Sleep-Wake and Cortical Synaptic Plasticity

C H A P T E R

29 Sleep-Wake and Cortical Synaptic Plasticity Igor Timofeev*,†, Sylvain Chauvette* *CERVO Brain Research Center, Qu
ebec, QC, Canada †Department of Psychiatry and Neuroscience, Universit
e Laval, Qu
ebec, QC, Canada

I INTRODUCTION During the waking state, the electroencephalogram (EEG) or local field potential (LFP) activities generally show an activated pattern (sometime referred to as desynchronized pattern). During waking, cortical neurons exhibit a relatively stable level of the membrane potential (overall average around 62 mV) (Okun, Naim, & Lampl, 2010; Steriade, Timofeev, & Grenier, 2001; Timofeev, Grenier, & Steriade, 2000), mediated mainly by a balance between excitatory and inhibitory synaptic activities (Haider, Duque, Hasenstaub, & McCormick, 2006; Haider, Hausser, & Carandini, 2013; Rudolph, Pospischil, Timofeev, & Destexhe, 2007). Patterns of field potentials and intracellular activities similar to those of the waking state are also recorded during rapid eye movement (REM) sleep (Steriade et al., 2001; Timofeev, Grenier, & Steriade, 2001). Slow-wave sleep (SWS), also referred to as non-rapid eye movement (non-REM) sleep, is characterized by the presence of large-amplitude slow waves in the EEG (Blake & Gerard, 1937) or LFP. When recording intracellularly, it becomes apparent that the depth-positive or surfacenegative component of field potential slow waves is mediated by hyperpolarized membrane potentials produced mainly due to disfacilitation, with virtually no synaptic activities. These periods, referred to as silent states or DOWN states, alternate with active states (also referred to as UP states) corresponding to periods rich in synaptic activities, action potentials, and a depolarized membrane potential (Chauvette, Volgushev, & Timofeev, 2010; Steriade et al., 2001; Timofeev et al., 2001; for details of neuronal activities during the sleep-wake cycle, see Chapter 1 of this book). One of the known functions of sleep is memory consolidation ( Jenkins & Dallenbach, 1924; Maquet, 2001; Rasch & Born, 2013). Synaptic plasticity is a prime

Handbook of Sleep Research, Volume 30 ISSN: 1569-7339 https://doi.org/10.1016/B978-0-12-813743-7.00029-3

mechanism in memory formation (Nabavi et al., 2014). Therefore, synaptic plasticity induced by sleep is likely responsible for sleep-dependent memory consolidation. The question is as follows: what kind of synaptic plasticity is induced by sleep? A prolonged and regular activation of presynaptic fibers sets some steady-state level of synaptic plasticity, typically depression, and a short break in such presynaptic firing recovers synaptic strength (Galarreta & Hestrin, 1998, 2000). A pattern of neuronal activity lasting for hours, either during wake or sleep, should preset a steady-state of neuronal plasticity. Subsequently, a change in the activity pattern (during the transition from wake/ REM sleep to SWS or SWS to wake/REM) should change this preset level of synaptic plasticity. An influential hypothesis suggests that slow-wave sleep induces synaptic downscaling in the neocortex (Tononi & Cirelli, 2003, 2006, 2014). However, multiple lines of evidence supporting the downscaling hypothesis are indirect and not always convincing, and several experimental studies directly oppose the sleep-dependent synaptic downscaling hypothesis (Frank, 2013, 2017; Timofeev & Chauvette, 2017, 2018). Therefore, this chapter will focus on reviewing the major experimental findings and discussing the predominant hypotheses regarding the phenomenon of sleep-dependent synaptic plasticity.

II NEURONAL PLASTICITY Neuronal plasticity is the ability of neurons to modify responses to incoming stimuli due to previous activities (Timofeev, 2011), and the sign of plasticity can either increase or decrease those responses. In this process, a leading role is usually played by synaptic plasticity, but the neuronal output is also modified by intrinsic currents, which also reveal several forms of plasticity. One form of

443

© 2019 Elsevier B.V. All rights reserved.

444

29. SLEEP-WAKE AND CORTICAL SYNAPTIC PLASTICITY

neuronal plasticity is structural plasticity, which can involve dynamic changes or formation/elimination of dendritic branches, spines, etc. Synaptic plasticity, when occurring in the same pathway (connection), is called homosynaptic plasticity, while it is termed heterosynaptic plasticity if it occurs in an input-unspecific fashion, when synaptic modification in a given synapse does not depend on the presynaptic activity of the same synapse, but rather on the activity of a different presynaptic terminal formed on the same neuron. Homeostatic plasticity down- (or up-) regulates neuronal excitability, depending on high (or low) levels of network activity. Homeostatic plasticity occurs not necessarily at a synapse with altered levels of activity, making it a form of heterosynaptic plasticity. A particular form of synaptic plasticity is called spike-timing-dependent plasticity. Neuronal plasticity can roughly be subdivided into short-, mid-, and long-term timescales, with effects occurring on (a) subsecond, (b) second-to-minute, and (c) minute-to-hour scales, respectively. All activitydependent increases in neuronal responses are usually called facilitation and/or potentiation, and a decrease in neuronal responses is called depression. Short-term plasticity (STP) is implicated in the network operations facilitating or preventing signal transmission. Mid- and long-term plasticity might be implicated in the formation of working and short-term memories and may contribute to the consolidation of long-term memory. Throughout life, neurons of the thalamocortical (TC) system remain spontaneously active and fire action potentials, which acts, among other things, to preset conditions for the induction of neuronal plasticity. During quiet wakefulness and REM sleep, most neurons in the TC system fire spontaneously in a tonic mode, presetting a steady-state of homosynaptic plasticity in the implicated synapses of the TC system. During SWS, neurons within the TC system fire single action potentials and/ or bursts of spikes separated by long-lasting periods of silence. During these silent periods, there should be a recovery from steady-state synaptic depression or facilitation. Because silent periods occur nearly simultaneously in large neuronal constellations (Chauvette et al., 2010; Nir et al., 2011; Sheroziya & Timofeev, 2014; Volgushev, Chauvette, Mukovski, & Timofeev, FIG. 29.1

Does slow-wave sleep neuronal activity lead to LTD or LTP? Low-frequency (A) and highfrequency (B) firing in a neuron recorded intracellularly during slow-wave sleep in the suprasylvian gyrus (area 7) of a cat. The upper trace corresponds to the local field potential recorded contralateral to the intracellular activities. Shaded areas correspond to silent (DOWN) states.

A

2006; Vyazovskiy et al., 2011), SWS is likely favorable for the development of heterosynaptic plasticity. The patterns of neuronal firing during SWS are reminiscent to the “classical” patterns of electric stimulation used to evoke both forms of long-term plasticity: Repeated activation around 1 Hz (the approximate frequency of slow waves) is reminiscent of long-term depression (LTD) induction protocols, while high-frequency spike trains (the neuronal firing within active periods of slow waves) are reminiscent of long-term potentiation (LTP) induction protocols (Fig. 29.1). Below, we will briefly summarize the available data on how sleep-wake activities may influence neuronal plasticity.

A Short-Term Plasticity The majority of interactions between neurons are based on chemical synapses. While some synapses do not show short-term synaptic changes (Arenz, Silver, Schaefer, & Margrie, 2008), most chemical synapses do exhibit activity-dependent modulations of efficacy. STP can appear in two forms: short-term facilitation (STF) or short-term depression (STD) (Schwarz, 2003; Zucker & Regehr, 2002). STF depends primarily on the elevation of presynaptic Ca2+ due to preceding presynaptic spikes (Shahrezaei & Delaney, 2005; Tank, Regehr, & Delaney, 1995), and STD depends mainly on the usedependent depletion of the readily releasable pool of synaptic vesicles (Zucker & Regehr, 2002). Most studies of STP have been conducted in vitro and revealed that the type of neuronal cells and connections (i.e., pyramid to pyramid and pyramid to interneuron) dictates whether a specific connection is facilitating or depressing (reviewed in Timofeev, 2011). However, there are at least two problems with these studies. First, they are mostly performed in slices in a silent network. Active network states have a profound influence on both intrinsic and synaptic properties of neurons, including synaptic plasticity (Arieli, Sterkin, Grinvald, & Aertsen, 1996; Borg-Graham, Monier, & Fregnac, 1998; Contreras, Timofeev, & Steriade, 1996; Crochet, Chauvette, Boucetta, & Timofeev, 2005; Crochet, Fuentealba, Cisse, Timofeev, & Steriade, 2006; Haider, Duque, Hasenstaub, Yu, & McCormick, 2007; Hasenstaub, Sachdev, & Long-term depression?

LFP area 7R

20 mV Intracell area 7L –60 mV

0.5 s

PART E. SLEEP, PLASTICITY, AND MEMORY

B

Long-term potentiation?

