Hippocampal Information Processing and Homeostatic Regulation During REM and Non-REM Sleep

C H A P T E R

4 Hippocampal Information Processing and Homeostatic Regulation During REM and Non-REM Sleep Kenji Mizuseki, Hiroyuki Miyawaki Department of Physiology, Graduate School of Medicine, Osaka City University, Osaka, Japan

I INTRODUCTION

information is necessary for the generation of grid cell signals (Burgess, Barry, & O’Keefe, 2007; Hasselmo, Giocomo, & Zilli, 2007; McNaughton, Battaglia, Jensen, Moser, & Moser, 2006), disruption of the head direction cell network in the anterior thalamic nuclei has been shown to significantly impair both the functioning of grid cells and head direction signaling in the entorhinal cortex and parasubiculum (Winter, Clark, & Taube, 2015). Speed is encoded by context-invariant, speedresponsive cells known as speed cells in the medial entorhinal cortex (MEC) and the hippocampus. While most speed cells in the MEC encode speed prospectively, those in the hippocampus encode speed retrospectively (Kropff, Carmichael, Moser, & Moser, 2015). Thus, it is thought that place cells, grid cells, head direction cells, and speed cells all work together to coordinate spatial navigation. It has been proposed that the mechanisms underlying episodic and semantic memory may have evolved from those underlying spatial navigation and use the same (or very similar) neural circuitry (Buzsaki & Moser, 2013). During sleep, the hippocampus and related structures actively process information without external inputs, and it has been hypothesized that this process is important for memory consolidation. Sleep is also thought to have a homeostatic function; synapses are potentiated during the waking period and depotentiated during sleep, and thus, the sum of their weights stays constant (Tononi & Cirelli, 2006). In this chapter, we review recent progress in the understanding of hippocampal information processing and homeostatic regulation during REM sleep and non-REM sleep.

The hippocampus and entorhinal cortex are responsible for episodic memory (Scoville & Milner, 1957). Episodic memory enables the individual to mentally travel back in time to reference personal experiences in the context of both time and space (Tulving, 1972). There has been controversy over the question of whether episodic memory is unique to humans or if other animals might have a similar capacity to remember particular episodes (Babb & Crystal, 2006; Clayton & Dickinson, 1998; Roberts et al., 2008; Squire, 1992; Tulving, 1972). In rodents, the most conspicuous behavioral correlate of hippocampal and entorhinal neuron activity is spatial coding, which is thought to underlie spatial memory and navigation. Place cells in the hippocampus and grid cells in the entorhinal cortex fire when an animal is at particular locations in the environment (O’Keefe & Nadel, 1978; Rowland, Roudi, Moser, & Moser, 2016). It has been proposed that the representation of space by the hippocampus may form the framework for memorizing experiences in their spatial context (O’Keefe & Nadel, 1978). To update a spatial representation that reflects ongoing movement, place cells and grid cells must access information regarding moment-to-moment direction and speed. Head direction cells (Taube, Muller, & Ranck Jr, 1990), which are distributed across multiple brain regions (including the preand postsubiculum, anterior dorsal thalamus, retrosplenial cortex, and entorhinal cortex), indicate the direction of the head, which is typically the same as the overall direction of movement. Consistent with the concept that head direction

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© 2019 Elsevier B.V. All rights reserved.

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II BRAIN STATE-DEPENDENT NETWORK DYNAMICS IN THE HIPPOCAMPUS Neuronal information is processed differently during different brain states (Mizuseki & Miyawaki, 2017), and activity sequences that occur when awake are often replayed during sleep (Buzsaki, 2015). Therefore, to fully understand the mechanisms of information processing during sleep, it is necessary to compare the neuronal activity that occurs during waking and sleep. The hippocampus is composed of laminar structures, in which projections from different brain areas terminate in distinct layers (Andersen, Morris, Amaral, Bliss, & O’Keefe, 2006). Local field potential (LFP) patterns that occur along these laminar structures reflect various behavior-dependent network dynamics (Buzsáki, 2006). Different kinds of network oscillations, such as theta (Mizuseki, Sirota, Pastalkova, & Buzsaki, 2009), gamma (Buzsaki & Wang, 2012), sharpwave ripples (SPW-Rs) (Buzsaki, 2015), and slow oscillations (Isomura et al., 2006; Wolansky, Clement, Peters, Palczak, & Dickson, 2006) secure temporal coordination of neuronal activity in the entorhinal-hippocampal circuit and provide the scaffolding for neuronal computation. During exploration and REM sleep, theta oscillations are prominent in the hippocampus and entorhinal cortex. The brain states associated with these oscillations are called theta states (Buzsaki, 2002). In contrast, during wakeful immobility, consummatory behavior (grooming, eating, drinking, etc.), and non-REM sleep, SPW-Rs events are prominent in the hippocampal CA1 field, and the brain states associated with these events are classified as nontheta states, since theta oscillations are not noticeable (Buzsaki, 2015). In addition to LFPs, neuronal spiking patterns depend on brain states (Mizuseki & Miyawaki, 2017). During active exploration, the hippocampal neurons known as place cells occasionally increase their firing rates for short periods (1 s), with low levels of population synchrony. Likewise, during REM sleep, any given neuron may increase its firing rate occasionally and temporally, but overall neuronal synchrony is low (Mizuseki & Buzsaki, 2014). In contrast, when an animal is immobile or in non-REM sleep, highly synchronous population bursts, which are associated with SPW-Rs, are flanked by relatively silent periods in the hippocampus (Buzsaki, 2015). SPW-Rs are ripple oscillations at 140–230 Hz in the hippocampus that transiently occur in the CA1 pyramidal cell layer and are associated with negative polarity deflections known as sharp waves in the CA1 stratum radiatum. SPW-Rs are self-organized patterns that emerge from the extensive excitatory recurrent system of the CA3 region, potentially triggered by CA2 (Oliva, Fernandez-Ruiz, Buzsaki, & Berenyi, 2016a), and represent synchronous population activity in the CA3-CA1-subicular complex and entorhinal cortex (Buzsaki, 2015).

