Sleep-dependent engram reactivation during hippocampal memory consolidation associated with subregion-specific biosynthetic changes

Post-learning sleep is essential for hippocampal memory processing, including contextual fear memory consolidation. We labeled context-encoding engram neurons in the hippocampal dentate gyrus (DG) and assessed reactivation of these neurons after fear learning. Post-learning sleep deprivation (SD) selectively disrupted reactivation of inferior blade DG engram neurons, linked to SD-induced suppression of neuronal activity in the inferior, but not superior DG blade. Subregion-specific spatial profiling of transcripts revealed that transcriptomic responses to SD differed greatly between hippocampal CA1, CA3, and DG inferior blade, superior blade, and hilus. Activity-driven transcripts, and those associated with cytoskeletal remodeling, were selectively suppressed in the inferior blade. Critically, learning-driven transcriptomic changes differed dramatically between the DG blades and were absent from all other regions. Together, these data suggest that the DG is critical for sleep-dependent memory consolidation, and that the effects of sleep loss on the hippocampus are highly subregion-specific.

Neuromodulation via muscarinic acetylcholine pathway can facilitate distinct, complementary, and sequential roles for NREM and REM states during sleep-dependent memory consolidation

Across vertebrate species, sleep consists of repeating cycles of NREM followed by REM. However, the respective functions of NREM, REM, and their stereotypic cycling pattern are not well understood. Using a simplified biophysical network model, we show that NREM and REM sleep can play differential and critical roles in memory consolidation primarily regulated, based on state-specific changes in cholinergic signaling. Within this network, decreasing and increasing muscarinic acetylcholine (ACh) signaling during bouts of NREM and REM, respectively, differentially alters neuronal excitability and excitatory/inhibitory balance. During NREM, deactivation of inhibitory neurons leads to network-wide disinhibition and bursts of synchronized activity led by firing in engram neurons. These features strengthen connections from the original engram neurons to less-active network neurons. In contrast, during REM, an increase in network inhibition suppresses firing in all but the most-active excitatory neurons, leading to competitive strengthening/pruning of the memory trace. We tested the predictions of the model against in vivo recordings from mouse hippocampus during active sleep-dependent memory storage. Consistent with modeling results, we find that functional connectivity between CA1 neurons changes differentially at transition from NREM to REM sleep during learning. Returning to the model, we find that an iterative sequence of state-specific activations during NREM/REM cycling is essential for memory storage in the network, serving a critical role during simultaneous consolidation of multiple memories. Together these results provide a testable mechanistic hypothesis for the respective roles of NREM and REM sleep, and their universal relative timing, in memory consolidation.

Significance statement

Using a simplified computational model and in vivo recordings from mouse hippocampus, we show that NREM and REM sleep can play differential roles in memory consolidation. The specific neurophysiological features of the two sleep states allow for expansion of memory traces (during NREM) and prevention of overlap between different memory traces (during REM). These features are likely essential in the context of storing more than one new memory simultaneously within a brain network.

Hypnotic treatment reverses NREM sleep disruption and EEG desynchronization in a mouse model of Fragile X syndrome to rescue memory consolidation deficits

Fragile X syndrome (FXS) is a highly-prevalent genetic cause of intellectual disability, associated with disrupted cognition and sleep abnormalities. Sleep loss itself negatively impacts cognitive function, yet the contribution of sleep loss to impaired cognition in FXS is vastly understudied. One untested possibility is that disrupted cognition in FXS is exacerbated by abnormal sleep. We hypothesized that restoration of sleep-dependent mechanisms could improve functions such as memory consolidation in FXS. We examined whether administration of ML297, a hypnotic drug acting on G-protein-activated inward-rectifying potassium channels, could restore sleep phenotypes and improve disrupted memory consolidation in Fmr1 -/y mice. Using 24-h polysomnographic recordings, we found that Fmr1 -/y mice exhibit reduced non-rapid eye movement (NREM) sleep and fragmented NREM sleep architecture, alterations in NREM EEG spectral power (including reductions in sleep spindles), and reduced EEG coherence between cortical areas. These alterations were reversed in the hours following ML297 administration. Hypnotic treatment following contextual fear or spatial learning also ameliorated disrupted memory consolidation in Fmr1 -/y mice. Hippocampal activation patterns during memory recall was altered in Fmr1 -/y mice, reflecting an altered balance of activity among principal neurons vs. parvalbumin-expressing (PV+) interneurons. This phenotype was partially reversed by post-learning ML297 administration. These studies suggest that sleep disruption could have a major impact on neurophysiological and behavioral phenotypes in FXS, and that hypnotic therapy may significantly improve disrupted cognition in this disorder.

Enriched binocular experience followed by sleep optimally restores binocular visual cortical responses in a mouse model of amblyopia

Studies of primary visual cortex have furthered our understanding of amblyopia, long-lasting visual impairment caused by imbalanced input from the two eyes during childhood, which is commonly treated by patching the dominant eye. However, the relative impacts of monocular vs. binocular visual experiences on recovery from amblyopia are unclear. Moreover, while sleep promotes visual cortex plasticity following loss of input from one eye, its role in recovering binocular visual function is unknown. Using monocular deprivation in juvenile male mice to model amblyopia, we compared recovery of cortical neurons' visual responses after identical-duration, identical-quality binocular or monocular visual experiences. We demonstrate that binocular experience is quantitatively superior in restoring binocular responses in visual cortex neurons. However, this recovery was seen only in freely-sleeping mice; post-experience sleep deprivation prevented functional recovery. Thus, both binocular visual experience and subsequent sleep help to optimally renormalize bV1 responses in a mouse model of amblyopia.

Atypical hypnotic compound ML297 restores sleep architecture immediately following emotionally valenced learning, to promote memory consolidation and hippocampal network activation during recall

Sleep plays a critical role in consolidating many forms of hippocampus-dependent memory. While various classes of hypnotic drugs have been developed in recent years, it remains unknown whether, or how, some of them affect sleep-dependent memory consolidation mechanisms. We find that ML297, a recently developed candidate hypnotic agent targeting a new mechanism (activating GIRK1/2-subunit containing G-protein coupled inwardly rectifying potassium [GIRK] channels), alters sleep architecture in mice over the first 6 hr following a single-trial learning event. Following contextual fear conditioning (CFC), ML297 reversed post-CFC reductions in NREM sleep spindle power and REM sleep amounts and architecture, renormalizing sleep features to what was observed at baseline, prior to CFC. Renormalization of post-CFC REM sleep latency, REM sleep amounts, and NREM spindle power were all associated with improved contextual fear memory (CFM) consolidation. We find that improvements in CFM consolidation due to ML297 are sleep-dependent, and are associated with increased numbers of highly activated dentate gyrus (DG), CA1, and CA3 neurons during CFM recall. Together our findings suggest that GIRK1/2 channel activation restores normal sleep architecture- including REM sleep, which is normally suppressed following CFC-and increases the number of hippocampal neurons incorporated into the CFM engram during memory consolidation.

Perspective - ultrastructural analyses reflect the effects of sleep and sleep loss on neuronal cell biology

Recent electron microscopic analyses of neurons in the Drosophila and rodent brain demonstrate that acute or chronic sleep loss can alter the structures of various organelles, including mitochondria, nucleus, and Golgi apparatus. Here, we discuss these findings in the context of biochemical findings from the sleep deprived brain, to clarify how these morphological changes may related to altered organelle function. We discuss how, taken together, the available data suggest that sleep loss (particularly chronic sleep loss) disrupts such fundamental cellular processes as transcription, translation, intracellular transport, and metabolism. A better understanding of these effects will have broad implications for understanding the biological importance of sleep, and the relationship of sleep loss to neuropathology.

Heterogeneous mechanisms for synchronization of networks of resonant neurons under different E/I balance regimes

Rhythmic synchronization of neuronal firing patterns is a widely present phenomenon in the brain-one that seems to be essential for many cognitive processes. A variety of mechanisms contribute to generation and synchronization of network oscillations, ranging from intrinsic cellular excitability to network mediated effects. However, it is unclear how these mechanisms interact together. Here, using computational modeling of excitatory-inhibitory neural networks, we show that different synchronization mechanisms dominate network dynamics at different levels of excitation and inhibition (i.e. E/I levels) as synaptic strength is systematically varied. Our results show that with low synaptic strength networks are sensitive to external oscillatory drive as a synchronizing mechanism-a hallmark of resonance. In contrast, in a strongly-connected regime, synchronization is driven by network effects via the direct interaction between excitation and inhibition, and spontaneous oscillations and cross-frequency coupling emerge. Unexpectedly, we find that while excitation dominates network synchrony at low excitatory coupling strengths, inhibition dominates at high excitatory coupling strengths. Together, our results provide novel insights into the oscillatory modulation of firing patterns in different excitation/inhibition regimes.

Dynamical Mechanism Underlying Scale-Free Network Reorganization in Low Acetylcholine States Corresponding to Slow Wave Sleep

Sleep is indispensable for most animals' cognitive functions, and is hypothesized to be a major factor in memory consolidation. Although we do not fully understand the mechanisms of network reorganisation driving memory consolidation, available data suggests that sleep-associated neurochemical changes may be important for such processes. In particular, global acetylcholine levels change across the sleep/wake cycle, with high cholinergic tone during wake and REM sleep and low cholinergic tone during slow wave sleep. Furthermore, experimental perturbation of cholinergic tone has been shown to impact memory storage. Through in silico modeling of neuronal networks, we show how spiking dynamics change in highly heterogenous networks under varying levels of cholinergic tone, with neuronal networks under high cholinergic modulation firing asynchronously and at high frequencies, while those under low cholinergic modulation exhibit synchronous patterns of activity. We further examined the network's dynamics and its reorganization mediated via changing levels of acetylcholine within the context of different scale-free topologies, comparing network activity within the hub cells, a small group of neurons having high degree connectivity, and with the rest of the network. We show a dramatic, state-dependent change in information flow throughout the network, with highly active hub cells integrating information in a high-acetylcholine state, and transferring it to rest of the network in a low-acetylcholine state. This result is experimentally corroborated by frequency-dependent frequency changes observed in vivo experiments. Together, these findings provide insight into how new neurons are recruited into memory traces during sleep, a mechanism which may underlie system memory consolidation.

The Engram's Dark Horse: How Interneurons Regulate State-Dependent Memory Processing and Plasticity

Brain states such as arousal and sleep play critical roles in memory encoding, storage, and recall. Recent studies have highlighted the role of engram neurons–populations of neurons activated during learning–in subsequent memory consolidation and recall. These engram populations are generally assumed to be glutamatergic, and the vast majority of data regarding the function of engram neurons have focused on glutamatergic pyramidal or granule cell populations in either the hippocampus, amygdala, or neocortex. Recent data suggest that sleep and wake states differentially regulate the activity and temporal dynamics of engram neurons. Two potential mechanisms for this regulation are either via direct regulation of glutamatergic engram neuron excitability and firing, or via state-dependent effects on interneuron populations–which in turn modulate the activity of glutamatergic engram neurons. Here, we will discuss recent findings related to the roles of interneurons in state-regulated memory processes and synaptic plasticity, and the potential therapeutic implications of understanding these mechanisms.

