Sequential structure of neocortical spontaneous activity in vivo

Hyperpolarization-activated currents drive neuronal activation sequences in sleep

Sequential neuronal patterns are believed to support information processing in the cortex, yet their origin is still a matter of debate. We report that neuronal activity in the mouse postsubiculum (PoSub), where a majority of neurons are modulated by the animal's head direction, was sequentially activated along the dorsoventral axis during sleep at the transition from hyperpolarized "DOWN" to activated "UP" states, while representing a stable direction. Computational modeling suggested that these dynamics could be attributed to a spatial gradient of hyperpolarization-activated currents (Ih), which we confirmed in ex vivo slice experiments and corroborated in other cortical structures. These findings open up the possibility that varying amounts of Ih across cortical neurons could result in sequential neuronal patterns and that traveling activity upstream of the entorhinal-hippocampal circuit organizes large-scale neuronal activity supporting learning and memory during sleep.

SYNAPTIC PLASTICITY IN THE INJURED BRAIN DEPENDS ON THE TEMPORAL PATTERN OF STIMULATION

Neurostimulation protocols are increasingly used as therapeutic interventions, including for brain injury. In addition to the direct activation of neurons, these stimulation protocols are also likely to have downstream effects on those neurons' synaptic outputs. It is well known that alterations in the strength of synaptic connections (long-term potentiation, LTP; long-term depression, LTD) are sensitive to the frequency of stimulation used for induction, however little is known about the contribution of the temporal pattern of stimulation to the downstream synaptic plasticity that may be induced by neurostimulation in the injured brain. We explored interactions of the temporal pattern and frequency of neurostimulation in the normal cerebral cortex and after mild traumatic brain injury (mTBI), to inform therapies to strengthen or weaken neural circuits in injured brains, as well as to better understand the role of these factors in normal brain plasticity. Whole-cell (WC) patch-clamp recordings of evoked postsynaptic potentials (PSPs) in individual neurons, as well as field potential (FP) recordings, were made from layer 2/3 of visual cortex in response to stimulation of layer 4, in acute slices from control (naïve), sham operated, and mTBI rats. We compared synaptic plasticity induced by different stimulation protocols, each consisting of a specific frequency (1 Hz, 10 Hz, or 100 Hz), continuity (continuous or discontinuous), and temporal pattern (perfectly regular, slightly irregular, or highly irregular). At the individual neuron level, dramatic differences in plasticity outcome occurred when the highly irregular stimulation protocol was used at 1 Hz or 10 Hz, producing an overall LTD in controls and shams, but a robust overall LTP after mTBI. Consistent with the individual neuron results, the plasticity outcomes for simultaneous FP recordings were similar, indicative of our results generalizing to a larger scale synaptic network than can be sampled by individual WC recordings alone. In addition to the differences in plasticity outcome between control (naïve or sham) and injured brains, the dynamics of the changes in synaptic responses that developed during stimulation were predictive of the final plasticity outcome. Our results demonstrate that the temporal pattern of stimulation plays a role in the polarity and magnitude of synaptic plasticity induced in the cerebral cortex while highlighting differences between normal and injured brain responses. Moreover, these results may be useful for optimization of neurostimulation therapies to treat mTBI and other brain disorders, in addition to providing new insights into downstream plasticity signaling mechanisms in the normal brain.

Ex Vivo Cortical Circuits Learn to Predict and Spontaneously Replay Temporal Patterns

It has been proposed that prediction and timing are computational primitives of neocortical microcircuits, specifically, that neural mechanisms are in place to allow neocortical circuits to autonomously learn the temporal structure of external stimuli and generate internal predictions. To test this hypothesis, we trained cortical organotypic slices on two specific temporal patterns using dual-optical stimulation. After 24-hours of training, whole-cell recordings revealed network dynamics consistent with training-specific timed prediction. Unexpectedly, there was replay of the learned temporal structure during spontaneous activity. Furthermore, some neurons exhibited timed prediction errors. Mechanistically our results indicate that learning relied in part on asymmetric connectivity between distinct neuronal ensembles with temporally-ordered activation. These findings further suggest that local cortical microcircuits are intrinsically capable of learning temporal information and generating predictions, and that the learning rules underlying temporal learning and spontaneous replay can be intrinsic to local cortical microcircuits and not necessarily dependent on top-down interactions.

Anti-Hebbian plasticity drives sequence learning in striatum

Spatio-temporal activity patterns have been observed in a variety of brain areas in spontaneous activity, prior to or during action, or in response to stimuli. Biological mechanisms endowing neurons with the ability to distinguish between different sequences remain largely unknown. Learning sequences of spikes raises multiple challenges, such as maintaining in memory spike history and discriminating partially overlapping sequences. Here, we show that anti-Hebbian spike-timing dependent plasticity (STDP), as observed at cortico-striatal synapses, can naturally lead to learning spike sequences. We design a spiking model of the striatal output neuron receiving spike patterns defined as sequential input from a fixed set of cortical neurons. We use a simple synaptic plasticity rule that combines anti-Hebbian STDP and non-associative potentiation for a subset of the presented patterns called rewarded patterns. We study the ability of striatal output neurons to discriminate rewarded from non-rewarded patterns by firing only after the presentation of a rewarded pattern. In particular, we show that two biological properties of striatal networks, spiking latency and collateral inhibition, contribute to an increase in accuracy, by allowing a better discrimination of partially overlapping sequences. These results suggest that anti-Hebbian STDP may serve as a biological substrate for learning sequences of spikes.

Microstimulation reveals anesthetic state-dependent effective connectivity of neurons in cerebral cortex

Complex neuronal interactions underlie cortical information processing that can be compromised in altered states of consciousness. Here intracortical microstimulation was applied to investigate the state-dependent effective connectivity of neurons in rat visual cortex in vivo. Extracellular activity was recorded at 32 sites in layers 5/6 while stimulating with charge-balanced discrete pulses at each electrode in random order. The same stimulation pattern was applied at three levels of anesthesia with desflurane and in wakefulness. Spikes were sorted and classified by their waveform features as putative excitatory and inhibitory neurons. Microstimulation caused early (<10ms) increase followed by prolonged (11-100ms) decrease in spiking of all neurons throughout the electrode array. The early response of excitatory but not inhibitory neurons decayed rapidly with distance from the stimulation site over 1mm. Effective connectivity of neurons with significant stimulus response was dense in wakefulness and sparse under anesthesia. Network motifs were identified in graphs of effective connectivity constructed from monosynaptic cross-correlograms. The number of motifs, especially those of higher order, increased rapidly as the anesthesia was withdrawn indicating a substantial increase in network connectivity as the animals woke up. The results illuminate the impact of anesthesia on functional integrity of local circuits affecting the state of consciousness.