Hippocampal place cells discharge at progressively earlier theta phases as an animal moves through the place field, a phenomenon known as phase precession (O’Keefe & Recce, 1993). As a result, when an animal traverses across place fields consisting of multiple place cells, the cell assembly representing the current location of the animal fires at the trough of the theta cycle, whereas assemblies representing the previously and subsequently visited locations fire on the descending and ascending phases, respectively. Thus, the neuronal sequence at a behaviorally relevant timescale (on the order of seconds) is embedded in the ongoing theta rhythm (on the order of 100 ms) in a time-compressed manner (Dragoi & Buzsaki, 2006; Foster & Wilson, 2007; Skaggs, McNaughton, Wilson, & Barnes, 1996). As a result, cells with overlapping place fields fire sequentially in close temporal proximity, which can trigger spike-timing-dependent plastic changes in the synapses between the cells, thus providing a cellular mechanism for sequence learning (Skaggs et al., 1996). Place fields and theta phase precession of place cells are regarded as prominent examples of “rate coding” and “temporal coding,” respectively. It is still debated whether these two codes are intrinsically coupled (Harris et al., 2002; Mehta, Lee, & Wilson, 2002) or independent (Huxter, Burgess, & O’Keefe, 2003; Souza & Tort, 2017). A critical issue is whether theta phase precession is tied to spatial computation. Theta phase precession has been observed during wheel running with stationary head position (Harris et al., 2002; Pastalkova, Itskov, Amarasingham, & Buzsaki, 2008), odor and visual cue combination tasks with fixed head position (Terada, Sakurai, Nakahara, & Fujisawa, 2017), and even during REM sleep (Harris et al., 2002). Therefore, it seems that theta phase precession is not tied to spatial computation, but instead reflects a general mechanism in the circuitry (Harris et al., 2002). Waking theta states in rodents are predominantly observed during walking, sniffing, whisking, jumping, and rearing periods, during which an animal intensely and actively processes sensory inputs (Buzsáki, 2006). During waking theta states, higher levels of neuromodulators (e.g., acetylcholine) in the hippocampus enhance the influence of external inputs relative to intrinsic activity, likely favoring sensory processing and information flow from the neocortex to the hippocampus (Hasselmo, 1999). In contrast, sleep-associated loss of neuromodulatory tone changes the properties of ion channels and extracellular ion composition (Ding et al., 2016; Marder & Goaillard, 2006), thereby increasing the efficacy of local synapses among excitatory neurons (Costa, Born, Claussen, & Martinetz, 2016; Hasselmo & McGaughy, 2004) and decreasing that of synapses from excitatory to inhibitory neurons (Hasselmo & McGaughy, 2004; Mielke, Ahuja, Comas, & Mealing, 2011; Mizuseki & Buzsaki, 2013; Munoz & Rudy, 2014).

PART A. BRAIN ACTIVITY DURING SLEEP AND WAKING

III INFORMATION PROCESSING DURING NON-REM SLEEP

Accumulating evidence supports the notion that sequential activity that occurs while awake is replayed during non-REM sleep in a time-compressed manner (Buzsaki, 2015). In sharp contrast, others have reported that sequential activity that occurs while awake is replayed during REM sleep at an equivalent timescale (Louie & Wilson, 2001). Interestingly, the temporal structure of head direction neuron firing in the anterodorsal thalamic nucleus and postsubiculum during waking is preserved during sleep (Peyrache, Lacroix, Petersen, & Buzsaki, 2015). During REM sleep, the speed of the spontaneous drift of the activity packet of the head direction neurons is similar to what occurs while awake, although it is accelerated 10-fold during non-REM sleep (Peyrache et al., 2015).

III INFORMATION PROCESSING DURING NON-REM SLEEP It has been proposed that memory trace formation involves two distinct steps. The first occurs during waking theta states, promoting “on-line” rapid information acquisition, and the second during nontheta states of subsequent rest and sleep, with “off-line” reactivation of acquired information (Buzsaki, 1989). According to this hypothesis, during waking theta states, afferent activity from the neocortex/entorhinal cortex to the hippocampus induces transient synaptic potentiation in the hippocampal CA3 area, where the newly acquired information is temporally held as a labile form of the memory trace. During subsequent nontheta states, the highly synchronous hippocampal population bursts that occur during SPW-Rs facilitate the transfer of temporally held information from the hippocampus to the neocortex and induce long-term synaptic reorganization in the circuit, thereby converting labile traces into long-lasting memory traces (Buzsaki, 1989, 2015). Consistent with the hypothesized role of SPW-Rs in memory consolidation, off-line reactivation of neuron populations during SPW-Rs could help in promoting synaptic plasticity (Buzsaki, 2015; Sadowski, Jones, & Mellor, 2016). A recent study reported that hippocampal SPW-Rs depotentiate synapses, which are potentiated during waking states, thereby playing a significant role in the homeostatic regulation of synaptic strength (Norimoto et al., 2018). Silencing SPW-Rs would prevent the downregulation of net synaptic weights and impair the subsequent learning of new memories. Importantly, while SPW-Rs reduce recent memory-irrelevant neuronal activity, they spare the neuronal activity induced by novel experiences, suggesting that SPW-Rs remove unnecessary synapses to avoid memory saturation while sparing behaviorally important information (Norimoto et al., 2018).