Acetylcholine-gated current translates wake neuronal firing rate information into a spike timing-based code in Non-REM sleep, stabilizing neural network dynamics during memory consolidation

Sleep is critical for memory consolidation, although the exact mechanisms mediating this process are unknown. Combining reduced network models and analysis of in vivo recordings, we tested the hypothesis that neuromodulatory changes in acetylcholine (ACh) levels during non-rapid eye movement (NREM) sleep mediate stabilization of network-wide firing patterns, with temporal order of neurons’ firing dependent on their mean firing rate during wake. In both reduced models and in vivo recordings from mouse hippocampus, we find that the relative order of firing among neurons during NREM sleep reflects their relative firing rates during prior wake. Our modeling results show that this remapping of wake-associated, firing frequency-based representations is based on NREM-associated changes in neuronal excitability mediated by ACh-gated potassium current. We also show that learning-dependent reordering of sequential firing during NREM sleep, together with spike timing-dependent plasticity (STDP), reconfigures neuronal firing rates across the network. This rescaling of firing rates has been reported in multiple brain circuits across periods of sleep. Our model and experimental data both suggest that this effect is amplified in neural circuits following learning. Together our data suggest that sleep may bias neural networks from firing rate-based towards phase-based information encoding to consolidate memories.

We show that neuromodulatory changes during non-rapid eye movement (NREM) sleep generate stable spike timing relationships between neurons, the ordering of which reflects the neurons’ relative firing rates during wake. Learning-dependent ordering of firing in the hippocampus during NREM, acting in tandem with spike timing-dependent plasticity, reconfigures neuronal firing rates across the hippocampal network. This “rescaling” of neuronal firing rates has recently been reported in multiple brain circuits across periods of sleep. Together, our results suggest that the brain is remapping frequency-biased representations of information formed during wake into timing biased-representations during NREM sleep.

Sleep loss drives acetylcholine- and somatostatin interneuron-mediated gating of hippocampal activity to inhibit memory consolidation

Sleep loss disrupts consolidation of hippocampus-dependent memory. To characterize effects of learning and sleep loss, we quantified activity-dependent phosphorylation of ribosomal protein S6 (pS6) across the dorsal hippocampus of mice. We find that pS6 is enhanced in dentate gyrus (DG) following single-trial contextual fear conditioning (CFC) but is reduced throughout the hippocampus after brief sleep deprivation (SD; which disrupts contextual fear memory [CFM] consolidation). To characterize neuronal populations affected by SD, we used translating ribosome affinity purification sequencing to identify cell type-specific transcripts on pS6 ribosomes (pS6-TRAP). Cell type-specific enrichment analysis revealed that SD selectively activated hippocampal somatostatin-expressing (Sst+) interneurons and cholinergic and orexinergic hippocampal inputs. To understand the functional consequences of SD-elevated Sst+ interneuron activity, we used pharmacogenetics to activate or inhibit hippocampal Sst+ interneurons or cholinergic input from the medial septum. The activation of either cell population was sufficient to disrupt sleep-dependent CFM consolidation by gating activity in granule cells. The inhibition of either cell population during sleep promoted CFM consolidation and increased S6 phosphorylation among DG granule cells, suggesting their disinhibition by these manipulations. The inhibition of either population across post-CFC SD was insufficient to fully rescue CFM deficits, suggesting that additional features of sleeping brain activity are required for consolidation. Together, our data suggest that state-dependent gating of DG activity may be mediated by cholinergic input and local Sst+ interneurons. This mechanism could act as a sleep loss-driven inhibitory gate on hippocampal information processing.

Theta-gamma coupling emerges from spatially heterogeneous cholinergic neuromodulation

Theta and gamma rhythms and their cross-frequency coupling play critical roles in perception, attention, learning, and memory. Available data suggest that forebrain acetylcholine (ACh) signaling promotes theta-gamma coupling, although the mechanism has not been identified. Recent evidence suggests that cholinergic signaling is both temporally and spatially constrained, in contrast to the traditional notion of slow, spatially homogeneous, and diffuse neuromodulation. Here, we find that spatially constrained cholinergic stimulation can generate theta-modulated gamma rhythms. Using biophysically-based excitatory-inhibitory (E-I) neural network models, we simulate the effects of ACh on neural excitability by varying the conductance of a muscarinic receptor-regulated K+ current. In E-I networks with local excitatory connectivity and global inhibitory connectivity, we demonstrate that theta-gamma-coupled firing patterns emerge in ACh modulated network regions. Stable gamma-modulated firing arises within regions with high ACh signaling, while theta or mixed theta-gamma activity occurs at the peripheries of these regions. High gamma activity also alternates between different high-ACh regions, at theta frequency. Our results are the first to indicate a causal role for spatially heterogenous ACh signaling in the emergence of localized theta-gamma rhythmicity. Our findings also provide novel insights into mechanisms by which ACh signaling supports the brain region-specific attentional processing of sensory information.

Aligning one's sights: The pulvinar provides context for visual information processing

The pulvinar (lateral posterior [LP]), like other higher-order thalamic nuclei, receives input from-and sends output to-multiple neocortical structures. In this issue of Neuron, Blot et al. (2021) demonstrate that LP integrates multimodal inputs to put visual information in context.

Sleep Loss Drives Brain Region-Specific and Cell Type-Specific Alterations in Ribosome-Associated Transcripts Involved in Synaptic Plasticity and Cellular Timekeeping

Sleep and sleep loss are thought to impact synaptic plasticity, and recent studies have shown that sleep and sleep deprivation (SD) differentially affect gene transcription and protein translation in the mammalian forebrain. However, much less is known regarding how sleep and SD affect these processes in different microcircuit elements within the hippocampus and neocortex, for example, in inhibitory versus excitatory neurons. Here, we use translating ribosome affinity purification (TRAP) and in situ hybridization to characterize the effects of sleep versus SD on abundance of ribosome-associated transcripts in Camk2a-expressing (Camk2a+) pyramidal neurons and parvalbumin-expressing (PV+) interneurons in the hippocampus and neocortex of male mice. We find that while both Camk2a+ neurons and PV+ interneurons in neocortex show concurrent SD-driven increases in ribosome-associated transcripts for activity-regulated effectors of plasticity and transcriptional regulation, these transcripts are minimally affected by SD in hippocampus. Similarly, we find that while SD alters several ribosome-associated transcripts involved in cellular timekeeping in neocortical Camk2a+ and PV+ neurons, effects on circadian clock transcripts in hippocampus are minimal, and restricted to Camk2a+ neurons. Taken together, our results indicate that SD effects on transcripts associated with translating ribosomes are both cell type-specific and brain region-specific, and that these effects are substantially more pronounced in the neocortex than the hippocampus. We conclude that SD-driven alterations in the strength of synapses, excitatory-inhibitory (E-I) balance, and cellular timekeeping are likely more heterogeneous than previously appreciated.SIGNIFICANCE STATEMENT Sleep loss-driven changes in transcript and protein abundance have been used as a means to better understand the function of sleep for the brain. Here, we use translating ribosome affinity purification (TRAP) to characterize changes in abundance of ribosome-associated transcripts in excitatory and inhibitory neurons in mouse hippocampus and neocortex after a brief period of sleep or sleep loss. We show that these changes are not uniform, but are generally more pronounced in excitatory neurons than inhibitory neurons, and more pronounced in neocortex than in hippocampus.

Impacts of Sleep Loss versus Waking Experience on Brain Plasticity: Parallel or Orthogonal?

Recent studies on the effects of sleep deprivation on synaptic plasticity have yielded discrepant results. Sleep deprivation studies using novelty exposure as a means to keep animals awake suggests that sleep (compared with wake) leads to widespread reductions in net synaptic strength. By contrast, sleep deprivation studies using approaches avoiding novelty-induced arousal (i.e., gentle handling) suggest that sleep can promote synaptic growth and strengthening. How can these discrepant findings be reconciled? Here, we discuss how varying methodologies for the experimental disruption of sleep (with differential introduction of novel experiences) could fundamentally alter the experimental outcome with regard to synaptic plasticity. Thus, data from experiments aimed at assessing the relative impact of sleep versus wake on the brain may instead reflect the quality of the waking experience itself. The highlighted work suggests that brain plasticity resulting from novel experiences versus wake per se has unique and distinct features.

Network and cellular mechanisms underlying heterogeneous excitatory/inhibitory balanced states

Recent work has explored spatiotemporal relationships between excitatory (E) and inhibitory (I) signaling within neural networks, and the effect of these relationships on network activity patterns. Data from these studies have indicated that excitation and inhibition are maintained at a similar level across long time periods and that excitatory and inhibitory currents may be tightly synchronized. Disruption of this balance-leading to an aberrant E/I ratio-is implicated in various brain pathologies. However, a thorough characterization of the relationship between E and I currents in experimental settings is largely impossible, due to their tight regulation at multiple cellular and network levels. Here, we use biophysical neural network models to investigate the emergence and properties of balanced states by heterogeneous mechanisms. Our results show that a network can homeostatically regulate the E/I ratio through interactions among multiple cellular and network factors, including average firing rates, synaptic weights and average neural depolarization levels in excitatory/inhibitory populations. Complex and competing interactions between firing rates and depolarization levels allow these factors to alternately dominate network dynamics in different synaptic weight regimes. This leads to the emergence of distinct mechanisms responsible for determining a balanced state and its dynamical correlate. Our analysis provides a comprehensive picture of how E/I ratio changes when manipulating specific network properties, and identifies the mechanisms regulating E/I balance. These results provide a framework to explain the diverse, and in some cases, contradictory experimental observations on the E/I state in different brain states and conditions.

Hippocampal Network Oscillations Rescue Memory Consolidation Deficits Caused by Sleep Loss

Oscillations in the hippocampal network during sleep are proposed to play a role in memory storage by patterning neuronal ensemble activity. Here we show that following single-trial fear learning, sleep deprivation (which impairs memory consolidation) disrupts coherent firing rhythms in hippocampal area CA1. State-targeted optogenetic inhibition of CA1 parvalbumin-expressing (PV+) interneurons during postlearning NREM sleep, but not REM sleep or wake, disrupts contextual fear memory (CFM) consolidation in a manner similar to sleep deprivation. NREM-targeted inhibition disrupts CA1 network oscillations which predict successful memory storage. Rhythmic optogenetic activation of PV+ interneurons following learning generates CA1 oscillations with coherent principal neuron firing. This patterning of CA1 activity rescues CFM consolidation in sleep-deprived mice. Critically, behavioral and optogenetic manipulations that disrupt CFM also disrupt learning-induced stabilization of CA1 ensembles’ communication patterns in the hours following learning. Conversely, manipulations that promote CFM also promote long-term stability of CA1 communication patterns. We conclude that sleep promotes memory consolidation by generating coherent rhythms of CA1 network activity, which provide consistent communication patterns within neuronal ensembles. Most importantly, we show that this rhythmic patterning of activity is sufficient to promote long-term memory storage in the absence of sleep.

