromodulatory systems, LC neurons can directly influence the preganglionic cells of the autonomic nervous system that control the smooth muscles in the iris; specifically, the preganglionic cells in the intermediolateral (IML) cell column of the spinal cord (sympathetic) and preganglionic cells in the Edinger-Westphal nucleus (EWN) in the brainstem (parasympathetic). These preganglionic cells affect the postganglionic cells in the periphery that control the smooth muscle. LC neurons can counteract pupil constriction because they innervate the EWN, whereas they drive pupil dilation by sparse projections (or denser projections from the sub-coeruleus) to the IML (Breen et al., 1983; Bruinstroop et al., 2012; Fleetwood-Walker and Coote, 1981; Fritschy and Grzanna, 1990; Y. Liu et al., 2017; Romagnano et al., 1991; Westlund and Coulter, 1980). While it is clear that the LC is closely integrated with the autonomic nervous system, allowing the pupil to be used as a readout of noradrenergic neuromodulation during cognitive tasks, the delay between iris muscle activations and LC activity highlight the importance of direct LC neuron recordings during cognitive tasks. Despite the connectivity between the LC and the autonomic nervous system, the extent to which the pupil may be used to purely index LC-NE system activity versus the activity of other neuromodulatory systems is indeterminate. Animal models are essential for determining the contributions of different neuromodulatory systems using invasive recordings combined with tracing and inactivating structures.

The emerging view on targeted NE neuromodulation by LC cell ensembles The general consensus in the field has been that the noradrenergic system is an evolutionarily ancient collection of cells, which serve the most basic survival functions by sending a redundant and global “pulse” to much of the forebrain (Aston-Jones and Cohen, 2005; Bouret and Sara, 2005; Corbetta et al., 2008; McGinley et al., 2015b). For example, fascinating work has recently revealed specific circuit inputs to the LC, yet the findings are still conveyed in terms of a noradrenergic output that is global for controlling arousal (see Figure 4G in Yackle, et al. 2017). An analogy may be drawn between the brainstem LC nucleus and the heart, both of which are ancient and evolutionarily-conserved, in that both structures need to widely distribute a substance (norepinephrine or blood, respectively) from a single distribution location using a limited number of cells. The LC-NE cells are localized to a small nucleus consisting of approximately 47x103 LC cells out of 8x1010 cells in the human brain (Azevedo et al., 2009; Haug, 1986; Herculano-Houzel, 2009; Sharma et al., 2010), while the human heart consists of 3.2x109 myocardial cells out of 3x1013 cells in the human body (Bergmann et al., 2015; Sender et al., 2016). This analogy is also supported by the fact that both LC neurons and myocardial cells have in common some synchrony-promoting physiological properties, such as gap junctions and prominent shared inputs across cells (Alvarez et al., 2002; Ishimatsu and Williams, 1996; Keener, 1990; Kim and Fishman, 2013; Rash et al., 2007; Van Bockstaele et al., 2004; Zaglia and Mongillo, 2017). Global distribution of norepinephrine (or blood) is achieved by synchronized and concerted activity across large numbers of cells (and, in the case of the heart, with phase delays between populations in different compartments of the heart). We define neuronal synchrony as any coincidental spike timing or correlated variability of spike rate occurring over any time range (from sub-

millisecond to hours or longer). In summary, synchronous activity of broadly-projecting noradrenergic neurons for controlling brain-wide state (e.g., arousal) appear to be an evolutionarily-conserved organization of this neuromodulatory system across species. Although this function of the LC appears to be conserved, there are neurochemical and neuroanatomical differences across species that might affect LC neuronal synchrony and function. In general, a broadly-projecting noradrenergic pontine nucleus is present in mammals and birds, but not amphibians (Dubé and Parent, 1981; Tohyama et al., 1976). In the laboratory rat (Rattus norvegicus domesticus) and mouse (Mus musculus) and other members of the Order Rodentia, as well as numerous primates and humans, the LC is a densely compacted collection of NE cells adjacent to the 4th ventricle in the pontine periventricular grey matter (Dahlström and Fuxe, 1964; Felten et al., 1974; Garver and Sladek, 1975; German and Bowden, 1975; Grzanna and Molliver, 1980; Hubbard and Di Carlo, 1973; Kruger et al., 2012; Nobin and Björklund, 1973; Olszewski and Baxter, 1954). However, other members of the Order Rodentia instead have a diffuse collection of NE cells adjacent to the 4th ventricle (Sweigers et al., 2017); moreover, in some Rodentia species this less compact LC is located ventrolaterally in the pontine periventricular grey matter away from the 4th ventricle (Bhagwandin et al., 2008; Da Silva et al., 2006; Limacher et al., 2008). On the other hand, species in the Order Carnivora (e.g., cats and dogs), have a diffuse collection of NE cells within the pontine periventricular grey matter (Chu and Bloom, 1974; Ishikawa et al., 1975; Jones and Moore, 1974; Maeda et al., 1973) that are intermingled with serotonin neurons (Léger and Hernandez-Nicaise, 1980; Léger et al., 1979). Other mammals, such as those in the Orders Artiodactyla (e.g., giraffe, onyx, pig, hippo, and deer), Proboscidea (e.g., elephants), Didelphimorphia (e.g., Opossum), Lagomorpha (e.g., rabbit), or Hydracoidea (e.g.,

rock hyrax) also have NE cells arranged in a diffuse collection (Blessing et al., 1978; Bux et al., 2010; Crutcher et al., 1978; Davimes et al., 2017; Gravett et al., 2009; Maseko et al., 2013), while the Orders Macroscelidea (e.g., elephant shrew) and aquatic cetaceans that evolved from Artiodactyla have a compact LC resembling laboratory rodents (Manger et al., 2003; Pieters et al., 2010). The Tree Pangolin (Manis tricuspis) in the Order Pholidota lacks an LC altogether, but has an increased density of NE cells in the pontine tegmentum that extend dendrites into the pontine periventricular grey matter by the floor of the 4th ventricle and receive the typical neurochemical inputs of LC neurons, such as orexin and serotonin (Imam et al., 2018). While the LC organization of many Rodentia species – including commonly-used laboratory rodents – and primate species have a compact “core”, primates also have a diffuse cellular surround (Calvey et al., 2015). Intriguingly, the combined compact core and diffuse surround cellular organization of the LC in some species of bats (Order: Chiropatera) resembles the primate LC more than the laboratory rat or mouse (Dell et al., 2010). For an overview of LC structure across mammals, we suggest reading Dell, et al. (2010). These various structural differences may affect physiological mechanisms that underlie synchrony, such as gap junctions. While there have not been any studies on this topic (to our knowledge), the effects of LC structure on synchrony and the functional implications on an LC ensemble code are an important topic for future study and will hopefully encourage the field to study other animal models. The notion that LC-NE neurons respond as a synchronized population to globally distribute NE is based on considerable physiological evidence from (i) intracellular membrane potential recordings, (ii) extracellular local field potential recordings at different sites within the LC nucleus, (iii) extracellular spike recordings of multi-unit activity, and (iiii) earlier studies of

single unit pair correlations (Alvarez et al., 2002; Aston-Jones and Bloom, 1981b; 1981a; Chen and Sara, 2007; Finlayson and Marshall, 1988; Ishimatsu and Williams, 1996; Usher et al., 1999). The following quotes express an historical perspective of synchrony in the LC: “The LC response [to foot shock stimuli] was characterized… by a remarkable homogeneity, with isolated single units and the population of surrounding neighbors, recorded as multiunit activity, showing similar patterns of response to foot shock.” (Chen and Sara, 2007) “The synchronization of activation of many locus coeruleus neurons could result in almost simultaneous release of neurotransmitter in the widespread target areas of locus coeruleus projections.” (Finlayson and Marshall, 1988) “Physiological studies suggest that the majority of cells in the…LC are activated simultaneously. It is likely that any one LC cell innervates all layers of a large region of cortex…. It is thus probable that LC activity results in the release of NE in the majority of the cortex simultaneously.” (Loughlin et al., 1982) “LC neurons exhibit uniform, concerted activity [so] it is unlikely that this nucleus is engaged in complex processing...of information. Rather, the LC may...widely distribute a uniform message over its divergent efferents.” (Aston-Jones et al., 1986) “Nearly all LC neurons are phasically activated in a similar manner by meaningful stimuli…These neurons give rise to a uniquely ubiquitous efferent network of fibers that simultaneously innervates nearly the entire neuraxis. The synchronous phasic activation of LC neurons presumably elicits elevated NE levels throughout the CNS.” (Aston-Jones et al., 1997) “[The activity patterns of] LC neurons are broadcast globally across functionally diverse regions of the brain.” (Berridge and Waterhouse, 2003) The viewpoint that the LC-NE system is a homogenous one originated, not only from physiological evidence for population synchrony, but also from the fact that the LC nucleus consists of predominantly (90%) noradrenergic neurons and does not contain local glutamate or GABA neurons that could sculpt more heterogeneous population activity patterns (Corteen

et al., 2011; Swanson, 1976). However, the peri-LC zone where LC neurons collectively ramify their dendrites may have interneurons that serve as a peri-dendritic pool (Aston-Jones et al., 2004). This local inhibitory pool may regulate afferent inputs and could even regulate input to subsets of LC neurons in order to dampen synchrony. Gap junctions and shared synaptic inputs are considered additional factors driving correlated activity of LC neurons (Alvarez et al., 2002; Aston-Jones et al., 1986; Ishimatsu and Williams, 1996; Rash et al., 2007; Van Bockstaele et al., 2004). LC gap junctions have been reported in both neonatal and adult rats (Alvarez et al., 2002; Ishimatsu and Williams, 1996). An anatomical study demonstrated the presence of gap junctions in the adult rat LC, estimating 20 and 22 connexin-36 gap junctions between LC neurons per 100,000 um2 in the developing and adult rat, respectively (Rash et al., 2007). Immunoblot analysis of protein levels also suggests that the amount of connexin-36 is similar at post-natal days 7, 14, 21, and 60 in rats (Van Bockstaele et al., 2004). However, it should be noted that gap junctions can also desynchronize a neuronal population (Connors, 2017). Despite the predominate views on LC synchrony, there have also been some in vitro studies demonstrating that synchrony can be quite low between LC neurons or may fluctuate as afferent input to the LC changes (Christie et al., 1989; Rancic et al., 2018; van den Pol et al., 2002). It is also likely that the degree of synchrony varies across behavioral/cognitive states (Usher et al., 1999). Recent studies have further reinforced this alternative view by presenting evidence for a target-specific, ensemble-based LC neuronal code (Chandler et al., 2014; Hirschberg et al., 2017; Kebschull et al., 2016; Uematsu et al., 2017; Wagatsuma et al., 2018).

Careful review of early studies that used antidromic stimulation of LC single unit spikes to infer projection targets indicate that the notion of broad projections may be over-generalized. For example, when comparing the antidromic responses of rat LC single units to stimulation of the visual cortex (VC), frontal cortex (FC), hippocampus (HP), and cerebellum (CR), antidromic responses were driven from stimulation of only a single region in 45% of the units (11 – HP, 18 – FC, 3 – VC, 1 – CR out of N = 74 units recorded) (Nakamura, 1977). Another study that tested antidromic responses from 10 cortical sites in the rat brain found that 23% of LC units responded to stimulation of only 1 to 2 cortical sites (Sakaguchi and Nakamura, 1987). Consistent with these early experiments, by stimulating 15 cortical and sub-cortical sites, we have recently revealed that the average number of projection targets of a rat LC single unit was 2.0±0.3 (Totah et al., 2018). LC neuron target-specificity has been further supported by recent work using Multiplexed Analysis of Projections by Sequencing (MAPseq), a novel method for studying neuronal connectivity (Kebschull et al., 2016). Whereas anterograde tracers can indicate the regions to which the LC projects, they cannot indicate if individual neurons project to the same brain regions, different brain regions, or share only some overlapping projection targets. MAPseq effectively labeled all ~2000 neurons in the mouse LC and provided a density measure for whole-brain projection patterns for each LC neuron. Zador and colleagues reported that the average number of primary projection targets per mouse LC neuron to be 1.6±0.8 (although they may very weakly innervate other forebrain sites). MAPseq will surely be critical to advancing our understanding of the degree of specificity in all diffusely projecting neuromodulatory nuclei. Recent retrograde tracing studies have also revealed target specificity of rat LC neurons using 10,000 Da molecular weight tracers (Chandler et al., 2014; 2013).