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Interactions between hippocampal pyramidal cells and interneurons maintain an exquisite balance between excitation and inhibition during SPW-Rs (Buzsaki, 2015). A recent study showed that neurons that are activated in mice when exploring novel environments are preferentially activated during SPW-Rs and reactivated neurons have a higher excitation/inhibition synaptic ratio, suggesting that unbalanced excitation underlies the off-linereactivation of neurons encoding novel information (Mizunuma et al., 2014). It has been shown that SPW-Rs are more frequent in the hour following a training session of an odor-reward association task (Eschenko & Sara, 2008) and a radial maze task, which positively correlated with a significant improvement in performance (Ramadan, Eschenko, & Sara, 2009). Moreover, hippocampal firing patterns associated with spatial novelty or reward learning have been found to be reactivated more strongly than those associated with familiar and unrewarded experiences (McNamara, Tejero-Cantero, Trouche, Campo-Urriza, & Dupret, 2014; O’Neill, Senior, Allen, Huxter, & Csicsvari, 2008; Singer & Frank, 2009). In support of the information transfer hypothesis, it has been shown that there are coordinated interactions between hippocampal SPW-Rs and neocortical oscillations. Hippocampal SPW-Rs are modulated by the sleep spindles, both events are modulated by the slow oscillation, and all three oscillations are biased by the phase of the ultraslow rhythm (approximately 0.1 Hz) (Sirota, Csicsvari, Buhl, & Buzsaki, 2003). Such cross frequency modulation of rhythms, driven from lower to higher frequencies, forms the basis of the hierarchical organization of multiple timescales in the brain (Buzsáki, 2006; Sirota et al., 2003). SPW-Rs are more likely to occur at the transition from DOWN to UP states and at the transition from UP to DOWN states (Battaglia, Sutherland, & McNaughton, 2004; Isomura et al., 2006; Molle, Eschenko, Gais, Sara, & Born, 2009; Peyrache, Battaglia, & Destexhe, 2011; Peyrache, Khamassi, Benchenane, Wiener, & Battaglia, 2009; Sirota et al., 2003) of slow oscillations (Steriade, 2003) and tend to coincide with neocortical spindles during sleep (Clemens et al., 2011; Molle et al., 2009; Siapas & Wilson, 1998; Sirota et al., 2003). The reactivation of ensemble firing patterns in neocortical areas coincides with hippocampal SPW-Rs at a coarser timescale ( Jadhav, Rothschild, Roumis, & Frank, 2016; Ji & Wilson, 2006; Peyrache et al., 2009). Cell assemblies formed in the medial prefrontal cortex while awake are transiently reactivated during non-REM sleep in a time-compressed manner (Euston, Tatsuno, & McNaughton, 2007; Peyrache et al., 2009). Prefrontal reactivation is positively correlated with DOWN-to-UP state transition density ( Johnson, Euston, Tatsuno, & McNaughton, 2010) and most often occurs near the beginning and ending of the UP state (Peyrache et al., 2009). Moreover, as soon as a rat had learned a behavioral rule, prefrontal neurons were

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IV “LOW STATES” IN NON-REM SLEEP

recruited into cell assemblies that were coordinated with the hippocampal theta rhythm, and the same assemblies were then preferentially reactivated during subsequent sleep (Benchenane et al., 2010). These findings suggest that coordinated hippocampal-prefrontal reactivation may play a role in selective memory consolidation (Mizuseki & Miyawaki, 2017). The perturbation of SPW-Rs during sleep by electric stimulation leads to memory impairment (Girardeau, Benchenane, Wiener, Buzsáki, & Zugaro, 2009). Moreover, the induction of delta waves and spindles in the neocortex by SPW-R-triggered electric stimulation was found to reinforce coordination between the hippocampus and prefrontal cortex and to improve memory performance in a novel object recognition task, suggesting that hippocampal-prefrontal coupling mediates memory consolidation (Maingret, Girardeau, Todorova, Goutierre, & Zugaro, 2016). Furthermore, off-line reactivation of newly formed cell assemblies, but not of previously formed cell assemblies, positively correlates with future context-dependent reinstatement (van de Ven, Trouche, McNamara, Allen, & Dupret, 2016). Reactivation during SPW-Rs is required for consolidating cell assemblies only when the assemblies are newly formed and are gradually strengthened during the first exposure to a novel environment (van de Ven et al., 2016).

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Although non-REM sleep is characterized by slow oscillations (0.1–4 Hz, depending on the species), slower fluctuations in the electric activity of the brain, on the order of 0.01–0.1 Hz, have also been observed (Pickenhain & Klingberg, 1967). Less active periods of this fluctuation are associated with a long-lasting suppression of electric brain activity in a wide frequency range (from 0.5 to 50 Hz) and spiking activity of neurons ( Jarosiewicz, McNaughton, & Skaggs, 2002; Miyawaki, Billeh, & Diba, 2017; Watson, Levenstein, Greene, Gelinas, & Buzsaki, 2016) (Fig. 4.1). Similar quiescent periods have also been observed in anesthetized (Aladjalova, 1957; Hughes, Lorincz, Parri, & Crunelli, 2011) and awake animals (also known as SIA: small-amplitude irregular activity) (Vanderwolf, 1971; Whishaw, 1972). Scientists have assigned various names to the quiescent period of non-REM for more than half a century. Initial reports were based on examinations of the hippocampus, where this was referred to as “low-amplitude irregular activity” (Pickenhain & Klingberg, 1967). Subsequent works referred to this substate as “arousal-like periods” (Roldan, Weiss, & Fifkova, 1963), “low-amplitude sleep” (Bergmann, Winter, Rosenberg, & Rechtschaffen, 1987),

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FIG. 4.1 LOW state is a long-lasting, sporadic, global substate of quiescence during non-REM sleep. (A) LOW states are characterized by suppression of spiking activity and power of local field potential (LFP) in a wide band range that occurred simultaneously across brain regions. ADn, anterodorsal thalamic nucleus; EMG, electromyogram; mPFC, medial prefrontal cortex. (B) Sporadic LOW states regulate oscillatory neuronal activities. In LOW states, various LFP oscillations, such as slow waves, spindles, and sharp-wave ripples (SPW-Rs), are strongly diminished, and slowwave amplitudes and the occurrence rate of SPW-Rs rebound on the offset of LOW states. Firing activity of pyramidal cells and interneurons are also reduced drastically in LOW states. Reproduced from Miyawaki, H., Billeh, Y. N., & Diba, K. (2017). Low activity microstates during sleep. Sleep, 40(6). doi:10.1093/sleep/zsx066, under the terms of the Creative Commons Attribution Non-commercial License.