How rhythms of the sleeping brain tune memory and synaptic plasticity

Decades of neurobehavioral research has linked sleep-associated rhythms in various brain areas to improvements in cognitive performance. However, it remains unclear what synaptic changes might underlie sleep-dependent declarative memory consolidation and procedural task improvement, and why these same changes appear not to occur across a similar interval of wake. Here we describe recent research on how one specific feature of sleep-network rhythms characteristic of rapid eye movement and non-rapid eye movement-could drive synaptic strengthening or weakening in specific brain circuits. We provide an overview of how these rhythms could affect synaptic plasticity individually and in concert. We also present an overarching hypothesis for how all network rhythms occurring across the sleeping brain could aid in encoding new information in neural circuits.

How Sleep Shapes Thalamocortical Circuit Function in the Visual System

Recent data have shown that sleep plays a beneficial role for cognitive functions such as declarative memory consolidation and perceptual learning. In this article, we review recent findings on the role of sleep in promoting adaptive visual response changes in the lateral geniculate nucleus and primary visual cortex following novel visual experiences. We discuss these findings in the context of what is currently known about how sleep affects the activity and function of thalamocortical circuits and current hypotheses regarding how sleep facilitates synaptic plasticity.

Scn1b deletion in adult mice results in seizures and SUDEP

Pathogenic loss‐of‐function variants in SCN1B are linked to Dravet syndrome (DS). Previous work suggested that neuronal pathfinding defects underlie epileptogenesis and SUDEP in the Scn1b null mouse model of DS. We tested this hypothesis by inducing Scn1b deletion in adult mice that had developed normally. Epilepsy and SUDEP, which occur by postnatal day 21 in Scn1b null animals, were observed within 20 days of induced Scn1b deletion in adult mice, suggesting that epileptogenesis in SCN1B‐DS does not result from defective brain development. Thus, the developmental brain defects observed previously in Scn1b null mice may model other co‐morbidities of DS.

Sleep Promotes, and Sleep Loss Inhibits, Selective Changes in Firing Rate, Response Properties and Functional Connectivity of Primary Visual Cortex Neurons

Recent studies suggest that sleep differentially alters the activity of cortical neurons based on firing rates during preceding wake—increasing the firing rates of sparsely firing neurons and decreasing those of faster firing neurons. Because sparsely firing cortical neurons may play a specialized role in sensory processing, sleep could facilitate sensory function via selective actions on sparsely firing neurons. To test this hypothesis, we analyzed longitudinal electrophysiological recordings of primary visual cortex (V1) neurons across a novel visual experience which induces V1 plasticity (or a control experience which does not), and a period of subsequent ad lib sleep or partial sleep deprivation. We find that across a day of ad lib sleep, spontaneous and visually-evoked firing rates are selectively augmented in sparsely firing V1 neurons. These sparsely firing neurons are more highly visually responsive, and show greater orientation selectivity than their high firing rate neighbors. They also tend to be “soloists” instead of “choristers”—showing relatively weak coupling of firing to V1 population activity. These population-specific changes in firing rate are blocked by sleep disruption either early or late in the day, and appear to be brought about by increases in neuronal firing rates across bouts of rapid eye movement (REM) sleep. Following a patterned visual experience that induces orientation-selective response potentiation (OSRP) in V1, sparsely firing and weakly population-coupled neurons show the highest level of sleep-dependent response plasticity. Across a day of ad lib sleep, population coupling strength increases selectively for sparsely firing neurons—this effect is also disrupted by sleep deprivation. Together, these data suggest that sleep may optimize sensory function by augmenting the functional connectivity and firing rate of highly responsive and stimulus-selective cortical neurons, while simultaneously reducing noise in the network by decreasing the activity of less selective, faster-firing neurons.

Sleep loss disrupts Arc expression in dentate gyrus neurons

Sleep loss affects many aspects of cognition, and memory consolidation processes occurring in the hippocampus seem particularly vulnerable to sleep loss. The immediate-early gene Arc plays an essential role in both synaptic plasticity and memory formation, and its expression is altered by sleep. Here, using a variety of techniques, we have characterized the effects of brief (3-h) periods of sleep vs. sleep deprivation (SD) on the expression of Arc mRNA and Arc protein in the mouse hippocampus and cortex. By comparing the relative abundance of mature Arc mRNA with unspliced pre-mRNA, we see evidence that during SD, increases in Arc across the cortex, but not hippocampus, reflect de novo transcription. Arc increases in the hippocampus during SD are not accompanied by changes in pre-mRNA levels, suggesting that increases in mRNA stability, not transcription, drives this change. Using in situ hybridization (together with behavioral observation to quantify sleep amounts), we find that in the dorsal hippocampus, SD minimally affects Arc mRNA expression, and decreases the number of dentate gyrus (DG) granule cells expressing Arc. This is in contrast to neighboring cortical areas, which show large increases in neuronal Arc expression after SD. Using immunohistochemistry, we find that Arc protein expression is also differentially affected in the cortex and DG with SD - while larger numbers of cortical neurons are Arc+, fewer DG granule cells are Arc+, relative to the same regions in sleeping mice. These data suggest that with regard to expression of plasticity-regulating genes, sleep (and SD) can have differential effects in hippocampal and cortical areas. This may provide a clue regarding the susceptibility of performance on hippocampus-dependent tasks to deficits following even brief periods of sleep loss.

Linking Network Activity to Synaptic Plasticity during Sleep: Hypotheses and Recent Data

Research findings over the past two decades have supported a link between sleep states and synaptic plasticity. Numerous mechanistic hypotheses have been put forth to explain this relationship. For example, multiple studies have shown structural alterations to synapses (including changes in synaptic volume, spine density, and receptor composition) indicative of synaptic weakening after a period of sleep. Direct measures of neuronal activity and synaptic strength support the idea that a period of sleep can reduce synaptic strength. This has led to the synaptic homeostasis hypothesis (SHY), which asserts that during slow wave sleep, synapses are downscaled throughout the brain to counteract net strengthening of network synapses during waking experience (e.g., during learning). However, neither the cellular mechanisms mediating these synaptic changes, nor the sleep-dependent activity changes driving those cellular events are well-defined. Here we discuss potential cellular and network dynamic mechanisms which could underlie reductions in synaptic strength during sleep. We also discuss recent findings demonstrating circuit-specific synaptic strengthening (rather than weakening) during sleep. Based on these data, we explore the hypothetical role of sleep-associated network activity patterns in driving synaptic strengthening. We propose an alternative to SHY—namely that depending on experience during prior wake, a variety of plasticity mechanisms may operate in the brain during sleep. We conclude that either synaptic strengthening or synaptic weakening can occur across sleep, depending on changes to specific neural circuits (such as gene expression and protein translation) induced by experiences in wake. Clarifying the mechanisms underlying these different forms of sleep-dependent plasticity will significantly advance our understanding of how sleep benefits various cognitive functions.

Sleep deprivation causes memory deficits by negatively impacting neuronal connectivity in hippocampal area CA1

Brief periods of sleep loss have long-lasting consequences such as impaired memory consolidation. Structural changes in synaptic connectivity have been proposed as a substrate of memory storage. Here, we examine the impact of brief periods of sleep deprivation on dendritic structure. In mice, we find that five hours of sleep deprivation decreases dendritic spine numbers selectively in hippocampal area CA1 and increased activity of the filamentous actin severing protein cofilin. Recovery sleep normalizes these structural alterations. Suppression of cofilin function prevents spine loss, deficits in hippocampal synaptic plasticity, and impairments in long-term memory caused by sleep deprivation. The elevated cofilin activity is caused by cAMP-degrading phosphodiesterase-4A5 (PDE4A5), which hampers cAMP-PKA-LIMK signaling. Attenuating PDE4A5 function prevents changes in cAMP-PKA-LIMK-cofilin signaling and cognitive deficits associated with sleep deprivation. Our work demonstrates the necessity of an intact cAMP-PDE4-PKA-LIMK-cofilin activation-signaling pathway for sleep deprivation-induced memory disruption and reduction in hippocampal spine density.

DOI:http://dx.doi.org/10.7554/eLife.13424.001

The demands of modern society means that millions of people do not get sufficient sleep on a daily basis. Sleep deprivation, even if only for brief periods, can impair learning and memory. In many cases, this impairment appears to be related to changes in the activity of a brain region called the hippocampus. However, the exact processes responsible for producing the effects of sleep deprivation remain unclear.

During learning or forming a new memory, the connections between the relevant neurons in the brain change. Havekes et al. found that depriving mice of sleep for just five hours dramatically reduced the connectivity between neurons in the hippocampus. This reduction is caused by the increased activity of cofilin, a protein that breaks down the actin filaments that shape the connections between neurons.

Havekes et al. then used a virus to introduce an inactive version of cofilin into hippocampal neurons to suppress the activity of the naturally present cofilin. This manipulation prevented both the loss of the connections between neurons and the memory deficits normally associated with sleep deprivation. Havekes et al. also found that recovery sleep leads to the re-wiring of neurons in the hippocampus. Future studies are now needed to determine how the neurons are able to re-wire themselves during recovery sleep.

DOI:http://dx.doi.org/10.7554/eLife.13424.002

25th Annual Computational Neuroscience Meeting: CNS-2016

A1 Functional advantages of cell-type heterogeneity in neural circuits

Tatyana O. Sharpee

A2 Mesoscopic modeling of propagating waves in visual cortex

Alain Destexhe

A3 Dynamics and biomarkers of mental disorders

Mitsuo Kawato

F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons

Vladislav Sekulić, Frances K. Skinner

F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains

Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári

F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks.

Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić

O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators

Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona

O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain

Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi

O3 Modeling auditory stream segregation, build-up and bistability

James Rankin, Pamela Osborn Popp, John Rinzel

O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields

Alejandro Tabas, André Rupp, Emili Balaguer-Ballester

O5 A simple model of retinal response to multi-electrode stimulation

Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin

O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task

Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer

O7 Input-location dependent gain modulation in cerebellar nucleus neurons

Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Volker Steuber

O8 Analytic solution of cable energy function for cortical axons and dendrites

Huiwen Ju, Jiao Yu, Michael L. Hines, Liang Chen, Yuguo Yu

O9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal network

Jimin Kim, Will Leahy, Eli Shlizerman

O10 Is the model any good? Objective criteria for computational neuroscience model selection

Justas Birgiolas, Richard C. Gerkin, Sharon M. Crook

O11 Cooperation and competition of gamma oscillation mechanisms

Atthaphon Viriyopase, Raoul-Martin Memmesheimer, Stan Gielen

O12 A discrete structure of the brain waves

Yuri Dabaghian, Justin DeVito, Luca Perotti

O13 Direction-specific silencing of the Drosophila gaze stabilization system

Anmo J. Kim, Lisa M. Fenk, Cheng Lyu, Gaby Maimon

O14 What does the fruit fly think about values? A model of olfactory associative learning

Chang Zhao, Yves Widmer, Simon Sprecher,Walter Senn

O15 Effects of ionic diffusion on power spectra of local field potentials (LFP)

Geir Halnes, Tuomo Mäki-Marttunen, Daniel Keller, Klas H. Pettersen,Ole A. Andreassen, Gaute T. Einevoll

O16 Large-scale cortical models towards understanding relationship between brain structure abnormalities and cognitive deficits

Yasunori Yamada

O17 Spatial coarse-graining the brain: origin of minicolumns

Moira L. Steyn-Ross, D. Alistair Steyn-Ross

O18 Modeling large-scale cortical networks with laminar structure

Jorge F. Mejias, John D. Murray, Henry Kennedy, Xiao-Jing Wang

O19 Information filtering by partial synchronous spikes in a neural population

Alexandra Kruscha, Jan Grewe, Jan Benda, Benjamin Lindner

O20 Decoding context-dependent olfactory valence in Drosophila

Laurent Badel, Kazumi Ohta, Yoshiko Tsuchimoto, Hokto Kazama

P1 Neural network as a scale-free network: the role of a hub

B. Kahng

P2 Hemodynamic responses to emotions and decisions using near-infrared spectroscopy optical imaging

Nicoladie D. Tam

P3 Phase space analysis of hemodynamic responses to intentional movement directions using functional near-infrared spectroscopy (fNIRS) optical imaging technique

Nicoladie D.Tam, Luca Pollonini, George Zouridakis

P4 Modeling jamming avoidance of weakly electric fish

Jaehyun Soh, DaeEun Kim

P5 Synergy and redundancy of retinal ganglion cells in prediction

Minsu Yoo, S. E. Palmer

P6 A neural field model with a third dimension representing cortical depth

Viviana Culmone, Ingo Bojak

P7 Network analysis of a probabilistic connectivity model of the Xenopus tadpole spinal cord

Andrea Ferrario, Robert Merrison-Hort, Roman Borisyuk

P8 The recognition dynamics in the brain

Chang Sub Kim

P9 Multivariate spike train analysis using a positive definite kernel

Taro Tezuka

P10 Synchronization of burst periods may govern slow brain dynamics during general anesthesia

Pangyu Joo

P11 The ionic basis of heterogeneity affects stochastic synchrony

Young-Ah Rho, Shawn D. Burton, G. Bard Ermentrout, Jaeseung Jeong, Nathaniel N. Urban

P12 Circular statistics of noise in spike trains with a periodic component

Petr Marsalek

P14 Representations of directions in EEG-BCI using Gaussian readouts

Hoon-Hee Kim, Seok-hyun Moon, Do-won Lee, Sung-beom Lee, Ji-yong Lee, Jaeseung Jeong

P15 Action selection and reinforcement learning in basal ganglia during reaching movements

Yaroslav I. Molkov, Khaldoun Hamade, Wondimu Teka, William H. Barnett, Taegyo Kim, Sergey Markin, Ilya A. Rybak

P17 Axon guidance: modeling axonal growth in T-Junction assay

Csaba Forro, Harald Dermutz, László Demkó, János Vörös

P19 Transient cell assembly networks encode persistent spatial memories

Yuri Dabaghian, Andrey Babichev

P20 Theory of population coupling and applications to describe high order correlations in large populations of interacting neurons

Haiping Huang

P21 Design of biologically-realistic simulations for motor control

Sergio Verduzco-Flores

P22 Towards understanding the functional impact of the behavioural variability of neurons

Filipa Dos Santos, Peter Andras

P23 Different oscillatory dynamics underlying gamma entrainment deficits in schizophrenia

Christoph Metzner, Achim Schweikard, Bartosz Zurowski

P24 Memory recall and spike frequency adaptation

James P. Roach, Leonard M. Sander, Michal R. Zochowski

P25 Stability of neural networks and memory consolidation preferentially occur near criticality

Quinton M. Skilling, Nicolette Ognjanovski, Sara J. Aton, Michal Zochowski

P26 Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems

Sheng-Jun Wang, Guang Ouyang, Jing Guang, Mingsha Zhang, K. Y. Michael Wong, Changsong Zhou

P27 Neurofield: a C++ library for fast simulation of 2D neural field models

Peter A. Robinson, Paula Sanz-Leon, Peter M. Drysdale, Felix Fung, Romesh G. Abeysuriya, Chris J. Rennie, Xuelong Zhao

P28 Action-based grounding: Beyond encoding/decoding in neural code

Yoonsuck Choe, Huei-Fang Yang

P29 Neural computation in a dynamical system with multiple time scales

Yuanyuan Mi, Xiaohan Lin, Si Wu

P30 Maximum entropy models for 3D layouts of orientation selectivity

Joscha Liedtke, Manuel Schottdorf, Fred Wolf

P31 A behavioral assay for probing computations underlying curiosity in rodents

Yoriko Yamamura, Jeffery R. Wickens

P32 Using statistical sampling to balance error function contributions to optimization of conductance-based models

Timothy Rumbell, Julia Ramsey, Amy Reyes, Danel Draguljić, Patrick R. Hof, Jennifer Luebke, Christina M. Weaver

P33 Exploration and implementation of a self-growing and self-organizing neuron network building algorithm

Hu He, Xu Yang, Hailin Ma, Zhiheng Xu, Yuzhe Wang

P34 Disrupted resting state brain network in obese subjects: a data-driven graph theory analysis

Kwangyeol Baek, Laurel S. Morris, Prantik Kundu, Valerie Voon

P35 Dynamics of cooperative excitatory and inhibitory plasticity

Everton J. Agnes, Tim P. Vogels

P36 Frequency-dependent oscillatory signal gating in feed-forward networks of integrate-and-fire neurons

William F. Podlaski, Tim P. Vogels

P37 Phenomenological neural model for adaptation of neurons in area IT

Martin Giese, Pradeep Kuravi, Rufin Vogels

P38 ICGenealogy: towards a common topology of neuronal ion channel function and genealogy in model and experiment

Alexander Seeholzer, William Podlaski, Rajnish Ranjan, Tim Vogels

P39 Temporal input discrimination from the interaction between dynamic synapses and neural subthreshold oscillations

Joaquin J. Torres, Fabiano Baroni, Roberto Latorre, Pablo Varona

P40 Different roles for transient and sustained activity during active visual processing

Bart Gips, Eric Lowet, Mark J. Roberts, Peter de Weerd, Ole Jensen, Jan van der Eerden

P41 Scale-free functional networks of 2D Ising model are highly robust against structural defects: neuroscience implications

Abdorreza Goodarzinick, Mohammad D. Niry, Alireza Valizadeh

P42 High frequency neuron can facilitate propagation of signal in neural networks

Aref Pariz, Shervin S. Parsi, Alireza Valizadeh

P43 Investigating the effect of Alzheimer’s disease related amyloidopathy on gamma oscillations in the CA1 region of the hippocampus

Julia M. Warburton, Lucia Marucci, Francesco Tamagnini, Jon Brown, Krasimira Tsaneva-Atanasova

P44 Long-tailed distributions of inhibitory and excitatory weights in a balanced network with eSTDP and iSTDP

Florence I. Kleberg, Jochen Triesch

P45 Simulation of EMG recording from hand muscle due to TMS of motor cortex

Bahar Moezzi, Nicolangelo Iannella, Natalie Schaworonkow, Lukas Plogmacher, Mitchell R. Goldsworthy, Brenton Hordacre, Mark D. McDonnell, Michael C. Ridding, Jochen Triesch

P46 Structure and dynamics of axon network formed in primary cell culture

Martin Zapotocky, Daniel Smit, Coralie Fouquet, Alain Trembleau

P47 Efficient signal processing and sampling in random networks that generate variability

Sakyasingha Dasgupta, Isao Nishikawa, Kazuyuki Aihara, Taro Toyoizumi

P48 Modeling the effect of riluzole on bursting in respiratory neural networks

Daniel T. Robb, Nick Mellen, Natalia Toporikova

P49 Mapping relaxation training using effective connectivity analysis

Rongxiang Tang, Yi-Yuan Tang

P50 Modeling neuron oscillation of implicit sequence learning

Guangsheng Liang, Seth A. Kiser, James H. Howard, Jr., Yi-Yuan Tang

P51 The role of cerebellar short-term synaptic plasticity in the pathology and medication of downbeat nystagmus

Julia Goncharenko, Neil Davey, Maria Schilstra, Volker Steuber

P52 Nonlinear response of noisy neurons

Sergej O. Voronenko, Benjamin Lindner

P53 Behavioral embedding suggests multiple chaotic dimensions underlie C. elegans locomotion

Tosif Ahamed, Greg Stephens

P54 Fast and scalable spike sorting for large and dense multi-electrodes recordings

Pierre Yger, Baptiste Lefebvre, Giulia Lia Beatrice Spampinato, Elric Esposito, Marcel Stimberg et Olivier Marre

P55 Sufficient sampling rates for fast hand motion tracking

Hansol Choi, Min-Ho Song

P56 Linear readout of object manifolds

SueYeon Chung, Dan D. Lee, Haim Sompolinsky

P57 Differentiating models of intrinsic bursting and rhythm generation of the respiratory pre-Bötzinger complex using phase response curves

Ryan S. Phillips, Jeffrey Smith

P58 The effect of inhibitory cell network interactions during theta rhythms on extracellular field potentials in CA1 hippocampus

Alexandra Pierri Chatzikalymniou, Katie Ferguson, Frances K. Skinner

P59 Expansion recoding through sparse sampling in the cerebellar input layer speeds learning

N. Alex Cayco Gajic, Claudia Clopath, R. Angus Silver

P60 A set of curated cortical models at multiple scales on Open Source Brain

Padraig Gleeson, Boris Marin, Sadra Sadeh, Adrian Quintana, Matteo Cantarelli, Salvador Dura-Bernal, William W. Lytton, Andrew Davison, R. Angus Silver

P61 A synaptic story of dynamical information encoding in neural adaptation

Luozheng Li, Wenhao Zhang, Yuanyuan Mi, Dahui Wang, Si Wu

P62 Physical modeling of rule-observant rodent behavior

Youngjo Song, Sol Park, Ilhwan Choi, Jaeseung Jeong, Hee-sup Shin

P64 Predictive coding in area V4 and prefrontal cortex explains dynamic discrimination of partially occluded shapes