III SPIKE-TIMING-DEPENDENT PLASTICITY

McCormick, 2007; Hirsch, Alonso, Reid, & Martinez, 1998; Reig, Gallego, Nowak, & Sanchez-Vives, 2006; Reig & Sanchez-Vives, 2007; Rosanova & Timofeev, 2005; Steriade et al., 2001; Timofeev, Contreras, & Steriade, 1996; Timofeev et al., 2002). The mechanisms of these effects are unclear, but it is likely that they involve, at least in part, the extracellular ion dynamics. During the waking state, when the cortical network is continuously active, [K+]o is increased, and [Ca2+, Mg2+, and H+]o is decreased compared with SWS (Ding et al., 2016). On a finer timescale, during the slow oscillation, the [Ca2+]o oscillates (higher during silent states and lower during active states), which affects neurotransmitter release and STP (Crochet et al., 2005; Massimini & Amzica, 2001). Such changes affect the reversal potential for the implicated ions and the overall expression of transmembrane currents. For example, higher [Ca2+]o concentration during silent states increases release probability, and the majority of synapses show STD in these conditions, while during active states (lower [Ca2+]o), the synapses either do not show short-term dynamics or show STF (Crochet et al., 2005). Physiological changes of [Ca2+]o also affect the intrinsic neuronal responsiveness, and lower [Ca2+]o is associated with increased neuronal bursting (Boucetta, Crochet, Chauvette, Seigneur, & Timofeev, 2013). However, in quiet awake mice exhibiting brief (around 100 ms) periods of hyperpolarizations, the cellular responses during active states were enhanced in parvalbumin-positive interneurons, but not in somatostatin-positive interneurons (Pala & Petersen, 2018). It is unclear whether this enhancement was synaptic, because during active states, the stimuli were applied at depolarized levels of the membrane potential and, therefore, could be enhanced by a persistent sodium current (Crill, 1996). A second problem with most STP studies is that the presynaptic stimulation consisted of either paired-pulse stimulation or rhythmic stimuli. At least for TC synapses, interspike intervals are critically important for the responsiveness of target cortical neurons (Stoelzel, Bereshpolova, Gusev, & Swadlow, 2008; Swadlow & Gusev, 2001). Rhythmic low-frequency cortical stimulation induces LTD, which is not true for stimulation applied at a similar frequency but at irregular intervals (Perrett, Dudek, Eagleman, Montague, & Friedlander, 2001). Natural patterns of presynaptic stimulation mimicking either spindles (Rosanova & Ulrich, 2005) or involving spontaneous spindles themselves (Timofeev et al., 2002) or spontaneous SWS (Chauvette, Seigneur, & Timofeev, 2012) typically induced LTP in the majority of investigated cortical neurons. Interestingly, wake-mimicking stimulation induced only short-term potentiation, but not LTP (Chauvette et al., 2012).

B Mid- and Long-Term Plasticity Mid-term plasticity occurs at a second-to-minute scale, while LTP and LTD can last for hours. LTP was

445

discovered by Lømo in 1966 (first publication dated 1973 (Bliss & Lomo, 1973)) and is the long-lasting enhancement of communication between two neurons; later, the opposite phenomenon of LTD was discovered (Levy & Steward, 1979). Tetanic stimulation of presynaptic fibers with a train of 100 Hz for 1 second induces LTP. LTD can be induced either by low-frequency (e.g., 1 Hz) stimulations (homosynaptic LTD) or as a result of inactivity in synapses on a neuron that also has active synapses (heterosynaptic LTD). The high-frequency stimulation required for LTP induction is clearly an artificial phenomenon, because such a firing pattern does not exist in the brain. Subsequently, a theta-burst stimulation protocol for the induction of LTP has been employed, which closer mimics the spontaneous activity of hippocampal pyramidal neurons (Kandel & Spencer, 1961; Larson & Lynch, 1986; Larson, Wong, & Lynch, 1986). Theta-burst stimulation typically consists of three to four stimuli delivered at 100 Hz, applied every 200 ms. Classical LTP protocols and theta-burst stimulation protocols share some molecular mechanisms in the plasticity that is induced following their application (Nguyen & Kandel, 1997). The physiological effects of LTP in vivo depend on the exact conditions of stimulation. In human, using the transcranial magnetic stimulation method, continuous thetaburst stimulation reduced motor-evoked potentials and short-interval intracortical inhibition (not enhanced, as would be predicted from in vitro studies), whereas intermittent theta-burst stimulation increased motor-evoked potentials and short-interval intracortical inhibition (Suppa et al., 2008). Whether potentiating or depressing effects are elicited by theta-burst stimulation largely depends on the level of previous neuronal activity. Without prior activity (as is the case in isolated preparations), theta-burst stimulation facilitated cortical responses, but when theta-burst stimulation was preceded by activity, cortical responses were depressed (Gentner, Wankerl, Reinsberger, Zeller, & Classen, 2008). Therefore, the direction of long-term plasticity depends not only on the parameters of stimulation but also on the actual network state. It is likely that both LTP and LTD play a role in memory consolidation. For example, a reduction of excitability via LTD induction in the amygdala can inactivate a memory, while LTP can reactivate a memory (Nabavi et al., 2014); further, a reduction of activity via chemogenetic inhibition of basal pyramidal neurons in the amygdala promotes fear learning (Tipps, Marron Fernandez de Velasco, Schaeffer, & Wickman, 2018).

III SPIKE-TIMING-DEPENDENT PLASTICITY The phenomenon of spike-timing-dependent plasticity was initially described in neuronal cultures and cortical slices: When synaptic depolarization is followed by a

PART E. SLEEP, PLASTICITY, AND MEMORY

446

29. SLEEP-WAKE AND CORTICAL SYNAPTIC PLASTICITY

postsynaptic spike, the synapse develops long-term potentiation, and when a postsynaptic spike is followed by synaptic excitation, the implicated synapse develops long-term depression (Bi & Poo, 1998; Markram, Lubke, Frotscher, & Sakmann, 1997). Induction of spike-timingdependent potentiation requires backpropagation of action potentials in postsynaptic neuron, which triggers dendritic Ca2+ spikes and helps to activate N-methyl-Daspartate (NMDA) receptors (Kampa, Letzkus, & Stuart, 2006). The mechanisms of spike-timing-dependent depression are less well understood. Spike-timing-dependent plasticity can also occur at inhibitory synapses (Lamsa, Kullmann, & Woodin, 2010; Woodin, Ganguly, & Poo, 2003).

IV HOMEOSTATIC PLASTICITY Homeostatic plasticity is a form of plasticity that functions to maintain a given, optimal level of neuronal excitability, which can be achieved via up-/downregulation of synaptic activities or intrinsic currents (Turrigiano, 2011). The overall concept of homeostatic plasticity is rather simple: If network activity is suppressed for some period of time, individual excitatory synaptic or intrinsic currents increase in amplitude; in contrast, if network activity is increased for a while, the excitatory drive is reduced, and the inhibitory drive is increased (Desai, Rutherford, & Turrigiano, 1999; Turrigiano, Leslie, Desai, Rutherford, & Nelson, 1998). Homeostatic plasticity can play a critical role in sleep homeostasis (Borbely, 1982). Indeed, during the waking state, the cortical network is persistently active, which should result in a decreased excitability; and by contrast, during slowwave sleep, the cortical network oscillates between active and silent states that should result in increased network excitability because of an accumulation of network silence. An alternative hypothesis of sleep-dependent downscaling of cortical synapses is proposed below.

V SLEEP AND MEMORY There is a general agreement that long-term plasticity (LTP, LTD, or both) contributes to memory formation. Strong evidence in support of this contention was proved by a study employing optogenetic stimulation in rodents to induce LTP and LTD. The results showed that a fear-conditioning memory was inactivated using an LTD stimulation protocol and subsequently reactivated using an LTP stimulation protocol (Nabavi et al., 2014), demonstrating a causal role of long-term plasticity in memory. For obvious reasons, learning occurs only during wakefulness. However, the consolidation of learning into memory occurs, at least in part, during sleep, a