Chaotic neural dynamics facilitate probabilistic computations through sampling

Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation. We show that the emergent chaotic dynamics provide neural substrates for generating samples not only of a static variable but also of a dynamical trajectory, where generic recurrent networks acquire these abilities with a biologically plausible learning rule through trial and error. Furthermore, the networks generalize their experience in the stimulus-evoked samples to the inference without partial or all sensory information, which suggests a computational role of spontaneous activity as a representation of the priors as well as a tractable biological computation for marginal distributions. These findings suggest that chaotic neural dynamics may serve for the brain function as a Bayesian generative model.

Global spatiotemporal synchronizing structures of spontaneous neural activities in different cell types

Increasing evidence has revealed the large-scale nonstationary synchronizations as traveling waves in spontaneous neural activity. However, the interplay of various cell types in fine-tuning these spatiotemporal patters remains unclear. Here, we performed comprehensive exploration of spatiotemporal synchronizing structures across different cell types, states (awake, anesthesia, motion) and developmental axis in male mice. We found traveling waves in glutamatergic neurons exhibited greater variety than those in GABAergic neurons. Moreover, the synchronizing structures of GABAergic neurons converged toward those of glutamatergic neurons during development, but the evolution of waves exhibited varying timelines for different sub-type interneurons. Functional connectivity arises from both standing and traveling waves, and negative connections can be elucidated by the spatial propagation of waves. In addition, some traveling waves were correlated with the spatial distribution of gene expression. Our findings offer further insights into the neural underpinnings of traveling waves, functional connectivity, and resting-state networks, with cell-type specificity and developmental perspectives.

Hippocampal replay sequence governed by spontaneous brain-wide dynamics

Neurons in the hippocampus exhibit spontaneous spiking activity during rest that appears to recapitulate previously experienced events. While this replay activity is frequently linked to memory consolidation and learning, the underlying mechanisms are not well understood. Recent large-scale neural recordings in mice have demonstrated that resting-state spontaneous activity is expressed as quasi-periodic cascades of spiking activity that pervade the forebrain, with each cascade engaging a high proportion of recorded neurons. Hippocampal ripples are known to be coordinated with cortical dynamics; however, less is known about the occurrence of replay activity relative to other brain-wide spontaneous events. Here we analyzed responses across the mouse brain to multiple viewings of natural movies, as well as subsequent patterns of neural activity during rest. We found that hippocampal neurons showed time-selectivity, with individual neurons responding consistently during particular moments of the movie. During rest, the population of time-selective hippocampal neurons showed both forward and time-reversed replay activity that matched the sequence observed in the movie. Importantly, these replay events were strongly time-locked to brain-wide spiking cascades, with forward and time-reversed replay activity associated with distinct cascade types. Thus, intrinsic hippocampal replay activity is temporally structured according to large-scale spontaneous physiology affecting areas throughout the forebrain. These findings shed light on the coordination between hippocampal and cortical circuits thought to be critical for memory consolidation.

Neuronal ensembles: Building blocks of neural circuits

Neuronal ensembles, defined as groups of neurons displaying recurring patterns of coordinated activity, represent an intermediate functional level between individual neurons and brain areas. Novel methods to measure and optically manipulate the activity of neuronal populations have provided evidence of ensembles in the neocortex and hippocampus. Ensembles can be activated intrinsically or in response to sensory stimuli and play a causal role in perception and behavior. Here we review ensemble phenomenology, developmental origin, biophysical and synaptic mechanisms, and potential functional roles across different brain areas and species, including humans. As modular units of neural circuits, ensembles could provide a mechanistic underpinning of fundamental brain processes, including neural coding, motor planning, decision-making, learning, and adaptability.

Specific afterdischarge properties can enhance the clinical utility of electrical stimulation mapping during intracranial monitoring

OBJECTIVE

Extraoperative electrical cortical stimulation (ECS) facilitates defining the seizure onset zone (SOZ) and eloquent cortex. The clinical relevance of stimulation-induced afterdischarges (ADs) is not well defined.

METHODS

Fifty-five patients who underwent intracranial electroencephalogram evaluations with ECS were retrospectively identified. ADs were identified in these recordings and categorized by pattern, location, and association with stimulation-induced seizures.

RESULTS

ADs were generated in 1774/9285 (19%) trials. Rhythmic spikes and irregular ADs within the stimulated bipolar contact pair were predictive of location within the SOZ compared to non-epileptogenic/non-irritative cortex (rhythmic spikes OR 2.24, p = 0.0098; irregular OR 1.39; p = 0.013). ADs immediately preceding stimulated seizures occurred at lower stimulation intensity thresholds compared to other stimulations (mean 2.94 ± 0.28 mA vs. 4.16 ± 0.05 mA respectively; p = 0.0068).

CONCLUSIONS

Changes in AD properties can provide clinically relevant data in extraoperative stimulation mapping.

SIGNIFICANCE

Although not exclusive to the SOZ, the generation of rhythmic spikes may suggest that a stimulation location is within the SOZ, while decreased stimulation intensity thresholds eliciting ADs may alert clinicians to a heightened probability of seizure generation with subsequent stimulation.

