GluN2C/D-containing NMDA receptors enhance temporal summation and increase sound-evoked and spontaneous firing in the inferior colliculus.

Along the ascending auditory pathway, there is a broad shift from temporal coding, which is common in the lower auditory brainstem, to rate coding, which predominates in auditory cortex. This temporal-to-rate transition is particularly prominent in the inferior colliculus (IC), the midbrain hub of the auditory system, but the mechanisms that govern how individual IC neurons integrate information across time remain largely unknown. Here, we report the widespread expression of Glun2c and Glun2d mRNA in IC neurons. GluN2C/D-containing NMDA receptors are relatively insensitive to voltage-dependent Mg2+ blockade, and thus can conduct current at resting membrane potential. Using in situ hybridization and pharmacology, we show that vasoactive intestinal peptide neurons in the IC express GluN2D-containing NMDA receptors that are activatable by commissural inputs from the contralateral IC. In addition, GluN2C/D-containing receptors have much slower kinetics than other NMDA receptors, and we found that GluN2D-containing receptors facilitate temporal summation of synaptic inputs in vasoactive intestinal peptide neurons. In a model neuron, we show that a GluN2C/D-like conductance interacts with the passive membrane properties of the neuron to alter temporal and rate coding of stimulus trains. Consistent with this, we show in vivo that blocking GluN2C/D-containing receptors decreases both the spontaneous firing rate and the overall firing rate elicited by amplitude-modulated sounds in many IC neurons. These results suggest that GluN2C/D-containing NMDA receptors influence rate coding for auditory stimuli in the IC by facilitating the temporal integration of synaptic inputs. KEY POINTS: NMDA receptors are critical components of most glutamatergic circuits in the brain, and the diversity of NMDA receptor subtypes yields receptors with a variety of functions. We found that many neurons in the auditory midbrain express GluN2C and/or GluN2D NMDA receptor subunits, which are less sensitive to Mg2+ blockade than the more commonly expressed GluN2A/B subunits. We show that GluN2C/D-containing receptors conducted current at resting membrane potential and enhanced temporal summation of synaptic inputs. In a model, we show that GluN2C/D-containing receptors provide additive gain for input-output functions driven by trains of synaptic inputs. In line with this, we found that blocking GluN2C/D-containing NMDA receptors in vivo decreased both spontaneous firing rates and firing evoked by amplitude-modulated sounds.

Data-driven mathematical modeling of sleep consolidation in early childhood.

Across early childhood development, sleep behavior transitions from a biphasic pattern (a daytime nap and nighttime sleep) to a monophasic pattern (only nighttime sleep). The transition to consolidated nighttime sleep, which occurs in most children between 2- and 5-years-old, is a major developmental milestone and reflects interactions between the developing homeostatic sleep drive and circadian system. Using a physiologically-based mathematical model of the sleep-wake regulatory network constrained by observational and experimental data from preschool-aged participants, we analyze how developmentally-mediated changes in the homeostatic sleep drive may contribute to the transition from napping to non-napping sleep patterns. We establish baseline behavior by identifying parameter sets that model typical 2-year-old napping behavior and 5-year-old non-napping behavior. Then we vary six model parameters associated with the dynamics of and sensitivity to the homeostatic sleep drive between the 2-year-old and 5-year-old parameter values to induce the transition from biphasic to monophasic sleep. We analyze the individual contributions of these parameters to sleep patterning by independently varying their age-dependent developmental trajectories. Parameters vary according to distinct evolution curves and produce bifurcation sequences representing various ages of transition onset, transition durations, and transitional sleep patterns. Finally, we consider the ability of napping and non-napping light schedules to reinforce napping or promote a transition to consolidated sleep, respectively. These modeling results provide insight into the role of the homeostatic sleep drive in promoting interindividual variability in developmentally-mediated transitions in sleep behavior and lay foundations for the identification of light- or behavior-based interventions that promote healthy sleep consolidation in early childhood.

Modeling cortical synaptic effects of anesthesia and their cholinergic reversal.