Prior retrograde tracing studies have used lower molecular weight tracers that can pass through gap junctions and this may have labeled not only LC neurons that projected to the injected forebrain region, but also labeled LC neurons that do not project to that region by allowing the label to diffuse through gap junctions (Chandler et al., 2013). Although such retrograde tracing is dependent on injecting target sites and cannot provide a whole-brain picture, the work by Chandler, Waterhouse and colleagues has made a key advance in understanding LC target-specificity by showing that LC neurons projecting to motor cortex rarely send projections to frontal associative cortex (e.g., projections to motor cortex cooccurred with projections to orbitofrontal, medial prefrontal, and anterior cingulate cortex in only 5.2%, 3.9%, and 6.3% of all retrograde labeled LC neurons, respectively) (Chandler et al., 2014). Another recent study combined anterograde and retrograde viral labeling to demonstrate target specificity of rat LC neurons (Uematsu et al., 2017). A retrograde canine adenoviral vector (CAV2) for transfecting neurons with Cre-recombinase was injected into the basolateral amygdala (BLA) or the infralimbic division of the medial prefrontal cortex (IL). Following that injection, the LC was injected with an anterograde adenovirus containing a Credependent synaptophysin (a protein expressed at neurotransmitter release sites) with a fluorescent label (mCherry). This method allows the Cre-dependent synaptophysin+mCherry to only be expressed in those LC cells that were retrogradely transfected with the Crerecombinase. Following the synaptophysin+mCherry expression in those LC neurons, the mCherry florescence can be observed in their terminals. After injecting CAV2 into the BLA, the axons of retrograde labeled BLA-projecting LC neurons will be visible with the red fluorophore (mCherry) throughout the forebrain. Johansen and colleagues reported that many BLAprojecting neurons also projected to other amygdala sub-regions and avoided most other brain

regions, including IL, whereas IL-projecting neurons tended to project to other medial prefrontal regions, but avoided many other brain regions, including the amygdala (Uematsu et al., 2017). Intriguingly, Uematsu, et al. (2017) found that LC neurons that project to different forebrain targets were engaged during different behaviors (i.e., fear learning and fear extinction) and optogenetic activation and inactivation experiments demonstrated that subpopulations of BLA- and IL-projecting LC neurons were involved in fear learning and extinction, respectively. Collectively, recent studies have provided the first evidence that the LC may be organized as sub-sets of neurons that act as separate ensembles to modulate specific brain circuits. Critically, however, if the activity of LC neurons is largely synchronous, then in spite of targeted projections diffuse NE modulation in the forebrain will still occur. Therefore, a necessary step in resolving the question of an LC ensemble code is to record a large population of LC single units simultaneously and assess if most LC spiking is en masse or if sub-sets of LC neurons can spike synchronously with other members of their ensemble. If LC ensembles exist, it will be imperative to characterize their temporal dynamics and relate them to temporal fluctuations in cognition and behavior (Table 1). Large-scale population recordings reveal LC cell ensembles We overcame the technical challenges that have prevented simultaneous recordings of numerous LC single units in rats by implanting high-density, 32 channel multi-electrode recording arrays contained on a single, 15 um thick (in coronal plane) silicone shank (Figure 1), which minimized tissue damage (Totah et al., 2018). Typically, for extracellular recordings, a single electrode or small bundle of two, four, or sometimes eight wire electrodes are implanted in the LC so as to avoid damage of the small nucleus (Aghajanian et al., 1977; Arakawa et al., 1997; Aston-Jones et al., 1997; 1994; Aston-Jones and Bloom, 1981b; 1981a;

Aston-Jones et al., 1985; 1980; Bari and Aston-Jones, 2012; Bouret and Richmond, 2015; Bouret and Sara, 2004; Cedarbaum and Aghajanian, 1978a; Chen and Sara, 2007; Chiang and Aston-Jones, 1993; Clayton et al., 2004; Curtis et al., 1997; Ennis and Aston-Jones, 1986a; Eschenko et al., 2011; Eschenko and Sara, 2008; Faiers and Mogenson, 1976; Foote et al., 1980; Grant et al., 1988; Hervé-Minvielle and Sara, 1995; Hirata and Aston-Jones, 1994; Jodo et al., 1998; Joshi et al., 2016; Kalwani et al., 2014; Mana and Grace, 1997; Manohar et al., 2017; Marzo et al., 2014; Nakamura, 1977; Neves et al., 2018; Rajkowski et al., 2004; 1994; Sakaguchi and Nakamura, 1987; Sara and Hervé-Minvielle, 1995; Swick et al., 1994; Takeuchi et al., 2016; Uematsu et al., 2017; Usher et al., 1999; Valentino et al., 1991; Vankov et al., 1995; Varazzani et al., 2015). (We have cited, to our knowledge, the majority of studies using such methods; note that many of these studies were in anesthetized animals or in vitro slice recordings, except for recordings from awake rats by Aston-Jones and Bloom 1981a, 1981b; Bouret and Sara 2004; Curtis et al. 1997; Foote,et al. 1989; Eschenko et al. 2008; Herve-Minvielle and Sara 1995; Manohar et al. 2017; Takeuchi et al. 2016; Uematsu et al. 2017; and Vankov et al. 1995 or recordings from awake primates by Aston-Jones et al. 1994, 1997; Bouret and Richmond 2015; Clayton et al. 2004; Foote et al. 1980; Grant et al. 1988; Joshi et al. 2016; Kalwani et al. 2014; Swick et al. 1994; Usher et al. 1999; and Varazzani et al. 2015). However, in all of these studies, the densely packed soma in the LC and the limited number of electrodes typically presented a challenge to isolating spikes of single units, yielding either multi-unit spiking or recording only one to two single units. A small number of studies in the anesthetized rat, awake rat, and awake monkey have isolated spikes of multiple single units on the same (or nearby) electrodes with the latter permitting examination of synchrony among approximately 20 LC cell pairs (Curtis et al., 2012; Usher et al., 1999; Watabe, 1980).

In order to assess the potential for an LC ensemble code, much larger samples of simultaneously recorded single units are necessary. In recently published work, we recorded 234 units from 12 rats under urethane anesthesia and analyzed 3,164 LC single unit pairs (Totah et al., 2018). For detailed methods, including how the data were collected and analyzed, we refer the reader to Totah, et al. (2018). In this review, we briefly review data collection and analysis methods and, without any new analyses, we reproduce plots from the data and figures in Totah, et al. (2018) in order to help the reader visualize our findings in relation to the perspectives and ideas presented here. Briefly, our data were collected after confirming that the entire recording array (spanning ~300 um in the dorsal-ventral axis) was positioned within the LC core by documenting total inhibition of spiking (the 600 Hz high pass signal) on each contact of the recording array after systemic (intra-peritoneal) administration of clonidine. Clonidine is an agonist for the auto-inhibitory noradrenergic alpha2 receptor on LC neurons; thus, clonidine has been routinely used to disambiguate LC spiking activity and spiking from adjacent structures because those structures do not contain alpha2 receptors (Lee et al., 1998; McCune et al., 1993). The average spontaneous firing rate of LC single units under urethane anesthesia was 0.89 Hz, which is similar to the spontaneous average firing rate of ~1.0 Hz in the awake rat and monkey (Aston-Jones and Bloom, 1981a; Bari and Aston-Jones, 2012; Bouret and Sara, 2004; Foote et al., 1980; Kalwani et al., 2014; Takeuchi et al., 2016; Vankov et al., 1995). The median interspike interval in our data set was 733 msec (Totah et al., 2018). These long time lags are likely due to the mixture of self-inhibition and lateral inhibition from neighboring neurons (Aghajanian et al., 1977; Ennis and Aston-Jones, 1986a). It is imperative that future studies of extracellular single unit spiking in the LC, especially those concerning synchrony, confirm a gap in the auto-

correlogram of 60 msec (to accommodate burst firing) or at least 100 msec (non-bursting), rather than relying on a lack of spiking during a ~1 msec refractory period as a criterion for accepting a spike train as a LC single unit. Although the inter-spike interval distribution likely depends on state (e.g., anesthetized, sleep, awake, cognitively-engaged), they are typically >50 msec in the awake state (Aston-Jones et al., 1994; Kalwani et al., 2014) and this interval could be enforced by the profuse auto-inhibition and lateral-inhibition in the LC. Our data set was sufficient for not only characterizing a potential LC ensemble code, but it also allowed an assessment of the temporal dynamics of LC ensemble activity, which may be relevant to fluctuations in cognition over multiple timescales.

Figure 1. A high-density electrode array was used to record well-isolated single units along the dorsal-ventral aspect of the LC. (A) A light microscope picture of the recording array. The recording array was a silicone probe that was positioned in the coronal plane, such that it produced minimal damage. The array contained 32 recording channels (circular metallic objects). The electrodes were evenly spaced (25 um center-to-center) and tiled 275 um in the dorsal-ventral direction. The dorso-ventral length of the LC is estimated to be approximately 500 um using the scale bar in various figures (Figure 5B in Grzanna & Molliver 1980; Figure 2CC in Shipley, et al. 1996; and Figure 1 in Swanson & Hartman 1975) (Grzanna and Molliver, 1980; Shipley et al., 1996; Swanson and Hartman, 1975). Prior work from our lab using

laminar arrays has also demonstrated that LC-NE neurons can be recorded over a 500 um dorso-ventral distance (Marzo et al., 2014). (B) Two simultaneously recorded single unit waveforms (average across all spikes) from are shown. The auto-correlogram for each unit is plotted between ±1 sec. The briefest inter-spike intervals (~100 to 200 msec) are much longer than a typical refractory period of 1 msec due to the self- and lateral-inhibition typical of the LC nucleus. Plots are reproduced from the data and figures in Totah, et al. (2018) using the analyses and methods in that paper.

We assessed the propensity of LC neurons to respond synchronously by measuring pairwise spike train cross-correlograms of more than 3,000 single unit pairs. This measure of synchrony considered spontaneous spiking. Cross-correlograms were examined in two time frames: 0.5 msec bins over a ±3 msec window and 20 msec bins over a ±2 sec window. These time frames were chosen because they reflect two potential mechanisms for synchronous spiking in the LC: shared synaptic input and gap junctions. Shared synaptic input can be inferred from a broad peak in the cross-correlogram that lasts tens of milliseconds (Perkel et al., 1967). Pharmacologically and genetically confirmed gap junctions in the retina, cortex, and cerebellum are associated with a brief (~0.5 to 1 msec) peak shifted 0.5 to 1 millisecond from zero (Connors et al., 1999; Trenholm et al., 2014; van Welie et al., 2016). We chose a longer time window (20 msec bins over a ±2 sec window) for direct comparison with existing studies (Curtis et al., 2012; Usher et al., 1999). Example cross-correlograms are shown in Figure 2. We used the method of Fujisawa et al. (2008) to exclude peaks in the cross-correlograms that were due to interactions at slower timescales that could be induced by under urethane anesthesia and artificially increase correlated activity between neurons (Aasebø et al., 2017; Clement et al., 2008; Ecker et al., 2014; Erchova et al., 2002; Greenberg et al., 2008; Petersen et al., 2003; Sakata and Harris, 2012; 2009). Accordingly, significantly synchronous spiking between two LC single units was assessed by comparing the coincidental spike count to a

distribution of 1000 surrogate cross-correlograms that were generated by jittering spike times on a uniform interval (Fujisawa et al., 2008). For each surrogate cross-correlogram, spike times of one neuron were jittered randomly over a uniform interval of ±1 msec (for putative gap junctions driving synchronous spiking) and ±200 msec (for putative common synaptic inputs driving synchronous spiking). In order for a cross-correlogram peak to be significant, the coincidental spike count had to exceed both a 1% pairwise expected chance coincidental spike count at each time bin (Figure 2, green line) and a 1% globally expected chance coincidental spike count (Figure 2, orange line).

Figure 2. A minority of LC single unit pairs spiked synchronously over less than a millisecond and up to tens of milliseconds. Two example cross-correlograms are shown. The black histogram is an un-normalized coincidental spike count. The green line is the largest 1% of coincidental spike counts observed at each time bin calculated from 1000 surrogate spike timing jittered cross-correlograms. The orange line is the largest global 1% coincidental spike count observed at any time across 1000 surrogate cross-correlograms. The left panel illustrates an interaction between a unit pair that lasted approximately 60 msec (three 20 msec bins crossed both significance thresholds). By comparison, the x-axis of the right panel is an order of magnitude briefer (±3 msec) and shows an interaction that lasts only a single 0.5 msec bin. Plots are reproduced from the data and figures in Totah, et al. (2018) using the analyses and methods in that paper.

Overall, pairwise synchrony in the LC nucleus was exceptionally low with the majority of cell pairs (85%) not having significant coincidental spiking at either time window examined

(Totah et al., 2018). The same result was obtained when spike count correlation coefficients (RSC) were assessed in 200 msec bins. The mean RSC across 3,164 LC cell pairs was 0.044±0.001, which is similar to values obtained in the cortex, a structure that is considered to have heterogeneous, ensemble-based firing patterns (Cohen and Kohn, 2011). RSC did not change when we only considered cell pairs with geometric mean firing rates that were similar to average LC firing rates during wakefulness, which suggests that anesthesia did not affect synchrony through minor suppression of LC firing rate (Totah et al., 2018). If anything, anesthesia may have increased synchrony among LC neurons; anesthesia does so in multiple cortical regions under multiple types of anesthetic (including urethane) by temporally compacting the spiking of active cortical neurons into windows corresponding to synchronized membrane potential up states and slow waves in the EEG or LFP (Aasebø et al., 2017; Ecker et al., 2014; Erchova et al., 2002; Greenberg et al., 2008; Sakata and Harris, 2009). LC population spiking is locked to cortical slow oscillations and is temporally compacted into the cortical population up state; this may be enabled by the direct reciprocal connections between LC and cortex, as well as indirect connections between LC and cortex via other afferents (Arnsten and Goldman-Rakic, 1984; Aston-Jones and Bloom, 1981a; Eschenko et al., 2011; Eschenko and Sara, 2008; Jodo et al., 1998; Lestienne et al., 1997; Sara and Hervé-Minvielle, 1995). Therefore, it seems likely that anesthesia would only increase synchrony in the LC, just as it does in cortex. This view is in agreement with our findings that LC synchrony (measured by RSC) was lower during epochs of less cortical population synchronization (Totah et al., 2018). Overall, given that LC and cortical regions may be nodes of an interconnected network and that anesthesia increases network synchrony, it is unlikely that anesthesia artificially decreased LC synchrony below its level in the awake state. In line with this, a study in the

awake, spontaneously behaving rat provided some evidence for rather low LC synchrony although this was based on a relatively small data set (~20 to 30 LC unit pairs) and the crosscorrelograms were not formally quantified (Curtis et al., 2012). Our results rely on the study of a large population of LC unit pairs (~3,000) and our findings of strikingly low synchrony suggest that the noradrenergic signal is not merely diffuse and broadly acting. However, a small proportion of pairs (15%) did have synchronous spiking quantified in cross-correlograms (of which, 62% suggested putative common input, 13% indicated putative gap junction, 25% exhibited both). These cell pairs could function as ensembles. While the spike train cross-correlograms and spike count correlation coefficients characterize synchrony among pairs of neurons, we also assessed beyond-pairwise ensembles, as to the best of our knowledge this data set was unique in its simultaneous recordings of more than two LC single units simultaneously (on average, 20 single units were simultaneously recorded). In order to reveal the existence of LC ensembles (defined as subsets of synchronized LC neurons), we used graph theory analysis. For this analysis, each neuron was considered a node and a link was drawn between neurons that had a significantly correlated spiking. A community detection algorithm was used to define ensembles in the graph by grouping together single units that are correlated with one another, while separating them from other groups of correlated single units, with which they are not correlated. Using this framework, between 1 and 3 ensembles were identified in each rat. Each ensemble consisted of between 2 and 9 units. Figure 3 illustrates graphs showing multiple, discrete ensembles within the larger LC network.