phenomenon that was first observed almost 100 years ago ( Jenkins & Dallenbach, 1924). It is now well accepted that sleep is beneficial for memory and it was shown to enhance the retention of declarative memories or to improve the performance of procedural motor skills (Born, Rasch, & Gais, 2006; Born & Wilhelm, 2012; Huber, Ghilardi, Massimini, & Tononi, 2004; Payne et al., 2012; Rasch, B€ uchel, Gais, & Born, 2007; Stickgold, James, & Hobson, 2000; Stickgold & Walker, 2007). In fact, substantial evidence has shown that even short naps (60–90 minutes) are beneficial for the consolidation of various types of memory (Lahl, Wispel, Willigens, & Pietrowsky, 2008; Mednick et al., 2002; Mednick, Nakayama, & Stickgold, 2003; Nishida & Walker, 2007; Takashima et al., 2006; Tucker et al., 2006). In contrast, sleep fragmentation due to obstructive sleep apnea in humans was shown to reduce the overnight improvement in a motor sequence learning task (Djonlagic, Saboisky, Carusona, Stickgold, & Malhotra, 2012). Boosting SWS using transcranial direct current or low-frequency stimulation (Antonenko, Diekelmann, Olsen, Born, & M€ olle, 2013; Binder, Berg, et al., 2014; Binder, Rawohl, Born, & Marshall, 2014; Marshall, Helgadottir, Molle, & Born, 2006) or using auditory clicks (Ngo, Martinetz, Born, & M€ olle, 2013; Shimizu et al., 2018) was shown to enhance memory. Therefore, the main issue is no longer whether sleep is important for memory or not, but concerns the mechanisms that allow sleep to mediate the consolidation process, a question that is under intense debate. According to the hypothesis of sleep-dependent downregulation of synaptic efficacy, currently called synaptic homeostasis hypothesis (sometimes SHY), during waking state, cortical synapses are continuously potentiated, and SWS induces synaptic downscaling. This process ensures that, upon awakening, depotentiated synapses become available for new potentiation and, thus, new learning (Tononi & Cirelli, 2003, 2006, 2014). A contrasting hypothesis of active consolidation during sleep essentially suggests the opposite: Persistent activities of cortical neurons during the waking state induce synaptic fatigue, and silent states of SWS, due to the absence of neuronal activities, play a role in the recovery from this fatigue and allow for synaptic consolidation to occur in the absence of interfering inputs and activity (Diekelmann & Born, 2010; Rasch & Born, 2013; Timofeev & Chauvette, 2017).

VI SYNAPTIC HOMEOSTASIS HYPOTHESIS According to synaptic homeostasis hypothesis, sleep contributes to memory formation by downscaling all synapses and, by doing so, improving the signal-to-noise ratio in synapses that were potentiated during the previous wake episode (Tononi & Cirelli, 2003, 2006, 2014).

PART E. SLEEP, PLASTICITY, AND MEMORY

VI SYNAPTIC HOMEOSTASIS HYPOTHESIS

Further, the downscaling of synaptic strength during sleep would make synapses ready for the next learning episode. This hypothesis is based on four main assumptions: (1) Wakefulness is associated with synaptic potentiation in several cortical circuits, (2) synaptic potentiation is tied to the homeostatic regulation of slow-wave activity, (3) slow-wave activity leads to synaptic downscaling, and (4) synaptic downscaling is tied to the beneficial effect of sleep (Tononi & Cirelli, 2003, 2006). There are several lines of molecular, electrophysiological, and structural evidence that are cited as supporting SHY (Tononi & Cirelli, 2014); however, criticisms were raised as to whether these data actually support SHY (Durkin & Aton, 2016; Durkin et al., 2017; Frank, 2012, 2013, 2017; Timofeev & Chauvette, 2017). We recently reviewed the inconsistencies and major gaps of evidences used to support SHY (Timofeev & Chauvette, 2017), which we will briefly review below.

A Molecular Evidence Some studies suggest that protein levels of GluR1containing AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) receptors controlled by Homer1a were increased by about 45% in the cortex and hippocampus of rats that were mainly awake (>75% of time) in the first 6 hours of the dark cycle compared with rats that were mainly asleep (>75% of time) during the same time interval (Diering et al., 2017; Vyazovskiy, Cirelli, PfisterGenskow, Faraguna, & Tononi, 2008). However, a contrasting finding demonstrates that in calcium-calmodulindependent kinase II-a (CaMKIIa)-positive cortical neurons (typically pyramidal neurons), AMPA receptors are progressively removed, and GABAa receptors are progressively increased over prolonged wake periods (del CidPellitero, Plavski, Mainville, & Jones, 2017). While AMPA receptors play a major role in multiple brain functions, long-term synaptic plasticity is typically associated with NMDA and metabotropic glutamate receptor 5 (mGluR5) receptors, CaMKII, extracellular signal-regulated kinase (ERK), protein kinase A (PKA), activity-regulated cytoskeleton-associated protein (ARC), and brain-derived neurotrophic factor (BDNF). A number of studies demonstrate that sleep promotes cortical mRNA translation associated with these molecules, resulting in synaptic strengthening, a key factor in memory consolidation (Aton et al., 2009; Elmenhorst et al., 2016; Seibt et al., 2012). Therefore, considerable molecular evidence suggests an upregulation of long-term synaptic plasticity during sleep.

B Electrophysiological Evidences The slope of the first negative component of evoked potentials induced by transcallosal electric or local

447

transcranial magnetic stimulation increases after a prolonged awake period (Huber et al., 2013; Vyazovskiy et al., 2008). These findings were interpreted as a reflection of increased synaptic excitability, which is likely not true. The first component of the field potential response to transcallosal stimulation is a reflection of the antidromic spike, not synaptic responses (Chang, 1953). TMS excites primarily axons and not neuronal bodies (Di & Rothwell, 2014; Siebner, Hartwigsen, Kassuba, & Rothwell, 2009). Therefore, the available data point to a possible increase in axonal, but not synaptic excitability. Another electrophysiological study cited as a support of SHY was performed in cortical slices from rats and mice that either were mainly awake, mainly asleep, and sleep-deprived or had sleep recovery following sleep deprivation. Results showed that the frequency of miniature excitatory postsynaptic currents (mEPSCs) was increased by wake and by sleep deprivation, and their frequency was reduced following sleep recovery, results that were interpreted as a direct demonstration that cortical synaptic efficacy increases after wake and is restored to lower levels after periods of sleep (Liu, Faraguna, Cirelli, Tononi, & Gao, 2010). However, the data presented in that study are very confusing. Different sets of experiments compared control versus mainly awake rat, control versus sleep-deprived rats, and sleepdeprived versus rats that had a sleep recovery period. The frequency of mEPSCs in control rats or in experimental rats in different sets of experiments fluctuated to such an extent that the results of control and experimental groups largely overlapped. The authors do acknowledge that rats from the various experiments had a highly variable frequency of mEPSCs and mention that this is most likely due to slight changes in the experimental conditions used to prepare the slices, although those “slight changes” were not described. Therefore, the reliability of these results appears questionable. Multiunit recordings from the cortex of behaving animals revealed that neuronal firing increases during wake and decreases during sleep (Vyazovskiy et al., 2009), results that were interpreted as a sign of stronger synapses during wake and weaker synapses during sleep. Another study confirmed that while neurons with high firing rates decrease firing during sleep, neurons with low firing rates increase firing during sleep (Watson Brendon, Levenstein, Greene, Gelinas Jennifer, & Buzsáki, 2016). It is important to note that, at least in rodents, the vast majority of cortical neurons have low firing rates (Barth & Poulet, 2012), implying that the network activity change should be an increase in firing during sleep. In the hippocampus, non-REM sleep increases the firing of principal cells, while REM sleep decreases it (Grosmark, Mizuseki, Pastalkova, Diba, & Buzski, 2012; Miyawaki & Diba, 2016), which is at odds with SHY predictions.

PART E. SLEEP, PLASTICITY, AND MEMORY

448

29. SLEEP-WAKE AND CORTICAL SYNAPTIC PLASTICITY

C Structural Evidence Dendritic spine turnover was investigated in young mice (1 month old) across the sleep/wake cycle, and it was found that sleep results in a net spine loss, while wake results in a net spine gain (de Vivo et al., 2017; Maret, Faraguna, Nelson, Cirelli, & Tononi, 2011). In addition, larger (stronger) synapses were preserved during sleep, while smaller (weaker) synapses were reduced (de Vivo et al., 2017), in agreement with SHY predictions. However, very importantly, these sleep/wake effects were not observed in 3-month-old (young adult) mice (Maret et al., 2011) casting serious doubts about the structural evidence supporting SHY. Moreover, a study performed on adult mice revealed sleep-dependent, branch-specific spine formation following learning (Yang et al., 2014), which is at odds with SHY predictions. Finally, another study demonstrated that the density of excitatory synapses was higher during the light cycle (in which rodents are mainly asleep) than during the dark cycle (Jasinska et al., 2015). Therefore, the available structural evidence does not support the predictions of SHY in adult animals.