General anesthetics work through a variety of molecular mechanisms while resulting in the common end point of sedation and loss of consciousness. Generally, the administration of common anesthetics induces reduction in synaptic excitation while promoting synaptic inhibition. Exogenous modulation of the anesthetics' synaptic effects can help determine the neuronal pathways involved in anesthesia. For example, both animal and human studies have shown that exogenously induced increases in acetylcholine in the brain can elicit wakeful-like behavior despite the continued presence of the anesthetic. However, the underlying mechanisms of anesthesia reversal at the cellular level have not been investigated. Here we apply a computational model of a network of excitatory and inhibitory neurons to simulate the network-wide effects of anesthesia, due to changes in synaptic inhibition and excitation, and their reversal by cholinergic activation through muscarinic receptors. We use a differential evolution algorithm to fit model parameters to match measures of spiking activity, neuronal connectivity, and network dynamics recorded in the visual cortex of rodents during anesthesia with desflurane in vivo. We find that facilitating muscarinic receptor effects of acetylcholine on top of anesthetic-induced synaptic changes predicts the reversal of anesthetic suppression of neurons' spiking activity, functional connectivity, as well as pairwise and population interactions. Thus, our model predicts a specific neuronal mechanism for the cholinergic reversal of anesthesia consistent with experimental behavioral observations.

Modelling learning in Caenorhabditis elegans chemosensory and locomotive circuitry for T-maze navigation.

Recently, a new type of Caenorhabditis elegans associative learning was reported, where nematodes learn to reach a target arm in an empty T-maze, after they have successfully located reward (food) in the same side arm of a similar, baited, training maze. Here, we present a simplified mathematical model of C. elegans chemosensory and locomotive circuitry that replicates C. elegans navigation in a T-maze and predicts the underlying mechanisms generating maze learning. Based on known neural circuitry, the model circuit responds to food-released chemical cues by modulating motor neuron activity that drives simulated locomotion. We show that, through modulation of interneuron activity, such a circuit can mediate maze learning by acquiring a turning bias, even after a single training session. Simulated nematode maze navigation during training conditions in food-baited mazes and during testing conditions in empty mazes is validated by comparing simulated behaviour with new experimental video data, extracted through the implementation of a custom-made maze tracking algorithm. Our work provides a mathematical framework for investigating the neural mechanisms underlying this novel learning behaviour in C. elegans. Model results predict neuronal components involved in maze and spatial learning and identify target neurons and potential neural mechanisms for future experimental investigations into this learning behaviour.

Correction: Contrasting mechanisms for hidden hearing loss: Synaptopathy vs myelin defects.

[This corrects the article DOI: 10.1371/journal.pcbi.1008499.].

Contrasting mechanisms for hidden hearing loss: Synaptopathy vs myelin defects.

Hidden hearing loss (HHL) is an auditory neuropathy characterized by normal hearing thresholds but reduced amplitudes of the sound-evoked auditory nerve compound action potential (CAP). In animal models, HHL can be caused by moderate noise exposure or aging, which induces loss of inner hair cell (IHC) synapses. In contrast, recent evidence has shown that transient loss of cochlear Schwann cells also causes permanent auditory deficits in mice with similarities to HHL. Histological analysis of the cochlea after auditory nerve remyelination showed a permanent disruption of the myelination patterns at the heminode of type I spiral ganglion neuron (SGN) peripheral terminals, suggesting that this defect could be contributing to HHL. To shed light on the mechanisms of different HHL scenarios observed in animals and to test their impact on type I SGN activity, we constructed a reduced biophysical model for a population of SGN peripheral axons whose activity is driven by a well-accepted model of cochlear sound processing. We found that the amplitudes of simulated sound-evoked SGN CAPs are lower and have greater latencies when heminodes are disorganized, i.e. they occur at different distances from the hair cell rather than at the same distance as in the normal cochlea. These results confirm that disruption of heminode positions causes desynchronization of SGN spikes leading to a loss of temporal resolution and reduction of the sound-evoked SGN CAP. Another mechanism resulting in HHL is loss of IHC synapses, i.e., synaptopathy. For comparison, we simulated synaptopathy by removing high threshold IHC-SGN synapses and found that the amplitude of simulated sound-evoked SGN CAPs decreases while latencies remain unchanged, as has been observed in noise exposed animals. Thus, model results illuminate diverse disruptions caused by synaptopathy and demyelination on neural activity in auditory processing that contribute to HHL as observed in animal models and that can contribute to perceptual deficits induced by nerve damage in humans.