PART A. BRAIN ACTIVITY DURING SLEEP AND WAKING

V COMPARISON OF NETWORK DYNAMICS DURING REM SLEEP AND WAKING THETA STATES

“microarousals” (MA) (Halasz, Terzano, Parrino, & Bodizs, 2004; Watson et al., 2016), “sleep SIA” (S-SIA) (Jarosiewicz et al., 2002; Jarosiewicz & Skaggs, 2004), and “LOW states” (Miyawaki et al., 2017). It is worth noting that the inconsistency of the term SIA in the literature, which originally referred to suppression periods during wakefulness (Vanderwolf, 1971; Whishaw, 1972), was then extended to sleep, referred to as S-SIA (Jarosiewicz et al., 2002), and recently was used to describe the quiescent periods in non-REM sleep (Jarosiewicz & Skaggs, 2004; Kay et al., 2016). Because a significant fraction of the quiescent periods during non-REM sleep is accompanied by transient increases in muscle tone, they are sometimes regarded as a brief waking period. However, it has been recently shown that there are at least two types of quiescent states in non-REM sleep, one that accompanies transient high electromyography (EMG) signals, whereas the other occurs with low EMG signals. These have been described as MA and LOW states, respectively (Miyawaki et al., 2017). Interestingly, while the majority of hippocampal cells quit firing during MA/LOW, a subset of neurons increased their activity, and this ensemble actually encoded the location where the animal fell asleep (Jarosiewicz et al., 2002; Jarosiewicz & Skaggs, 2004; Kay et al., 2016). When a sleeping animal was moved without disturbing its sleep, the neuronal ensemble during MA/LOW kept representing the location where the animal fell asleep but updated immediately after waking (Jarosiewicz & Skaggs, 2004). This clearly indicates that MA/LOW states are not brief sessions of wakefulness, but an integral part of sleep. Delta amplitude, which is low during both MA/LOW and wakefulness, reanimates quickly after transitioning back to non-REM from MA/ LOW but increases gradually after “true” waking as the animal transitions back into sleep (Miyawaki et al., 2017; Watson et al., 2016), which also suggests that MA/LOW is different from wakefulness. Although some studies have not distinguished MA and LOW clearly, hereafter, we refer to the quiescent state in nonREM sleep as “LOW states,” following the first report (Pickenhain & Klingberg, 1967), with emphasis on the fact that these are microstates spreading across multiple brain regions, including at least in the CA1, EC, mPFC, postsubiculum, and anterodorsal thalamic nucleus (Miyawaki et al., 2017). Although the physiological importance of LOW states is not yet known, accumulating evidence indicates that LOW states are associated with sleep function. First, LOW states modulate oscillatory events in non-REM sleep, such as delta waves, sleep spindles, and SPW-Rs (Lecci et al., 2017; Miyawaki et al., 2017; Fig. 4.1). During LOW states, these oscillations are strongly suppressed, and the occurrence rates of SPW-Rs and delta amplitudes increase right after the transition out of LOW states

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(Miyawaki et al., 2017). Second, the fraction of LOW states in non-REM sleep increased across sleep and decreased after long-lasting wakefulness (Miyawaki et al., 2017). Third, there is evidence that the activity of neuronal populations during LOW may encode the location where the animal fell asleep, as discussed above (Jarosiewicz et al., 2002; Jarosiewicz & Skaggs, 2004; Kay et al., 2016). Moreover, the neurons representing the location of the animal when it fell asleep may be overrepresented in the CA2 area, and these cells may also be active specifically during immobile waking epochs (Kay et al., 2016). Additionally, LOW states may be beneficial to homeostatic regulation of neuronal activity, at least in the frontal cortex (Watson et al., 2016).

V COMPARISON OF NETWORK DYNAMICS DURING REM SLEEP AND WAKING THETA STATES REM sleep is considered a paradoxical state because, despite the high arousal threshold, the physiological patterns in the forebrain during REM sleep resemble those observed while awake (Mizuseki & Miyawaki, 2017). Indeed, theta oscillations in the hippocampus and lowvoltage desynchronized LFP in the neocortex are prominent during both REM sleep and waking theta states (Mizuseki & Miyawaki, 2017). Despite the apparent similarity, the tone of various neuromodulators is different between these states. During REM sleep, acetylcholine levels are similar to or even higher than when awake, but norepinephrine, serotonin, and histamine levels are lower (Hasselmo, 1999). During the waking theta state, theta oscillations are believed to play an important role in the temporal packaging and transfer of neuronal information (Skaggs et al., 1996), encoding and retrieval of episodic and spatial memories (Hasselmo, 2005; Jensen & Lisman, 2005; O’Keefe & Burgess, 2005), and synaptic plasticity (Pavlides, Greenstein, Grudman, & Winson, 1988). Consistent with these hypotheses, interfering with theta rhythms results in memory impairment (Mizumori, Perez, Alvarado, Barnes, & McNaughton, 1990; Robbe & Buzsaki, 2009; Wang, Romani, Lustig, Leonardo, & Pastalkova, 2015; Winson, 1978). In contrast to the relatively well-understood role of theta oscillations during waking theta states, little is known about the role of theta oscillations during REM sleep. One of the main differences between REM sleep and waking theta states is the frequency of oscillations; theta oscillations are faster during waking than during REM sleep except for periods of phasic REM (Montgomery, Sirota, & Buzsaki, 2008). Moreover, this study demonstrated that theta and gamma synchrony between dentate gyrus (DG) and CA3 is significantly higher during REM sleep than during waking theta