Figure 3. LC neurons form discrete, beyond-pairwise ensembles. The plots illustrate functional connectivity from recordings of LC single units in 3 rats. The white circles are simultaneously recorded units. Significant pairwise correlations are depicted as yellow lines. Ensembles were defined by iteratively clustering units until the number of links within a cluster was maximized and the number of outgoing links was minimized (see methods in Totah et al. 2018). The label of each unit indicates its ensemble membership. Units that were not part of an ensemble are unlabeled. The hue of purple of each units indicates the proportion of links that enter the node. The left plot shows a recording with three ensembles. The middle plot shows a single ensemble embedded in other neurons that spike independently. The plot on the right illustrates another LC recording in which members within two ensembles are synchronized with little connectivity between the two ensembles. In the right panel, is shown. Plots are reproduced from the data and figures in Totah, et al. (2018) using the analyses and methods in that paper.

One pragmatic scenario for an LC ensemble-based code is that multiple LC ensembles, each targeting a specific brain region, could in principle activate together to simultaneously modulate – not the entire brain – but a sub-set of neuronal circuits controlling behavior. For example, attention re-orienting to a surprising stimulus could be mediated by such a scenario. This cognitive process requires fast integration of many component behaviors and coordinated activity of many brain circuits regulating response thresholds and tuning for stimuli (involving cortex and thalamus), an “interruption” of ongoing cognitive processes such as tasks or thoughts (involving multiple brain regions), relocating sensory organs toward the unexpected stimulus (involving brainstem regions, superior colliculus, and the frontal eye field), and an

autonomic response (heart rate, breathing, thermoregulation) that musters the required energy resources for reacting to the surprising stimulus. Re-orienting attention, therefore, requires simultaneous noradrenergic neuromodulation of multiple neural circuits, but does not require totally global and non-specific modulation of the entire brain. In other words, the LC may need to provide semi-global modulation. LC ensemble activity could provide neuromodulation that is not purely one-to-one modulation and yet is also not fully global. Interestingly, we reported that, under anesthesia, noxious foot shocks only evoked a synchronous response among 16% of LC cell pairs (Totah et al., 2018). These data suggest that sensory stimuli may not drive an en masse response in the LC that would provide completely global neuromodulation; instead, sub-sets of LC neurons may be activated to modulate only the brain circuits needed. Of course, our study using a noxious stimulus in the anesthetized rat is more relevant to saliency processing and much less relevant to complex cognitive behaviors in the awake organism. It is unknown if semi-global, ensemble-based noradrenergic neuromodulation occurs during reorienting to salient stimuli in the awake state. One hint of LC ensemble activity can be inferred from the data presented by Bouret & Richmond (2009), which reported peri-stimulus spike raster plots of LC single units recorded in the awake monkey (Bouret and Richmond, 2009). We examined their raster plots and inferred that the response of some LC neurons to a salient visual stimulus is highly variable from trial-to-trial (see Figure 8 from Bouret & Richmond, 2009), while other LC units respond in a highly consistent way on each trial (see Figure 10 from Bouret & Richmond, 2009). In their study, the LC units were recorded one-at-a-time, but if LC units with different response variability would be recorded in this study simultaneously, it would result in low trial-by-trial LC single unit synchrony occurring in the awake state. Our findings of low synchrony during sensory-evoked spiking, as well as inferences from the

recordings of Bouret & Richmond (2009) during wakefulness, suggest that the LC may use an ensemble code to respond to sensory stimuli. This ensemble code may provide a more spatially and temporally selective modulation of neural circuits mediating various cognitive processes and behaviors, such as attentional re-orienting. LC ensemble activations over diverse behaviorally-relevant timescales In the above section, we have presented recent evidence for LC ensemble activity (Totah et al., 2018) and evidence that sub-sets of LC neurons have projection target-specificity that modulates specific circuits to influence particular behaviors (Chandler et al., 2014; 2013; Hirschberg et al., 2017; Kebschull et al., 2016; Uematsu et al., 2017; Wagatsuma et al., 2018). An important question is whether LC ensemble activity spans multiple time scales that are relevant to behavior and cognitive operations. Our analysis of LC cell pairs revealed that LC ensembles can be active over multiple timescales: sub-millisecond, tens of milliseconds, and multiple seconds. Given that LC firing rate can change over the time course of less than a second or multiple minutes and hours, it was important to assess the degree of synchrony occurring over a large range of timescales. We used the method of integrating the cross-correlogram to assess synchrony over the range of 1 msec up to 40 sec (Bair et al., 2001). The cross-correlogram is gradually integrated in steps () of ±5 msec around time = 0 sec. The integral will change rapidly over time bins when there is a large number of coincidental spikes (i.e., a peak in the cross-correlogram) and eventually plateau when the cross-correlogram flattens. By measuring the time when the integral plateaus (we chose 80% of its maximum), a read-out of the width of the crosscorrelogram peak is obtained. We found that interactions for some pairs lasted up to 20 seconds (Totah et al., 2018). We then measured the duration of the cross-correlogram peaks

(coincidental spike count exceeding the 1% pairwise and global threshold) focusing on cell pair interaction timeframes of up to 20 sec. We found that the vast majority of synchronous spiking was confined to a time scale of less than 100 msec (Figure 4).

Figure 4. LC neurons pairwise ensembles were active over diverse timescales of less than 100 msec. The histogram shows the percent of pairs with a significant peak at a particular time ranging from 0.5 msec to 90 msec. The time between 1.5 msec and 10 msec is removed from the axis because no pairs interacted on this timescale. Among the minority of LC unit pairs with synchronous spiking (i.e., significant cross-correlogram peaks) the timing of the synchronous interaction was either very brief (0.5 msec) or spanned a range from 10 to 80 msec. Plots are reproduced from the data and figures in Totah, et al. (2018) using the analyses and methods in that paper.

This finding is consistent with the timescale of pairwise synchrony reported in the behaving monkey (Usher et al., 1999) and in slice recordings from the rat LC (Alvarez et al., 2002; Rancic et al., 2018). We also found cell pairs that spiked synchronously with a 0.5 millisecond delay, consistent with these interactions being mediated by putative gap junctions. In support of this interpretation of the data, we found that the proportion of cell pairs with sub-millisecond synchrony fell with distance between the cells (Totah et al., 2018). On the other hand, longer duration (tens of milliseconds to ~100 msec) cell pair interactions were invariant to distance between cells, suggesting that they could be driven by non-topographical afferent input to the

LC. Intriguingly, putative gap junction coupled LC neurons were more likely to project to the same areas of the forebrain, as assessed by evoking antidromic responses in LC neurons after stimulation of 15 different forebrain (cortical and sub-cortical) brain regions (Totah et al., 2018). Thus, LC cells can form projection target-specific ensembles at the very brief (~1 msec) timescale of gap junctions. These findings reveal that LC ensembles may function at timescales ranging from sub-millisecond to hundreds of milliseconds. We also observed LC neuronal interactions at a longer timescale of many seconds. It has been speculated that the diffuse projections of the LC-NE system could influence infraslow (<1Hz) rhythmic changes in forebrain neuronal excitability that are associated with brainwide resting state or other large-scale cognitive networks (Drew et al., 2008; Leopold et al., 2003; Mateo et al., 2017; Mittner et al., 2016). This scenario would require LC-NE neuronal spike rate to oscillate over an infra-slow time scale. In order to assess such a possibility, we converted each of the 234 single unit spike trains into 234 continuous spike density functions by convolving them with a 250 msec Gaussian kernel and then calculated power spectra of spike rate oscillations (Totah et al., 2018). The width of the kernel will allow assessment of frequencies up to a Nyquist frequency of 2 Hz. The spike rate of many LC single units oscillated in the infra-slow range, specifically at frequencies of 0.09 Hz and ~0.4 to 0.5 Hz, because the average power spectral density (N = 234 units) had large peaks at those frequency bands (Figure 5A). These frequencies correspond to rhythmic changes in spike rate with a period of 11 – 12 sec and 2.0 – 2.5 sec, respectively. Indeed, it is possible that the entire LC population increases and decreases its firing rate, en masse, in an infra-slow oscillatory pattern; thus providing completely global and periodic elevations of NE release that last multiple seconds (i.e., during the peaks of the 0.09 Hz and ~0.4 to 0.5 Hz fluctuations in

spike rate). In order to test this possibility, we characterized the phase difference between spike rate oscillations of pairs of LC single units. Figure 5B shows that some LC unit pairs oscillated in phase (mean phase angle,  = 0 degrees). The phase difference was highly consistent for the duration of the recording, suggesting that these units have a stable relationship for hours. Figure 5C shows that other LC single unit pairs oscillated anti-phase; when one unit was firing more, the other unit was firing less (mean phase angle,  = 180 degrees). The mean phase angle difference () was collected for each pair of units (3,164 pairs) and plotted (Figure 5D). The results show that LC unit pairs show every possible phase relationship, from in-phase, to anti-phase, and any number of out-of-phase possibilities. Our results support the hypothesis that spike rates of sub-sets of LC neurons can synchronously oscillate in an infra-slow range, yet segregate their firing rate increases from other LC neurons.

Figure 5. The spike rate of LC single units oscillated at infra-slow frequencies that were phase-locked between unit pairs with a range of phase relations. (A) The average power spectral density (N = 234 single units) of all spike density functions. Peaks occurred at 0.09 Hz and 0.4 to 0.5 Hz. Additional sharp peaks are harmonics of the 0.09 Hz peak. (B) The left panel shows infra-slow oscillations of spike rate for 4 different unit pairs. The spike density function of each unit is drawn as green and purple lines. The spike rates of the two units in pair #1 oscillate at 0.4 to 0.5 Hz (shown for a random 10 sec recording epoch) nearly synchronously. The right panel illustrates the instantaneous phase differences between these two spike rate oscillations across the entire recording session. The synchronized oscillations are highly coherent. Pair #2 spike rates oscillate in-phase at 0.09 Hz. Note the larger recording epoch of 60 sec. (C) Pairs #3 and #4 spike rates oscillate in an anti-phase relationship at 0.4 to 0.5 Hz (upper panel) and 0.09 Hz (lower panel). (D) The mean of the pairwise phase relation distributions (e.g., in panels B and C) were collected for all 3,164 LC unit pairs. The left panel shows the distribution of those mean phase relations for 0.09 Hz oscillations. The right panel shows the same for 0.4 to 0.5 Hz oscillations. The phase relationships are uniformly distributed, such that some unit pairs are in-phase, some unit pairs are anti-phase, and some unit pairs are out-of-phase with delays. Plots are reproduced from the data and figures in Totah, et al. (2018) using the analyses and methods in that paper.

LC ensemble activity over multiple seconds may have direct implications for synchronization of brain regions within resting state networks in states of rest, sleep, or anesthesia. Resting state networks are formed by the coordination of neuronal excitability among brain regions that could promote communication among them; coordination of brain regions into resting state networks appears as synchronized infra-slow LFP, EEG, or BOLD oscillations across forebrain regions (Buzsáki and Draguhn, 2004; Chan et al., 2015; Lakatos, 2005; Leopold et al., 2003; Steriade et al., 1993; Vanhatalo et al., 2004). LC spiking and associated NE release regulates cortical excitability (Haider and McCormick, 2009; McCormick, 1992; McGinley et al., 2015a). We propose that sub-sets of synchronously firing LC neurons in an infra-slow oscillation pattern could coordinate infra-slow cortical fMRI BOLD / EEG oscillations among sub-sets of cortical regions to form resting state networks (Figure 6). Regardless of speculating on the implications of LC ensembles that are rhythmically active

over multiple seconds, it is apparent that our findings at least provide the first experimental support for a theoretically predicted role for the LC in modulating infra-slow changes in forebrain cortical excitability (Drew et al., 2008; Leopold et al., 2003; Mateo et al., 2017; Mittner et al., 2016). It is possible that LC infra-slow oscillations of spike rate are specific to anesthesia, however, cortical infra-slow oscillations can occur in the anesthetized, sleeping, and awake states (Buzsáki and Draguhn, 2004; Chan et al., 2015; Lakatos, 2005; Leopold et al., 2003; Steriade et al., 1993; Vanhatalo et al., 2004). It remains critical that our findings are reproduced in the awake animal.

Figure 6. Diverse timing of infra-slow oscillations between LC units' spike rates may provide targeted neuromodulation of specific cortical networks. (A) Separate LC cell assemblies may target separate cortical regions to synchronize cortical infra-slow oscillations within each network, thus providing targeted neuromodulation at the level of resting state networks. (B) The various phase relationships between infra-slow spike rate oscillations of LC units would lead to sub-sets of LC units that have synchronous infra-slow oscillations. The grey line illustrates the phase differences across LC neuron spike rate fluctuations. The red LC single units (ensemble #1) increase and decrease their firing rates synchronously with one

another over a time scale of a few seconds (i.e., infra-slow). The blue LC single units (ensemble #2) firing rates oscillate out of phase with the red LC single units. Our finding of strong spike-spike coherence suggests that these patterns are consistent over time, which would generate LC ensembles whose member units’ spike rates consistently oscillate in phase with one another, but out of phase with units in other ensembles. Hypothetically, differing projections between the red and blue LC single units could entrain infra-slow fluctuations in cortical neuronal excitability (reflected in LFP, EEG, and BOLD signals) resulting in sub-sets of cortical regions with synchronized fluctuations in excitability.