VII ACTIVE CONSOLIDATION DURING SLEEP Another current theory argues that an active memory consolidation process occurs during sleep. The cortical slow oscillation is known for grouping other sleep rhythms (M€ olle & Born, 2011; Molle, Marshall, Gais, & Born, 2002; Steriade, 2006; Steriade & Timofeev, 2003). Both spindles and hippocampal sharp-wave ripples occur predominantly during the active state of the slow oscillation, and sharp-wave ripples occur mainly in the spindle trough (Latchoumane, Ngo, Born, & Shin, 2017; Staresina et al., 2015). It has been suggested that this coordination between active states of slow waves, thalamocortical spindles, and hippocampal sharp-wave ripples allows memory traces to be transferred from the hippocampus to the neocortex (Diekelmann & Born, 2010). Spindles generated in the thalamocortical system during sleep were also shown to be related to learning and memory in both rodents and humans. In rats, an object recognition task increased the power in the spindle band during a subsequent sleep episode (Binder et al., 2012), and a reward-association task increased the spindle density after learning (Eschenko, Molle, Born, & Sara, 2006). In humans, different declarative and nondeclarative learning tasks increased the spindle density in the following sleep episode in regions involved in the learning task (Bergmann, M€ olle, Diedrichs, Born, & Siebner, 2012; Fogel & Smith, 2006; Gais, Molle, Helms, & Born, 2002; Holz et al., 2012; Meier-Koll,

Bussmann, Schmidt, & Neuschwander, 1999; Morin et al., 2008). Of note, stimulating cortical slices using a spindle pattern resulted in the induction of LTP (Rosanova & Ulrich, 2005). Another oscillation, hippocampal sharp-wave ripples that are generated either during SWS or wake, has also been linked to memory consolidation. According to the two-stage model of memory formation, short-term memory is encoded in the hippocampus during wake, followed the transformation into a long-term memory during subsequent sleep (Buzsaki, 1989). Importantly, the firing sequence of hippocampal neurons observed during a learning task is replayed in ripples during SWS (Buzsaki, 1989; Girardeau, Inema, & Buzsáki, 2017; Nádasdy, Hirase, Czurkó, Csicsvari, & Buzsáki, 1999; O’Neill, Pleydell-Bouverie, Dupret, & Csicsvari, 2010; Peyrache, Khamassi, Benchenane, Wiener, & Battaglia, 2009; Wilson & McNaughton, 1994). Further, replay of activity sequences during postlearning sleep is not exclusive to the hippocampus; it was also observed in the prefrontal cortex (Euston, Tatsuno, & McNaughton, 2007; Peyrache et al., 2009), visual cortex ( Ji & Wilson, 2007), and the striatum (Lansink, Goltstein, Lankelma, McNaughton, & Pennartz, 2009). Selective disruption of hippocampal ripples using electric stimulation in the postlearning period impaired the formation of spatial memory (Ego-Stengel & Wilson, 2009; Girardeau, Benchenane, Wiener, Buzsáki, & Zugaro, 2009), while increasing the coupling between sharp-wave ripples and cortically recorded slow waves and spindles using closed-loop optogenetic stimulation increased the performance in a memory task on the following day (Maingret, Girardeau, Todorova, Goutierre, & Zugaro, 2016), suggesting a crucial role for ripples in the memory formation. Recently, reactivations during sharp-wave ripples were shown to induce LTP in vivo at CA3 to CA1 synapses during rest or sleep periods (Sadowski, Jones, & Mellor, 2016). In contrast, another study demonstrated that hippocampal ripples downregulate hippocampal synapses formed by Schaffer collaterals on CA1 neurons (Norimoto et al., 2018). Exactly why these two studies found opposite results is unclear, but the latter indirectly supports the two-stage hypothesis of memory in which short-term memory is formed during wake primarily in the hippocampal formation, while long-term memory is cortical and formed during sleep (Buzsaki, 1989). It seems that hippocampal synapses downregulate synaptic efficacy during SWS, when most sharp-wave ripples are generated, in order to be able to acquire new short-term memory during following wake, while cortical synapses upregulate synaptic efficacy during SWS (Chauvette et al., 2012; Timofeev & Chauvette, 2017), thereby allowing the consolidation of sleepdependent long-term memory.

PART E. SLEEP, PLASTICITY, AND MEMORY

449

X CONCLUSION

VIII LTP DURING SLEEP A common technique used to investigate the plasticity in the visual cortex consists of monocular deprivation during a critical developmental period. This procedure leads to ocular dominance plasticity, in which the responses of visual cortex neurons shift toward the nondeprived eye (Frank, 2017). This ocular dominance plasticity is sleep-dependent (Aton et al., 2013; Aton, Suresh, Broussard, & Frank, 2014; Durkin & Aton, 2016; Frank, Issa, & Stryker, 2001), requires the activation of NMDA receptors (Aton et al., 2009), and is considered as a form of LTP. Using low-frequency (1 Hz) electric stimulation of prethalamic (medial lemniscus) fibers, we showed that the evoked potential recorded in the somatosensory cortex was modulated by states of vigilance (Chauvette et al., 2012). We consistently observed an increased amplitude of the evoked potential in a wake episode following a slow-wave sleep episode compared with the presleep wake episode. The response amplitude was increased whether or not stimulations were applied during slowwave sleep. Although the responses during REM sleep were larger in amplitude, REM sleep did not appear to play any role in the sleep-dependent potentiation outlasting the sleep episodes. The evoked response recorded intracellularly was also increased following a slow-wave sleep episode in five out of six neurons investigated. Complementary in vitro experiments revealed that this long-lasting potentiation required the bimodal distribution of the membrane potential and was NMDA-, AMPA-, and calcium-dependent, consistent with traditional LTP mechanisms. These results and the sleepdependent ocular dominance plasticity are at odds with the SHY hypothesis.

IX SPIKE-TIMING DEPENDENT PLASTICITY DURING SLEEP Spike-timing-dependent plasticity (STDP) is another form of LTP in which the relative timing between the presynaptic inputs and postsynaptic firing dictates the direction of the synaptic strength modulation. Presynaptic firing followed by a postsynaptic spike within typically 20 ms or less will induce potentiation, while postsynaptic firing occurring before presynaptic firing will lead to depression (Bi & Poo, 1998, 2001). Very recently, a study investigated STDP in projections from layer 4 to layer 2/3 neurons in sleeplike conditions using young (P16–P21) mice anesthetized with urethane (Gonzalez-Rueda, Pedrosa, Feord, Clopath, & Paulsen, 2018). During silent or DOWN states, the authors essentially reported the

same STDP rules as those in previous studies on cell cultures or cortical slices maintained in vitro. Further, during UP states, if a postsynaptic spike occurred 20 ms after the presynaptic spike, no plasticity was observed; however, all other conditions (post- before presynaptic, presynaptic alone, and postsynaptic spike 50 ms after presynaptic) lead to long-term depression, likely mediated be presynaptic and NMDA-dependent mechanisms (Gonzalez-Rueda et al., 2018). However, among other actions, urethane partially blocks NMDA receptors (Hara & Harris, 2002), and therefore, the involvement of NMDA receptors in this form of plasticity in behaving animals might be different from the results obtained under urethane anesthesia, as used by Gonzalez-Rueda et al. (2018). Also, the neuronal activity recorded during UP states is very similar to activity recorded during wake or REM sleep (Destexhe, Hughes, Rudolph, & Crunelli, 2007). Therefore, if depression occurs during the UP state of sleeplike oscillations lasting hundreds of milliseconds, one would expect the same to occur during REM sleep or wake lasting hours. Thus, it is likely that wakerelated long-lasting active states contribute to spiketiming-dependent synaptic depression (Timofeev & Chauvette, 2018). Several studies point to an infragranular cortical origin of UP states (Beltramo et al., 2013; Chauvette et al., 2010; Sanchez-Vives & McCormick, 2000; Stroh et al., 2013; Wester & Contreras, 2012). Fig. 29.2A and B shows an example of a dual intracellular recording during slowwave sleep, with the deeper neuron firing a few milliseconds before the more superficial one. LFP during slowwave sleep was recorded using a linear 16-channel silicon probe (Fig. 29.2C and D), and multiunit activities revealed that electrode contacts located at a depth corresponding to layer 5 showed the earliest spiking activity after the active state onset (Fig. 29.2E and F). If STDP rules apply during slow-wave sleep and UP states start in layer 5, with neurons typically firing within a few milliseconds after the onset of UP state (Fig. 29.2E and F), then projections from layer 5 to superficial layers, which are still in DOWN state, should exhibit potentiation (Fig. 29.3). Therefore, it is highly likely that STDP rules contribute to sleep-dependent synaptic facilitation. This hypothesis will need to be tested during natural slow-wave sleep conditions in technically challenging experiments.