Spontaneous slow wave oscillations in extracellular field potential recordings reflect the alternating dominance of excitation and inhibition

In the resting state, cortical neurons can fire action potentials spontaneously but synchronously (Up state), followed by a quiescent period (Down state) before the cycle repeats. Extracellular recordings in the infragranular layer of cortex with a micro-electrode display a negative deflection (depth-negative) during Up states and a positive deflection (depth-positive) during Down states. The resulting slow wave oscillation (SWO) has been studied extensively during sleep and under anaesthesia. However, recent research on the balanced nature of synaptic excitation and inhibition has highlighted our limited understanding of its genesis. Specifically, are excitation and inhibition balanced during SWOs? We analyse spontaneous local field potentials (LFPs) during SWOs recorded from anaesthetised rats via a multi-channel laminar micro-electrode and show that the Down state consists of two distinct synaptic states: a Dynamic Down state associated with depth-positive LFPs and a prominent dipole in the extracellular field, and a Static Down state with negligible (≈ 0 mV $ \approx 0{\mathrm{\;mV}}$ ) LFPs and a lack of dipoles extracellularly. We demonstrate that depth-negative and -positive LFPs are generated by a shift in the balance of synaptic excitation and inhibition from excitation dominance (depth-negative) to inhibition dominance (depth-positive) in the infragranular layer neurons. Thus, although excitation and inhibition co-tune overall, differences in their timing lead to an alternation of dominance, manifesting as SWOs. We further show that Up state initiation is significantly faster if the preceding Down state is dynamic rather than static. Our findings provide a coherent picture of the dependence of SWOs on synaptic activity. KEY POINTS: Cortical neurons can exhibit repeated cycles of spontaneous activity interleaved with periods of relative silence, a phenomenon known as 'slow wave oscillation' (SWO). During SWOs, recordings of local field potentials (LFPs) in the neocortex show depth-negative deflection during the active period (Up state) and depth-positive deflection during the silent period (Down state). Here we further classified the Down state into a dynamic phase and a static phase based on a novel method of classification and revealed non-random, stereotypical sequences of the three states occurring with significantly different transitional kinetics. Our results suggest that the positive and negative deflections in the LFP reflect the shift of the instantaneous balance between excitatory and inhibitory synaptic activity of the local cortical neurons. The differences in transitional kinetics may imply distinct synaptic mechanisms for Up state initiation. The study may provide a new approach for investigating spontaneous brain rhythms.

Excitation-Inhibition Balance, Neural Criticality, and Activities in Neuronal Circuits

Neural activities in local circuits exhibit complex and multilevel dynamic features. Individual neurons spike irregularly, which is believed to originate from receiving balanced amounts of excitatory and inhibitory inputs, known as the excitation-inhibition balance. The spatial-temporal cascades of clustered neuronal spikes occur in variable sizes and durations, manifested as neural avalanches with scale-free features. These may be explained by the neural criticality hypothesis, which posits that neural systems operate around the transition between distinct dynamic states. Here, we summarize the experimental evidence for and the underlying theory of excitation-inhibition balance and neural criticality. Furthermore, we review recent studies of excitatory-inhibitory networks with synaptic kinetics as a simple solution to reconcile these two apparently distinct theories in a single circuit model. This provides a more unified understanding of multilevel neural activities in local circuits, from spontaneous to stimulus-response dynamics.

Short-term Hebbian learning can implement transformer-like attention

Transformers have revolutionized machine learning models of language and vision, but their connection with neuroscience remains tenuous. Built from attention layers, they require a mass comparison of queries and keys that is difficult to perform using traditional neural circuits. Here, we show that neurons can implement attention-like computations using short-term, Hebbian synaptic potentiation. We call our mechanism the match-and-control principle and it proposes that when activity in an axon is synchronous, or matched, with the somatic activity of a neuron that it synapses onto, the synapse can be briefly strongly potentiated, allowing the axon to take over, or control, the activity of the downstream neuron for a short time. In our scheme, the keys and queries are represented as spike trains and comparisons between the two are performed in individual spines allowing for hundreds of key comparisons per query and roughly as many keys and queries as there are neurons in the network.

The Interactions of Temporal and Sensory Representations in the Basal Ganglia

In rodents and primates, interval estimation has been associated with a complex network of cortical and subcortical structures where the dorsal striatum plays a paramount role. Diverse evidence ranging from individual neurons to population activity has demonstrated that this area hosts temporal-related neural representations that may be instrumental for the perception and production of time intervals. However, little is known about how temporal representations interact with other well-known striatal representations, such as kinematic parameters of movements or somatosensory representations. An attractive hypothesis suggests that somatosensory representations may serve as the scaffold for complex representations such as elapsed time. Alternatively, these representations may coexist as independent streams of information that could be integrated into downstream nuclei, such as the substantia nigra or the globus pallidus. In this review, we will revise the available information suggesting an instrumental role of sensory representations in the construction of temporal representations at population and single-neuron levels throughout the basal ganglia.

Propagating population activity patterns during spontaneous slow waves in the thalamus of rodents

Slow waves (SWs) represent the most prominent electrophysiological events in the thalamocortical system under anesthesia and during deep sleep. Recent studies have revealed that SWs have complex spatiotemporal dynamics and propagate across neocortical regions. However, it is still unclear whether neuronal activity in the thalamus exhibits similar propagation properties during SWs. Here, we report propagating population activity in the thalamus of ketamine/xylazine-anesthetized rats and mice visualized by high-density silicon probe recordings. In both rodent species, propagation of spontaneous thalamic activity during up-states was most frequently observed in dorsal thalamic nuclei such as the higher order posterior (Po), lateral posterior (LP) or laterodorsal (LD) nuclei. The preferred direction of thalamic activity spreading was along the dorsoventral axis, with over half of the up-states exhibiting a gradual propagation in the ventral-to-dorsal direction. Furthermore, simultaneous neocortical and thalamic recordings collected under anesthesia demonstrated that there is a weak but noticeable interrelation between propagation patterns observed during cortical up-states and those displayed by thalamic population activity. In addition, using chronically implanted silicon probes, we detected propagating activity patterns in the thalamus of naturally sleeping rats during slow-wave sleep. However, in comparison to propagating up-states observed under anesthesia, these propagating patterns were characterized by a reduced rate of occurrence and a faster propagation speed. Our findings suggest that the propagation of spontaneous population activity is an intrinsic property of the thalamocortical network during synchronized brain states such as deep sleep or anesthesia. Additionally, our data implies that the neocortex may have partial control over the formation of propagation patterns within the dorsal thalamus under anesthesia.