Overall, our findings indicate that LC ensemble activations can occur over various time scales: (i) 1 msec, (ii) 10 to 100 msec, and (iii) multiple seconds. A significant question is then how LC ensemble activity at these varied time courses contributes to different behaviors or cognitive processes. Aston-Jones and colleagues provided some initial answers by demonstrating that the degree of synchrony among LC neuron spiking varied with performance accuracy in a visual discrimination task in the awake macaque monkey (Usher et al., 1999). Specifically, they analyzed 23 pairs of nearby LC single units and found significant correlated firing lasting ~100 msec for 18 of 23 pairs and only during good task performance (high hit rate and low false alarm responses to distracting stimuli), but not during poor task performance. Although stimulus-evoked LC spiking was removed in their study, it is possible that other taskrelated events such as reward delivery and consumption could have induced some of the synchronous firing. The 10 to 100 msec time scale may be an important range for LC ensemble activity to modulate cognitive processes. In summary, LC ensemble activity occurring over discrete time periods (1 msec, 10 to 100 msec, and multiple seconds) may allow spatially and temporally differentiated modulation of distinct brain circuits over timescales that are relevant to NE-dependent brain functions (see Table 1). Future studies that characterize LC ensemble activity over multiple timescales

relevant for different aspects of behavior (e.g., stimulus expectancy and perception, stimulusguided action preparation and execution, reward expectation and consumption, etc.) and in various cognitive contexts will bring further insights on the roles that LC ensembles may play in NE-dependent brain functions. Two types of LC extracellular waveforms form cell-type specific ensembles Although the LC has long been considered a homogenous nucleus in the rat and primate brain, there exist some hints of LC cell diversity. Several morphologically distinct LC neurons have been described: the medium-sized multipolar and fusiform cells and small-sized oval-shaped cells (Loughlin et al., 1986b). Moreover, LC-NE neurons co-release other neurotransmitters, among them glutamate and a variety of neuropeptides such as enkephalin, galanin, and dopamine (Aston-Jones et al., 1991; Beas et al., 2018; Devoto et al., 2005; Fung et al., 1994; Holets et al., 1988; Le Maître et al., 2013; McCall et al., 2015; Reyes et al., 2008; Simpson et al., 1999; Takeuchi et al., 2016). Although approximately 90% of LC neurons are NE-producing, it is interesting to note that the non-noradrenergic minority appears to selectively express GABA-A receptors with alpha-1 subunits which may lend special synaptic response properties that should be further investigated (Corteen et al., 2011; Devoto et al., 2005; Everitt et al., 1984; Fung et al., 1994; Holets et al., 1988; Le Maître et al., 2013; Takeuchi et al., 2016). There is a topography in the anatomical organization of the LC nucleus according to the predominant projection targets of each LC subdivision, such as cortex, hippocampus, hypothalamus, or spinal cord (Hirschberg et al., 2017; Li et al., 2016; Loughlin et al., 1986a; Simpson et al., 1999; 1997). Recent work employing a viral-based trans-synaptic tracing method reported a somewhat broader topography in terms of hindbrain versus forebrain (Schwarz et al., 2015). However, until recently LC cells have not been mapped onto

any particular genetic, neurochemical, electrophysiological traits; none of these features were linked to cell type specific functional interactions. Chandler, Waterhouse and colleagues have further advanced our understanding of LC cell diversity by demonstrating that LC neurons have genetic variation for encoding of glutamate receptors. As a result of this genetic heterogeneity, two groups of LC neurons differ in their synaptic excitability and intracellular spike waveform width and after-hyperpolarization amplitude; the differences in excitability contribute to a cell type difference in spontaneous firing rate (Chandler et al., 2014). Consistent with the observations of Chandler, et al. (2014), we could identify two cell types differing by waveform width in our extracellular recordings (Totah et al., 2018). The waveform shapes clustered into two groups characterized by narrow and wide spike width (Figure 8A). Narrow units also spiked more frequently (Figure 8B). Furthermore, we found that narrow waveform units were preferentially localized in the ventral aspect of the LC nucleus, but were also still observed throughout the nucleus (Figure 7C). The two types of LC neurons also had different local circuit properties. Specifically, in response to sensory stimuli (noxious stimuli consisting of a single 5.0 mA foot shock of 0.5 msec pulse duration repeated 50 times every 2000±500 msec), wide units were anti-correlated with other wide and narrow units (Figure 7D). These results suggested that the discharge of wide, but not narrow cells might cause NE-mediated local lateral inhibition. Most strikingly, LC ensembles detected by graph theory analysis and community detection algorithms (Figure 3) often consisted of primarily a single unit type (Figure 7E). In summary, our results demonstrate heterogeneity in LC extracellular waveforms, which map onto differential location in the nucleus, intrinsic excitability, local circuit properties, and propensity for ensemble formation.

It is, however, unknown how these extracellular waveforms map onto the cell classes reported by Chandler, et al. (2014). Moreover, the neurochemical nature and morphology of these narrow and wide LC unit types are currently unknown. The notion that narrow waveform units are the “small oval” cell morphology can be ruled out because such cells project only to hypothalamus or do not project out of the nucleus (Cintra et al., 1982; Loughlin et al., 1986b), whereas the narrow and wide waveform units had similar projection topology, as examined by antidromic responses to stimulation of 15 forebrain sites (Totah et al., 2018). With regard to neurochemical content, it is also unlikely that the narrow units were local interneurons. First, the composition of the rat LC is predominantly (90%) noradrenergic (Corteen et al., 2011; Swanson, 1976). Second, GABA interneurons are located outside of the LC core. For example, Jin, et al. (2016) have described GABA neurons in the dorsomedial area outside of the LC core (Jin et al., 2016). GABA neurons are also found dorsolateral and ventromedial to the LC core; however, at a distance of hundreds of micrometers (Aston-Jones et al., 2004), which is beyond the ~60 um radius that is typically required for reliable isolation of single units (Buzsáki, 2004; Henze et al., 2000; Mechler et al., 2011). Finally, GABA interneurons nearby the LC have a much higher firing rate than LC neurons. Jin, et al. (2016) reported that the GABA neurons dorsomedial to LC fired spontaneously at approximately 11 Hz. The typical firing rate of LC neurons is 0.5 – 5Hz (Aston-Jones and Bloom, 1981a). The narrow units observed in our experiments had a rather low spontaneous spike rate (median of 1.28±0.73 spikes/sec). We observed that both cell types were inhibited by systemic injection of the alpha2 adrenergic receptor agonist, clonidine, which is a marker of noradrenergic neurons. One method to provide further evidence of neurochemical identity of the identified cell types is to use optogenetic “tagging” of NE neurons by viral transfection of the neurons with

channelrhodopsin under a promoter for tyrosine hydroxylase (TH) or dopamine--hydroxylase (DBH), which are two enzymes in the NE synthesis pathway (Carter et al., 2010; Hickey et al., 2014; Hirschberg et al., 2017; Li et al., 2016; Martins and Froemke, 2015; Uematsu et al., 2017). However, using these new methods may not provide 100% certainty about neurochemical identity of the recorded units. A recent study (Uematsu et al., 2017) reported that 25% of rat LC neurons that were optogenetically-identified as NE neurons spiked at 10 – 20 Hz (their Suppl. Fig. 3, panel f). These unusually high firing rates and short inter-spike intervals (<10 msec, their Suppl. Fig. 3, panel d) are intriguingly different from what one might expect from intracellular and extracellular studies which have demonstrated the characteristics of LC-NE cells as ~1 – 5 spikes per sec and an inter-spike interval of >100 msec. These highly active neurons may be due to TH-Cre-dependent channelrhodopsin ectopic expression in nonTH neurons, which has been observed and leads to potentially inaccurate tagging (Lammel et al., 2015; Z. Liu et al., 2016). Moreover, in studies without ectopic expression, channelrhodopsin was extremely under-expressed (Lohani et al., 2018; 2016). In those studies, 60% of TH+ neurons did not express channelrhodopsin and over half the sample of neurons would therefore not respond to channelrhodopsin activation. The current TH-Cre method may therefore also lead to under-sampling of the data. Undoubtedly, the TH-Cre method will continue to improve and, while results from TH-Cre tagged putative LC-NE neurons are seemingly reliable and fascinating, the use of optogenetics nevertheless introduces some perplexing new questions and is therefore not necessarily yet suitable for unambiguous identification of LC cell identity. The DBH targeting method using the PRS promoter, is an alternative that appears to be currently quite promising (Hickey et al., 2014; Hirschberg et al., 2017; Li et al., 2016). Regardless of the type of optogenetic manipulation

used, attempts to determine neurochemical identity using optogenetic tagging should still be coupled with “classic” methods using, for example, clonidine to, first, prevent false negatives due to under-expression and false positives due to ectopic expression and to, second, compare recent advances with the vast prior LC literature that used clonidine to confirm recordings from LC-NE neurons. Future work that combines large-scale population recording from the LC in the awake animal will be instrumental to studying the role of LC cell heterogeneity in cognitive processes and behavior.

Figure 7. Extracellular LC single unit waveforms split into two shapes with a number of unique characteristics. A. The example waveforms of two units are shown (narrow waveform – pink and wide waveform – blue). The example waveforms are the mean across all spikes for each unit. Analysis revealed a separation between units that had a pronounced afterhyperpolarization (AHP) and a narrow spike width (trough to AHP peak duration less than 700 sec) versus a smaller amplitude AHP and a wide spike width (greater than 700 sec). B. Box plots illustrate the median firing rate (white circle) for narrow (N) type and wide (W) type single units. The notches are shown as triangles. Narrow units had a higher firing rate under conditions of both spontaneous activity and evoked activity (trial-averaged maximum spike rate

in 50 msec window after a single foot shock. Asterisks indicate p<0.001 of an adequately powered (>80%) Wilcoxon-Mann-Whitney test on 34 narrow units and 200 wide units recorded across 12 rats. C. The narrow units were preferentially recorded on the ventral aspect of the recording array, as shown by the mean probability distribution across 12 rats. The high-density electrode arrays spanned 300 m from the ventral to the dorsal aspect of the LC nucleus. This was confirmed by foot shocks evoking biphasic multi-unit responses (excitation followed by inhibition) on all channels and clonidine suppression of single unit spiking and multi-unit spiking on all channels. D. Some units increased spike rate and other units to decreased spike rate on a trial-by-trial basis during the 50 msec window after a foot shock stimulus. This anticorrelated response between unit pairs resulted in a negative Pearson’s correlation coefficient between the units’ trial-by-trial spike count in that 50 msec window. The pie charts show the proportion of unit pairs with positive or negative correlations. Anti-correlated responses to foot shock were observed for pairs involving a wide unit (blue), but much less so for pairs of exclusively narrow units (pink waveforms). Similar results were observed for other time windows. The simultaneous enhancement of spiking of one neuron and depression of spiking in another may result from local, noradrenergic lateral inhibition between the two neurons. The pattern of results suggests that narrow units do not exhibit lateral inhibition with one another, but are themselves susceptible to lateral inhibition from wide units. E. The plot shows the percent of units in each ensemble that were either narrow (pink) or wide (blue). Plots are reproduced from the data and figures in Totah, et al. (2018) using the analyses and methods in that paper.

LC ensemble formation and putative afferent and local mechanisms Another emergent question concerns a mechanistic account of how various afferent inputs and local LC circuit mechanisms enable finely-structured LC ensemble activity. We briefly review the afferent and local mechanisms that come into play. The LC receives afferent input from much of the brain (Figure 8). One of the most systematic assessments was recently made using trans-synaptic rabies retrograde tracing of synaptic inputs into DBH+ cells in the mouse LC (Schwarz et al., 2015). This work revealed the breadth of inputs to the LC. The new findings are consistent with prior studies that used various “classical” tracing methods to demonstrate inputs from many structures to the LC (Arnsten and Goldman-Rakic, 1984; Aston-

Jones et al., 1986; Beckstead et al., 1979; Cedarbaum and Aghajanian, 1978b; Ennis and Aston-Jones, 1989a; 1986b; Luppi et al., 1995; Reyes et al., 2008; Simon et al., 1979; Van Bockstaele et al., 1998). If these synaptic inputs are temporally correlated, then they may increase synchronous activity among the LC neurons receiving shared synaptic input (Perkel et al., 1967). On the other hand, LC neurons receive a diverse mixture of excitatory and inhibitory afferents from many brain regions – inputs that may be asynchronous (and/or opposing in excitatory/inhibitory valence) with respect to each other (Aston-Jones et al., 1991; Ennis and Aston-Jones, 1989b; 1986b). This may lead to desynchronization of spiking activity (Doiron et al., 2016). Furthermore, refractory periods, auto-inhibition, and lateral inhibition could set LC neurons to a dynamic mixture of membrane potentials. Thus, LC neurons that are in a state of enhanced excitability may spike and then inhibit other LC neurons from responding to excitatory input through lateral inhibition (Aghajanian et al., 1977; Ennis and Aston-Jones, 1986a; Marzo et al., 2014). As discussed above, we propose that wide waveform LC units may, in particular, play an important role in structuring LC ensemble activity by providing this local lateral inhibition.

Figure 8. The LC integrates a broad set of afferents with single afferents contacting the dendrites of many LC neurons. The drawing summarizes the literature and is based directly

on a figure created by E. Szbadi (Szabadi, 2013). Anatomical abbreviations: PFC – prefrontal cortex, AMY – amygdala, VLPO – ventrolateral preoptic nucleus, LH – lateral hypothalamus, TMN – tuberomammillary nucleus, PVN – paraventricular nucleus of the hypothalamus, PrH – nucleus prepositus hypoglossi, VTA – ventral tegmental area, PAG – pariaqueductal gray, PPT – pedunculopontine tegmental nucleus, LDT – laterodorsal tegmental nucleus, DRN – dorsal raphe nucleus, RVLM – rostral ventrolateral medulla, DH – dorsal horn of the spinal cord. Neurochemical abbreviations: Glut – glutamate, CRF – corticotropin-releasing factor, GABA – -aminobutyric acid, Oxy – oxytocin, Hist – histamine, VP – vasopressin, DA – dopamine, ACh – acetylcholine, 5HT – serotonin, NA – noradrenaline, Enc – enkephalin.