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.

Phasic cholinergic signaling promotes emergence of local gamma rhythms in excitatory-inhibitory networks.

Recent experimental results have shown that the detection of cues in behavioral attention tasks relies on transient increases of acetylcholine (ACh) release in frontal cortex and cholinergically driven oscillatory activity in the gamma frequency band (Howe et al. Journal of Neuroscience, 2017, 37, 3215). The cue-induced gamma rhythmic activity requires stimulation of M1 muscarinic receptors. Using biophysical computational modeling, we show that a network of excitatory (E) and inhibitory (I) neurons that initially displays asynchronous firing can generate transient gamma oscillatory activity in response to simulated brief pulses of ACh. ACh effects are simulated as transient modulation of the conductance of an M-type K+ current which is blocked by activation of muscarinic receptors and has significant effects on neuronal excitability. The ACh-induced effects on the M current conductance, gKs , change network dynamics to promote the emergence of network gamma rhythmicity through a Pyramidal-Interneuronal Network Gamma mechanism. Depending on connectivity strengths between and among E and I cells, gamma activity decays with the simulated gKs transient modulation or is sustained in the network after the gKs transient has completely dissipated. We investigated the sensitivity of the emergent gamma activity to synaptic strengths, external noise and simulated levels of gKs modulation. To address recent experimental findings that cholinergic signaling is likely spatially focused and dynamic, we show that localized gKs modulation can induce transient changes of cellular excitability in local subnetworks, subsequently causing population-specific gamma oscillations. These results highlight dynamical mechanisms underlying localization of ACh-driven responses and suggest that spatially localized, cholinergically induced gamma may contribute to selectivity in the processing of competing external stimuli, as occurs in attentional tasks.

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.

Habitual sleep duration affects recovery from acute sleep deprivation: A modeling study.

Adult humans exhibit high interindividual variation in habitual sleep durations, with short sleepers typically sleeping less than 6 h per night and long sleepers typically sleeping more than 9 h per night. Analysis of the time course of homeostatic sleep drive in habitual short and long sleepers has not identified differences between these groups, leading to the hypothesis that habitual short sleep results from increased tolerance to high levels of homeostatic sleep drive. Using a physiologically-based mathematical model of the sleep-wake regulatory network, we investigate responses to acute sleep deprivation in simulated populations of habitual long, regular and short sleepers that differ in daily levels of homeostatic sleep drive. The model predicts timing and durations of wake, rapid eye movement (REM), and non-REM (NREM) sleep episodes as modulated by the homeostatic sleep drive and the circadian rhythm, which is entrained to an external light cycle. Model parameters are fit to experimental measures of baseline sleep durations to construct simulated populations of individuals of each sleeper type. The simulated populations are validated against data for responses to specific acute sleep deprivation protocols. We use the model to predict responses to a wide range of sleep deprivation durations for each sleeper type. Model results predict that all sleeper types exhibit shorter sleep durations during recovery sleep that occurs in the morning, but, for recovery sleep times occurring later in the day, long and regular sleepers show longer and more variable sleep durations, and can suffer longer lasting disruption of daily sleep patterns compared to short sleepers. Additionally, short sleepers showed more resilience to sleep deprivation with longer durations of waking episodes following recovery sleep. These results support the hypothesis that differential responses to sleep deprivation between short and long sleepers result from differences in the tolerance for homeostatic sleep pressure.

Modeling the daily rhythm of human pain processing in the dorsal horn.