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states. In contrast, the amplitude of gamma oscillations in CA1 and the coherence at gamma frequencies between CA3 and CA1 are significantly lower during REM sleep than during waking theta states. Importantly, during phasic bursts of local field potential power (phasic REM), theta and gamma synchrony among the DG, CA3, and CA1 regions transiently increases, as well as the firing of CA1 pyramidal neurons. Therefore, it has been hypothesized that information processing within the DG-CA3 network is enhanced, but CA3-CA1 coordination is reduced during tonic REM sleep, thereby supporting off-line mnemonic processing (Montgomery et al., 2008). On the other hand, phasic REM provides an opportunity to synchronize the hippocampal trisynaptic loop and increase output to cortical targets, thereby promoting memory consolidation (Montgomery et al., 2008). Consistent with this hypothesis, perturbation of theta rhythms during REM sleep has been shown to impair contextual memory consolidation (Boyce, Glasgow, Williams, & Adamantidis, 2016). Another difference between waking theta states and REM sleep involves the preferred theta phase of CA1 pyramidal neurons (Mizuseki, Diba, Pastalkova, & Buzsaki, 2011; Poe, Nitz, McNaughton, & Barnes, 2000). It has been shown that CA1 pyramidal cells that are active in novel places fire at the troughs of theta waves during both exploration and subsequent REM sleep, while cells that are active in familiar places discharge at the troughs of theta waves during exploration and at peaks during subsequent REM sleep (Poe et al., 2000). A

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Since the firing phase of neurons can influence whether synapses are strengthened or weakened (Holscher, Anwyl, & Rowan, 1997; Huerta & Lisman, 1995; Pavlides et al., 1988), it has been suggested that, during REM sleep, recently acquired memories are strengthened and older ones are weakened (Poe et al., 2000). A recent study could not detect any noticeable effect of novelty on theta phase preference during REM sleep (Mizuseki et al., 2011). Instead, it was found that there is a strong correlation between the preferred phase of theta waves, which are recorded from the middle of the CA1 pyramidal layer, and the somal positions of CA1 pyramidal neurons along the CA1 radial axis (Mizuseki et al., 2011). Superficial pyramidal neurons fire at the trough of theta oscillations during both waking theta states and REM sleep. In contrast, deep pyramidal cells fire at the trough of theta during waking theta states but shift their firing phase to the peak of theta during REM, when entorhinal layer 3 (EC3) input to the CA1 is maximum (Mizuseki et al., 2011) (Fig. 4.2A). The hippocampal CA1 region receives different spatiotemporal input patterns from the CA3 region and entorhinal cortex during REM sleep and waking theta states (Fernandez-Ruiz et al., 2017; Schomburg et al., 2014). Compared with waking theta states, the firing rate of EC3 principal neurons increases during REM sleep, while the firing rate of CA3 pyramidal neurons decreases (Mizuseki & Buzsaki, 2013; Mizuseki, Royer, Diba, & Buzsaki, 2012; Schomburg et al., 2014). Firing rates of upstream neurons are reliably reflected in the gamma B

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FIG. 4.2 Entorhinal-CA3 dual-input control of spike timing in the hippocampus during waking theta states and REM sleep. (A) Distribution of the preferred theta phase of CA1 pyramidal cells in the superficial and deep layers during waking theta states and REM sleep. Gray curves at top indicate the idealized reference theta cycle in the CA1 pyramidal layer. (B and C) Diagrams summarizing the entorhinal-CA3 dual-input control of spike timing of CA1 pyramidal cells. (B) Entorhinal layer 3 (EC3) middle gamma input (60–100 Hz) in the stratum lacunosum-moleculare (LM) is maximum at the positive peak of the CA1 pyramidal layer theta, followed by CA3 slow gamma (30–60 Hz) input in stratum radiatum (rad) on the descending theta phase. pyr, CA1 pyramidal cell layer; ori, stratum oriens. Black indicates theta oscillations in CA1 pyramidal cell layer and in stratum lacunosum-moleculare. (C) The relative strengths of phase-separated CA3 and EC3 inputs are hypothesized to determine the theta phase of spiking of CA1 pyramidal cells. During REM sleep, CA3 drive is weaker, and EC3 is stronger compared with those during waking theta states. As a result, the preferred phase of CA1 pyramidal cells (green) moves toward the peak of theta during REM. Black traces, theta oscillations in CA1 pyramidal cell layer. (A) to (C) Positive polarity is up. (A): Modified from Mizuseki, K., Diba, K., Pastalkova, E., & Buzsaki, G. (2011). Hippocampal CA1 pyramidal cells form functionally distinct sublayers. Nature Neuroscience, 14(9), 1174–1181. https://doi.org/ 10.1038 /nn.2894. (B) Reproduced from Fernandez-Ruiz, A., Oliva, A., Nagy, G. A., Maurer, A. P., Berenyi, A., & Buzsaki, G. (2017). Entorhinal-CA3 dual-input control of spike timing in the hippocampus by thetagamma coupling. Neuron, 93(5), 1213–1226.e1215. doi:10.1016/j.neuron.2017.02.017. (C): Modified from Fernandez-Ruiz, A., Oliva, A., Nagy, G. A., Maurer, A. P., Berenyi, A., & Buzsaki, G. (2017). Entorhinal-CA3 dual-input control of spike timing in the hippocampus by theta-gamma coupling. Neuron, 93(5), 1213–1226.e1215. doi:10.1016/j.neuron.2017.02.017, with permission from Elsevier.