Hannah Choi, Anitha Pasupathy, Eric Shea-Brown

P65 Stability of FORCE learning on spiking and rate-based networks

Dongsung Huh, Terrence J. Sejnowski

P66 Stabilising STDP in striatal neurons for reliable fast state recognition in noisy environments

Simon M. Vogt, Arvind Kumar, Robert Schmidt

P67 Electrodiffusion in one- and two-compartment neuron models for characterizing cellular effects of electrical stimulation

Stephen Van Wert, Steven J. Schiff

P68 STDP improves speech recognition capabilities in spiking recurrent circuits parameterized via differential evolution Markov Chain Monte Carlo

Richard Veale, Matthias Scheutz

P69 Bidirectional transformation between dominant cortical neural activities and phase difference distributions

Sang Wan Lee

P70 Maturation of sensory networks through homeostatic structural plasticity

Júlia Gallinaro, Stefan Rotter

P71 Corticothalamic dynamics: structure, number of solutions and stability of steady-state solutions in the space of synaptic couplings

Paula Sanz-Leon, Peter A. Robinson

P72 Optogenetic versus electrical stimulation of the parkinsonian basal ganglia. Computational study

Leonid L. Rubchinsky, Chung Ching Cheung, Shivakeshavan Ratnadurai-Giridharan

P73 Exact spike-timing distribution reveals higher-order interactions of neurons

Safura Rashid Shomali, Majid Nili Ahmadabadi, Hideaki Shimazaki, S. Nader Rasuli

P74 Neural mechanism of visual perceptual learning using a multi-layered neural network

Xiaochen Zhao, Malte J. Rasch

P75 Inferring collective spiking dynamics from mostly unobserved systems

Jens Wilting, Viola Priesemann

P76 How to infer distributions in the brain from subsampled observations

Anna Levina, Viola Priesemann

P77 Influences of embedding and estimation strategies on the inferred memory of single spiking neurons

Lucas Rudelt, Joseph T. Lizier, Viola Priesemann

P78 A nearest-neighbours based estimator for transfer entropy between spike trains

Joseph T. Lizier, Richard E. Spinney, Mikail Rubinov, Michael Wibral, Viola Priesemann

P79 Active learning of psychometric functions with multinomial logistic models

Ji Hyun Bak, Jonathan Pillow

P81 Inferring low-dimensional network dynamics with variational latent Gaussian process

Yuan Zaho, Il Memming Park

P82 Computational investigation of energy landscapes in the resting state subcortical brain network

Jiyoung Kang, Hae-Jeong Park

P83 Local repulsive interaction between retinal ganglion cells can generate a consistent spatial periodicity of orientation map

Jaeson Jang, Se-Bum Paik

P84 Phase duration of bistable perception reveals intrinsic time scale of perceptual decision under noisy condition

Woochul Choi, Se-Bum Paik

P85 Feedforward convergence between retina and primary visual cortex can determine the structure of orientation map

Changju Lee, Jaeson Jang, Se-Bum Paik

P86 Computational method classifying neural network activity patterns for imaging data

Min Song, Hyeonsu Lee, Se-Bum Paik

P87 Symmetry of spike-timing-dependent-plasticity kernels regulates volatility of memory

Youngjin Park, Woochul Choi^, Se-Bum Paik

P88 Effects of time-periodic coupling strength on the first-spike latency dynamics of a scale-free network of stochastic Hodgkin-Huxley neurons

Ergin Yilmaz, Veli Baysal, Mahmut Ozer

P89 Spectral properties of spiking responses in V1 and V4 change within the trial and are highly relevant for behavioral performance

Veronika Koren, Klaus Obermayer

P90 Methods for building accurate models of individual neurons

Daniel Saska, Thomas Nowotny

P91 A full size mathematical model of the early olfactory system of honeybees

Ho Ka Chan, Alan Diamond, Thomas Nowotny

P92 Stimulation-induced tuning of ongoing oscillations in spiking neural networks

Christoph S. Herrmann, Micah M. Murray, Silvio Ionta, Axel Hutt, Jérémie Lefebvre

P93 Decision-specific sequences of neural activity in balanced random networks driven by structured sensory input

Philipp Weidel, Renato Duarte, Abigail Morrison

P94 Modulation of tuning induced by abrupt reduction of SST cell activity

Jung H. Lee, Ramakrishnan Iyer, Stefan Mihalas

P95 The functional role of VIP cell activation during locomotion

Jung H. Lee, Ramakrishnan Iyer, Christof Koch, Stefan Mihalas

P96 Stochastic inference with spiking neural networks

Mihai A. Petrovici, Luziwei Leng, Oliver Breitwieser, David Stöckel, Ilja Bytschok, Roman Martel, Johannes Bill, Johannes Schemmel, Karlheinz Meier

P97 Modeling orientation-selective electrical stimulation with retinal prostheses

Timothy B. Esler, Anthony N. Burkitt, David B. Grayden, Robert R. Kerr, Bahman Tahayori, Hamish Meffin

P98 Ion channel noise can explain firing correlation in auditory nerves

Bahar Moezzi, Nicolangelo Iannella, Mark D. McDonnell

P99 Limits of temporal encoding of thalamocortical inputs in a neocortical microcircuit

Max Nolte, Michael W. Reimann, Eilif Muller, Henry Markram

P100 On the representation of arm reaching movements: a computational model

Antonio Parziale, Rosa Senatore, Angelo Marcelli

P101 A computational model for investigating the role of cerebellum in acquisition and retention of motor behavior

Rosa Senatore, Antonio Parziale, Angelo Marcelli

P102 The emergence of semantic categories from a large-scale brain network of semantic knowledge

K. Skiker, M. Maouene

P103 Multiscale modeling of M1 multitarget pharmacotherapy for dystonia

Samuel A. Neymotin, Salvador Dura-Bernal, Alexandra Seidenstein, Peter Lakatos, Terence D. Sanger, William W. Lytton

P104 Effect of network size on computational capacity

Salvador Dura-Bernal, Rosemary J. Menzies, Campbell McLauchlan, Sacha J. van Albada, David J. Kedziora, Samuel Neymotin, William W. Lytton, Cliff C. Kerr

P105 NetPyNE: a Python package for NEURON to facilitate development and parallel simulation of biological neuronal networks

Salvador Dura-Bernal, Benjamin A. Suter, Samuel A. Neymotin, Cliff C. Kerr, Adrian Quintana, Padraig Gleeson, Gordon M. G. Shepherd, William W. Lytton

P107 Inter-areal and inter-regional inhomogeneity in co-axial anisotropy of Cortical Point Spread in human visual areas

Juhyoung Ryu, Sang-Hun Lee

P108 Two bayesian quanta of uncertainty explain the temporal dynamics of cortical activity in the non-sensory areas during bistable perception

Joonwon Lee, Sang-Hun Lee

P109 Optimal and suboptimal integration of sensory and value information in perceptual decision making

Hyang Jung Lee, Sang-Hun Lee

P110 A Bayesian algorithm for phoneme Perception and its neural implementation

Daeseob Lim, Sang-Hun Lee

P111 Complexity of EEG signals is reduced during unconsciousness induced by ketamine and propofol

Jisung Wang, Heonsoo Lee

P112 Self-organized criticality of neural avalanche in a neural model on complex networks

Nam Jung, Le Anh Quang, Seung Eun Maeng, Tae Ho Lee, Jae Woo Lee

P113 Dynamic alterations in connection topology of the hippocampal network during ictal-like epileptiform activity in an in vitro rat model

Chang-hyun Park, Sora Ahn, Jangsup Moon, Yun Seo Choi, Juhee Kim, Sang Beom Jun, Seungjun Lee, Hyang Woon Lee

P114 Computational model to replicate seizure suppression effect by electrical stimulation

Sora Ahn, Sumin Jo, Eunji Jun, Suin Yu, Hyang Woon Lee, Sang Beom Jun, Seungjun Lee

P115 Identifying excitatory and inhibitory synapses in neuronal networks from spike trains using sorted local transfer entropy

Felix Goetze, Pik-Yin Lai

P116 Neural network model for obstacle avoidance based on neuromorphic computational model of boundary vector cell and head direction cell

Seonghyun Kim, Jeehyun Kwag

P117 Dynamic gating of spike pattern propagation by Hebbian and anti-Hebbian spike timing-dependent plasticity in excitatory feedforward network model

Hyun Jae Jang, Jeehyun Kwag

P118 Inferring characteristics of input correlations of cells exhibiting up-down state transitions in the rat striatum

Marko Filipović, Ramon Reig, Ad Aertsen, Gilad Silberberg, Arvind Kumar

P119 Graph properties of the functional connected brain under the influence of Alzheimer’s disease

Claudia Bachmann, Simone Buttler, Heidi Jacobs, Kim Dillen, Gereon R. Fink, Juraj Kukolja, Abigail Morrison

P120 Learning sparse representations in the olfactory bulb

Daniel Kepple, Hamza Giaffar, Dima Rinberg, Steven Shea, Alex Koulakov

P121 Functional classification of homologous basal-ganglia networks

Jyotika Bahuguna,Tom Tetzlaff, Abigail Morrison, Arvind Kumar, Jeanette Hellgren Kotaleski

P122 Short term memory based on multistability

Tim Kunze, Andre Peterson, Thomas Knösche

P123 A physiologically plausible, computationally efficient model and simulation software for mammalian motor units

Minjung Kim, Hojeong Kim

P125 Decoding laser-induced somatosensory information from EEG

Ji Sung Park, Ji Won Yeon, Sung-Phil Kim

P126 Phase synchronization of alpha activity for EEG-based personal authentication

Jae-Hwan Kang, Chungho Lee, Sung-Phil Kim

P129 Investigating phase-lags in sEEG data using spatially distributed time delays in a large-scale brain network model

Andreas Spiegler, Spase Petkoski, Matias J. Palva, Viktor K. Jirsa

P130 Epileptic seizures in the unfolding of a codimension-3 singularity

Maria L. Saggio, Silvan F. Siep, Andreas Spiegler, William C. Stacey, Christophe Bernard, Viktor K. Jirsa

P131 Incremental dimensional exploratory reasoning under multi-dimensional environment

Oh-hyeon Choung, Yong Jeong

P132 A low-cost model of eye movements and memory in personal visual cognition

Yong-il Lee, Jaeseung Jeong

P133 Complex network analysis of structural connectome of autism spectrum disorder patients

Su Hyun Kim, Mir Jeong, Jaeseung Jeong

P134 Cognitive motives and the neural correlates underlying human social information transmission, gossip

Jeungmin Lee, Jaehyung Kwon, Jerald D. Kralik, Jaeseung Jeong

P135 EEG hyperscanning detects neural oscillation for the social interaction during the economic decision-making

Jaehwan Jahng, Dong-Uk Hwang, Jaeseung Jeong

P136 Detecting purchase decision based on hyperfrontality of the EEG

Jae-Hyung Kwon, Sang-Min Park, Jaeseung Jeong

P137 Vulnerability-based critical neurons, synapses, and pathways in the Caenorhabditis elegans connectome

Seongkyun Kim, Hyoungkyu Kim, Jerald D. Kralik, Jaeseung Jeong

P138 Motif analysis reveals functionally asymmetrical neurons in C. elegans

Pyeong Soo Kim, Seongkyun Kim, Hyoungkyu Kim, Jaeseung Jeong

P139 Computational approach to preference-based serial decision dynamics: do temporal discounting and working memory affect it?