X CONCLUSION The functions of sleep are not well understood (Siegel, 2005). One of the most consistently supported functions of sleep in multiple species is that of sleep-dependent memory consolidation (Maquet, 2001; Rasch & Born, 2013). According to a generally accepted hypothesis,

PART E. SLEEP, PLASTICITY, AND MEMORY

450

29. SLEEP-WAKE AND CORTICAL SYNAPTIC PLASTICITY

C

A

D 1 mV

DC field

4 20

mV

0.1 mV

1s

Cell 1 1052 µm

0.1 s

D E

F

–64 mV Cell 2 1835 µm

0.25 s

*

*

–66 mV

5% B

40 ms

2 mV 10

–250

0

250

ms

–50

0

50

100

ms

FIG. 29.2 Onset of active states in layer 5 neurons. (A) Simultaneously recorded DC field potential and intracellular activity of two closely located (<100 μm in lateral distance) neurons. (B) Two cycles from (A) shown at expanded timescale. Action potentials are truncated. Note that the deeper (red) neuron fires shortly (<20 ms) before the more superficial (blue) neuron. (C) LFP (top traces) and multiunit activity (bottom trace) recordings at the border of cortical areas 5 and 7 (associative cortex) during short episode of slow-wave sleep. Recordings were performed with a 16-channel silicon probe inserted perpendicularly to the cortical surface. The recording sites were separated by 100 μm; the upper recording site was just below the cortical surface. LFP and multiunit activity traces were obtained from the same electrodes by band-pass filtering 0.1 Hz to 10 kHz and 0.5–10 kHz, respectively. (D) A segment from (C) as indicated by the bar, at higher temporal resolution. (E) Probability distributions of multiunit firing during a cycle of slow oscillation. Pooled data from 290 cycles from five different sleep episodes. Spikes were detected as all peaks that exceeded a threshold (gray lines in C and D) set at 5  SD of the noise fluctuations during silent states. * Indicates the electrode at which the polarity of slow waves was reversed. Vertical line shows onset of active state determined in the LFP recorded from the deepest electrode. (F) Zoom in on the silent to active state transition from (E). Note that the first firing occurs in neurons located between 800 and 1200 μm from the cortical surface. Figure modified from Chauvette, S., Volgushev, M., & Timofeev, I. (2010). Origin of active states in local neocortical networks during slow sleep oscillation. Cerebral Cortex, 20, 2660–2674.

FIG. 29.3 STDP during slow-wave sleep. Left, schematic of active state propagation with initial depolarization occurring first in layer 5 neurons (red), followed by layer 2/3 neurons (blue), and layer 6 neurons (green). Right, schematic of the relative timing of neuronal firing during slow waves. Note that if STDP is operational during slow-wave sleep, the balance should be toward potentiation of synapses.

Layer 1 Layer 2/3

Onset of active state in layer 5, then propagation to other layers

Layer 4

Do slow waves induce LTP via spike-timing dependent plasticity during slow-wave sleep?

Layer 5

Layer 6

Synchronous silent state onset

PART E. SLEEP, PLASTICITY, AND MEMORY

Earlier firing in layer 5

REFERENCES

memory formation occurs as a two-step process (Buzsaki, 1989): (a) Learning and short-term memory formation depends mainly on the hippocampal formation and occurs during wake; (b) long-term memory consolidation occurs during sleep and may depend on frontal cortical areas. Even though it is clear that memory formation is mediated by synaptic plasticity processes (Nabavi et al., 2014), the direction, mechanisms, and functions of synaptic plasticity during sleep are highly contentious. An influential hypothesis suggests that sleep downregulates neocortical synaptic efficacy (Tononi & Cirelli, 2003, 2006, 2014). However, direct evidence for this hypothesis is either absent or limited to studies in very young animals. On the other hand, several lines of evidence point to a facilitation of excitatory synaptic connections during sleep (Aton et al., 2009; Chauvette et al., 2012; del Cid-Pellitero et al., 2017; Durkin & Aton, 2016; Yang et al., 2014). The coming years have the potential to be promising in terms of formulating a more complete theory of cellular plasticity mechanisms mediating sleepdependent memory consolidation; we predict that it is highly likely that sleep-associated synaptic facilitation will be considered as the main mechanism.

References Antonenko, D., Diekelmann, S., Olsen, C., Born, J., & M€ olle, M. (2013). Napping to renew learning capacity: enhanced encoding after stimulation of sleep slow oscillations. European Journal of Neuroscience, 37, 1142–1151. Arenz, A., Silver, R. A., Schaefer, A. T., & Margrie, T. W. (2008). The contribution of single synapses to sensory representation in vivo. Science, 321, 977–980. Arieli, A., Sterkin, A., Grinvald, A., & Aertsen, A. (1996). Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses. Science, 273, 1868–1871. Aton, S. J., Broussard, C., Dumoulin, M., Seibt, J., Watson, A., Coleman, T., et al. (2013). Visual experience and subsequent sleep induce sequential plastic changes in putative inhibitory and excitatory cortical neurons. Proceedings of the National Academy of Sciences of the United States of America, 110, 3101–3106. Aton, S. J., Seibt, J., Dumoulin, M., Jha, S. K., Steinmetz, N., Coleman, T., et al. (2009). Mechanisms of sleep-dependent consolidation of cortical plasticity. Neuron, 61, 454–466. Aton, S. J., Suresh, A., Broussard, C., & Frank, M. G. (2014). Sleep promotes cortical response potentiation following visual experience. Sleep, 37, 1163–1170. Barth, A. L., & Poulet, J. F. A. (2012). Experimental evidence for sparse firing in the neocortex. Trends in Neurosciences, 35, 345–355. Beltramo, R., D’Urso, G., Dal Maschio, M., Farisello, P., Bovetti, S., Clovis, Y., et al. (2013). Layer-specific excitatory circuits differentially control recurrent network dynamics in the neocortex. Nature Neuroscience, 16, 227–234. Bergmann, T. O., M€ olle, M., Diedrichs, J., Born, J., & Siebner, H. R. (2012). Sleep spindle-related reactivation of category-specific cortical regions after learning face-scene associations. NeuroImage, 59, 2733–2742. Bi, G. -q., & Poo, M. -m. (1998). Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. The Journal of Neuroscience, 18, 10464–10472.

451

Bi, G. -q., & Poo, M. -m. (2001). Synaptic modification by correlated activity: Hebb’s postulate revisited. Annual Review of Neuroscience, 24, 139–166. Binder, S., Baier, P. C., M€ olle, M., Inostroza, M., Born, J., & Marshall, L. (2012). Sleep enhances memory consolidation in the hippocampusdependent object-place recognition task in rats. Neurobiology of Learning and Memory, 97, 213–219. Binder, S., Berg, K., Gasca, F., Lafon, B., Parra, L. C., Born, J., et al. (2014). Transcranial slow oscillation stimulation during sleep enhances memory consolidation in rats. Brain Stimulation, 7, 508–515. Binder, S., Rawohl, J., Born, J., & Marshall, L. (2014). Transcranial slow oscillation stimulation during NREM sleep enhances acquisition of the radial maze task and modulates cortical network activity in rats. Frontiers in Behavioral Neuroscience, 7, 220. Blake, H., & Gerard, R. W. (1937). Brain potentials during sleep. The American Journal of Physiology, 119, 692–703. Bliss, T. V., & Lomo, T. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. The Journal of Physiology, 232, 331–356. Borbely, A. A. (1982). A two process model of sleep regulation. Human Neurobiology, 1, 195–204. Borg-Graham, L. J., Monier, C., & Fregnac, Y. (1998). Visual input evokes transient and strong shunting inhibition in visual cortical neurons. Nature, 393, 369–373. Born, J., Rasch, B., & Gais, S. (2006). Sleep to remember. The Neuroscientist, 12, 410–424. Born, J., & Wilhelm, I. (2012). System consolidation of memory during sleep. Psychological Research, 76, 192–203. Boucetta, S., Crochet, S., Chauvette, S., Seigneur, J., & Timofeev, I. (2013). Extracellular Ca2+ fluctuations in vivo affect afterhyperpolarization potential and modify firing patterns of neocortical neurons. Experimental Neurology, 245, 5–14. Buzsaki, G. (1989). Two-stage model of memory trace formation: a role for “noisy” brain states. Neuroscience, 31, 551–570. Chang, H. T. (1953). Cortical response to activity of callosal neurons. Journal of Neurophysiology, 16, 117–131. Chauvette, S., Seigneur, J., & Timofeev, I. (2012). Sleep oscillations in the thalamocortical system induce long-term neuronal plasticity. Neuron, 75, 1105–1113. Chauvette, S., Volgushev, M., & Timofeev, I. (2010). Origin of active states in local neocortical networks during slow sleep oscillation. Cerebral Cortex, 20, 2660–2674. Contreras, D., Timofeev, I., & Steriade, M. (1996). Mechanisms of long-lasting hyperpolarizations underlying slow sleep oscillations in cat corticothalamic networks. The Journal of Physiology, 494, 251–264. Crill, W. E. (1996). Persistent sodium current in mammalian central neurons. Annual Review of Physiology, 58, 349–362. Crochet, S., Chauvette, S., Boucetta, S., & Timofeev, I. (2005). Modulation of synaptic transmission in neocortex by network activities. The European Journal of Neuroscience, 21, 1030–1044. Crochet, S., Fuentealba, P., Cisse, Y., Timofeev, I., & Steriade, M. (2006). Synaptic plasticity in local cortical network in vivo and its modulation by the level of neuronal activity. Cerebral Cortex, 16, 618–631. de Vivo, L., Bellesi, M., Marshall, W., Bushong, E. A., Ellisman, M. H., Tononi, G., et al. (2017). Ultrastructural evidence for synaptic scaling across the wake/sleep cycle. Science, 355, 507–510. del Cid-Pellitero, E., Plavski, A., Mainville, L., & Jones, B. E. (2017). Homeostatic changes in GABA and glutamate receptors on excitatory cortical neurons during sleep deprivation and recovery. Frontiers in Systems Neuroscience, 11, 17. https://doi.org/10.3389/fnsys.2017.00017. Desai, N. S., Rutherford, L. C., & Turrigiano, G. G. (1999). Plasticity in the intrinsic excitability of cortical pyramidal neurons. Nature Neuroscience, 2, 515–520.