Protosequences in human cortical organoids model intrinsic states in the developing cortex

Neuronal firing sequences are thought to be the basic building blocks of neural coding and information broadcasting within the brain. However, when sequences emerge during neurodevelopment remains unknown. We demonstrate that structured firing sequences are present in spontaneous activity of human brain organoids and ex vivo neonatal brain slices from the murine somatosensory cortex. We observed a balance between temporally rigid and flexible firing patterns that are emergent phenomena in human brain organoids and early postnatal murine somatosensory cortex, but not in primary dissociated cortical cultures. Our findings suggest that temporal sequences do not arise in an experience-dependent manner, but are rather constrained by an innate preconfigured architecture established during neurogenesis. These findings highlight the potential for brain organoids to further explore how exogenous inputs can be used to refine neuronal circuits and enable new studies into the genetic mechanisms that govern assembly of functional circuitry during early human brain development.

Sensory cortical ensembles exhibit differential coupling to ripples in distinct hippocampal subregions

Cortical neurons activated during recent experiences often reactivate with dorsal hippocampal CA1 ripples during subsequent rest. Less is known about cortical interactions with intermediate hippocampal CA1, whose connectivity, functions, and ripple events differ from dorsal CA1. We identified three clusters of putative excitatory neurons in mouse visual cortex that are preferentially excited together with either dorsal or intermediate CA1 ripples or suppressed before both ripples. Neurons in each cluster were evenly distributed across primary and higher visual cortices and co-active even in the absence of ripples. These ensembles exhibited similar visual responses but different coupling to thalamus and pupil-indexed arousal. We observed a consistent activity sequence preceding and predicting ripples: (1) suppression of ripple-suppressed cortical neurons, (2) thalamic silence, and (3) activation of intermediate CA1-ripple-activated cortical neurons. We propose that coordinated dynamics of these ensembles relay visual experiences to distinct hippocampal subregions for incorporation into different cognitive maps.

3D Functional Neuronal Networks in Free-Standing Bioprinted Hydrogel Constructs

The composition, elasticity, and organization of the extracellular matrix within the central nervous system contribute to the architecture and function of the brain. From an in vitro modeling perspective, soft biomaterials are needed to mimic the 3D neural microenvironments. While many studies have investigated 3D culture and neural network formation in bulk hydrogel systems, these approaches have limited ability to position cells to mimic sophisticated brain architectures. In this study, cortical neurons and astrocytes acutely isolated from the brains of rats are bioprinted in a hydrogel to form 3D neuronal constructs. Successful bioprinting of cellular and acellular strands in a multi-bioink approach allows the subsequent formation of gray- and white-matter tracts reminiscent of cortical structures. Immunohistochemistry shows the formation of dense, 3D axon networks. Calcium signaling and extracellular electrophysiology in these 3D neuronal networks confirm spontaneous activity in addition to evoked activities under pharmacological and electrical stimulation. The system and bioprinting approaches are capable of fabricating soft, free-standing neuronal structures of different bioink and cell types with high resolution and throughput, which provide a promising platform for understanding fundamental questions of neural networks, engineering neuromorphic circuits, and for in vitro drug screening.

Learning heterogeneous delays in a layer of spiking neurons for fast motion detection

The precise timing of spikes emitted by neurons plays a crucial role in shaping the response of efferent biological neurons. This temporal dimension of neural activity holds significant importance in understanding information processing in neurobiology, especially for the performance of neuromorphic hardware, such as event-based cameras. Nonetheless, many artificial neural models disregard this critical temporal dimension of neural activity. In this study, we present a model designed to efficiently detect temporal spiking motifs using a layer of spiking neurons equipped with heterogeneous synaptic delays. Our model capitalizes on the diverse synaptic delays present on the dendritic tree, enabling specific arrangements of temporally precise synaptic inputs to synchronize upon reaching the basal dendritic tree. We formalize this process as a time-invariant logistic regression, which can be trained using labeled data. To demonstrate its practical efficacy, we apply the model to naturalistic videos transformed into event streams, simulating the output of the biological retina or event-based cameras. To evaluate the robustness of the model in detecting visual motion, we conduct experiments by selectively pruning weights and demonstrate that the model remains efficient even under significantly reduced workloads. In conclusion, by providing a comprehensive, event-driven computational building block, the incorporation of heterogeneous delays has the potential to greatly improve the performance of future spiking neural network algorithms, particularly in the context of neuromorphic chips.

Spontaneous and Visual Stimulation Evoked Firing Sequences Are Distinct Under Desflurane Anesthesia

Recurring spike sequences are thought to underlie cortical computations and may be essential for information processing in the conscious state. How anesthesia at graded levels may influence spontaneous and stimulus-related spike sequences in visual cortex has not been fully elucidated. We recorded extracellular single-unit activity in the rat primary visual cortex in vivo during wakefulness and three levels of anesthesia produced by desflurane. The latencies of spike sequences within 0-200 ms from the onset of spontaneous UP states and visual flash-evoked responses were compared. During wakefulness, spike latency patterns linked to the local field potential theta cycle were similar to stimulus-evoked patterns. Under desflurane anesthesia, spontaneous UP state sequences differed from flash-evoked sequences due to the recruitment of low-firing excitatory neurons to the UP state. Flash-evoked spike sequences showed higher reliability and longer latency when stimuli were applied during DOWN states compared to UP states. At deeper levels, desflurane altered both UP state and flash-evoked spike sequences by selectively suppressing inhibitory neuron firing. The results reveal desflurane-induced complex changes in cortical firing sequences that may influence visual information processing.

Backbone spiking sequence as a basis for preplay, replay, and default states in human cortex

Sequences of spiking activity have been heavily implicated as potential substrates of memory formation and retrieval across many species. A parallel line of recent evidence also asserts that sequential activity may arise from and be constrained by pre-existing network structure. Here we reconcile these two lines of research in the human brain by measuring single unit spiking sequences in the temporal lobe cortex as participants perform an episodic memory task. We find the presence of an average backbone spiking sequence identified during pre-task rest that is stable over time and different cognitive states. We further demonstrate that these backbone sequences are composed of both rigid and flexible sequence elements, and that flexible elements within these sequences serve to promote memory specificity when forming and retrieving new memories. These results support the hypothesis that pre-existing network dynamics serve as a scaffold for ongoing neural activity in the human cortex.