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VI DISTRIBUTION OF FIRING RATES DURING DISTINCT BRAIN STATES

power in the target layers in the hippocampus (FernandezRuiz et al., 2017; Schomburg et al., 2014). Similar to the firing-rate changes in CA3 and EC3 between waking theta states and REM sleep, it has been shown that lowfrequency gamma oscillations (30–60 Hz) in the stratum radiatum, whose power is maximum at the descending phase of the CA1 pyramidal cell layer theta oscillations, decrease during REM sleep when compared with the waking theta states (Fernandez-Ruiz et al., 2017; Schomburg et al., 2014). In contrast, mid-frequency gamma oscillations (60–100 Hz) in the stratum lacunosummoleculare, whose power is maximal at the peak of the CA1 pyramidal layer theta oscillations, increase during REM sleep when compared with waking theta states (Fernandez-Ruiz et al., 2017; Schomburg et al., 2014; Fig. 4.2B). Because EC3 principal neurons fire on the peak of theta and CA3 pyramidal neurons fire at the descending phase during both REM sleep and waking theta states (Mizuseki et al., 2011; Oliva, Fernandez-Ruiz, Buzsaki, & Berenyi, 2016b), it has been suggested that deep pyramidal cells, relative to superficial pyramidal cells, in the CA1 area are more strongly influenced by direct EC3 input (Fernandez-Ruiz et al., 2017; Mizuseki et al., 2011; Mizuseki & Miyawaki, 2017; Fig. 4.2B and C). Consistent with this notion, deep cells, relative to superficial cells, are more strongly phase-locked by entorhinal slow oscillations in non-REM sleep, suggesting that deep cells are under stronger control by direct EC3 input (Mizuseki et al., 2011; Mizuseki & Miyawaki, 2017). Deep and superficial CA1 pyramidal neurons, which have distinct physiological functions (Geiller, Royer, & Choi, 2017; Slomianka, Amrein, Knuesel, Sørensen, & Wolfer, 2011; Soltesz & Losonczy, 2018), fire at the same theta phase during waking theta states but at different phases during REM sleep, thus influencing their targets jointly or differentially, depending on the brain state (Mizuseki et al., 2011). The functional importance of such state-dependent integration and segregation of neuronal information by theta oscillations remains unknown (Mizuseki & Miyawaki, 2017).

VI DISTRIBUTION OF FIRING RATES DURING DISTINCT BRAIN STATES Many important parameters of brain structure and activity follow skewed distributions with a heavy tail, including the firing rate of individual neurons, degree of synchrony, probability of spike transmission, number of synaptic contacts between neurons, size of synaptic boutons, and synaptic strength. This suggests that skewed, typically lognormal, distributions are fundamental to the structural and functional organization of the brain (Buzsaki & Mizuseki, 2014). In the hippocampus and entorhinal cortex, the firing rates of principal

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neurons show a lognormal-like distribution in all brain states. Mean and peak firing rates within place fields of hippocampal place cells also follow lognormal-like patterns (Mizuseki & Buzsaki, 2013). Further, both mean spontaneous and stimulus-evoked firing rates of individual neocortical neurons in the intact brain span at least three orders of magnitude and their distributions obey a long-tailed, typically lognormal distribution (Buzsaki & Mizuseki, 2014; Watson et al., 2016). This skewed distribution creates a wide-ranging rate spectrum that spans from a majority of slow-firing neurons to a small fraction of fast-firing cells. Theoretical works suggest that skewed firing-rate distributions may be beneficial for network performance (Gilson & Fukai, 2011; Ikegaya et al., 2013; Teramae, Tsubo, & Fukai, 2012). Highly active and stable minority neurons may endow networks with the flexibility to continuously remodel without compromising stability and function (Panas et al., 2015), and network hubs composed of highly active neurons (Yassin et al., 2010) may be beneficial for information transfer between subnetworks (Jahnke, Memmesheimer, & Timme, 2014; Shimono & Beggs, 2015). The firing rates of the same neuron are robustly and positively correlated across brain states, and therefore, the relative rank of individual cells in the rate distribution does not change much across brain states. Furthermore, the mean firing rates of the same hippocampal neuron in different situations during waking exploration remain positively correlated (Buzsaki & Mizuseki, 2014; Mizuseki & Buzsaki, 2013; Mizuseki & Miyawaki, 2017). A relatively fixed firing rate of individual neurons has also been reported in the neocortex (Buzsaki & Mizuseki, 2014; Mizuseki & Miyawaki, 2017). In the mouse visual cortex, firing rates of regular spiking neurons differ by several orders of magnitude, and after recovery from long-term perturbation by visual deprivation, a given neuron returns to its initial firing-rate set point (Hengen, Torrado Pacheco, McGregor, Van Hooser, & Turrigiano, 2016). Collectively, it has been suggested that the skewed distribution of firing rates reflects an intrinsic biophysical heterogeneity in neuronal populations and that such a skewed distribution is actively maintained by homeostatic mechanisms (Hengen et al., 2016). Recent evidence suggests that slow- and fast-firing neurons exhibit differences in plasticity and dynamics (Mizuseki & Miyawaki, 2017). Highly active hippocampal neurons are more likely to be assigned place fields (Alme et al., 2014; Mizuseki & Buzsaki, 2013; Rich, Liaw, & Lee, 2014; Witharana et al., 2016). When CA1 pyramidal neurons divide into rigid and plastic cells based on the changes in contribution to the SPW-Rassociated neuronal sequence between sleep before and after the novel maze exploration, plastic neurons fire at lower firing rates, exhibit higher spatial specificity, and

PART A. BRAIN ACTIVITY DURING SLEEP AND WAKING

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gain higher place specificity during exploration than rigid neurons (Grosmark & Buzsaki, 2016). The coexistence of fast-firing, rigid neurons and slow-firing, plastic neurons may be beneficial for both system stability and mnemonic function (Grosmark & Buzsaki, 2016). Further, a recent study reported that during memory encoding, only a fraction of CA1 place cells express an early immediate gene, C-fos, and therefore function as “engram cells” (Tanaka et al., 2018). Such cells fired at higher firing rates than C-fos negative place cells during memory encoding. During memory recall, the engram cells exhibited higher context specificity, yet with higher tendency for remapping within the same context, than C-fos negative place cells (Tanaka et al., 2018). Although both rigid cells and engram cells have higher firing rates and lower spatial information, elucidating the relationship between the dichotomy of the rigid versus plastic cells and that of the engram versus nonengram cells warrants further investigation.