PART E. SLEEP, PLASTICITY, AND MEMORY

452

29. SLEEP-WAKE AND CORTICAL SYNAPTIC PLASTICITY

Destexhe, A., Hughes, S. W., Rudolph, M., & Crunelli, V. (2007). Are corticothalamic ‘up’ states fragments of wakefulness? Trends in Neurosciences, 30, 334–342. Di Lazzaro, V., & Rothwell, J. C. (2014). Corticospinal activity evoked and modulated by non-invasive stimulation of the intact human motor cortex. The Journal of Physiology, 592, 4115–4128. Diekelmann, S., & Born, J. (2010). The memory function of sleep. Nature Reviews Neuroscience, 11, 114–126. Diering, G. H., Nirujogi, R. S., Roth, R. H., Worley, P. F., Pandey, A., & Huganir, R. L. (2017). Homer1a drives homeostatic scaling-down of excitatory synapses during sleep. Science, 355, 511–515. Ding, F., O’Donnell, J., Xu, Q., Kang, N., Goldman, N., & Nedergaard, M. (2016). Changes in the composition of brain interstitial ions control the sleep-wake cycle. Science, 352, 550–555. Djonlagic, I., Saboisky, J., Carusona, A., Stickgold, R., & Malhotra, A. (2012). Increased sleep fragmentation leads to impaired off-line consolidation of motor memories in humans. PLoS One, 7, e34106. Durkin, J., & Aton, S. J. (2016). Sleep-dependent potentiation in the visual system is at odds with the synaptic homeostasis hypothesis. Sleep, 39, 155–159. Durkin, J., Suresh, A. K., Colbath, J., Broussard, C., Wu, J., Zochowski, M., et al. (2017). Cortically coordinated NREM thalamocortical oscillations play an essential, instructive role in visual system plasticity. Proceedings of the National Academy of Sciences of the United States of America, 114, 10485–10490. Ego-Stengel, V., & Wilson, M. A. (2009). Disruption of ripple-associated hippocampal activity during rest impairs spatial learning in the rat. Hippocampus, 20, 1–10. Elmenhorst, D., Mertens, K., Kroll, T., Oskamp, A., Ermert, J., Elmenhorst, E. -M., et al. (2016). Circadian variation of metabotropic glutamate receptor 5 availability in the rat brain. Journal of Sleep Research, 25, 754–761. Eschenko, O., Molle, M., Born, J., & Sara, S. J. (2006). Elevated sleep spindle density after learning or after retrieval in rats. The Journal of Neuroscience, 26, 12914–12920. Euston, D. R., Tatsuno, M., & McNaughton, B. L. (2007). Fast-forward playback of recent memory sequences in prefrontal cortex during sleep. Science, 318, 1147–1150. Fogel, S. M., & Smith, C. T. (2006). Learning-dependent changes in sleep spindles and Stage 2 sleep. Journal of Sleep Research, 15, 250–255. Frank, M. G. (2012). Erasing synapses in sleep: is it time to be SHY? Neural Plasticity, 2012, 264378. Frank, M. G. (2013). Why i am not shy: a reply to Tononi and Cirelli. Neural Plasticity, 2013, 3. Frank, M. G. (2017). Sleep and plasticity in the visual cortex: more than meets the eye. Current Opinion in Neurobiology, 44, 8–12. Frank, M. G., Issa, N. P., & Stryker, M. P. (2001). Sleep enhances plasticity in the developing visual cortex. Neuron, 30, 275–287. Gais, S., Molle, M., Helms, K., & Born, J. (2002). Learning-dependent increases in sleep spindle density. The Journal of Neuroscience, 22, 6830–6834. Galarreta, M., & Hestrin, S. (1998). Frequency-dependent synaptic depression and the balance of excitation and inhibition in the neocortex. Nature Neuroscience, 1, 587–594. Galarreta, M., & Hestrin, S. (2000). Burst firing induces a rebound of synaptic strength at unitary neocortical synapses. Journal of Neurophysiology, 83, 621–624. Gentner, R., Wankerl, K., Reinsberger, C., Zeller, D., & Classen, J. (2008). Depression of human corticospinal excitability induced by magnetic Theta-burst stimulation: evidence of rapid polarity-reversing metaplasticity. Cerebral Cortex, 18, 2046–2053. Girardeau, G., Benchenane, K., Wiener, S. I., Buzsáki, G., & Zugaro, M. B. (2009). Selective suppression of hippocampal ripples impairs spatial memory. Nature Neuroscience, 12, 1222. Girardeau, G., Inema, I., & Buzsáki, G. (2017). Reactivations of emotional memory in the hippocampus–amygdala system during sleep. Nature Neuroscience, 20, 1634.

Gonzalez-Rueda, A., Pedrosa, V., Feord, R. C., Clopath, C., & Paulsen, O. (2018). Activity-dependent downscaling of subthreshold synaptic inputs during slow-wave-sleep-like activity in vivo. Neuron, 97, 1244–1252 e5. Grosmark, A. D., Mizuseki, K., Pastalkova, E., Diba, K., & Buzski, G. (2012). REM sleep reorganizes hippocampal excitability. Neuron, 75, 1001–1007. Haider, B., Duque, A., Hasenstaub, A. R., & McCormick, D. A. (2006). Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition. The Journal of Neuroscience, 26, 4535–4545. Haider, B., Duque, A., Hasenstaub, A. R., Yu, Y., & McCormick, D. A. (2007). Enhancement of visual responsiveness by spontaneous local network activity In vivo. Journal of Neurophysiology, 97, 4186–4202. Haider, B., Hausser, M., & Carandini, M. (2013). Inhibition dominates sensory responses in the awake cortex. Nature, 493, 97–100. Hara, K., & Harris, R. A. (2002). The anesthetic mechanism of urethane: the effects on neurotransmitter-gated ion channels. Anesthesia and Analgesia, 94, 313–318. Hasenstaub, A., Sachdev, R. N. S., & McCormick, D. A. (2007). State changes rapidly modulate cortical neuronal responsiveness. The Journal of Neuroscience, 27, 9607–9622. Hirsch, J. A., Alonso, J. M., Reid, R. C., & Martinez, L. M. (1998). Synaptic integration in striate cortical simple cells. The Journal of Neuroscience, 18, 9517–9528. Holz, J., Piosczyk, H., Feige, B., Spiegelhalder, K. A. I., Baglioni, C., Riemann, D., et al. (2012). EEG sigma and slow-wave activity during NREM sleep correlate with overnight declarative and procedural memory consolidation. Journal of Sleep Research, 21, 612–619. Huber, R., Ghilardi, M. F., Massimini, M., & Tononi, G. (2004). Local sleep and learning. Nature, 430, 78–81. Huber, R., Maki, H., Rosanova, M., Casarotto, S., Canali, P., Casali, A. G., et al. (2013). Human cortical excitability increases with time awake. Cerebral Cortex, 23, 1–7. Jasinska, M., Grzegorczyk, A., Woznicka, O., Jasek, E., Kossut, M., Barbacka-Surowiak, G., et al. (2015). Circadian rhythmicity of synapses in mouse somatosensory cortex. European Journal of Neuroscience, 42, 2585–2594. Jenkins, J. G., & Dallenbach, K. M. (1924). Obliviscence during sleep and waking. The American Journal of Psychology, 35, 605–612. Ji, D., & Wilson, M. A. (2007). Coordinated memory replay in the visual cortex and hippocampus during sleep. Nature Neuroscience, 10, 100–107. Kampa, B. M., Letzkus, J. J., & Stuart, G. J. (2006). Requirement of dendritic calcium spikes for induction of spike-timing-dependent synaptic plasticity. The Journal of Physiology, 574, 283–290. Kandel, E. R., & Spencer, W. A. (1961). Electrophysiology of hippocampal neurons. II. After-potentials and repetitive firing. Journal of Neurophysiology, 24, 243–259. Lahl, O., Wispel, C., Willigens, B., & Pietrowsky, R. (2008). An ultra short episode of sleep is sufficient to promote declarative memory performance. Journal of Sleep Research, 17, 3–10. Lamsa, K. P., Kullmann, D. M., & Woodin, M. A. (2010). Spike-timing dependent plasticity in inhibitory circuits. Frontiers in Synaptic Neuroscience, 2, 8. https://doi.org/10.3389/fnsyn.2010.00008. Lansink, C. S., Goltstein, P. M., Lankelma, J. V., McNaughton, B. L., & Pennartz, C. M. A. (2009). Hippocampus leads ventral striatum in replay of place-reward information. PLoS Biology, 7, e1000173. Larson, J., & Lynch, G. (1986). Induction of synaptic potentiation in hippocampus by patterned stimulation involves two events. Science, 232, 985–988. Larson, J., Wong, D., & Lynch, G. (1986). Patterned stimulation at the theta frequency is optimal for the induction of hippocampal longterm potentiation. Brain Research, 368, 347–350. Latchoumane, C. -F. V., Ngo, H. -V. V., Born, J., & Shin, H. -S. (2017). Thalamic spindles promote memory formation during sleep