Offline memory replay in recurrent neuronal networks emerges from constraints on online dynamics

During spatial exploration, neural circuits in the hippocampus store memories of sequences of sensory events encountered in the environment. When sensory information is absent during 'offline' resting periods, brief neuronal population bursts can 'replay' sequences of activity that resemble bouts of sensory experience. These sequences can occur in either forward or reverse order, and can even include spatial trajectories that have not been experienced, but are consistent with the topology of the environment. The neural circuit mechanisms underlying this variable and flexible sequence generation are unknown. Here we demonstrate in a recurrent spiking network model of hippocampal area CA3 that experimental constraints on network dynamics such as population sparsity, stimulus selectivity, rhythmicity and spike rate adaptation, as well as associative synaptic connectivity, enable additional emergent properties, including variable offline memory replay. In an online stimulus-driven state, we observed the emergence of neuronal sequences that swept from representations of past to future stimuli on the timescale of the theta rhythm. In an offline state driven only by noise, the network generated both forward and reverse neuronal sequences, and recapitulated the experimental observation that offline memory replay events tend to include salient locations like the site of a reward. These results demonstrate that biological constraints on the dynamics of recurrent neural circuits are sufficient to enable memories of sensory events stored in the strengths of synaptic connections to be flexibly read out during rest and sleep, which is thought to be important for memory consolidation and planning of future behaviour. KEY POINTS: A recurrent spiking network model of hippocampal area CA3 was optimized to recapitulate experimentally observed network dynamics during simulated spatial exploration. During simulated offline rest, the network exhibited the emergent property of generating flexible forward, reverse and mixed direction memory replay events. Network perturbations and analysis of model diversity and degeneracy identified associative synaptic connectivity and key features of network dynamics as important for offline sequence generation. Network simulations demonstrate that population over-representation of salient positions like the site of reward results in biased memory replay.

Spontaneous activity in cortical neurons is stereotyped and non-Poisson

Neurons fire even in the absence of sensory stimulation or task demands. Numerous theoretical studies have modeled this spontaneous activity as a Poisson process with uncorrelated intervals between successive spikes and a variance in firing rate equal to the mean. Experimental tests of this hypothesis have yielded variable results, though most have concluded that firing is not Poisson. However, these tests say little about the ways firing might deviate from randomness. Nor are they definitive because many different distributions can have equal means and variances. Here, we characterized spontaneous spiking patterns in extracellular recordings from monkey, cat, and mouse cerebral cortex neurons using rate-normalized spike train autocorrelation functions (ACFs) and a logarithmic timescale. If activity was Poisson, this function should be flat. This was almost never the case. Instead, ACFs had diverse shapes, often with characteristic peaks in the 1-700 ms range. Shapes were stable over time, up to the longest recording periods used (51 min). They did not fall into obvious clusters. ACFs were often unaffected by visual stimulation, though some abruptly changed during brain state shifts. These behaviors may have their origin in the intrinsic biophysics and dendritic anatomy of the cells or in the inputs they receive.

Adaptive control of synaptic plasticity integrates micro- and macroscopic network function

Synaptic plasticity configures interactions between neurons and is therefore likely to be a primary driver of behavioral learning and development. How this microscopic-macroscopic interaction occurs is poorly understood, as researchers frequently examine models within particular ranges of abstraction and scale. Computational neuroscience and machine learning models offer theoretically powerful analyses of plasticity in neural networks, but results are often siloed and only coarsely linked to biology. In this review, we examine connections between these areas, asking how network computations change as a function of diverse features of plasticity and vice versa. We review how plasticity can be controlled at synapses by calcium dynamics and neuromodulatory signals, the manifestation of these changes in networks, and their impacts in specialized circuits. We conclude that metaplasticity-defined broadly as the adaptive control of plasticity-forges connections across scales by governing what groups of synapses can and can't learn about, when, and to what ends. The metaplasticity we discuss acts by co-opting Hebbian mechanisms, shifting network properties, and routing activity within and across brain systems. Asking how these operations can go awry should also be useful for understanding pathology, which we address in the context of autism, schizophrenia and Parkinson's disease.

Precise Spiking Motifs in Neurobiological and Neuromorphic Data

Why do neurons communicate through spikes? By definition, spikes are all-or-none neural events which occur at continuous times. In other words, spikes are on one side binary, existing or not without further details, and on the other, can occur at any asynchronous time, without the need for a centralized clock. This stands in stark contrast to the analog representation of values and the discretized timing classically used in digital processing and at the base of modern-day neural networks. As neural systems almost systematically use this so-called event-based representation in the living world, a better understanding of this phenomenon remains a fundamental challenge in neurobiology in order to better interpret the profusion of recorded data. With the growing need for intelligent embedded systems, it also emerges as a new computing paradigm to enable the efficient operation of a new class of sensors and event-based computers, called neuromorphic, which could enable significant gains in computation time and energy consumption-a major societal issue in the era of the digital economy and global warming. In this review paper, we provide evidence from biology, theory and engineering that the precise timing of spikes plays a crucial role in our understanding of the efficiency of neural networks.

Footshock-induced activation of the claustrum-entorhinal cortical pathway in freely moving mice

Footshock is frequently used as an unconditioned stimulus in fear conditioning behavior studies. The medial entorhinal cortex (MEC) contributes to fear learning and receives neuronal inputs from the claustrum. However, whether footshocks can induce a neuronal response in claustrum-MEC (CLA-MEC) projection remains unknown. Here, we combined fiber-based Ca2+ recordings with a retrograde AAV labeling method to investigate neuronal responses of MEC-projecting claustral neurons to footshock stimulation in freely moving mice. We achieved successful Ca2+ recordings in both anesthetized and freely exploring mice. We found that footshock stimulation reliably induced neuronal responses to MEC-projecting claustral neurons. Therefore, the footshock-induced response detected in the CLA-MEC projection suggests its potential role in fear processin.