Intriguingly, it is possible that the gap junctions in the LC actually prevent en masse synchronization of the LC nucleus and contribute to the existence of sub-sets of synchronized LC neurons (i.e., ensembles). Systems that are coupled through gap junctions are certainly predisposed to exhibit synchronous changes of both membrane potential and spike probability (Connors, 2017). For example, in vitro recordings of the relatively deafferenated LC in slice recordings have demonstrated gap junction-synchronized membrane potentials (Alvarez et al., 2002). However, when whole-brain afferent input is preserved (as in our experiments, (Totah et al., 2018)and the neurons also have action potentials with a large amplitude afterhyperpolarization (as is the case for LC neurons (Chandler et al., 2014), an excitatory afferent input can phase shift the relation between neurons’ membrane potentials, which results in overall desynchronization at the population level but synchronization among sub-sets of neurons (Connors, 2017). Thus, gap junctions in the LC may contribute to weak population synchrony and synchronization of activity within distinct cell ensembles. Future outlook: shifting perspectives on LC function The LC-NE system is evolutionarily-conserved across vertebrates, which suggests that it is involved in fundamental functions that are critical for survival (Smeets and González, 2000; Szabadi, 2013). Indeed, noradrenergic neuromodulation is implicated in maintaining

wakefulness (Aston-Jones et al., 2001; Aston-Jones and Bloom, 1981a; Carter et al., 2010), processing pain (Alba-Delgado et al., 2012; Hickey et al., 2014; Hirschberg et al., 2017), and “fight or flight” responses, such as escaping stressful or aversive conditions (Curtis et al., 2012; Hirschberg et al., 2017; McCall et al., 2015; Valentino et al., 1991). However, the LC-NE system is also involved in higher-order cognitive functions, such as attention, perception, working memory, decision making, and action planning (reviewed in (Aston-Jones and Cohen, 2005; Sara, 2009). The LC connectivity with evolutionarily “ancient” brain stem regions controlling autonomic functions and more “modern” forebrain regions may allow the LC to integrate survival functions with cognition such that the LC “serves as the cognitive limb of a globally conceived sympathetic nervous system” (Aston-Jones et al., 1991; Szabadi, 2013). For example, the LC may mediate the presumably “calming” effects of breathing on mental state (Yackle et al., 2017). The LC may do this by integrating and influencing brain regions involved in both the cognitive and autonomic processes. It will be critical to understand how sub-sets of LC neurons with particular input-output connectivity profiles function as ensembles to contribute to this integration of mind and body. Moreover, by recording LC ensemble activity in cognitive tasks, we can begin to understand the nuanced role of the LC in behaviors and cognitive processes that span time scales. We are witnessing the field shifting its longstanding views on the noradrenergic system as an evolutionarily conserved brain structure mediating survival functions toward the emerging perspective that the LC is also central to cognitive functions spanning numerous timescales. In this transition process, large-scale LC population data, particularly those collected in the behaving animal, will be essential to establish the role of LC ensembles for various behaviors and cognitive functions. An LC ensemble code may present a mechanism

for noradrenergic neuromodulation to affect those moment-to-moment fluctuations in perception, thought, and action.

Acknowledgements The authors thank Dr. Hamid Noori and Dr. Masataka Watanabe for comments on the manuscript. This work was supported by the European Union’s Marie Curie Fellowship in the FP7 funding scheme to N.K.T. (PIIF-GA-2012-331122) and the European Union’s FET Open in the FP7 funding scheme (SICODE) to O.E. and N.K.L.

References Aasebø, I.E.J., Lepperød, M.E., Stavrinou, M., Nøkkevangen, S., Einevoll, G., Hafting, T., Fyhn, M., 2017. Temporal Processing in the Visual Cortex of the Awake and Anesthetized Rat. 4, ENEURO.0059–17.2017. Aghajanian, G.K., Cedarbaum, J.M., Wang, R.Y., 1977. Evidence for norepinephrine-mediated collateral inhibition of locus coeruleus neurons. Brain Research 136, 570–577. Alba-Delgado, C., Mico, J.A., Sánchez-Blázquez, P., Berrocoso, E., 2012. Analgesic antidepressants promote the responsiveness of locus coeruleus neurons to noxious stimulation: implications for neuropathic pain. Pain 153, 1438–1449. Alvarez, V.A., Chow, C.C., Van Bockstaele, E.J., Williams, J.T., 2002. Frequency-dependent synchrony in locus ceruleus: role of electrotonic coupling. Proc. Natl. Acad. Sci. U.S.A. 99, 4032–4036. Arakawa, S., Nakamura, S., Kawashima, N., Nishiike, S., Fujii, Y., 1997. Antidromic burst activity of locus coeruleus neurons during cortical spreading depression. Neuroscience 78, 1147–1158. Arnsten, A.F., Goldman-Rakic, P.S., 1984. Selective prefrontal cortical projections to the region of the locus coeruleus and raphe nuclei in the rhesus monkey. Brain Research 306, 9–18. Arnsten, A.F.T., 2011. Catecholamine Influences on Dorsolateral Prefrontal Cortical Networks. BPS 69, e89–e99. Aston-Jones, G., Bloom, F.E., 1981a. Activity of norepinephrine-containing locus coeruleus neurons in behaving rats anticipates fluctuations in the sleep-waking cycle. 1, 876–886. Aston-Jones, G., Bloom, F.E., 1981b. Norepinephrine-containing locus coeruleus neurons in behaving rats exhibit pronounced responses to non-noxious environmental stimuli. 1, 887– 900. Aston-Jones, G., Chen, S., Zhu, Y., Oshinsky, M.L., 2001. A neural circuit for circadian regulation of arousal. Nat Neurosci 4, 732–738. Aston-Jones, G., Cohen, J.D., 2005. An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. Annu. Rev. Neurosci. 28, 403–450. Aston-Jones, G., Ennis, M., Pieribone, V.A., Nickell, W.T., Shipley, M.T., 1986. The brain nucleus locus coeruleus: restricted afferent control of a broad efferent network. Science

234, 734–737. Aston-Jones, G., Foote, S.L., Segal, M., 1985. Impulse conduction properties of noradrenergic locus coeruleus axons projecting to monkey cerebrocortex. Neuroscience 15, 765–777. Aston-Jones, G., Rajkowski, J., Kubiak, P., 1997. Conditioned responses of monkey locus coeruleus neurons anticipate acquisition of discriminative behavior in a vigilance task. Neuroscience 80, 697–715. Aston-Jones, G., Rajkowski, J., Kubiak, P., Alexinsky, T., 1994. Locus coeruleus neurons in monkey are selectively activated by attended cues in a vigilance task. 14, 4467–4480. Aston-Jones, G., Segal, M., Bloom, F.E., 1980. Brain aminergic axons exhibit marked variability in conduction velocity. Brain Research 195, 215–222. Aston-Jones, G., Shipley, M.T., Chouvet, G., Ennis, M., van Bockstaele, E., Pieribone, V., Shiekhattar, R., Akaoka, H., Drolet, G., Astier, B., 1991. Afferent regulation of locus coeruleus neurons: anatomy, physiology and pharmacology. Prog. Brain Res. 88, 47–75. Aston-Jones, G., Zhu, Y., Card, J.P., 2004. Numerous GABAergic afferents to locus ceruleus in the pericerulear dendritic zone: possible interneuronal pool. Journal of Neuroscience 24, 2313–2321. Azevedo, F.A.C., Carvalho, L.R.B., Grinberg, L.T., Farfel, J.M., Ferretti, R.E.L., Leite, R.E.P., Jacob Filho, W., Lent, R., Herculano-Houzel, S., 2009. Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. Journal of Comparative Neurology 513, 532–541. Bair, W., Zohary, E., Newsome, W.T., 2001. Correlated firing in macaque visual area MT: time scales and relationship to behavior. Journal of Neuroscience 21, 1676–1697. Bari, A., Aston-Jones, G., 2012. Atomoxetine modulates spontaneous and sensory-evoked discharge of locus coeruleus noradrenergic neurons. Neuropharmacology. Beas, B.S., Wright, B.J., Skirzewski, M., Leng, Y., Hyun, J.H., Koita, O., Ringelberg, N., Kwon, H.-B., Buonanno, A., Penzo, M.A., 2018. The locus coeruleus drives disinhibition in the midline thalamus via a dopaminergic mechanism. Nat Neurosci 54, 89. Beckstead, R.M., Domesick, V.B., Nauta, W.J., 1979. Efferent connections of the substantia nigra and ventral tegmental area in the rat. Brain Research 175, 191–217. Bergmann, O., Zdunek, S., Felker, A., Salehpour, M., Alkass, K., Bernard, S., Sjostrom, S.L., Szewczykowska, M., Jackowska, T., Remedios, dos, C., Malm, T., Andrä, M., Jashari, R., Nyengaard, J.R., Possnert, G., Jovinge, S., Druid, H., Frisén, J., 2015. Dynamics of Cell Generation and Turnover in the Human Heart. Cell 161, 1566–1575. Berridge, C.W., Waterhouse, B.D., 2003. The locus coeruleus-noradrenergic system: modulation of behavioral state and state-dependent cognitive processes. Brain Res. Brain Res. Rev. 42, 33–84. Bhagwandin, A., Fuxe, K., Bennett, N.C., Manger, P.R., 2008. Nuclear organization and morphology of cholinergic, putative catecholaminergic and serotonergic neurons in the brains of two species of African mole-rat. J. Chem. Neuroanat. 35, 371–387. Blessing, W.W., Chalmers, J.P., Howe, P.R., 1978. Distribution of catecholamine-containing cell bodies in the rabbit central nervous system. J. Comp. Neurol. 179, 407–423. Bouret, S., Richmond, B.J., 2015. Sensitivity of locus ceruleus neurons to reward value for goal-directed actions. Journal of Neuroscience 35, 4005–4014. Bouret, S., Richmond, B.J., 2009. Relation of locus coeruleus neurons in monkeys to Pavlovian and operant behaviors. Journal of Neurophysiology 101, 898–911. Bouret, S., Sara, S.J., 2005. Network reset: a simplified overarching theory of locus coeruleus

noradrenaline function. Trends in Neurosciences 28, 574–582. Bouret, S., Sara, S.J., 2004. Reward expectation, orientation of attention and locus coeruleusmedial frontal cortex interplay during learning. European Journal of Neuroscience 20, 791– 802. Bouret, S., Sara, S.J., 2002. Locus coeruleus activation modulates firing rate and temporal organization of odour-induced single-cell responses in rat piriform cortex. Eur. J. Neurosci. 16, 2371–2382. Breen, L.A., Burde, R.M., Loewy, A.D., 1983. Brainstem connections to the Edinger-Westphal nucleus of the cat: a retrograde tracer study. Brain Research 261, 303–306. Browning, M., Behrens, T.E., Jocham, G., O'Reilly, J.X., Bishop, S.J., 2015. Anxious individuals have difficulty learning the causal statistics of aversive environments. Nat Neurosci 18, 590–596. Bruinstroop, E., Cano, G., Vanderhorst, V.G.J.M., Cavalcante, J.C., Wirth, J., Sena-Esteves, M., Saper, C.B., 2012. Spinal projections of the A5, A6 (locus coeruleus), and A7 noradrenergic cell groups in rats. Journal of Comparative Neurology 520, 1985–2001. Bux, F., Bhagwandin, A., Fuxe, K., Manger, P.R., 2010. Organization of cholinergic, putative catecholaminergic and serotonergic nuclei in the diencephalon, midbrain and pons of subadult male giraffes. J. Chem. Neuroanat. 39, 189–203. Buzsáki, G., 2010. Neural syntax: cell assemblies, synapsembles, and readers. Neuron 68, 362–385. Buzsáki, G., 2004. Large-scale recording of neuronal ensembles. Nat Neurosci 7, 446–451. doi:10.1038/nn1233 Buzsáki, G., Draguhn, A., 2004. Neuronal oscillations in cortical networks. Science 304, 1926– 1929. Calvey, T., Patzke, N., Kaswera-Kyamakya, C., Gilissen, E., Bertelsen, M.F., Pettigrew, J.D., Manger, P.R., 2015. Organization of cholinergic, catecholaminergic, serotonergic and orexinergic nuclei in three strepsirrhine primates: Galago demidoff, Perodicticus potto and Lemur catta. J. Chem. Neuroanat. 70, 42–57. Carter, M.E., Yizhar, O., Chikahisa, S., Nguyen, H., Adamantidis, A., Nishino, S., Deisseroth, K., de Lecea, L., 2010. Tuning arousal with optogenetic modulation of locus coeruleus neurons. Nat Neurosci 13, 1526–1533. Cedarbaum, J.M., Aghajanian, G.K., 1978a. Activation of locus coeruleus neurons by peripheral stimuli: modulation by a collateral inhibitory mechanism. Life Sciences 23, 1383–1392. Cedarbaum, J.M., Aghajanian, G.K., 1978b. Afferent projections to the rat locus coeruleus as determined by a retrograde tracing technique. Journal of Comparative Neurology 178, 116. Chan, A.W., LeDue, J.M., Mohajerani, M.H., Wang, Y.T., Murphy, T.H., 2015. Mesoscale infraslow spontaneous membrane potential fluctuations recapitulate high-frequency activity cortical motifs. Nat Commun 6, 7738. Chandler, D.J., Gao, W.-J., Waterhouse, B.D., 2014. Heterogeneous organization of the locus coeruleus projections to prefrontal and motor cortices. Proceedings of the National Academy of Sciences 111, 6816–6821. Chandler, D.J., Lamperski, C.S., Waterhouse, B.D., 2013. Identification and distribution of projections from monoaminergic and cholinergic nuclei to functionally differentiated subregions of prefrontal cortex. Brain Research 1522, 38–58.