PART E. SLEEP, PLASTICITY, AND MEMORY

REFERENCES

through triple phase-locking of cortical, thalamic, and hippocampal rhythms. Neuron, 95, 424–435.e6. Levy, W. B., & Steward, O. (1979). Synapses as associative memory elements in the hippocampal formation. Brain Research, 175, 233–245. Liu, Z. -W., Faraguna, U., Cirelli, C., Tononi, G., & Gao, X. -B. (2010). Direct evidence for wake-related increases and sleep-related decreases in synaptic strength in rodent cortex. The Journal of Neuroscience, 30, 8671–8675. Maingret, N., Girardeau, G., Todorova, R., Goutierre, M., & Zugaro, M. (2016). Hippocampo-cortical coupling mediates memory consolidation during sleep. Nature Neuroscience, 19, 959–964. Maquet, P. (2001). The role of sleep in learning and memory. Science, 294, 1048–1052. Maret, S., Faraguna, U., Nelson, A. B., Cirelli, C., & Tononi, G. (2011). Sleep and waking modulate spine turnover in the adolescent mouse cortex. Nature Neuroscience, 14, 1418–1420. Markram, H., Lubke, J., Frotscher, M., & Sakmann, B. (1997). Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science, 275, 213–215. Marshall, L., Helgadottir, H., Molle, M., & Born, J. (2006). Boosting slow oscillations during sleep potentiates memory. Nature, 444, 610–613. Massimini, M., & Amzica, F. (2001). Extracellular calcium fluctuations and intracellular potentials in the cortex during the slow sleep oscillation. Journal of Neurophysiology, 85, 1346–1350. Mednick, S., Nakayama, K., & Stickgold, R. (2003). Sleep-dependent learning: a nap is as good as a night. Nature Neuroscience, 6, 697–698. Mednick, S. C., Nakayama, K., Cantero, J. L., Atienza, M., Levin, A. A., Pathak, N., et al. (2002). The restorative effect of naps on perceptual deterioration. Nature Neuroscience, 5, 677. Meier-Koll, A., Bussmann, B., Schmidt, C., & Neuschwander, D. (1999). Walking through a maze alters the architecture of sleep. Perceptual and Motor Skills, 88, 1141–1159. Miyawaki, H., & Diba, K. (2016). Regulation of hippocampal firing by network oscillations during sleep. Current Biology, 26, 893–902. M€ olle, M., & Born, J. (2011). Slow oscillations orchestrating fast oscillations and memory consolidation. In V. S. EJW, Y. D. Van Der Werf, P. R. Roelfsema, H. D. Mansvelder, & F. H. Lopes Da Silva (Eds.), Progress in brain research (pp. 93–110). Elsevier (Chapter 7). Molle, M., Marshall, L., Gais, S., & Born, J. (2002). Grouping of spindle activity during slow oscillations in human non-rapid eye movement sleep. The Journal of Neuroscience, 22, 10941–10947. Morin, A., Doyon, J., Dostie, V., Barakat, M., Hadj Tahar, A., Korman, M., et al. (2008). Motor sequence learning increases sleep spindles and fast frequencies in post-training sleep. Sleep, 31, 1149–1156. Nabavi, S., Fox, R., Proulx, C. D., Lin, J. Y., Tsien, R. Y., & Malinow, R. (2014). Engineering a memory with LTD and LTP. Nature, 511, 348–352. Nádasdy, Z., Hirase, H., Czurkó, A., Csicsvari, J., & Buzsáki, G. (1999). Replay and time compression of recurring spike sequences in the hippocampus. The Journal of Neuroscience, 19, 9497. Ngo, H. -V. V., Martinetz, T., Born, J., & M€ olle, M. (2013). Auditory closed-loop stimulation of the sleep slow oscillation enhances memory. Neuron, 78, 545–553. Nguyen, P. V., & Kandel, E. R. (1997). Brief theta-burst stimulation induces a transcription-dependent late phase of LTP requiring cAMP in area CA1 of the mouse hippocampus. Learning & Memory, 4, 230–243. Nir, Y., Staba, R. J., Andrillon, T., Vyazovskiy Vladyslav, V., Cirelli, C., Fried, I., et al. (2011). Regional slow waves and spindles in human sleep. Neuron, 70, 153–169. Nishida, M., & Walker, M. P. (2007). Daytime naps, motor memory consolidation and regionally specific sleep spindles. PLoS One, 2, e341. Norimoto, H., Makino, K., Gao, M., Shikano, Y., Okamoto, K., Ishikawa, T., et al. (2018). Hippocampal ripples down-regulate synapses. Science, 359, 1524–1527.

453

O’Neill, J., Pleydell-Bouverie, B., Dupret, D., & Csicsvari, J. (2010). Play it again: reactivation of waking experience and memory. Trends in Neurosciences, 33, 220–229. Okun, M., Naim, A., & Lampl, I. (2010). The subthreshold relation between cortical local field potential and neuronal firing unveiled by intracellular recordings in awake rats. The Journal of Neuroscience, 30, 4440–4448. Pala, A., & Petersen, C. C. H. (2018). State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice. eLife, 7, e35869. Payne, J. D., Tucker, M. A., Ellenbogen, J. M., Wamsley, E. J., Walker, M. P., Schacter, D. L., et al. (2012). Memory for semantically related and unrelated declarative information: the benefit of sleep, the cost of wake. PLoS One, 7, e33079. Perrett, S. P., Dudek, S. M., Eagleman, D., Montague, P. R., & Friedlander, M. J. (2001). LTD induction in adult visual cortex: role of stimulus timing and inhibition. The Journal of Neuroscience, 21, 2308–2319. Peyrache, A., Khamassi, M., Benchenane, K., Wiener, S. I., & Battaglia, F. P. (2009). Replay of rule-learning related neural patterns in the prefrontal cortex during sleep. Nature Neuroscience, 12, 919–926. Rasch, B., & Born, J. (2013). About sleep’s role in memory. Physiological Reviews, 93, 681–766. Rasch, B., B€ uchel, C., Gais, S., & Born, J. (2007). Odor cues during slowwave sleep prompt declarative memory consolidation. Science, 315, 1426–1429. Reig, R., Gallego, R., Nowak, L. G., & Sanchez-Vives, M. V. (2006). Impact of cortical network activity on short-term synaptic depression. Cerebral Cortex, 16, 688–695. Reig, R., & Sanchez-Vives, M. V. (2007). Synaptic transmission and plasticity in an active cortical network. PLoS One, 2, e670. Rosanova, M., & Timofeev, I. (2005). Neuronal mechanisms mediating the variability of somatosensory evoked potentials during sleep oscillations in cats. The Journal of Physiology, 562(2), 569–582. Rosanova, M., & Ulrich, D. (2005). Pattern-specific associative long-term potentiation induced by a sleep spindle-related spike train. The Journal of Neuroscience, 25, 9398–9405. Rudolph, M., Pospischil, M., Timofeev, I., & Destexhe, A. (2007). Inhibition determines membrane potential dynamics and controls action potential generation in awake and sleeping cat cortex. The Journal of Neuroscience, 27, 5280–5290. Sadowski, J. H. L. P., Jones, M. W., & Mellor, J. R. (2016). Sharp-wave ripples orchestrate the induction of synaptic plasticity during reactivation of place cell firing patterns in the hippocampus. Cell Reports, 14, 1916–1929. Sanchez-Vives, M. V., & McCormick, D. A. (2000). Cellular and network mechanisms of rhythmic recurrent activity in neocortex. Nature Neuroscience, 3, 1027–1034. Schwarz, T. L. (2003). Release of neurotransmitters. In M. J. Zigmond, F. E. Bloom, S. C. Landis, J. L. Roberts, & L. R. Squire (Eds.), Fundamental neuroscience (pp. 197–224). San Diego, London, Boston, New York, Sydney, Tokyo, Toronto: Academic Press. Seibt, J., Dumoulin, M. C., Aton, S. J., Coleman, T., Watson, A., Naidoo, N., et al. (2012). Protein synthesis during sleep consolidates cortical plasticity in vivo. Current Biology, 22, 676–682. Shahrezaei, V., & Delaney, K. R. (2005). Brevity of the Ca2+ microdomain and active zone geometry prevent Ca2+-sensor saturation for neurotransmitter release. Journal of Neurophysiology, 94, 1912–1919. Sheroziya, M., & Timofeev, I. (2014). Global intracellular slow-wave dynamics of the thalamocortical system. The Journal of Neuroscience, 34, 8875–8893. Shimizu, R. E., Connolly, P. M., Cellini, N., Armstrong, D. M., Hernandez, L. T., Estrada, R., et al. (2018). Closed-loop targeted memory reactivation during sleep improves spatial navigation. Frontiers in Human Neuroscience, 12, 28. https://doi.org/10.3389/ fnhum.2018.00028.