Functional States Shape the Spatiotemporal Representation of Local and Cortex-wide Neural Activity in Mouse Sensory Cortex

The spatiotemporal representation of neural activity during rest and upon sensory stimulation in cortical areas is highly dynamic, and may be predominantly governed by cortical state. On the mesoscale level, intrinsic neuronal activity ranges from a persistent state, generally associated with a sustained depolarization of neurons, to a bimodal, slow-wave like state with bursts of neuronal activation, alternating with silent periods. These different activity states are prevalent under certain types of sedatives, or are associated with specific behavioral or vigilance conditions. Neurophysiological experiments assessing circuit activity, usually assume a constant underlying state, yet reports of variability of neuronal responses under seemingly constant conditions are common in the field. Even when a certain type of neural activity or cortical state can stably be maintained over time, the associated response properties are highly relevant for explaining experimental outcomes. Here we describe the spatiotemporal characteristics of ongoing activity and sensory evoked responses under two predominant functional states in the sensory cortices of mice: persistent activity (PA) and slow wave activity (SWA). Using electrophysiological recordings, and local and wide-field calcium recordings, we examine whether spontaneous and sensory evoked neuronal activity propagate throughout the cortex in a state dependent manner. We find that PA and SWA differ in their spatiotemporal characteristics which determine the cortical network's response to a sensory stimulus. During PA state, sensory stimulation elicits gamma-based short-latency responses which precisely follow each stimulation pulse and are prone to adaptation upon higher stimulation frequencies. Sensory responses during SWA are more variable, dependent on refractory periods following spontaneous slow waves. While spontaneous slow waves propagated in anterior-posterior direction in a majority of observations, the direction of propagation of stimulus-elicited wave depends on the sensory modality. These findings suggest that cortical state explains variance and should be considered when investigating multi-scale correlates of functional neurocircuit activity.

Modelling the modulation of cortical Up-Down state switching by astrocytes

Up-Down synchronization in neuronal networks refers to spontaneous switches between periods of high collective firing activity (Up state) and periods of silence (Down state). Recent experimental reports have shown that astrocytes can control the emergence of such Up-Down regimes in neural networks, although the molecular or cellular mechanisms that are involved are still uncertain. Here we propose neural network models made of three populations of cells: excitatory neurons, inhibitory neurons and astrocytes, interconnected by synaptic and gliotransmission events, to explore how astrocytes can control this phenomenon. The presence of astrocytes in the models is indeed observed to promote the emergence of Up-Down regimes with realistic characteristics. Our models show that the difference of signalling timescales between astrocytes and neurons (seconds versus milliseconds) can induce a regime where the frequency of gliotransmission events released by the astrocytes does not synchronize with the Up and Down phases of the neurons, but remains essentially stable. However, these gliotransmission events are found to change the localization of the bifurcations in the parameter space so that with the addition of astrocytes, the network enters a bistability region of the dynamics that corresponds to Up-Down synchronization. Taken together, our work provides a theoretical framework to test scenarios and hypotheses on the modulation of Up-Down dynamics by gliotransmission from astrocytes.

Neural networks in many brain regions can display synchronized activities. During the so-called “Up-Down” synchronization regimes for instance, the whole local population of neurons switches in a spontaneous and synchronized fashion between phases of high activity (Up states) and phases of low activity (Down states). The mechanisms responsible for this behaviour are still not well understood, but recent experimental reports have suggested that another type of brain cells, the astrocytes, at least partly control these oscillations. Astrocytes are increasingly believed to play a role in the propagation of signals between neurons, via their connections to neuronal synapses, but how this mechanism could control Up-Down regimes is not understood. To address this issue we present here simple mathematical models of neuronal networks that incorporate astrocytes in addition to neurons according to various levels of description. Using bifurcation analysis and numerical simulations we explore how astrocytes control Up-Down synchronization of the neuronal networks. In particular, astrocytes in the model are found to change the localization of the bifurcation points in the parameter space, so that the neurons enter the region of Up-Down regime when astrocytes are present. We also give some theoretical predictions that can be tested experimentally to test the validity of our models.

Transient brain-wide coactivations and structured transitions revealed in hemodynamic imaging data

Brain-wide patterns in resting human brains, as either structured functional connectivity (FC) or recurring brain states, have been widely studied in the neuroimaging literature. In particular, resting-state FCs estimated over windowed timeframe neuroimaging data from sub-minutes to minutes using correlation or blind source separation techniques have reported many brain-wide patterns of significant behavioral and disease correlates. The present pilot study utilized a novel whole-head cap-based high-density diffuse optical tomography (DOT) technology, together with data-driven analysis methods, to investigate recurring transient brain-wide patterns in spontaneous fluctuations of hemodynamic signals at the resolution of single timeframes from thirteen healthy adults in resting conditions. Our results report that a small number, i.e., six, of brain-wide coactivation patterns (CAPs) describe major spatiotemporal dynamics of spontaneous hemodynamic signals recorded by DOT. These CAPs represent recurring brain states, showing spatial topographies of hemispheric symmetry, and exhibit highly anticorrelated pairs. Moreover, a structured transition pattern among the six brain states is identified, where two CAPs with anterior-posterior spatial patterns are significantly involved in transitions among all brain states. Our results further elucidate two brain states of global positive and negative patterns, indicating transient neuronal coactivations and co-deactivations, respectively, over the entire cortex. We demonstrate that these two brain states are responsible for the generation of a subset of peaks and troughs in global signals (GS), supporting the recent reports on neuronal relevance of hemodynamic GS. Collectively, our results suggest that transient neuronal events (i.e., CAPs), global brain activity, and brain-wide structured transitions co-exist in humans and these phenomena are closely related, which extend the observations of similar neuronal events recently reported in animal hemodynamic data. Future studies on the quantitative relationship among these transient events and their relationships to windowed FCs along with larger sample size are needed to understand their changes with behaviors and diseased conditions.

Alterations of sleep oscillations in Alzheimer's disease: A potential role for GABAergic neurons in the cortex, hippocampus, and thalamus

Sleep abnormalities are widely reported in patients with Alzheimer's disease (AD) and are linked to cognitive impairments. Sleep abnormalities could be potential biomarkers to detect AD since they are often observed at the preclinical stage. Moreover, sleep could be a target for early intervention to prevent or slow AD progression. Thus, here we review changes in brain oscillations observed during sleep, their connection to AD pathophysiology and the role of specific brain circuits. Slow oscillations (0.1-1 Hz), sleep spindles (8-15 Hz) and their coupling during non-REM sleep are consistently reduced in studies of patients and in AD mouse models although the timing and magnitude of these alterations depends on the pathophysiological changes and the animal model studied. Changes in delta (1-4 Hz) activity are more variable. Animal studies suggest that hippocampal sharp-wave ripples (100-250 Hz) are also affected. Reductions in REM sleep amount and slower oscillations during REM are seen in patients but less consistently in animal models. Thus, changes in a variety of sleep oscillations could impact sleep-dependent memory consolidation or restorative functions of sleep. Recent mechanistic studies suggest that alterations in the activity of GABAergic neurons in the cortex, hippocampus and thalamic reticular nucleus mediate sleep oscillatory changes in AD and represent a potential target for intervention. Longitudinal studies of the timing of AD-related sleep abnormalities with respect to pathology and dysfunction of specific neural networks are needed to identify translationally relevant biomarkers and guide early intervention strategies to prevent or delay AD progression.