Chen, F.-J., Sara, S.J., 2007. Locus coeruleus activation by foot shock or electrical stimulation inhibits amygdala neurons. Neuroscience 144, 472–481. Chiang, C., Aston-Jones, G., 1993. Response of locus coeruleus neurons to footshock stimulation is mediated by neurons in the rostral ventral medulla. Neuroscience 53, 705– 715. Christie, M.J., Williams, J.T., North, R.A., 1989. Electrical coupling synchronizes subthreshold activity in locus coeruleus neurons in vitro from neonatal rats. 9, 3584–3589. Chu, N.-S., Bloom, F.E., 1974. The catecholamine-containing neurons in the cat dorsolateral pontine tegmentum: Distribution of the cell bodies and some axonal projections. Brain Research 66, 1–21. Cintra, L., Díaz-Cintra, S., Kemper, T., Morgane, P.J., 1982. Nucleus locus coeruleus: a morphometric Golgi study in rats of three age groups. Brain Research 247, 17–28. Clayton, E.C., Rajkowski, J., Cohen, J.D., Aston-Jones, G., 2004. Phasic activation of monkey locus ceruleus neurons by simple decisions in a forced-choice task. Journal of Neuroscience 24, 9914–9920. Clement, E.A., Richard, A., Thwaites, M., Ailon, J., Peters, S., Dickson, C.T., 2008. Cyclic and Sleep-Like Spontaneous Alternations of Brain State Under Urethane Anaesthesia. PLoS ONE 3, e2004. Cohen, M.R., Kohn, A., 2011. Measuring and interpreting neuronal correlations. Nature Publishing Group 14, 811–819. Connors, B.W., 2017. Synchrony and so much more: Diverse roles for electrical synapses in neural circuits. Devel Neurobio 77, 610–624. Connors, B.W., Gibson, J.R., Beierlein, M., 1999. Two networks of electrically coupled inhibitory neurons in neocortex. Nature 402, 75–79. Corbetta, M., Patel, G., Shulman, G.L., 2008. The reorienting system of the human brain: from environment to theory of mind. Neuron 58, 306–324. Corteen, N.L., Cole, T.M., Sarna, A., Sieghart, W., Swinny, J.D., 2011. Localization of GABA‐A receptor alpha subunits on neurochemically distinct cell types in the rat locus coeruleus. Eur. J. Neurosci. 34, 250–262. Courville, A.C., Daw, N.D., Touretzky, D.S., 2006. Bayesian theories of conditioning in a changing world. Trends in Cognitive Sciences 10, 294–300. Crutcher, K.A., Humbertson, A.O., Martin, G.F., 1978. The origin of brainstem-spinal pathways in the North American opossum (Didelphis virginiana). Studies using the horseradish peroxidase method. J. Comp. Neurol. 179, 169–193. Curtis, A.L., Lechner, S.M., Pavcovich, L.A., Valentino, R.J., 1997. Activation of the locus coeruleus noradrenergic system by intracoerulear microinfusion of corticotropin-releasing factor: effects on discharge rate, cortical norepinephrine levels and cortical electroencephalographic activity. J. Pharmacol. Exp. Ther. 281, 163–172. Curtis, A.L., Leiser, S.C., Snyder, K., Valentino, R.J., 2012. Predator stress engages corticotropin-releasing factor and opioid systems to alter the operating mode of locus coeruleus norepinephrine neurons. Neuropharmacology 62, 1737–1745. Da Silva, J.N., Fuxe, K., Manger, P.R., 2006. Nuclear parcellation of certain immunohistochemically identifiable neuronal systems in the midbrain and pons of the Highveld molerat (Cryptomys hottentotus). J. Chem. Neuroanat. 31, 37–50. Dahlström, A., Fuxe, K., 1964. Evidence for the existence of monoamine-containing neurons in the central nervous system. I. Demonstration of monoamines in the cell bodies of brain

stem neurons. Acta Physiol Scand Suppl 232:1–55. Dalley, J.W., McGaughy, J., O'Connell, M.T., Cardinal, R.N., Levita, L., Robbins, T.W., 2001. Distinct changes in cortical acetylcholine and noradrenaline efflux during contingent and noncontingent performance of a visual attentional task. Journal of Neuroscience 21, 4908– 4914. Davimes, J.G., Alagaili, A.N., Bennett, N.C., Mohammed, O.B., Bhagwandin, A., Manger, P.R., Gravett, N., 2017. Neurochemical organization and morphology of the sleep related nuclei in the brain of the Arabian oryx, Oryx leucoryx. J. Chem. Neuroanat. 81, 53–70. de Gee, J.W., Colizoli, O., Kloosterman, N.A., Knapen, T., Nieuwenhuis, S., Donner, T.H., 2017. Dynamic modulation of decision biases by brainstem arousal systems. Elife 6, 309. Dell, L.-A., Kruger, J.-L., Bhagwandin, A., Jillani, N.E., Pettigrew, J.D., Manger, P.R., 2010. Nuclear organization of cholinergic, putative catecholaminergic and serotonergic systems in the brains of two megachiropteran species. J. Chem. Neuroanat. 40, 177–195. Devauges, V., Sara, S.J., 1990. Activation of the noradrenergic system facilitates an attentional shift in the rat. Behavioural Brain Research 39, 19–28. Devoto, P., Flore, G., Saba, P., Fà, M., Gessa, G.L., 2005. Co-release of noradrenaline and dopamine in the cerebral cortex elicited by single train and repeated train stimulation of the locus coeruleus. BMC Neurosci 6, 31. Doiron, B., Litwin-Kumar, A., Rosenbaum, R., Ocker, G.K., Josić, K., 2016. The mechanics of state-dependent neural correlations. Nature Publishing Group 19, 383–393. Drew, P.J., Duyn, J.H., Golanov, E., Kleinfeld, D., 2008. Finding coherence in spontaneous oscillations. Nat Neurosci 11, 991–993. Dubé, L., Parent, A., 1981. The monoamine-containing neurons in avian brain: I. A study of the brain stem of the chicken (Gallus domesticus) by means of fluorescence and acetylcholinesterase histochemistry. J. Comp. Neurol. 196, 695–708. Ecker, A.S., Berens, P., Cotton, R.J., Subramaniyan, M., Denfield, G.H., Cadwell, C.R., Smirnakis, S.M., Bethge, M., Tolias, A.S., 2014. State dependence of noise correlations in macaque primary visual cortex. Neuron 82, 235–248. Edeline, J.-M., Manunta, Y., Hennevin, E., 2011. Induction of selective plasticity in the frequency tuning of auditory cortex and auditory thalamus neurons by locus coeruleus stimulation. Hear. Res. 274, 75–84. Einhäuser, W., Stout, J., Koch, C., Carter, O., 2008. Pupil dilation reflects perceptual selection and predicts subsequent stability in perceptual rivalry. Proceedings of the National Academy of Sciences 105, 1704–1709. Ennis, M., Aston-Jones, G., 1989a. Potent inhibitory input to locus coeruleus from the nucleus prepositus hypoglossi. Brain Research Bulletin 22, 793–803. Ennis, M., Aston-Jones, G., 1989b. GABA-mediated inhibition of locus coeruleus from the dorsomedial rostral medulla. 9, 2973–2981. Ennis, M., Aston-Jones, G., 1986a. Evidence for self- and neighbor-mediated postactivation inhibition of locus coeruleus neurons. Brain Research 374, 299–305. Ennis, M., Aston-Jones, G., 1986b. A potent excitatory input to the nucleus locus coeruleus from the ventrolateral medulla. Neuroscience Letters 71, 299–305. Erchova, I.A., Lebedev, M.A., Diamond, M.E., 2002. Somatosensory cortical neuronal population activity across states of anaesthesia. Eur. J. Neurosci. 15, 744–752. Eschenko, O., Magri, C., Panzeri, S., Sara, S.J., 2011. Noradrenergic Neurons of the Locus Coeruleus Are Phase Locked to Cortical Up-Down States during Sleep. Cerebral Cortex.

Eschenko, O., Sara, S.J., 2008. Learning-Dependent, Transient Increase of Activity in Noradrenergic Neurons of Locus Coeruleus during Slow Wave Sleep in the Rat: Brain Stem–Cortex Interplay for Memory Consolidation? Cereb. Cortex 18, 2596–2603. Everitt, B.J., Hökfelt, T., Terenius, L., Tatemoto, K., Mutt, V., Goldstein, M., 1984. Differential co-existence of neuropeptide Y (NPY)-like immunoreactivity with catecholamines in the central nervous system of the rat. Neuroscience 11, 443–462. Faiers, A.A., Mogenson, G.J., 1976. Electrophysiological identification of neurons in locus coeruleus. Exp. Neurol. 53, 254–266. Felten, D.L., Laties, A.M., Carpenter, M.B., 1974. Monoamine-containing cell bodies in the squirrel monkey brain. Am. J. Anat. 139, 153–165. Finlayson, P.G., Marshall, K.C., 1988. Synchronous bursting of locus coeruleus neurons in tissue culture. Neuroscience 24, 217–225. Fleetwood-Walker, S.M., Coote, J.H., 1981. The contribution of brain stem catecholamine cell groups to the innervation of the sympathetic lateral cell column. Brain Research 205, 141– 155. Foote, S.L., Aston-Jones, G., Bloom, F.E., 1980. Impulse activity of locus coeruleus neurons in awake rats and monkeys is a function of sensory stimulation and arousal. Proc. Natl. Acad. Sci. U.S.A. 77, 3033–3037. Foote, S.L., Berridge, C.W., Adams, L.M., Pineda, J.A., 1991. Electrophysiological evidence for the involvement of the locus coeruleus in alerting, orienting, and attending, in: Neurobiology of the Locus Coeruleus, Progress in Brain Research. Elsevier, pp. 521–532. Fritschy, J.M., Grzanna, R., 1990. Demonstration of two separate descending noradrenergic pathways to the rat spinal cord: evidence for an intragriseal trajectory of locus coeruleus axons in the superficial layers of the dorsal horn. J. Comp. Neurol. 291, 553–582. Fujisawa, S., Amarasingham, A., Harrison, M.T., Buzsáki, G., 2008. Behavior-dependent shortterm assembly dynamics in the medial prefrontal cortex. Nat Neurosci 11, 823–833. Fung, S.J., Reddy, V.K., Zhuo, H., Liu, R.H., 1994. Anatomical evidence for the presence of glutamate or enkephalin in noradrenergic projection neurons of the locus coeruleus. 29, 219–225. Garver, D.L., Sladek, J.R., 1975. Monoamine distribution in primate brain. I Catecholaminecontaining perikarya in the brain stem of Macaca speciosa. J. Comp. Neurol. 159, 289– 304. German, D.C., Bowden, D.M., 1975. Locus ceruleus in rhesus monkey (Macaca mulatta): a combined histochemical fluorescence, Nissl and silver study. J. Comp. Neurol. 161, 19–29. Grant, S.J., Aston-Jones, G., Redmond, D.E., 1988. Responses of primate locus coeruleus neurons to simple and complex sensory stimuli. Brain Research Bulletin 21, 401–410. doi:10.1016/0361-9230(88)90152-9 Gravett, N., Bhagwandin, A., Fuxe, K., Manger, P.R., 2009. Nuclear organization and morphology of cholinergic, putative catecholaminergic and serotonergic neurons in the brain of the rock hyrax, Procavia capensis. J. Chem. Neuroanat. 38, 57–74. Greenberg, D.S., Houweling, A.R., Kerr, J.N.D., 2008. Population imaging of ongoing neuronal activity in the visual cortex of awake rats. Nat Neurosci 11, 749–751. Grzanna, R., Molliver, M.E., 1980. The locus coeruleus in the rat: An immunohistochemical delineation. Neuroscience 5, 21–40. Haider, B., McCormick, D.A., 2009. Rapid neocortical dynamics: cellular and network mechanisms. Neuron 62, 171–189.

Haug, H., 1986. History of neuromorphometry. Journal of Neuroscience Methods 18, 1–17. Henze, D.A., Borhegyi, Z., Csicsvari, J., Mamiya, A., Harris, K.D., Buzsaki, G., 2000. Intracellular features predicted by extracellular recordings in the hippocampus in vivo. Journal of Neurophysiology 84, 390–400. Herculano-Houzel, S., 2009. The human brain in numbers: a linearly scaled-up primate brain. Front. Hum. Neurosci. 3. Hervé-Minvielle, A., Sara, S.J., 1995. Rapid habituation of auditory responses of locus coeruleus cells in anaesthetized and awake rats. Neuroreport 6, 1363–1368. Hickey, L., Li, Y., Fyson, S.J., Watson, T.C., Perrins, R., Hewinson, J., Teschemacher, A.G., Furue, H., Lumb, B.M., Pickering, A.E., 2014. Optoactivation of locus ceruleus neurons evokes bidirectional changes in thermal nociception in rats. Journal of Neuroscience 34, 4148–4160. Hirata, H., Aston-Jones, G., 1994. A novel long-latency response of locus coeruleus neurons to noxious stimuli: mediation by peripheral C-fibers. Journal of Neurophysiology 71, 1752– 1761. Hirschberg, S., Li, Y., Randall, A., Kremer, E.J., Pickering, A.E., 2017. Functional dichotomy in spinal- vs prefrontal-projecting locus coeruleus modules splits descending noradrenergic analgesia from ascending aversion and anxiety in rats. Elife 6, 570. Holdefer, R.N., Jacobs, B.L., 1994. Phasic stimulation of the locus coeruleus: effects on activity in the lateral geniculate nucleus. Exp Brain Res 100, 444–452. Holets, V.R., Hökfelt, T., Rökaeus, A., Terenius, L., Goldstein, M., 1988. Locus coeruleus neurons in the rat containing neuropeptide Y, tyrosine hydroxylase or galanin and their efferent projections to the spinal cord, cerebral cortex and hypothalamus. Neuroscience 24, 893–906. Hubbard, J.E., Di Carlo, V., 1973. Fluorescence histochemistry of monoamine-containing cell bodies in the brain stem of the squirrel monkey (Saimiri sciureus). I. The locus caeruleus. J. Comp. Neurol. 147, 553–566. Imam, A., Bhagwandin, A., Ajao, M.S., Ihunwo, A.O., Fuxe, K., Manger, P.R., 2018. Brain of the tree pangolin (Manis tricuspis). III. The unusual locus coeruleus complex. Journal of Comparative Neurology 526, 2570–2684. Ishikawa, M., Shimada, S., Tanaka, C., 1975. Histochemical mapping of catecholamine neurons and fiber pathways in the pontine tegmentum of the dog. Brain Research 86, 1– 16. Ishimatsu, M., Williams, J.T., 1996. Synchronous activity in locus coeruleus results from dendritic interactions in pericoerulear regions. 16, 5196–5204. Jepma, M., Nieuwenhuis, S., 2011. Pupil diameter predicts changes in the explorationexploitation trade-off: evidence for the adaptive gain theory. J Cogn Neurosci 23, 1587– 1596. Jin, X., Li, S., Bondy, B., Zhong, W., Oginsky, M.F., Wu, Y., Johnson, C.M., Zhang, S., Cui, N., Jiang, C., 2016. Identification of a Group of GABAergic Neurons in the Dorsomedial Area of the Locus Coeruleus. PLoS ONE 11, e0146470. Jodo, E., Chiang, C., Aston-Jones, G., 1998. Potent excitatory influence of prefrontal cortex activity on noradrenergic locus coeruleus neurons. Neuroscience 83, 63–79. Jones, B.E., Moore, R.Y., 1974. Catecholamine-containing neurons of the nucleus locus coeruleus in the cat. J. Comp. Neurol. 157, 43–51. Joshi, S., Li, Y., Kalwani, R.M., Gold, J.I., 2016. Relationships between Pupil Diameter and