PART E. SLEEP, PLASTICITY, AND MEMORY

454

29. SLEEP-WAKE AND CORTICAL SYNAPTIC PLASTICITY

Siebner, H. R., Hartwigsen, G., Kassuba, T., & Rothwell, J. C. (2009). How does transcranial magnetic stimulation modify neuronal activity in the brain? Implications for studies of cognition. Cortex, 45, 1035–1042. Siegel, J. M. (2005). Clues to the functions of mammalian sleep. Nature, 437, 1264–1271. Staresina, B. P., Bergmann, T. O., Bonnefond, M., van der Meij, R., Jensen, O., Deuker, L., et al. (2015). Hierarchical nesting of slow oscillations, spindles and ripples in the human hippocampus during sleep. Nature Neuroscience, 18, 1679–1686. Steriade, M. (2006). Grouping of brain rhythms in corticothalamic systems. Neuroscience, 137, 1087–1106. Steriade, M., & Timofeev, I. (2003). Neuronal plasticity in thalamocortical networks during sleep and waking oscillations. Neuron, 37, 563–576. Steriade, M., Timofeev, I., & Grenier, F. (2001). Natural waking and sleep states: a view from inside neocortical neurons. Journal of Neurophysiology, 85, 1969–1985. Stickgold, R., James, L., & Hobson, J. A. (2000). Visual discrimination learning requires sleep after training. Nature Neuroscience, 3, 1237–1238. Stickgold, R., & Walker, M. P. (2007). Sleep-dependent memory consolidation and reconsolidation. Sleep Medicine, 8, 331–343. Stoelzel, C. R., Bereshpolova, Y., Gusev, A. G., & Swadlow, H. A. (2008). The impact of an LGNd impulse on the awake visual cortex: synaptic dynamics and the sustained/transient distinction. The Journal of Neuroscience, 28, 5018–5028. Stroh, A., Adelsberger, H., Groh, A., R€ uhlmann, C., Fischer, S., Schierloh, A., et al. (2013). Making waves: initiation and propagation of corticothalamic Ca2+ waves in vivo. Neuron, 77, 1136–1150. Suppa, A., Ortu, E., Zafar, N., Deriu, F., Paulus, W., Berardelli, A., et al. (2008). Theta burst stimulation induces after-effects on contralateral primary motor cortex excitability in humans. The Journal of Physiology, 586, 4489–4500. Swadlow, H. A., & Gusev, A. G. (2001). The impact of ’bursting’ thalamic impulses at a neocortical synapse. Nature Neuroscience, 4, 402–408. Takashima, A., Petersson, K. M., Rutters, F., Tendolkar, I., Jensen, O., Zwarts, M. J., et al. (2006). Declarative memory consolidation in humans: a prospective functional magnetic resonance imaging study. Proceedings of the National Academy of Sciences of the United States of America, 103, 756–761. Tank, D. W., Regehr, W. G., & Delaney, K. R. (1995). A quantitative analysis of presynaptic calcium dynamics that contribute to short-term enhancement. The Journal of Neuroscience, 15, 7940–7952. Timofeev, I. (2011). Neuronal plasticity and thalamocortical sleep and waking oscillations. In E. J. W. V. Someren, Y. D. V. D. Werf, P. R. Roelfsema, H. D. Mansvelder, & F. Lopes Da Silva (Eds.), Progress in brain research (pp. 121–144). Elsevier. Timofeev, I., & Chauvette, S. (2017). Sleep slow oscillation and plasticity. Current Opinion in Neurobiology, 44, 116–126. Timofeev, I., & Chauvette, S. (2018). Sleep, anesthesia, and plasticity. Neuron, 97, 1200–1202. Timofeev, I., Contreras, D., & Steriade, M. (1996). Synaptic responsiveness of cortical and thalamic neurones during various phases of slow sleep oscillation in cat. The Journal of Physiology, 494, 265–278. Timofeev, I., Grenier, F., Bazhenov, M., Houweling, A. R., Sejnowski, T. J., & Steriade, M. (2002). Short- and medium-term plasticity associated with augmenting responses in cortical slabs and spindles in intact cortex of cats in vivo. The Journal of Physiology, 542, 583–598.

Timofeev, I., Grenier, F., & Steriade, M. (2000). Impact of intrinsic properties and synaptic factors on the activity of neocortical networks in vivo. Journal of Physiology, Paris, 94, 343–355. Timofeev, I., Grenier, F., & Steriade, M. (2001). Disfacilitation and active inhibition in the neocortex during the natural sleep-wake cycle: an intracellular study. Proceedings of the National Academy of Sciences of the United States of America, 98, 1924–1929. Tipps, M., Marron Fernandez de Velasco, E., Schaeffer, A., & Wickman, K. (2018). Inhibition of pyramidal neurons in the basal amygdala promotes fear learning. eNeuro, 5(5). https://doi.org/ 10.1523/ENEURO.0272-18.2018. Tononi, G., & Cirelli, C. (2003). Sleep and synaptic homeostasis: a hypothesis. Brain Research Bulletin, 62, 143–150. Tononi, G., & Cirelli, C. (2006). Sleep function and synaptic homeostasis. Sleep Medicine Reviews, 10, 49–62. Tononi, G., & Cirelli, C. (2014). Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration. Neuron, 81, 12–34. Tucker, M. A., Hirota, Y., Wamsley, E. J., Lau, H., Chaklader, A., & Fishbein, W. (2006). A daytime nap containing solely non-REM sleep enhances declarative but not procedural memory. Neurobiology of Learning and Memory, 86, 241–247. Turrigiano, G. (2011). Too many cooks? Intrinsic and synaptic homeostatic mechanisms in cortical circuit refinement. Annual Review of Neuroscience, 34, 89–103. Turrigiano, G. G., Leslie, K. R., Desai, N. S., Rutherford, L. C., & Nelson, S. B. (1998). Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature, 391, 892–896. Volgushev, M., Chauvette, S., Mukovski, M., & Timofeev, I. (2006). Precise long-range synchronization of activity and silence in neocortical neurons during slow-wave sleep. The Journal of Neuroscience, 26, 5665–5672. Vyazovskiy, V. V., Cirelli, C., Pfister-Genskow, M., Faraguna, U., & Tononi, G. (2008). Molecular and electrophysiological evidence for net synaptic potentiation in wake and depression in sleep. Nature Neuroscience, 11, 200–208. Vyazovskiy, V. V., Olcese, U., Hanlon, E. C., Nir, Y., Cirelli, C., & Tononi, G. (2011). Local sleep in awake rats. Nature, 472, 443–447. Vyazovskiy, V. V., Olcese, U., Lazimy, Y. M., Faraguna, U., Esser, S. K., Williams, J. C., et al. (2009). Cortical firing and sleep homeostasis. Neuron, 63, 865–878. Watson Brendon, O., Levenstein, D., Greene, J. P., Gelinas Jennifer, N., & Buzsáki, G. (2016). Network homeostasis and state dynamics of neocortical sleep. Neuron, 90, 839–852. Wester, J. C., & Contreras, D. (2012). Columnar interactions determine horizontal propagation of recurrent network activity in neocortex. The Journal of Neuroscience, 32, 5454–5471. Wilson, M. A., & McNaughton, B. L. (1994). Reactivation of hippocampal ensemble memories during sleep. Science, 265, 676–679. Woodin, M. A., Ganguly, K., & Poo, M. -m. (2003). Coincident pre- and postsynaptic activity modifies GABAergic synapses by postsynaptic changes in Cl transporter activity. Neuron, 39, 807–820. Yang, G., Lai, C. S. W., Cichon, J., Ma, L., Li, W., & Gan, W. -B. (2014). Sleep promotes branch-specific formation of dendritic spines after learning. Science, 344, 1173–1178. Zucker, R. S., & Regehr, W. G. (2002). Short-term synaptic plasticity. Annual Review of Physiology, 64, 355–405.

PART E. SLEEP, PLASTICITY, AND MEMORY