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VII REGULATION OF NEURONAL FIRING RATE BY SLEEP

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The synaptic homeostasis hypothesis (SHY) of Tononi and Cirelli postulates that synaptic weights are potentiated during waking and homeostatic mechanisms during sleep globally downscale all synapses (de Vivo et al., 2017; Diering et al., 2017; Tononi & Cirelli, 2006). Consistent with the SHY, the firing rate of neurons in the rat barrel cortex increases during waking and decreases during sleep (Vyazovskiy et al., 2009). During sleep, the decrease in firing rate correlates with a decrease in slow-wave activity, a marker of sleep homeostasis (Vyazovskiy et al., 2009). Further, model studies have shown that global downscaling of synaptic strength results in decreases in slow-wave amplitude and neuronal firing rates (Esser, Hill, & Tononi, 2007; Olcese, Esser, & Tononi, 2010). Similar to neocortical neurons, hippocampal neurons decrease their firing rate during sleep and increase firing upon waking across the circadian cycle (Miyawaki & Diba, 2016; Fig. 4.3C). Interestingly, fast- and slow-firing cells change their firing differently across sleep/wake cycles, with slow-firing neurons in the hippocampus having the largest relative firing decrease during sleep, whereas moderately-firing hippocampal cells exhibit the greatest relative increase in firing during wakefulness (Miyawaki & Diba, 2016; Miyawaki, Watson, & Diba, 2019). Recently, it was also reported that, in the neocortex, the excitabilities of slow- and fast-firing neurons are modulated differentially during sleep (Watson et al., 2016). SHY assumes that slow-wave oscillations drive global downscaling. It is supported by the observation that firing rates within UP states are positively correlated with slow-wave amplitudes (Vyazovskiy et al., 2009) and

Awake

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FIG. 4.3

Regulation of firing rates across sleep/wake cycles. (A) Timenormalized power spectra of adjacent non-REMn/REM/non-REMn+1 episodes and corresponding firing rates (mean  S.E.M.) of pyramidal cells (black) and interneurons (white) shown within intervals of non-REM and REM episodes. Firing rates gradually increase within non-REM episodes and rapidly decrease within REM episodes, resulting in an overall decrease in firing rate. Horizontal lines represent mean rates at the beginning of the non-REMn/REM/non-REMn+1 cycle. (B) Incidence rates of SPW-Rs (left) and sleep spindles (right) in non-REMn are predictive of changes in firing rates across non-REMn/REM/non-REMn+1 sequences. The mean and SEM of firing-rate changes are shown. (C) Schematic diagram of firing-rate changes across wake/sleep cycles (top) and during sleep (bottom). Overall, firing rates increase while awake and decrease during sleep. During sleep, firing rates gradually increase during non-REM sleep and rapidly decrease during REM sleep. SPW-Rs and sleep spindles occur during non-REM sleep, and their incidences predict firing rate decreases during subsequent REM sleep. (A): Reproduced from Grosmark, A. D., Mizuseki, K., Pastalkova, E., Diba, K., & Buzsaki, G. (2012). REM sleep reorganizes hippocampal excitability. Neuron, 75(6), 1001–1007. doi:10.1016/j.neuron.2012.08.015. (B and C): Modified from Miyawaki, H., & Diba, K. (2016). Regulation of hippocampal firing by network oscillations during sleep. Current Biology, 26(7), 893–902. doi:10.1016/j.cub.2016.02.024, with permission from Elsevier.

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recent findings in the neocortex that spikes within UP states induce long-term depression (LTD) unless followed by a postsynaptic spike (Gonzalez-Rueda, Pedrosa, Feord, Clopath, & Paulsen, 2018). In contrast, the slow oscillation in the hippocampus is not as prominent as that in the neocortex, which could be partially attributable to persistent activity during neocortical DOWN states inherited from MEC (Hahn, McFarland, Berberich, Sakmann, & Mehta, 2012). Hippocampal CA1 and CA3 pyramidal cells and dentate granule cells lack bimodality in their membrane potentials, whereas neocortical, entorhinal, and subicular neurons exhibit prominent bimodality (Isomura et al., 2006). Despite less prominent slow oscillations in the hippocampus, there is a possibility that neocortical slow oscillations play a role in the hippocampal downscaling. Hippocampal activities during non-REM sleep are strongly modulated by neocortical and entorhinal slow oscillations (Isomura et al., 2006; Mizuseki et al., 2011). Although lacking clear bimodality (Isomura et al., 2006), membrane potentials of hippocampal CA1 and CA3 pyramidal neurons and dentate granule cells are modulated by neocortical UP-DOWN states (Hahn, Sakmann, & Mehta, 2007). Further, membrane potentials of hippocampal CA1 interneurons are phase-locked to neocortical UP-DOWN states (Hahn, Sakmann, & Mehta, 2006). Importantly, the neuronal firing rate in the hippocampal CA1 field gradually increases during non-REM sleep episodes but rapidly decreases during interleaved REM episodes, resulting in a net decrease in firing rate during sleep (Grosmark, Mizuseki, Pastalkova, Diba, & Buzsaki, 2012; Miyawaki & Diba, 2016; Fig. 4.3A). The magnitude of the decrease in firing rate between non-REM sleep epochs is positively correlated with the power of theta oscillations during interleaved REM epochs; therefore, it has been proposed that one important function of REM sleep is to downregulate the firing rate of neurons, at least in the hippocampal CA1 region (Grosmark et al., 2012; Fig. 4.3A). It was recently reported that the incidence of sleep spindles and SPW-Rs during non-REM sleep also correlates with a decrease in the firing rate of hippocampal CA1 pyramidal neurons during subsequent REM sleep (Miyawaki & Diba, 2016; Fig. 4.3B). Consistent with this notion, SPW-Rs during non-REM sleep induce hippocampal LTD, rather than long-term potentiation (LTP) (Norimoto et al., 2018) as predicted previously (Mehta, 2007). Interestingly, the magnitude of decrease in firing rate during REM sleep epochs is correlated more strongly to spindle/SPW-R incidences in preceding non-REM epochs than theta power during the REM sleep epochs. Based on these observations, it has been proposed that the homeostatic changes in hippocampal firing are initiated by spindles and SPW-Rs during non-REM sleep and implemented during subsequent REM sleep (Miyawaki & Diba, 2016; Fig. 4.3C). Potentially related to these observations, the number of transitions from