Experimental studies show that human pain sensitivity varies across the 24-hour day, with the lowest sensitivity usually occurring during the afternoon. Patients suffering from neuropathic pain, or nerve damage, experience an inversion in the daily modulation of pain sensitivity, with the highest sensitivity usually occurring during the early afternoon. Processing of painful stimulation occurs in the dorsal horn (DH), an area of the spinal cord that receives input from peripheral tissues via several types of primary afferent nerve fibers. The DH circuit is composed of different populations of neurons, including excitatory and inhibitory interneurons, and projection neurons, which constitute the majority of the output from the DH to the brain. In this work, we develop a mathematical model of the dorsal horn neural circuit to investigate mechanisms for the daily modulation of pain sensitivity. The model describes average firing rates of excitatory and inhibitory interneuron populations and projection neurons, whose activity is directly correlated with experienced pain. Response in afferent fibers to peripheral stimulation is simulated by a Poisson process generating nerve fiber spike trains at variable firing rates. Model parameters for fiber response to stimulation and the excitability properties of neuronal populations are constrained by experimental results found in the literature, leading to qualitative agreement between modeled responses to pain and experimental observations. We validate our model by reproducing the wind-up of pain response to repeated stimulation. We apply the model to investigate daily modulatory effects on pain inhibition, in which response to painful stimuli is reduced by subsequent non-painful stimuli. Finally, we use the model to propose a mechanism for the observed inversion of the daily rhythmicity of pain sensation under neuropathic pain conditions. Underlying mechanisms for the shift in rhythmicity have not been identified experimentally, but our model results predict that experimentally-observed dysregulation of inhibition within the DH neural circuit may be responsible. The model provides an accessible, biophysical framework that will be valuable for experimental and clinical investigations of diverse physiological processes modulating pain processing in humans.

Network burst dynamics under heterogeneous cholinergic modulation of neural firing properties and heterogeneous synaptic connectivity.

The characteristics of neural network activity depend on intrinsic neural properties and synaptic connectivity in the network. In brain networks, both of these properties are critically affected by the type and levels of neuromodulators present. The expression of many of the most powerful neuromodulators, including acetylcholine (ACh), varies tonically and phasically with behavioural state, leading to dynamic, heterogeneous changes in intrinsic neural properties and synaptic connectivity properties. Namely, ACh significantly alters neural firing properties as measured by the phase response curve in a manner that has been shown to alter the propensity for network synchronization. The aim of this simulation study was to build an understanding of how heterogeneity in cholinergic modulation of neural firing properties and heterogeneity in synaptic connectivity affect the initiation and maintenance of synchronous network bursting in excitatory networks. We show that cells that display different levels of ACh modulation have differential roles in generating network activity: weakly modulated cells are necessary for burst initiation and provide synchronizing drive to the rest of the network, whereas strongly modulated cells provide the overall activity level necessary to sustain burst firing. By applying several quantitative measures of network activity, we further show that the existence of network bursting and its characteristics, such as burst duration and intraburst synchrony, are dependent on the fraction of cell types providing the synaptic connections in the network. These results suggest mechanisms underlying ACh modulation of brain oscillations and the modulation of seizure activity during sleep states.

The Association of Specific Executive Functions and Falls Risk in People with Mild Cognitive Impairment and Early-Stage Dementia.

BACKGROUND/AIMS: Impairment in executive function is associated with a heightened risk for falls in people with mild cognitive impairment (MCI) and dementia. The purpose of this study was to determine which aspects of executive function are associated with falls risk. METHODS: Forty-two participants with a mean age of 81.6 years and a diagnosis of MCI or mild dementia completed five different executive function tests from the computerised CANTAB test battery and a comprehensive falls risk assessment. RESULTS: A hierarchical regression analysis showed that falls risk was significantly associated with spatial memory abilities and inhibition of a pre-potent response. CONCLUSION: The concept of executive function may be too general to provide meaningful results in a research or clinical context, which should focus on spatial memory and inhibition of a pre-potent response.

A dynamical role for acetylcholine in synaptic renormalization.

Although sleep is a fundamental behavior observed in virtually all animal species, its functions remain unclear. One leading proposal, known as the synaptic renormalization hypothesis, suggests that sleep is necessary to counteract a global strengthening of synapses that occurs during wakefulness. Evidence for sleep-dependent synaptic downscaling (or synaptic renormalization) has been observed experimentally, but the physiological mechanisms which generate this phenomenon are unknown. In this study, we propose that changes in neuronal membrane excitability induced by acetylcholine may provide a dynamical mechanism for both wake-dependent synaptic upscaling and sleep-dependent downscaling. We show in silico that cholinergically-induced changes in network firing patterns alter overall network synaptic potentiation when synaptic strengths evolve through spike-timing dependent plasticity mechanisms. Specifically, network synaptic potentiation increases dramatically with high cholinergic concentration and decreases dramatically with low levels of acetylcholine. We demonstrate that this phenomenon is robust across variation of many different network parameters.