Sangsup Yoon, Jaehyung Kwon, Sewoong Lim, Jaeseung Jeong

P141 Social stress induced neural network reconfiguration affects decision making and learning in zebrafish

Choongseok Park, Thomas Miller, Katie Clements, Sungwoo Ahn, Eoon Hye Ji, Fadi A. Issa

P142 Descriptive, generative, and hybrid approaches for neural connectivity inference from neural activity data

JeongHun Baek, Shigeyuki Oba, Junichiro Yoshimoto, Kenji Doya, Shin Ishii

P145 Divergent-convergent synaptic connectivities accelerate coding in multilayered sensory systems

Thiago S. Mosqueiro, Martin F. Strube-Bloss, Brian Smith, Ramon Huerta

P146 Swinging networks

Michal Hadrava, Jaroslav Hlinka

P147 Inferring dynamically relevant motifs from oscillatory stimuli: challenges, pitfalls, and solutions

Hannah Bos, Moritz Helias

P148 Spatiotemporal mapping of brain network dynamics during cognitive tasks using magnetoencephalography and deep learning

Charles M. Welzig, Zachary J. Harper

P149 Multiscale complexity analysis for the segmentation of MRI images

Won Sup Kim, In-Seob Shin, Hyeon-Man Baek, Seung Kee Han

P150 A neuro-computational model of emotional attention

René Richter, Julien Vitay, Frederick Beuth, Fred H. Hamker

P151 Multi-site delayed feedback stimulation in parkinsonian networks

Kelly Toppin, Yixin Guo

P152 Bistability in Hodgkin–Huxley-type equations

Tatiana Kameneva, Hamish Meffin, Anthony N. Burkitt, David B. Grayden

P153 Phase changes in postsynaptic spiking due to synaptic connectivity and short term plasticity: mathematical analysis of frequency dependency

Mark D. McDonnell, Bruce P. Graham

P154 Quantifying resilience patterns in brain networks: the importance of directionality

Penelope J. Kale, Leonardo L. Gollo

P155 Dynamics of rate-model networks with separate excitatory and inhibitory populations

Merav Stern, L. F. Abbott

P156 A model for multi-stable dynamics in action recognition modulated by integration of silhouette and shading cues

Leonid A. Fedorov, Martin A. Giese

P157 Spiking model for the interaction between action recognition and action execution

Mohammad Hovaidi Ardestani, Martin Giese

P158 Surprise-modulated belief update: how to learn within changing environments?

Mohammad Javad Faraji, Kerstin Preuschoff, Wulfram Gerstner

P159 A fast, stochastic and adaptive model of auditory nerve responses to cochlear implant stimulation

Margriet J. van Gendt, Jeroen J. Briaire, Randy K. Kalkman, Johan H. M. Frijns

P160 Quantitative comparison of graph theoretical measures of simulated and empirical functional brain networks

Won Hee Lee, Sophia Frangou

P161 Determining discriminative properties of fMRI signals in schizophrenia using highly comparative time-series analysis

Ben D. Fulcher, Patricia H. P. Tran, Alex Fornito

P162 Emergence of narrowband LFP oscillations from completely asynchronous activity during seizures and high-frequency oscillations

Stephen V. Gliske, William C. Stacey, Eugene Lim, Katherine A. Holman, Christian G. Fink

P163 Neuronal diversity in structure and function: cross-validation of anatomical and physiological classification of retinal ganglion cells in the mouse

Jinseop S. Kim, Shang Mu, Kevin L. Briggman, H. Sebastian Seung, the EyeWirers

P164 Analysis and modelling of transient firing rate changes in area MT in response to rapid stimulus feature changes

Detlef Wegener, Lisa Bohnenkamp, Udo A. Ernst

P165 Step-wise model fitting accounting for high-resolution spatial measurements: construction of a layer V pyramidal cell model with reduced morphology

Tuomo Mäki-Marttunen, Geir Halnes, Anna Devor, Christoph Metzner, Anders M. Dale, Ole A. Andreassen, Gaute T. Einevoll

P166 Contributions of schizophrenia-associated genes to neuron firing and cardiac pacemaking: a polygenic modeling approach

Tuomo Mäki-Marttunen, Glenn T. Lines, Andy Edwards, Aslak Tveito, Anders M. Dale, Gaute T. Einevoll, Ole A. Andreassen

P167 Local field potentials in a 4 × 4 mm² multi-layered network model

Espen Hagen, Johanna Senk, Sacha J. van Albada, Markus Diesmann

P168 A spiking network model explains multi-scale properties of cortical dynamics

Maximilian Schmidt, Rembrandt Bakker, Kelly Shen, Gleb Bezgin, Claus-Christian Hilgetag, Markus Diesmann, Sacha Jennifer van Albada

P169 Using joint weight-delay spike-timing dependent plasticity to find polychronous neuronal groups

Haoqi Sun, Olga Sourina, Guang-Bin Huang, Felix Klanner, Cornelia Denk

P170 Tensor decomposition reveals RSNs in simulated resting state fMRI

Katharina Glomb, Adrián Ponce-Alvarez, Matthieu Gilson, Petra Ritter, Gustavo Deco

P171 Getting in the groove: testing a new model-based method for comparing task-evoked vs resting-state activity in fMRI data on music listening

Matthieu Gilson, Maria AG Witek, Eric F. Clarke, Mads Hansen, Mikkel Wallentin, Gustavo Deco, Morten L. Kringelbach^, Peter Vuust

P172 STochastic engine for pathway simulation (STEPS) on massively parallel processors

Guido Klingbeil, Erik De Schutter

P173 Toolkit support for complex parallel spatial stochastic reaction–diffusion simulation in STEPS

Weiliang Chen, Erik De Schutter

P174 Modeling the generation and propagation of Purkinje cell dendritic spikes caused by parallel fiber synaptic input

Yunliang Zang, Erik De Schutter

P175 Dendritic morphology determines how dendrites are organized into functional subunits

Sungho Hong, Akira Takashima, Erik De Schutter

P176 A model of Ca²⁺/calmodulin-dependent protein kinase II activity in long term depression at Purkinje cells

Criseida Zamora, Andrew R. Gallimore, Erik De Schutter

P177 Reward-modulated learning of population-encoded vectors for insect-like navigation in embodied agents

Dennis Goldschmidt, Poramate Manoonpong, Sakyasingha Dasgupta

P178 Data-driven neural models part II: connectivity patterns of human seizures

Philippa J. Karoly, Dean R. Freestone, Daniel Soundry, Levin Kuhlmann, Liam Paninski, Mark Cook

P179 Data-driven neural models part I: state and parameter estimation

Dean R. Freestone, Philippa J. Karoly, Daniel Soundry, Levin Kuhlmann, Mark Cook

P180 Spectral and spatial information processing in human auditory streaming

Jaejin Lee, Yonatan I. Fishman, Yale E. Cohen

P181 A tuning curve for the global effects of local perturbations in neural activity: Mapping the systems-level susceptibility of the brain

Leonardo L. Gollo, James A. Roberts, Luca Cocchi

P182 Diverse homeostatic responses to visual deprivation mediated by neural ensembles

Yann Sweeney, Claudia Clopath

P183 Opto-EEG: a novel method for investigating functional connectome in mouse brain based on optogenetics and high density electroencephalography

Soohyun Lee, Woo-Sung Jung, Jee Hyun Choi

P184 Biphasic responses of frontal gamma network to repetitive sleep deprivation during REM sleep

Bowon Kim, Youngsoo Kim, Eunjin Hwang, Jee Hyun Choi

P185 Brain-state correlate and cortical connectivity for frontal gamma oscillations in top-down fashion assessed by auditory steady-state response

Younginha Jung, Eunjin Hwang, Yoon-Kyu Song, Jee Hyun Choi

P186 Neural field model of localized orientation selective activation in V1

James Rankin, Frédéric Chavane

P187 An oscillatory network model of Head direction and Grid cells using locomotor inputs

Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy

P188 A computational model of hippocampus inspired by the functional architecture of basal ganglia

Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy

P189 A computational architecture to model the microanatomy of the striatum and its functional properties

Sabyasachi Shivkumar, Vignesh Muralidharan, V. Srinivasa Chakravarthy

P190 A scalable cortico-basal ganglia model to understand the neural dynamics of targeted reaching

Vignesh Muralidharan, Alekhya Mandali, B. Pragathi Priyadharsini, Hima Mehta, V. Srinivasa Chakravarthy

P191 Emergence of radial orientation selectivity from synaptic plasticity

Catherine E. Davey, David B. Grayden, Anthony N. Burkitt

P192 How do hidden units shape effective connections between neurons?

Braden A. W. Brinkman, Tyler Kekona, Fred Rieke, Eric Shea-Brown, Michael Buice

P193 Characterization of neural firing in the presence of astrocyte-synapse signaling

Maurizio De Pittà, Hugues Berry, Nicolas Brunel

P194 Metastability of spatiotemporal patterns in a large-scale network model of brain dynamics

James A. Roberts, Leonardo L. Gollo, Michael Breakspear

P195 Comparison of three methods to quantify detection and discrimination capacity estimated from neural population recordings

Gary Marsat, Jordan Drew, Phillip D. Chapman, Kevin C. Daly, Samual P. Bradley

P196 Quantifying the constraints for independent evoked and spontaneous NMDA receptor mediated synaptic transmission at individual synapses

Sat Byul Seo, Jianzhong Su, Ege T. Kavalali, Justin Blackwell

P199 Gamma oscillation via adaptive exponential integrate-and-fire neurons

LieJune Shiau, Laure Buhry, Kanishka Basnayake

P200 Visual face representations during memory retrieval compared to perception

Sue-Hyun Lee, Brandon A. Levy, Chris I. Baker

P201 Top-down modulation of sequential activity within packets modeled using avalanche dynamics

Timothée Leleu, Kazuyuki Aihara

Q28 An auto-encoder network realizes sparse features under the influence of desynchronized vascular dynamics

Ryan T. Philips, Karishma Chhabria, V. Srinivasa Chakravarthy

Sleep-Dependent Potentiation in the Visual System Is at Odds with the Synaptic Homeostasis Hypothesis

STUDY OBJECTIVES

Two commentaries recently published in SLEEP came to very different conclusions regarding how data from a mouse model of sleep-dependent neural plasticity (orientation-specific response potentiation; OSRP) fit with the synaptic homeostasis hypothesis (SHY). To assess whether SHY offers an explanatory mechanism for OSRP, we present new data on how cortical neuron firing rates are modulated as a function of novel sensory experience and subsequent sleep in this model system.