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non-REM to REM states was positively correlated with memory performance in a two-way active avoidance task in rats (Langella, Colarieti, Ambrosini, & Giuditta, 1992). It is worth noting that artificially prolonged and shortened REM sleeps are associated with increased and decreased slow-wave activity during subsequent nonREM sleep, respectively (Hayashi et al., 2015), suggesting that non-REM sleep is under the influence of REM sleep, as is the case for REM sleep, which is modulated by non-REM sleep (Benington & Heller, 1994). It is not clear how REM sleep implements synaptic changes, but theta oscillations may play a role. The density of spindles and SPW-Rs during non-REM sleep is also positively correlated with theta power in the subsequent REM epoch (Miyawaki & Diba, 2016). Although the possibility of a causal relationship between SPW-Rs and theta oscillations has not been tested formally yet, both the interruption of SPW-Rs in non-REM sleep (Girardeau et al., 2009) and the perturbation of theta rhythms during REM (Boyce et al., 2016) disturb memory consolidation, suggesting that SPW-Rs and theta oscillations work synergistically. The sleep spindles are generated through a thalamocortical circuit (Steriade, McCormick, & Sejnowski, 1993). In humans, spindles are observed across various cortical regions but generally stay local (Andrillon et al., 2011; Nir et al., 2011). Spindles are also present in the hippocampus in both humans and rodents (Andrillon et al., 2011; Sullivan, Mizuseki, Sorgi, & Buzsaki, 2014). Interestingly, theta and spindles have similar input patterns across layers of the hippocampus, whereas spindles regulate spiking activity more synchronously across the hippocampus and the entorhinal cortex (Sullivan et al., 2014). Downregulation of firing activity in the hippocampus is also positively correlated with spindle density in the hippocampus (Miyawaki & Diba, 2016). It is unclear whether SPW-Rs or spindles have dominant roles in downscaling neuronal activity or if both work coherently since both exhibit similar correlations with firing-rate changes (Miyawaki & Diba, 2016) and their occurrences are aligned to slow oscillations (Peyrache et al., 2011; Sirota et al., 2003). It is worth noting that the enhancement of endogenous coordination of SPW-Rs, delta waves, and spindles results in better spatial memory (Maingret et al., 2016), which indicates that cross frequency coupling among these rhythms plays an important role in memory storage. At the same time, it is still elusive whether such coupling has a role in downregulating network excitability. In summary, recent studies suggest that slow-wave activity, SPW-Rs, and spindles during non-REM sleep on one hand and theta oscillations during REM sleep on the other hand play important roles in the downscaling of firing activity and the process of memory storage. These observations lead to the question of whether downscaling contributes to memory functions such as consolidation. We will discuss this point in the following section.

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VIII MEMORY CONSOLIDATION AND HOMEOSTATIC FUNCTIONS OF SLEEP Sleep is hypothesized to mediate both memory consolidation and homeostatic functions. How does sleep implement both the selective plasticity induction necessary for memory consolidation and the general homeostatic plasticity required to maintain a functioning neural system (Mizuseki & Miyawaki, 2017)? The distribution of firing rates is wider during waking periods than in sleep in both the hippocampus (Mizuseki & Buzsaki, 2013) and the neocortex (Watson et al., 2016). It has been proposed that the most prominent effect of sleep is a narrowing of the firing-rate distribution (Watson et al., 2016). Namely, the activity of fast-firing neurons decreases, and the activity of slow-firing neurons increases during non-REM sleep, thereby homogenizing the firing-rate spectrum and adjusting network excitability in the neocortex (Hobson & McCarley, 1971; Watson et al., 2016; Miyawaki et al., 2019), especially during non-REM sleep. In contrast, firing rates across the entire rate spectrum decrease during REM sleep. Microarousals, interspersed within non-REM sleep, increase the firing rates of slowfiring neurons (Watson et al., 2016). In the neocortex, fast-firing neurons tend to fire earlier than slow-firing neurons at the DOWN-to-UP transition during non-REM sleep (Luczak, Bartho, Marguet, Buzsaki, & Harris, 2007; Watson et al., 2016). Based on these observations, it has been proposed that spiketiming-dependent plasticity at the DOWN-to-UP transition in the neocortex may account for the narrowing of the distribution of firing rate during non-REM sleep (Levenstein, Watson, Rinzel, & Buzsaki, 2017). According to this proposal, since fast-firing neurons tend to fire before those with a low firing rate, synapses from fastfiring neurons to slow-firing neurons are potentiated, while synapses from slow-firing neurons to fast-firing neurons tend to be weakened. This redistribution of synaptic weight would pull both ends of the firing-rate distribution closer to the mean (Levenstein et al., 2017). While narrowing of the firing-rate distribution during sleep could compensate for the widening of the distribution while awake, the firing rates of neurons still would not be identical, suggesting that alternating sleep/ waking cycles are a crucial determinant of the skewed statistical properties of elements of neuronal networks, such as synapses and the firing rate of neurons (Levenstein et al., 2017). Furthermore, it has been postulated that SPW-R-associated information transfer from the hippocampus to the neocortex “adds” spikes of memory-related slow-firing plastic neurons, which in turn perturb spike-timing-dependent plasticity at the DOWN-to-UP transition in the neocortex (Levenstein et al., 2017). From this perspective, the DOWN-to-UP transition may provide a window of opportunity for both

the homeostatic changes in firing rate and the selective plasticity needed for consolidating novel memory traces (Levenstein et al., 2017). Validation of this fascinating hypothesis awaits further investigation.

ACKNOWLEDGMENTS This work is supported by JSPS KAKENHI (18H05137, 17K19462, 16H04656, and 16H01279) (K. M.), (17K14937) (H. M.), Toray Science Foundation (K. M.), Takeda Science Foundation (K. M.), Kato Memorial Bioscience Foundation (H. M.), GSK Japan Research Grant 2017 (H. M.), The Uehara Memorial Foundation (H. M.), and The Osaka City University (OCU) “Think globally, act locally” Research Grant for young researchers 2018 through the hometown donation fund of Osaka City (H. M.).

CONFLICTS OF INTEREST The authors declare that they have no conflict of interest.

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PART A. BRAIN ACTIVITY DURING SLEEP AND WAKING