Brain-wide neural co-activations in resting human

Spontaneous neural activity in human as assessed with resting-state functional magnetic resonance imaging (fMRI) exhibits brain-wide coordinated patterns in the frequency of < 0.1 Hz. However, understanding of fast brain-wide networks at the timescales of neuronal events (milliseconds to sub-seconds) and their spatial, spectral, and transitional characteristics remain limited due to the temporal constraints of hemodynamic signals. With milli-second resolution and whole-head coverage, scalp-based electroencephalography (EEG) provides a unique window into brain-wide networks with neuronal-timescale dynamics, shedding light on the organizing principles of brain functions. Using the state-of-the-art signal processing techniques, we reconstructed cortical neural tomography from resting-state EEG and extracted component-based co-activation patterns (cCAPs). These cCAPs revealed brain-wide intrinsic networks and their dynamics, indicating the configuration/reconfiguration of resting human brains into recurring and transitional functional states, which are featured with the prominent spatial phenomena of global patterns and anti-state pairs of co-(de)activations. Rich oscillational structures across a wide frequency band (i.e., 0.6 Hz, 5 Hz, and 10 Hz) were embedded in the nonstationary dynamics of these functional states. We further identified a superstructure that regulated between-state immediate and long-range transitions involving the entire set of identified cCAPs and governed a significant aspect of brain-wide network dynamics. These findings demonstrated how resting-state EEG data can be functionally decomposed using cCAPs to reveal rich dynamic structures of brain-wide human neural activations.

Anesthetic modulations dissociate neuroelectric characteristics between sensory-evoked and spontaneous activities across bilateral rat somatosensory cortical laminae

Spontaneous neural activity has been widely adopted to construct functional connectivity (FC) amongst distant brain regions. Although informative, the functional role and signaling mechanism of the resting state FC are not intuitive as those in stimulus/task-evoked activity. In order to bridge the gap, we investigated anesthetic modulation of both resting-state and sensory-evoked activities. We used two well-studied GABAergic anesthetics of varying dose (isoflurane: 0.5–2.0% and α-chloralose: 30 and 60 mg/kg∙h) and recorded changes in electrophysiology using a pair of laminar electrode arrays that encompass the entire depth of the bilateral somatosensory cortices (S1fl) in rats. Specifically, the study focused to describe how varying anesthesia conditions affect the resting state activities and resultant FC between bilateral hemispheres in comparison to those obtained by evoked responses. As results, isoflurane decreased the amplitude of evoked responses in a dose-dependent manner mostly due to the habituation of repetitive responses. However, α-chloralose rather intensified the amplitude without exhibiting habituation. No such diverging trend was observed for the spontaneous activity, in which both anesthetics increased the signal power. For α-chloralose, overall FC was similar to that obtained with the lowest dose of isoflurane at 0.5% while higher doses of isoflurane displayed increased FC. Interestingly, only α-chloralose elicited relatively much greater increases in the ipsi-stimulus evoked response (i.e., in S1fl ipsilateral to the stimulated forelimb) than those associated with the contra-stimulus response, suggesting enhanced neuronal excitability. Taken together, the findings demonstrate modulation of the FC profiles by anesthesia is highly non-linear, possibly with a distinct underlying mechanism that affects either resting state or evoked activities differently. Further, the current study warrants thorough investigation of the basal neuronal states prior to the interpretation of resting state FC and evoked activities for accurate understanding of neural signal processing and circuitry.

Spatiotemporal patterns of spontaneous brain activity: a mini-review

The brain exists in a state of constant activity in the absence of any external sensory input. The spatiotemporal patterns of this spontaneous brain activity have been studied using various recording and imaging techniques. This has enabled considerable progress to be made in elucidating the cellular and network mechanisms that are involved in the observed spatiotemporal dynamics. This mini-review outlines different spatiotemporal dynamic patterns that have been identified in four commonly used modalities: electrophysiological recordings, optical imaging, functional magnetic resonance imaging, and electroencephalography. Signal sources for each modality, possible sources of the observed dynamics, and future directions are also discussed.

Ca2+-based neural activity recording for rapidly screening behavioral correlates of the claustrum in freely behaving mice

The claustrum has been hypothesized to participate in high-order brain functions, but experimental studies to demonstrate these functions are currently lacking. Neural activity recording of the claustrum in freely-behaving animals allows for correlating claustral activities with specific behaviors. However, previously utilized methods for studying the claustrum make it difficult to monitor neural activity patterns of freely-behaving animals in real time. Here we applied fiber photometry to monitor Ca²⁺ activity in the claustrum of freely-behaving mice. Using this method, we were able to achieve Ca²⁺ activity recording in both anesthetized and freely-behaving mice. We found that the dynamics of Ca²⁺ activity depended on anesthesia levels. As compared to the use of genetically encoded Ca²⁺ indicators that requires a few weeks of virus-dependent expression, we used a synthetic fluorescent Ca²⁺-sensitive dye, Oregon green 488 BAPTA-1, that allows for rapidly screening neural activity of interest within a few hours that relates to certain behaviors. In this way, we found the correlation between Ca²⁺ activity and specific behaviors, such as approaching an object. Our work offers an effective method for recording neural activity in the claustrum and thus for rapidly screening any behavioral relevance of the claustrum in freely-behaving mice.