Mechanisms for dysregulation of excitatory-inhibitory balance underlying allodynia in dorsal horn neural subcircuits.

Chronic pain is a wide-spread condition that is debilitating and expensive to manage, costing the United States alone around $600 billion in 2010. In a common type of chronic pain called allodynia, non-painful stimuli produce painful responses with highly variable presentations across individuals. While the specific mechanisms remain unclear, allodynia is hypothesized to be caused by the dysregulation of excitatory-inhibitory (E-I) balance in pain-processing neural circuitry in the dorsal horn of the spinal cord. In this work, we analyze biophysically-motivated subcircuit structures that represent common motifs in neural circuits in layers I-II of the dorsal horn. These circuits are hypothesized to be part of the neural pathways that mediate two different types of allodynia: static and dynamic. We use neural firing rate models to describe the activity of populations of excitatory and inhibitory interneurons within each subcircuit. By accounting for experimentally-observed responses under healthy conditions, we specify model parameters defining populations of subcircuits that yield typical behavior under normal conditions. Then, we implement a sensitivity analysis approach to identify the mechanisms most likely to cause allodynia-producing dysregulation of the subcircuit's E-I signaling. We find that disruption of E-I balance generally occurs either due to downregulation of inhibitory signaling so that excitatory neurons are "released" from inhibitory control, or due to upregulation of excitatory neuron responses so that excitatory neurons "escape" their inhibitory control. Which of these mechanisms is most likely to occur, the subcircuit components involved in the mechanism, and the proportion of subcircuits exhibiting the mechanism can vary depending on the subcircuit structure. These results suggest specific hypotheses about diverse mechanisms that may be most likely responsible for allodynia, thus offering predictions for the high interindividual variability observed in allodynia and identifying targets for further experimental studies on the underlying mechanisms of this chronic pain condition.

Acetylcholine facilitates localized synaptic potentiation and location specific feature binding.

Forebrain acetylcholine (ACh) signaling has been shown to drive attention and learning. Recent experimental evidence of spatially and temporally constrained cholinergic signaling has sparked interest to investigate how it facilitates stimulus-induced learning. We use biophysical excitatory-inhibitory (E-I) multi-module neural network models to show that external stimuli and ACh signaling can mediate spatially constrained synaptic potentiation patterns. The effects of ACh on neural excitability are simulated by varying the conductance of a muscarinic receptor-regulated hyperpolarizing slow K+ current (m-current). Each network module consists of an E-I network with local excitatory connectivity and global inhibitory connectivity. The modules are interconnected with plastic excitatory synaptic connections, that change via a spike-timing-dependent plasticity (STDP) rule. Our results indicate that spatially constrained ACh release influences the information flow represented by network dynamics resulting in selective reorganization of inter-module interactions. Moreover the information flow depends on the level of synchrony in the network. For highly synchronous networks, the more excitable module leads firing in the less excitable one resulting in strengthening of the outgoing connections from the former and weakening of its incoming synapses. For networks with more noisy firing patterns, activity in high ACh regions is prone to induce feedback firing of synchronous volleys and thus strengthening of the incoming synapses to the more excitable region and weakening of outgoing synapses. Overall, these results suggest that spatially and directionally specific plasticity patterns, as are presumed necessary for feature binding, can be mediated by spatially constrained ACh release.

Modeling homeostatic and circadian modulation of human pain sensitivity.