METHODS

We carried out longitudinal extracellular recordings of single-neuron activity in the primary visual cortex across a period of novel visual experience and subsequent sleep or sleep deprivation. Spontaneous neuronal firing rates and visual responses were recorded from the same population of visual cortex neurons before control (blank screen) or novel (oriented grating) stimulus presentation, immediately after stimulus presentation, and after a period of subsequent ad lib sleep or sleep deprivation.

RESULTS

Firing rate responses to visual stimuli were unchanged across waking experience, regardless of whether a blank screen or an oriented grating stimulus was presented. Firing rate responses to stimuli of the presented stimulus orientation were selectively enhanced across post-stimulus sleep, but these changes were blocked by sleep deprivation. Neuronal firing increased significantly across bouts of post-stimulus rapid eye movement (REM) sleep and slow wave sleep (SWS), but not across bouts of wake.

CONCLUSIONS

The current data suggest that following novel visual experience, potentiation of a subset of V1 synapses occurs across periods of sleep. This finding cannot be explained parsimoniously by SHY.

Rapid eye movement sleep promotes cortical plasticity in the developing brain

Rapid eye movement sleep is maximal during early life, but its function in the developing brain is unknown. We investigated the role of rapid eye movement sleep in a canonical model of developmental plasticity in vivo (ocular dominance plasticity in the cat) induced by monocular deprivation. Preventing rapid eye movement sleep after monocular deprivation reduced ocular dominance plasticity and inhibited activation of a kinase critical for this plasticity (extracellular signal-regulated kinase). Chronic single-neuron recording in freely behaving cats further revealed that cortical activity during rapid eye movement sleep resembled activity present during monocular deprivation. This corresponded to times of maximal extracellular signal-regulated kinase activation. These findings indicate that rapid eye movement sleep promotes molecular and network adaptations that consolidate waking experience in the developing brain.

Sleep promotes cortical response potentiation following visual experience

STUDY OBJECTIVES

Sleep has been hypothesized to globally reduce synaptic strength. However, recent findings suggest that in the context of learning and memory consolidation, sleep may promote synaptic potentiation. We tested the requirement for sleep in a naturally occurring form of experience-dependent synaptic potentiation in the adult mouse visual cortex (V1), which is initiated by patterned visual experience.

DESIGN

Visual responses were recorded in individual V1 neurons before and after presentation of an oriented grating stimulus, and after subsequent sleep or sleep deprivation.

MEASUREMENTS AND RESULTS

We find that V1 response potentiation-associated with a shift in orientation preference in favor of the presented stimulus-occurs only after sleep and only during the entrained circadian sleep phase, and is blocked by sleep deprivation. Induction of plasticity following stimulus presentation is associated with an increase in principal neuron firing in V1, which is present in all behavioral states and occurs regardless of time of day. Sleep dependent potentiation is proportional to phase-locking of neuronal activity with thalamocortical spindle oscillations.

CONCLUSIONS

Our results suggest that sleep can promote cortical synaptic potentiation in vivo, and that this potentiation may be mediated by slow wave sleep spindles.

CITATION

Aton SJ, Suresh A, Broussard C, Frank MG. Sleep promotes cortical response potentiation following visual experience.

CA1 hippocampal network activity changes during sleep-dependent memory consolidation

A period of sleep over the first few hours following single-trial contextual fear conditioning (CFC) is essential for hippocampally-mediated memory consolidation. Recent studies have uncovered intracellular mechanisms required for memory formation which are affected by post-conditioning sleep and sleep deprivation. However, almost nothing is known about the circuit-level activity changes during sleep that underlie activation of these intracellular pathways. Here we continuously recorded from the CA1 region of freely-behaving mice to characterize neuronal and network activity changes occurring during active memory consolidation. C57BL/6J mice were implanted with custom stereotrode recording arrays to monitor activity of individual CA1 neurons, local field potentials (LFPs), and electromyographic activity. Sleep architecture and state-specific CA1 activity patterns were assessed during a 24 h baseline recording period, and for 24 h following either single-trial CFC or Sham conditioning. We find that consolidation of CFC is not associated with significant sleep architecture changes, but is accompanied by long-lasting increases in CA1 neuronal firing, as well as increases in delta, theta, and gamma-frequency CA1 LFP activity. These changes occurred in both sleep and wakefulness, and may drive synaptic plasticity within the hippocampus during memory formation. We also find that functional connectivity within the CA1 network, assessed through functional clustering algorithm (FCA) analysis of spike timing relationships among recorded neurons, becomes more stable during consolidation of CFC. This increase in network stability was not present following Sham conditioning, was most evident during post-CFC slow wave sleep (SWS), and was negligible during post-CFC wakefulness. Thus in the interval between encoding and recall, SWS may stabilize the hippocampal contextual fear memory (CFM) trace by promoting CA1 network stability.

Set and setting: how behavioral state regulates sensory function and plasticity

Recently developed neuroimaging and electrophysiological techniques are allowing us to answer fundamental questions about how behavioral states regulate our perception of the external environment. Studies using these techniques have yielded surprising insights into how sensory processing is affected at the earliest stages by attention and motivation, and how new sensory information received during wakefulness (e.g., during learning) continues to affect sensory brain circuits (leading to plastic changes) during subsequent sleep. This review aims to describe how brain states affect sensory response properties among neurons in primary and secondary sensory cortices, and how this relates to psychophysical detection thresholds and performance on sensory discrimination tasks. This is not intended to serve as a comprehensive overview of all brain states, or all sensory systems, but instead as an illustrative description of how three specific state variables (attention, motivation, and vigilance [i.e., sleep vs. wakefulness]) affect sensory systems in which they have been best studied.

Extracellular signal-regulated kinase (ERK) activity during sleep consolidates cortical plasticity in vivo

Ocular dominance plasticity (ODP) in the cat primary visual cortex (V1) is induced during waking by monocular deprivation (MD) and consolidated during subsequent sleep. The mechanisms underlying this process are incompletely understood. Extracellular signal-regulated kinase (ERK) is activated in V1 during sleep after MD, but it is unknown whether ERK activation during sleep is necessary for ODP consolidation. We investigated the role of ERK in sleep-dependent ODP consolidation by inhibiting the ERK-activating enzyme MEK in V1 (via U0126) during post-MD sleep. ODP consolidation was then measured with extracellular microelectrode recordings. Western blot analysis was used to confirm the efficacy of U0126 and to examine proteins downstream of ERK. U0126 abolished ODP consolidation and reduced both phosphorylation of eukaryotic initiation factor 4E (eIF4E) and levels of the synaptic marker PSD-95. Furthermore, interfering with ERK-mediated translation by inhibiting MAP kinase-interacting kinase 1 (Mnk1) with CGP57380 mimicked the effects of U0126. These results demonstrate that ODP consolidation requires sleep-dependent activation of the ERK-Mnk1 pathway.

GABA networks destabilize genetic oscillations in the circadian pacemaker

Systems of coupled oscillators abound in nature. How they establish stable phase relationships under diverse conditions is fundamentally important. The mammalian suprachiasmatic nucleus (SCN) is a self-sustained, synchronized network of circadian oscillators that coordinates daily rhythms in physiology and behavior. To elucidate the underlying topology and signaling mechanisms that modulate circadian synchrony, we discriminated the firing of hundreds of SCN neurons continuously over days. Using an analysis method to identify functional interactions between neurons based on changes in their firing, we characterized a GABAergic network comprised of fast, excitatory, and inhibitory connections that is both stable over days and changes in strength with time of day. By monitoring PERIOD2 protein expression, we provide the first evidence that these millisecond-level interactions actively oppose circadian synchrony and inject jitter into daily rhythms. These results provide a mechanism by which circadian oscillators can tune their phase relationships under different environmental conditions.

Protein synthesis during sleep consolidates cortical plasticity in vivo

Sleep consolidates experience-dependent brain plasticity, but the precise cellular mechanisms mediating this process are unknown [1]. De novo cortical protein synthesis is one possible mechanism. In support of this hypothesis, sleep is associated with increased brain protein synthesis [2, 3] and transcription of messenger RNAs (mRNAs) involved in protein synthesis regulation [4, 5]. Protein synthesis in turn is critical for memory consolidation and persistent forms of plasticity in vitro and in vivo [6, 7]. However, it is unknown whether cortical protein synthesis in sleep serves similar functions. We investigated the role of protein synthesis in the sleep-dependent consolidation of a classic form of cortical plasticity in vivo (ocular dominance plasticity, ODP; [8, 9]) in the cat visual cortex. We show that intracortical inhibition of mammalian target of rapamycin (mTOR)-dependent protein synthesis during sleep abolishes consolidation but has no effect on plasticity induced during wakefulness. Sleep also promotes phosphorylation of protein synthesis regulators (i.e., 4E-BP1 and eEF2) and the translation (but not transcription) of key plasticity related mRNAs (ARC and BDNF). These findings show that sleep promotes cortical mRNA translation. Interruption of this process has functional consequences, because it abolishes the consolidation of experience in the cortex.

The sedating antidepressant trazodone impairs sleep-dependent cortical plasticity

Background

Recent findings indicate that certain classes of hypnotics that target GABA_A receptors impair sleep-dependent brain plasticity. However, the effects of hypnotics acting at monoamine receptors (e.g., the antidepressant trazodone) on this process are unknown. We therefore assessed the effects of commonly-prescribed medications for the treatment of insomnia (trazodone and the non-benzodiazepine GABA_A receptor agonists zaleplon and eszopiclone) in a canonical model of sleep-dependent, in vivo synaptic plasticity in the primary visual cortex (V1) known as ocular dominance plasticity.

Methodology/Principal Findings

After a 6-h baseline period of sleep/wake polysomnographic recording, cats underwent 6 h of continuous waking combined with monocular deprivation (MD) to trigger synaptic remodeling. Cats subsequently received an i.p. injection of either vehicle, trazodone (10 mg/kg), zaleplon (10 mg/kg), or eszopiclone (1–10 mg/kg), and were allowed an 8-h period of post-MD sleep before ocular dominance plasticity was assessed. We found that while zaleplon and eszopiclone had profound effects on sleeping cortical electroencephalographic (EEG) activity, only trazodone (which did not alter EEG activity) significantly impaired sleep-dependent consolidation of ocular dominance plasticity. This was associated with deficits in both the normal depression of V1 neuronal responses to deprived-eye stimulation, and potentiation of responses to non-deprived eye stimulation, which accompany ocular dominance plasticity.