Neuronal Activity in the Locus Coeruleus, Colliculi, and Cingulate Cortex. Neuron 89, 221– 234. Kahneman, D., Beatty, J., 1966. Pupil diameter and load on memory. Science 154, 1583– 1585. Kalwani, R.M., Joshi, S., Gold, J.I., 2014. Phasic activation of individual neurons in the locus ceruleus/subceruleus complex of monkeys reflects rewarded decisions to go but not stop. Journal of Neuroscience 34, 13656–13669. Kebschull, J.M., Garcia da Silva, P., Reid, A.P., Peikon, I.D., Albeanu, D.F., Zador, A.M., 2016. High-Throughput Mapping of Single-Neuron Projections by Sequencing of Barcoded RNA. Neuron 91, 975–987. Keener, J.P., 1990. The effects of gap junctions on propagation in myocardium: a modified cable theory. Annals of the New York Academy of Sciences 591, 257–277. Kim, E., Fishman, G.I., 2013. Designer gap junctions that prevent cardiac arrhythmias. Trends in Cardiovascular Medicine 23, 33–38. Kloosterman, N.A., Meindertsma, T., van Loon, A.M., Lamme, V.A.F., Bonneh, Y.S., Donner, T.H., 2015. Pupil size tracks perceptual content and surprise. European Journal of Neuroscience 41, 1068–1078. Kruger, J.-L., Patzke, N., Fuxe, K., Bennett, N.C., Manger, P.R., 2012. Nuclear organization of cholinergic, putative catecholaminergic, serotonergic and orexinergic systems in the brain of the African pygmy mouse (Mus minutoides): organizational complexity is preserved in small brains. J. Chem. Neuroanat. 44, 45–56. Lakatos, P., 2005. An Oscillatory Hierarchy Controlling Neuronal Excitability and Stimulus Processing in the Auditory Cortex. Journal of Neurophysiology 94, 1904–1911. Lammel, S., Steinberg, E.E., Földy, C., Wall, N.R., Beier, K., Luo, L., Malenka, R.C., 2015. Diversity of transgenic mouse models for selective targeting of midbrain dopamine neurons. Neuron 85, 429–438. Lapiz, M.D.S., Morilak, D.A., 2006. Noradrenergic modulation of cognitive function in rat medial prefrontal cortex as measured by attentional set shifting capability. Neuroscience 137, 1039–1049. Le Maître, E., Barde, S.S., Palkovits, M., Diaz-Heijtz, R., Hökfelt, T.G.M., 2013. Distinct features of neurotransmitter systems in the human brain with focus on the galanin system in locus coeruleus and dorsal raphe. Proceedings of the National Academy of Sciences 110, E536–45. Lee, A., Rosin, D.L., Van Bockstaele, E.J., 1998. alpha2A-adrenergic receptors in the rat nucleus locus coeruleus: subcellular localization in catecholaminergic dendrites, astrocytes, and presynaptic axon terminals. Brain Research 795, 157–169. Lenartowicz, A., Simpson, G.V., Cohen, M.S., 2013. Perspective: causes and functional significance of temporal variations in attention control. Front. Hum. Neurosci. 7, 381. Leopold, D.A., Murayama, Y., Logothetis, N.K., 2003. Very slow activity fluctuations in monkey visual cortex: implications for functional brain imaging. Cereb. Cortex 13, 422–433. Lestienne, R., Hervé-Minvielle, A., Robinson, D., Briois, L., Sara, S.J., 1997. Slow oscillations as a probe of the dynamics of the locus coeruleus-frontal cortex interaction in anesthetized rats. J. Physiol. Paris 91, 273–284. Léger, L., Hernandez-Nicaise, M.L., 1980. The cat locus coeruleus. Light and electron microscopic study of the neuronal somata. Anat. Embryol. 159, 181–198. Léger, L., Wiklund, L., Descarries, L., Persson, M., 1979. Description of an indolaminergic cell

component in the cat locus coeruleus: a fluorescence histochemical and radioautographic study. Brain Research 168, 43–56. Li, Y., Hickey, L., Perrins, R., Werlen, E., Patel, A.A., Hirschberg, S., Jones, M.W., Salinas, S., Kremer, E.J., Pickering, A.E., 2016. Retrograde optogenetic characterization of the pontospinal module of the locus coeruleus with a canine adenoviral vector. Brain Research 1641, 274–290. Limacher, A., Bhagwandin, A., Fuxe, K., Manger, P.R., 2008. Nuclear organization and morphology of cholinergic, putative catecholaminergic and serotonergic neurons in the brain of the Cape porcupine (Hystrix africaeaustralis): increased brain size does not lead to increased organizational complexity. J. Chem. Neuroanat. 36, 33–52. Liu, Y., Rodenkirch, C., Moskowitz, N., Schriver, B., Wang, Q., 2017. Dynamic Lateralization of Pupil Dilation Evoked by Locus Coeruleus Activation Results from Sympathetic, Not Parasympathetic, Contributions. Cell Rep 20, 3099–3112. Liu, Z., Brown, A., Fisher, D., Wu, Y., Warren, J., Cui, X., 2016. Tissue Specific Expression of Cre in Rat Tyrosine Hydroxylase and Dopamine Active Transporter-Positive Neurons. PLoS ONE 11, e0149379. Lohani, S., Martig, A.K., Underhill, S.M., DeFrancesco, A., Roberts, M.J., Rinaman, L., Amara, S., Moghaddam, B., 2018. Burst activation of dopamine neurons produces prolonged postburst availability of actively released dopamine. Neuropsychopharmacology 3, 21. Lohani, S., Poplawsky, A.J., Kim, S.-G., Moghaddam, B., 2016. Unexpected global impact of VTA dopamine neuron activation as measured by opto-fMRI. Mol Psychiatry 22, 585–594. Loughlin, S.E., Foote, S.L., Bloom, F.E., 1986a. Efferent projections of nucleus locus coeruleus: topographic organization of cells of origin demonstrated by three-dimensional reconstruction. Neuroscience 18, 291–306. Loughlin, S.E., Foote, S.L., Fallon, J.H., 1982. Locus coeruleus projections to cortex: topography, morphology and collateralization. Brain Research Bulletin 9, 287–294. Loughlin, S.E., Foote, S.L., Grzanna, R., 1986b. Efferent projections of nucleus locus coeruleus: morphologic subpopulations have different efferent targets. Neuroscience 18, 307–319. Luppi, P.H., Aston-Jones, G., Akaoka, H., Chouvet, G., Jouvet, M., 1995. Afferent projections to the rat locus coeruleus demonstrated by retrograde and anterograde tracing with cholera-toxin B subunit and Phaseolus vulgaris leucoagglutinin. Neuroscience 65, 119– 160. Maeda, T., Pin, C., Salvert, D., Ligier, M., Jouvet, M., 1973. Les neurones contenant des catécholamines du tegmentum pontique et leurs voies de projection chez le chat. Brain Research 57, 119–152. Mana, M.J., Grace, A.A., 1997. Chronic cold stress alters the basal and evoked electrophysiological activity of rat locus coeruleus neurons. Neuroscience 81, 1055–1064. Manger, P.R., Ridgway, S.H., Siegel, J.M., 2003. The locus coeruleus complex of the bottlenose dolphin (Tursiops truncatus) as revealed by tyrosine hydroxylase immunohistochemistry. J Sleep Res 12, 149–155. Manohar, A., Curtis, A.L., Zderic, S.A., Valentino, R.J., 2017. Brainstem network dynamics underlying the encoding of bladder information. Elife 6, 1377. doi:10.7554/eLife.29917 Martins, A.R.O., Froemke, R.C., 2015. Coordinated forms of noradrenergic plasticity in the locus coeruleus and primary auditory cortex. Nature Publishing Group 18, 1483–1492. Marzo, A., Totah, N.K., Neves, R.M., Logothetis, N.K., Eschenko, O., 2014. Unilateral electrical

stimulation of rat locus coeruleus elicits bilateral response of norepinephrine neurons and sustained activation of medial prefrontal cortex. Journal of Neurophysiology 111, 2570– 2588. Maseko, B.C., Jacobs, B., Spocter, M.A., Sherwood, C.C., Hof, P.R., Manger, P.R., 2013. Qualitative and quantitative aspects of the microanatomy of the African elephant cerebellar cortex. Brain Behav. Evol. 81, 40–55. Mateo, C., Knutsen, P.M., Tsai, P.S., Shih, A.Y., Kleinfeld, D., 2017. Entrainment of Arteriole Vasomotor Fluctuations by Neural Activity Is a Basis of Blood-Oxygenation-LevelDependent “Resting-State” Connectivity. Neuron 96, 936–948.e3. McCall, J.G., Al-Hasani, R., Siuda, E.R., Hong, D.Y., Norris, A.J., Ford, C.P., Bruchas, M.R., 2015. CRH Engagement of the Locus Coeruleus Noradrenergic System Mediates StressInduced Anxiety. Neuron 87, 605–620. McCormick, D.A., 1992. Neurotransmitter actions in the thalamus and cerebral cortex and their role in neuromodulation of thalamocortical activity. Progress in Neurobiology 39, 337–388. McCune, S.K., Voigt, M.M., Hill, J.M., 1993. Expression of multiple alpha adrenergic receptor subtype messenger RNAs in the adult rat brain. Neuroscience 57, 143–151. McGinley, M.J., David, S.V., McCormick, D.A., 2015a. Cortical Membrane Potential Signature of Optimal States for Sensory Signal Detection. Neuron 87, 179–192. McGinley, M.J., Vinck, M., Reimer, J., Batista-Brito, R., Zagha, E., Cadwell, C.R., Tolias, A.S., Cardin, J.A., McCormick, D.A., 2015b. Waking State: Rapid Variations Modulate Neural and Behavioral Responses. Neuron 87, 1143–1161. Mechler, F., Victor, J.D., Ohiorhenuan, I., Schmid, A.M., Hu, Q., 2011. Three-dimensional localization of neurons in cortical tetrode recordings. Journal of Neurophysiology 106, 828– 848. Mingote, S., de Bruin, J.P.C., Feenstra, M.G.P., 2004. Noradrenaline and dopamine efflux in the prefrontal cortex in relation to appetitive classical conditioning. Journal of Neuroscience 24, 2475–2480. Mitchell, K., Oke, A.F., Adams, R.N., 1994. In vivo dynamics of norepinephrine releasereuptake in multiple terminal field regions of rat brain. J. Neurochem. 63, 917–926. Mittner, M., Hawkins, G.E., Boekel, W., Forstmann, B.U., 2016. A Neural Model of Mind Wandering. Trends in Cognitive Sciences 20, 570–578. Mundorf, M.L., Joseph, J.D., Austin, C.M., Caron, M.G., Wightman, R.M., 2008. Catecholamine release and uptake in the mouse prefrontal cortex. J. Neurochem. 79, 130– 142. Nakamura, S., 1977. Some electrophysiological properties of neurones of rat locus coeruleus. The Journal of Physiology 267, 641–658. Nassar, M.R., Rumsey, K.M., Wilson, R.C., Parikh, K., Heasly, B., Gold, J.I., 2012. Rational regulation of learning dynamics by pupil-linked arousal systems. Nature Publishing Group 15, 1040–1046. Nasser, H.M., Calu, D.J., Schoenbaum, G., Sharpe, M.J., 2017. The Dopamine Prediction Error: Contributions to Associative Models of Reward Learning. Front. Psychology 8, 1026. Neves, R.M., van Keulen, S., Yang, M., Logothetis, N.K., Eschenko, O., 2018. Locus coeruleus phasic discharge is essential for stimulus-induced gamma oscillations in the prefrontal cortex. Journal of Neurophysiology 119, 904–920. Nobin, A., Björklund, A., 1973. Topography of the monoamine neuron systems in the human brain as revealed in fetuses. Acta Physiol Scand Suppl 388, 1–40.