Introduction: Mathematical modeling has played a significant role in understanding how homeostatic sleep pressure and the circadian rhythm interact to influence sleep-wake behavior. Pain sensitivity is also affected by these processes, and recent experimental results have measured the circadian and homeostatic components of the 24 h rhythm of thermal pain sensitivity in humans. To analyze how rhythms in pain sensitivity are affected by disruptions in sleep behavior and shifts in circadian rhythms, we introduce a dynamic mathematical model for circadian and homeostatic regulation of sleep-wake states and pain intensity. Methods: The model consists of a biophysically based, sleep-wake regulation network model coupled to data-driven functions for the circadian and homeostatic modulation of pain sensitivity. This coupled sleep-wake-pain sensitivity model is validated by comparison to thermal pain intensities in adult humans measured across a 34 h sleep deprivation protocol. Results: We use the model to predict dysregulation of pain sensitivity rhythms across different scenarios of sleep deprivation and circadian rhythm shifts, including entrainment to new environmental light and activity timing as occurs with jet lag and chronic sleep restriction. Model results show that increases in pain sensitivity occur under conditions of increased homeostatic sleep drive with nonlinear modulation by the circadian rhythm, leading to unexpected decreased pain sensitivity in some scenarios. Discussion: This model provides a useful tool for pain management by predicting alterations in pain sensitivity due to varying or disrupted sleep schedules.

NREM-REM alternation complicates transitions from napping to non-napping behavior in a three-state model of sleep-wake regulation.

The temporal structure of human sleep changes across development as it consolidates from the polyphasic sleep of infants to the single nighttime sleep episode typical in adults. Experimental studies have shown that changes in the dynamics of sleep need may mediate this developmental transition in sleep patterning, however, it is unknown how sleep architecture interacts with these changes. We employ a physiologically-based mathematical model that generates wake, rapid eye movement (REM) and non-REM (NREM) sleep states to investigate how NREM-REM alternation affects the transition in sleep patterns as the dynamics of the homeostatic sleep drive are varied. To study the mechanisms producing these transitions, we analyze the bifurcations of numerically-computed circle maps that represent key dynamics of the full sleep-wake network model by tracking the evolution of sleep onsets across different circadian (∼ 24 h) phases. The maps are non-monotonic and discontinuous, being composed of branches that correspond to sleep-wake cycles containing distinct numbers of REM bouts. As the rates of accumulation and decay of the homeostatic sleep drive are varied, we identify the bifurcations that disrupt a period-adding-like behavior of sleep patterns in the transition between biphasic and monophasic sleep. These bifurcations include border collision and saddle-node bifurcations that initiate new sleep patterns, period-doubling bifurcations leading to higher-order patterns of NREM-REM alternation, and intervals of bistability of sleep patterns with different NREM-REM alternations. Furthermore, patterns of NREM-REM alternation exhibit variable behaviors in different regimes of constant sleep-wake patterns. Overall, the sequence of sleep-wake behaviors, and underlying bifurcations, in the transition from biphasic to monophasic sleep in this three-state model is more complex than behavior observed in models of sleep-wake regulation that do not consider the dynamics of NREM-REM alternation. These results suggest that interactions between the dynamics of the homeostatic sleep drive and the dynamics of NREM-REM alternation may contribute to the wide interindividual variation observed when young children transition from napping to non-napping behavior.

Heterogeneity of voltage gated sodium current density between neurons decorrelates spiking and suppresses network synchronization in Scn1b null mouse models.

Voltage gated sodium channels (VGSCs) are required for action potential initiation and propagation in mammalian neurons. As with other ion channel families, VGSC density varies between neurons. Importantly, sodium current (INa) density variability is reduced in pyramidal neurons of Scn1b null mice. Scn1b encodes the VGSC ß1/ ß1B subunits, which regulate channel expression, trafficking, and voltage dependent properties. Here, we investigate how variable INa density in cortical layer 6 and subicular pyramidal neurons affects spike patterning and network synchronization. Constitutive or inducible Scn1b deletion enhances spike timing correlations between pyramidal neurons in response to fluctuating stimuli and impairs spike-triggered average current pattern diversity while preserving spike reliability. Inhibiting INa with a low concentration of tetrodotoxin similarly alters patterning without impairing reliability, with modest effects on firing rate. Computational modeling shows that broad INa density ranges confer a similarly broad spectrum of spike patterning in response to fluctuating synaptic conductances. Network coupling of neurons with high INa density variability displaces the coupling requirements for synchronization and broadens the dynamic range of activity when varying synaptic strength and network topology. Our results show that INa heterogeneity between neurons potently regulates spike pattern diversity and network synchronization, expanding VGSC roles in the nervous system.