Conclusions/Significance

Taken together, our data suggest that the monoamine receptors targeted by trazodone play an important role in sleep-dependent consolidation of synaptic plasticity. They also demonstrate that changes in sleep architecture are not necessarily reliable predictors of how hypnotics affect sleep-dependent neural functions.

Mechanisms of sleep-dependent consolidation of cortical plasticity

Sleep is thought to consolidate changes in synaptic strength, but the underlying mechanisms are unknown. We investigated the cellular events involved in this process during ocular dominance plasticity (ODP)-a canonical form of in vivo cortical plasticity triggered by monocular deprivation (MD) and consolidated by sleep via undetermined, activity-dependent mechanisms. We find that sleep consolidates ODP primarily by strengthening cortical responses to nondeprived eye stimulation. Consolidation is inhibited by reversible, intracortical antagonism of NMDA receptors (NMDARs) or cAMP-dependent protein kinase (PKA) during post-MD sleep. Consolidation is also associated with sleep-dependent increases in the activity of remodeling neurons and in the phosphorylation of proteins required for potentiation of glutamatergic synapses. These findings demonstrate that synaptic strengthening via NMDAR and PKA activity is a key step in sleep-dependent consolidation of ODP.

The non-benzodiazepine hypnotic zolpidem impairs sleep-dependent cortical plasticity

STUDY OBJECTIVES

The effects of hypnotics on sleep-dependent brain plasticity are unknown. We have shown that sleep enhances a canonical model of in vivo cortical plasticity, known as ocular dominance plasticity (ODP). We investigated the effects of 3 different classes of hypnotics on ODP.

DESIGN

Polysomnographic recordings were performed during the entire experiment (20 h). After a baseline sleep/wake recording (6 h), cats received 6 h of monocular deprivation (MD) followed by an i.p. injection of triazolam (1-10 mg/kg i.p.), zolpidem (10 mg/kg i.p.), ramelteon (0.1-1 mg/kg i.p.), or vehicle (DMSO i.p.). They were then allowed to sleep ad lib for 8 h, after which they were prepared for optical imaging of intrinsic cortical signals and single-unit electrophysiology.

SETTING

Basic neurophysiology laboratory

PATIENTS OR PARTICIPANTS

Cats (male and female) in the critical period of visual development (postnatal days 28-41)

INTERVENTIONS

N/A.

MEASUREMENTS AND RESULTS

Zolpidem reduced cortical plasticity by approximately 50% as assessed with optical imaging of intrinsic cortical signals. This was not due to abnormal sleep architecture because triazolam, which perturbed sleep architecture and sleep EEGs more profoundly than zolpidem, had no effect on plasticity. Ramelteon minimally altered sleep and had no effect on ODP.

CONCLUSIONS

Our findings demonstrate that alterations in sleep architecture do not necessarily lead to impairments in sleep function. Conversely, hypnotics that produce more "physiological" sleep based on polysomnography may impair critical brain processes, depending on their pharmacology.

Bioluminescence imaging of period1 gene expression in utero

The use of real-time reporters has accelerated our understanding of gene expression in vivo. This study examined the feasibility of a luciferase-based reporter to image spatiotemporal changes in fetal gene expression in utero. We chose to monitor Period1 (Per1) because it is expressed broadly in the body and plays a role in circadian rhythmicity. Using rats carrying a Per1::luc transgene, we repetitively imaged fetuses in utero throughout gestation. We found that bioluminescence was specific to transgenic pups, increased dramatically on embryonic day 10 (10 days after successful mating), and continued to increase logarithmically until birth. Diurnal fluctuations in Per1 expression were apparent several days prior to birth. These results demonstrate the feasibility of in utero imaging of mammalian gene expression, tracking of fetal gene expression from the same litter, and early detection of mammalian clock gene expression. We conclude that luciferase-based reporters can provide a sensitive, noninvasive measure of in utero gene expression.

GABA and Gi/o differentially control circadian rhythms and synchrony in clock neurons

Neurons in the mammalian suprachiasmatic nuclei (SCN) generate daily rhythms in physiology and behavior, but it is unclear how they maintain and synchronize these rhythms in vivo. We hypothesized that parallel signaling pathways in the SCN are required to synchronize rhythms in these neurons for coherent output. We recorded firing and clock-gene expression patterns while blocking candidate signaling pathways for at least 8 days. GABA(A) and GABA(B) antagonism increased circadian peak firing rates and rhythm precision of cultured SCN neurons, but G(i/o) did not impair synchrony or rhythmicity. In contrast, inhibiting G(i/o) with pertussis toxin abolished rhythms in most neurons and desynchronized the population, phenocopying the loss of vasoactive intestinal polypeptide (VIP). Daily VIP receptor agonist treatment restored synchrony and rhythmicity to VIP(-/-) SCN cultures during continuous GABA receptor antagonism but not during G(i/o) blockade. Pertussis toxin did not affect circadian cycling of the liver, suggesting that G(i/o) plays a specialized role in maintaining SCN rhythmicity. We conclude that endogenous GABA controls the amplitude of SCN neuronal rhythms by reducing daytime firing, whereas G(i/o) signaling suppresses nighttime firing, and it is necessary for synchrony among SCN neurons. We propose that G(i/o), not GABA activity, converges with VIP signaling to maintain and coordinate rhythms among SCN neurons.

Come together, right...now: synchronization of rhythms in a mammalian circadian clock

In mammals, the suprachiasmatic nuclei (SCN) of the hypothalamus act as a dominant circadian pacemaker, coordinating rhythms throughout the body and regulating daily and seasonal changes in physiology and behavior. This review focuses on the mechanisms that mediate synchronization of circadian rhythms between SCN neurons. Understanding how these neurons communicate as a network of circadian oscillators has begun to shed light on the adaptability and dysfunction of the brain's master clock.

Plasticity of circadian behavior and the suprachiasmatic nucleus following exposure to non-24-hour light cycles

Period aftereffects are a form of behavioral plasticity in which the free-running period of circadian behavior undergoes experience-dependent changes. It is unclear whether this plasticity is age dependent and whether the changes in behavioral period relate to changes in the SCN or the retina, 2 known circadian pacemakers in mammals. To determine whether these changes vary with age, Per1-luc transgenic mice (in which the luciferase gene is driven by the Period1 promoter) of different ages were exposed to short (10 h light: 10 h dark, T20) or long (14 h light: 14 h dark, T28) light cycles (T cycles). Recordings of running-wheel activity in constant darkness (DD) revealed that the intrinsic periods of T20 mice were significantly shorter than of T28 mice at all ages. Aftereffects following the shorter light cycle were significantly smaller in mice older than 3 months, corresponding with a decreased ability to entrain to T20. Age did not diminish entrainment or aftereffects in the 28-h light schedule. The behavioral period of pups born in DD depended on the T cycle experienced in utero, showing maternal transference of aftereffects. Recordings of Per1-luc activity from the isolated SCN in vitro revealed that the SCN of young mice expressed aftereffects, but the periods of behavior and SCN were negatively correlated. Enucleation in DD had no effect on behavioral aftereffects, indicating the eyes are not required for aftereffects expression. These data show that circadian aftereffects are an age-dependent form of plasticity mediated by stable changes in the SCN and, importantly, extra-SCN tissues.

Vasoactive intestinal polypeptide mediates circadian rhythmicity and synchrony in mammalian clock neurons

The mammalian suprachiasmatic nucleus (SCN) is a master circadian pacemaker. It is not known which SCN neurons are autonomous pacemakers or how they synchronize their daily firing rhythms to coordinate circadian behavior. Vasoactive intestinal polypeptide (VIP) and the VIP receptor VPAC(2) (encoded by the gene Vipr2) may mediate rhythms in individual SCN neurons, synchrony between neurons, or both. We found that Vip(-/-) and Vipr2(-/-) mice showed two daily bouts of activity in a skeleton photoperiod and multiple circadian periods in constant darkness. Loss of VIP or VPAC(2) also abolished circadian firing rhythms in approximately half of all SCN neurons and disrupted synchrony between rhythmic neurons. Critically, daily application of a VPAC(2) agonist restored rhythmicity and synchrony to VIP(-/-) SCN neurons, but not to Vipr2(-/-) neurons. We conclude that VIP coordinates daily rhythms in the SCN and behavior by synchronizing a small population of pacemaking neurons and maintaining rhythmicity in a larger subset of neurons.

Temporal precision in the mammalian circadian system: a reliable clock from less reliable neurons

The mammalian SCN contains a biological clock that drives remarkably precise circadian rhythms in vivo and in vitro. This study asks whether the cycle-to-cycle variability of behavioral rhythms in mice can be attributed to precision of individual circadian pacemakers within the SCN or their interactions. The authors measured the standard deviation of the cycle-to-cycle period from 7-day recordings of running wheel activity, Period1 gene expression in cultured SCN explants, and firing rate patterns of dispersed SCN neurons. Period variability of the intact tissue and animal was lower than single neurons. The median variability of running wheel and Period1 rhythms was less than 40 min per cycle compared to 2.1 h in firing rate rhythms of dispersed SCN neurons. The most precise SCN neuron, with a period deviation of 1.1 h, was 10 times noisier than the most accurate SCN explant (0.1 h) or mouse (0.1 h) but comparable to the least stable explant (2.1 h) and mouse (1.1 h). This variability correlated with intrinsic period in mice and SCN explants but not with single cells. Precision was unrelated to the amplitude of rhythms and did not change significantly with age up to 1 year after birth. Analysis of the serial correlation of cycle-to-cycle period revealed that approximately half of this variability is attributable to noise outside the pacemaker. These results indicate that cell-cell interactions within the SCN reduce pacemaker noise to determine the precision of circadian rhythms in the tissue and in behavior.

Olfactory bulb neurons express functional, entrainable circadian rhythms

Circadian pacemakers drive many daily molecular, physiological and behavioural rhythms. We investigated whether the main olfactory bulb is a functional circadian pacemaker in rats. Long-term, multielectrode recordings revealed that individual, cultured bulb neurons expressed near 24-h oscillations in firing rate. Real-time recordings of Period1 gene activity showed that a population of cells within the bulb expressed synchronized rhythmicity starting on embryonic day 19. This rhythmicity was intrinsic to the mitral, and not the granule, cell layer, entrainable to physiological temperature cycles and temperature compensated in its period. However, removal of the olfactory bulbs had no effect on running wheel behaviour. These results indicate that individual mitral/tufted cells are competent circadian pacemakers which normally synchronize to each other. The daily rhythms in gene expression and firing rate intrinsic to the olfactory bulb are not required for circadian patterns of locomotion, indicating that they are involved in rhythms outside the canonical circadian system.