Olszewski, J., Baxter, D., 1954. Cytoarchitecture of the Human Brain Stem. Philadelphia ; Montreal : J. B. Lippincott Company. Pajkossy, P., Szőllősi, Á., Demeter, G., Racsmány, M., 2018. Physiological Measures of Dopaminergic and Noradrenergic Activity During Attentional Set Shifting and Reversal. Front. Psychology 9, 506. Panzeri, S., Macke, J.H., Gross, J., Kayser, C., 2015. Neural population coding: combining insights from microscopic and mass signals. Trends in Cognitive Sciences 19, 162–172. Perkel, D.H., Gerstein, G.L., Moore, G.P., 1967. Neuronal Spike Trains and Stochastic Point Processes. Biophys. J. 7, 419–440. Petersen, C.C.H., Hahn, T.T.G., Mehta, M., Grinvald, A., Sakmann, B., 2003. Interaction of sensory responses with spontaneous depolarization in layer 2/3 barrel cortex. Proc. Natl. Acad. Sci. U.S.A. 100, 13638–13643. Pfeffer, T., Avramiea, A.-E., Nolte, G., Engel, A.K., Linkenkaer-Hansen, K., Donner, T.H., 2018. Catecholamines alter the intrinsic variability of cortical population activity and perception. PLoS Biol 16, e2003453. Pieters, R.P., Gravett, N., Fuxe, K., Manger, P.R., 2010. Nuclear organization of cholinergic, putative catecholaminergic and serotonergic nuclei in the brain of the eastern rock elephant shrew, Elephantulus myurus. J. Chem. Neuroanat. 39, 175–188. Rajkowski, J., Kubiak, P., Aston-Jones, G., 1994. Locus coeruleus activity in monkey: Phasic and tonic changes are associated with altered vigilance. Brain Research Bulletin 35, 607– 616. Rajkowski, J., Majczynski, H., Clayton, E., Aston-Jones, G., 2004. Activation of monkey locus coeruleus neurons varies with difficulty and performance in a target detection task. Journal of Neurophysiology 92, 361–371. Rancic, V., Rawal, B., Panaitescu, B., Ruangkittisakul, A., Ballanyi, K., 2018. Suction electrode recording in locus coeruleus of newborn rat brain slices reveals network bursting comprising summated non-synchronous spiking. Neuroscience Letters 671, 103–107. Rash, J.E., Olson, C.O., Davidson, K.G.V., Yasumura, T., Kamasawa, N., Nagy, J.I., 2007. Identification of connexin36 in gap junctions between neurons in rodent locus coeruleus. Neuroscience 147, 938–956. Reimer, J., McGinley, M.J., Liu, Y., Rodenkirch, C., Wang, Q., McCormick, D.A., Tolias, A.S., 2016. Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex. Nat Commun 7, 13289. Reyes, B.A.S., Drolet, G., Van Bockstaele, E.J., 2008. Dynorphin and stress-related peptides in rat locus coeruleus: contribution of amygdalar efferents. Journal of Comparative Neurology 508, 663–675. Robbins, T.W., Arnsten, A.F.T., 2009. The Neuropsychopharmacology of Fronto-Executive Function: Monoaminergic Modulation. Annu. Rev. Neurosci. 32, 267–287. Roelfsema, P.R., van Ooyen, A., Watanabe, T., 2010. Perceptual learning rules based on reinforcers and attention. Trends in Cognitive Sciences 14, 64–71. Romagnano, M.A., Harshbarger, R.J., Hamill, R.W., 1991. Brainstem enkephalinergic projections to spinal autonomic nuclei. 11, 3539–3555. Room, P., Postema, F., Korf, J., 1981. Divergent axon collaterals of rat locus coeruleus neurons: demonstration by a fluorescent double labeling technique. Brain Research 221, 219–230. Runyan, C.A., Piasini, E., Panzeri, S., Harvey, C.D., 2017. Distinct timescales of population

coding across cortex. Nature 548, 92–96. Sakaguchi, T., Nakamura, S., 1987. The mode of projections of single locus coeruleus neurons to the cerebral cortex in rats. Neuroscience 20, 221–230. Sakata, S., Harris, K.D., 2012. Laminar-dependent effects of cortical state on auditory cortical spontaneous activity. Front. Neural Circuits 6, 109. Sakata, S., Harris, K.D., 2009. Laminar structure of spontaneous and sensory-evoked population activity in auditory cortex. Neuron 64, 404–418. Sara, S.J., 2009. The locus coeruleus and noradrenergic modulation of cognition. Nat Rev Neurosci 10, 211–223. Sara, S.J., Hervé-Minvielle, A., 1995. Inhibitory influence of frontal cortex on locus coeruleus neurons. Proc. Natl. Acad. Sci. U.S.A. 92, 6032–6036. Sara, S.J., Segal, M., 1991. Plasticity of sensory responses of locus coeruleus neurons in the behaving rat: implications for cognition. Prog. Brain Res. 88, 571–585. Schwarz, L.A., Miyamichi, K., Gao, X.J., Beier, K.T., Weissbourd, B., DeLoach, K.E., Ren, J., Ibanes, S., Malenka, R.C., Kremer, E.J., Luo, L., 2015. Viral-genetic tracing of the inputoutput organization of a central noradrenaline circuit. Nature, 524, 88-92. Seitz, A., Watanabe, T., 2005. A unified model for perceptual learning. Trends in Cognitive Sciences 9, 329–334. Sender, R., Fuchs, S., Milo, R., 2016. Revised Estimates for the Number of Human and Bacteria Cells in the Body. PLoS Biol 14, e1002533. Sharma, Y., Xu, T., Graf, W.M., Fobbs, A., Sherwood, C.C., Hof, P.R., Allman, J.M., Manaye, K.F., 2010. Comparative anatomy of the locus coeruleus in humans and nonhuman primates. Journal of Comparative Neurology 518, 963–971. Shipley, M.T., Fu, L., Ennis, M., Liu, W.L., Aston-Jones, G., 1996. Dendrites of locus coeruleus neurons extend preferentially into two pericoerulear zones. J. Comp. Neurol. 365, 56–68. Simon, H., Le Moal, M., Calas, A., 1979. Efferents and afferents of the ventral tegmental-A10 region studied after local injection of [3H]leucine and horseradish peroxidase. Brain Research 178, 17–40. Simpson, K.L., Altman, D.W., Wang, L., Kirifides, M.L., Lin, R.C., Waterhouse, B.D., 1997. Lateralization and functional organization of the locus coeruleus projection to the trigeminal somatosensory pathway in rat. J. Comp. Neurol. 385, 135–147. Simpson, K.L., Waterhouse, B.D., Lin, R.C., 1999. Origin, distribution, and morphology of galaninergic fibers in the rodent trigeminal system. J. Comp. Neurol. 411, 524–534. Smeets, W.J., González, A., 2000. Catecholamine systems in the brain of vertebrates: new perspectives through a comparative approach. Brain Res. Brain Res. Rev. 33, 308–379. Steriade, M., Nuñez, A., Amzica, F., 1993. A novel slow (< 1 Hz) oscillation of neocortical neurons in vivo: depolarizing and hyperpolarizing components. 13, 3252–3265. Swanson, L.W., 1976. The locus coeruleus: a cytoarchitectonic, Golgi and immunohistochemical study in the albino rat. Brain Research 110, 39–56. Swanson, L.W., Hartman, B.K., 1975. The central adrenergic system. An immunofluorescence study of the location of cell bodies and their efferent connections in the rat utilizing dopamine-beta-hydroxylase as a marker. J. Comp. Neurol. 163, 467–505. Sweigers, J., Bhagwandin, A., Spocter, M.A., Kaswera-Kyamakya, C., Gilissen, E., Manger, P.R., Maseko, B.C., 2017. Nuclear organisation of cholinergic, catecholaminergic, serotonergic and orexinergic neurons in two relatively large-brained rodent species-The springhare (Pedetes capensis) and Beecroft's scaly-tailed squirrel (Anomalurus beecrofti).

J. Chem. Neuroanat. 86, 78–91. Swick, D., Pineda, J.A., Schacher, S., Foote, S.L., 1994. Locus coeruleus neuronal activity in awake monkeys: relationship to auditory P300-like potentials and spontaneous EEG. Exp Brain Res 101, 86–92. Szabadi, E., 2013. Functional neuroanatomy of the central noradrenergic system. J. Psychopharmacol. (Oxford) 27, 659–693. Takeuchi, T., Duszkiewicz, A.J., Sonneborn, A., Spooner, P.A., Yamasaki, M., Watanabe, M., Smith, C.C., Fernández, G., Deisseroth, K., Greene, R.W., Morris, R.G.M., 2016. Locus coeruleus and dopaminergic consolidation of everyday memory. Nature 537, 357–362. Tervo, D.G.R., Proskurin, M., Manakov, M., Kabra, M., Vollmer, A., Branson, K., Karpova, A.Y., 2014. Behavioral variability through stochastic choice and its gating by anterior cingulate cortex. Cell 159, 21–32. 7 Tognoli, E., Kelso, J.A.S., 2014. The metastable brain. Neuron 81, 35–48. Tohyama, M., Maeda, T., Shimizu, N., 1976. Comparative anatomy of the locus coeruleus. II. Organization and projection of the catecholamine containing neurons in the upper rhombencephalon of the frog, Rana catesbiana. J Hirnforsch 17, 81–89. Totah, N.K., Logothetis, N.K., Eschenko, O., 2015. Atomoxetine accelerates attentional set shifting without affecting learning rate in the rat. Psychopharmacology 232, 3697–3707. Totah, N.K., Neves, R.M., Panzeri, S., Logothetis, N.K., Eschenko, O., 2018. The Locus Coeruleus Is a Complex and Differentiated Neuromodulatory System. Neuron 99, 1055– 1068.e6. Trenholm, S., McLaughlin, A.J., Schwab, D.J., Turner, M.H., Smith, R.G., Rieke, F., Awatramani, G.B., 2014. Nonlinear dendritic integration of electrical and chemical synaptic inputs drives fine-scale correlations. Nat Neurosci 17, 1759–1766. Tronel, S., Feenstra, M.G.P., Sara, S.J., 2004. Noradrenergic action in prefrontal cortex in the late stage of memory consolidation. Learn. Mem. 11, 453–458. Uematsu, A., Tan, B.Z., Ycu, E.A., Cuevas, J.S., Koivumaa, J., Junyent, F., Kremer, E.J., Witten, I.B., Deisseroth, K., Johansen, J.P., 2017. Modular organization of the brainstem noradrenaline system coordinates opposing learning states. Nat Neurosci 20, 1602–1611. Unsworth, N., Robison, M.K., 2018. Tracking arousal state and mind wandering with pupillometry. Cogn Affect Behav Neurosci 14, 1–27. Unsworth, N., Robison, M.K., 2014. Individual differences in the allocation of attention to items in working memory: Evidence from pupillometry. Psychonomic Bulletin & Review 22, 757– 765. Urai, A.E., Braun, A., Donner, T.H., 2017. Pupil-linked arousal is driven by decision uncertainty and alters serial choice bias. Nature Publishing Group 8, 14637. Usher, M., Cohen, J.D., Servan-Schreiber, D., Rajkowski, J., Aston-Jones, G., 1999. The role of locus coeruleus in the regulation of cognitive performance. Science 283, 549–554. Valentino, R.J., Page, M.E., Curtis, A.L., 1991. Activation of noradrenergic locus coeruleus neurons by hemodynamic stress is due to local release of corticotropin-releasing factor. Brain Research 555, 25–34. Van Bockstaele, E.J., Colago, E.E., Valentino, R.J., 1998. Amygdaloid corticotropin-releasing factor targets locus coeruleus dendrites: substrate for the co-ordination of emotional and cognitive limbs of the stress response. J. Neuroendocrinol. 10, 743–757. Van Bockstaele, E.J., Garcia-Hernandez, F., Fox, K., Alvarez, V.A., Williams, J.T., 2004. Expression of connexins during development and following manipulation of afferent input in

the rat locus coeruleus. Neurochem. Int. 45, 421–428. van den Brink, R.L., Murphy, P.R., Nieuwenhuis, S., 2016. Pupil Diameter Tracks Lapses of Attention. PLoS ONE 11, e0165274. van den Pol, A.N., Ghosh, P.K., Liu, R.-J., Li, Y., Aghajanian, G.K., Gao, X.-B., 2002. Hypocretin (orexin) enhances neuron activity and cell synchrony in developing mouse GFP-expressing locus coeruleus. The Journal of Physiology 541, 169–185. van Welie, I., Roth, A., Ho, S.S.N., Komai, S., Häusser, M., 2016. Conditional Spike Transmission Mediated by Electrical Coupling Ensures Millisecond Precision-Correlated Activity among Interneurons In Vivo. Neuron 90, 810–823. Vanhatalo, S., Palva, J.M., Holmes, M.D., Miller, J.W., Voipio, J., Kaila, K., 2004. Infraslow oscillations modulate excitability and interictal epileptic activity in the human cortex during sleep. Proc. Natl. Acad. Sci. U.S.A. 101, 5053–5057. Vankov, A., Hervé-Minvielle, A., Sara, S.J., 1995. Response to novelty and its rapid habituation in locus coeruleus neurons of the freely exploring rat. Eur. J. Neurosci. 7, 1180–1187. Varazzani, C., San-Galli, A., Gilardeau, S., Bouret, S., 2015. Noradrenaline and dopamine neurons in the reward/effort trade-off: a direct electrophysiological comparison in behaving monkeys. Journal of Neuroscience 35, 7866–7877. Vinera, J., Kermen, F., Sacquet, J., Didier, A., Mandairon, N., Richard, M., 2015. Olfactory perceptual learning requires action of noradrenaline in the olfactory bulb: comparison with olfactory associative learning. Learn. Mem. 22, 192–196. Wagatsuma, A., Okuyama, T., Sun, C., Smith, L.M., Abe, K., Tonegawa, S., 2018. Locus coeruleus input to hippocampal CA3 drives single-trial learning of a novel context. Proceedings of the National Academy of Sciences 115, E310–E316. Watabe, K., 1980. Mode of neuronal interaction in rat locus coeruleus. Archives italiennes de biologie 118, 303-329. Waterhouse, B.D., Moises, H.C., Woodward, D.J., 1998. Phasic activation of the locus coeruleus enhances responses of primary sensory cortical neurons to peripheral receptive field stimulation. Brain Research 790, 33–44. Westlund, K.N., Coulter, J.D., 1980. Descending projections of the locus coeruleus and subcoeruleus/medial parabrachial nuclei in monkey: axonal transport studies and dopamine-beta-hydroxylase immunocytochemistry. Brain Research 2, 235–264. Yackle, K., Schwarz, L.A., Kam, K., Sorokin, J.M., Huguenard, J.R., Feldman, J.L., Luo, L., Krasnow, M.A., 2017. Breathing control center neurons that promote arousal in mice. Science 355, 1411–1415. Zaglia, T., Mongillo, M., 2017. Cardiac sympathetic innervation, from a different point of (re)view. The Journal of Physiology 595, 3919–3930.

Highlights 

Recent evidence supports the existence of LC ensemble activity and targeted noradrenergic neuromodulation of specific neural circuits



Finely-structured, cell type-specific LC ensemble activity may increase the information in phasic and tonic signals to provide differentiated and targeted neuromodulation of cognitive processes



LC ensembles may be active over a variety of timescales that influence cognitive processes and behaviors occurring over similarly diverse timescales