Metastable dynamics of neural circuits and networks

Cortical neurons emit seemingly erratic trains of action potentials or "spikes," and neural network dynamics emerge from the coordinated spiking activity within neural circuits. These rich dynamics manifest themselves in a variety of patterns, which emerge spontaneously or in response to incoming activity produced by sensory inputs. In this Review, we focus on neural dynamics that is best understood as a sequence of repeated activations of a number of discrete hidden states. These transiently occupied states are termed "metastable" and have been linked to important sensory and cognitive functions. In the rodent gustatory cortex, for instance, metastable dynamics have been associated with stimulus coding, with states of expectation, and with decision making. In frontal, parietal, and motor areas of macaques, metastable activity has been related to behavioral performance, choice behavior, task difficulty, and attention. In this article, we review the experimental evidence for neural metastable dynamics together with theoretical approaches to the study of metastable activity in neural circuits. These approaches include (i) a theoretical framework based on non-equilibrium statistical physics for network dynamics; (ii) statistical approaches to extract information about metastable states from a variety of neural signals; and (iii) recent neural network approaches, informed by experimental results, to model the emergence of metastable dynamics. By discussing these topics, we aim to provide a cohesive view of how transitions between different states of activity may provide the neural underpinnings for essential functions such as perception, memory, expectation, or decision making, and more generally, how the study of metastable neural activity may advance our understanding of neural circuit function in health and disease.

Phase-specific pooling of sparse assembly activity by respiration-related brain oscillations

Neuronal assemblies activate phase-coupled to ongoing respiration-related oscillations (RROs) in the medial prefrontal cortex of mice. The phase coupling strength of assemblies exceeds that of individual neurons. Assemblies preferentially activate during the descending phase of RRO. Despite higher assembly frequency during descending RRO, overlap between active assemblies remains constant across RRO phase. Putative GABAergic interneurons are preferentially recruited by assembly neurons during descending RRO, suggesting that interneurons might contribute to the segregation of active assemblies during the descending phase of RRO. ABSTRACT: Nasal breathing affects cognitive functions, but it has remained largely unclear how respiration-driven inputs shape information processing in neuronal circuits. Current theories emphasize the role of neuronal assemblies, coalitions of transiently active pyramidal cells, as the core unit of cortical network computations. Here, we show that the phase of respiration-related oscillations (RROs) influences the likelihood of activation of a subset of neuronal assemblies in the medial prefrontal cortex (mPFC) of awake mice. RROs bias the activation of neuronal assemblies more efficiently than that of individual neurons by entraining the coactivity of assembly neurons. Moreover, the activation of assemblies is moderately biased towards the descending phase of RROs. Despite the enriched activation of assemblies during descending RRO, the overlap between individual assemblies remains constant across RRO phases. Putative GABAergic interneurons are shown to coactivate with assemblies and receive enhanced excitatory drive from assembly neurons during descending RRO, suggesting that the phase-specific recruitment of putative interneurons might help to keep the activation of different assemblies separated from each other during times of preferred assembly activation. Our results thus identify respiration-synchronized brain rhythms as drivers of neuronal assemblies and point to a role of RROs in defining time windows of enhanced yet segregated assembly activity. Abstract figure legend. Nasal breathing affects cognitive functions, but it has remained largely unclear how respiration-driven inputs shape information processing in neuronal circuits. We show that the phase of respiration-related oscillations (RROs) influences the likelihood of the activation of a subset of neuronal assemblies in the medial prefrontal cortex (mPFC) of awake mice. The activation of assemblies is moderately biased towards the descending phase of RROs, while the overlap between individual assemblies remains constant across RRO phases. Putative GABAergic interneurons are shown to coactivate with assemblies and receive enhanced excitatory drive from assembly neurons during descending RRO, suggesting that the phase-specific recruitment of putative interneurons might help to keep the activation of different assemblies separated from each other. This article is protected by copyright. All rights reserved.

High-frequency oscillations and sequence generation in two-population models of hippocampal region CA1

Hippocampal sharp wave/ripple oscillations are a prominent pattern of collective activity, which consists of a strong overall increase of activity with superimposed (140 − 200 Hz) ripple oscillations. Despite its prominence and its experimentally demonstrated importance for memory consolidation, the mechanisms underlying its generation are to date not understood. Several models assume that recurrent networks of inhibitory cells alone can explain the generation and main characteristics of the ripple oscillations. Recent experiments, however, indicate that in addition to inhibitory basket cells, the pattern requires in vivo the activity of the local population of excitatory pyramidal cells. Here, we study a model for networks in the hippocampal region CA1 incorporating such a local excitatory population of pyramidal neurons. We start by investigating its ability to generate ripple oscillations using extensive simulations. Using biologically plausible parameters, we find that short pulses of external excitation triggering excitatory cell spiking are required for sharp/wave ripple generation with oscillation patterns similar to in vivo observations. Our model has plausible values for single neuron, synapse and connectivity parameters, random connectivity and no strong feedforward drive to the inhibitory population. Specifically, whereas temporally broad excitation can lead to high-frequency oscillations in the ripple range, sparse pyramidal cell activity is only obtained with pulse-like external CA3 excitation. Further simulations indicate that such short pulses could originate from dendritic spikes in the apical or basal dendrites of CA1 pyramidal cells, which are triggered by coincident spike arrivals from hippocampal region CA3. Finally we show that replay of sequences by pyramidal neurons and ripple oscillations can arise intrinsically in CA1 due to structured connectivity that gives rise to alternating excitatory pulse and inhibitory gap coding; the latter denotes phases of silence in specific basket cell groups, which induce selective disinhibition of groups of pyramidal neurons. This general mechanism for sequence generation leads to sparse pyramidal cell and dense basket cell spiking, does not rely on synfire chain-like feedforward excitation and may be relevant for other brain regions as well.

Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits

Adaptive sequential behavior is a hallmark of human cognition. In particular, humans can learn to produce precise spatiotemporal sequences given a certain context. For instance, musicians can not only reproduce learned action sequences in a context-dependent manner, they can also quickly and flexibly reapply them in any desired tempo or rhythm without overwriting previous learning. Existing neural network models fail to account for these properties. We argue that this limitation emerges from the fact that sequence information (i.e., the position of the action) and timing (i.e., the moment of response execution) are typically stored in the same neural network weights. Here, we augment a biologically plausible recurrent neural network of cortical dynamics to include a basal ganglia-thalamic module which uses reinforcement learning to dynamically modulate action. This “associative cluster-dependent chain” (ACDC) model modularly stores sequence and timing information in distinct loci of the network. This feature increases computational power and allows ACDC to display a wide range of temporal properties (e.g., multiple sequences, temporal shifting, rescaling, and compositionality), while still accounting for several behavioral and neurophysiological empirical observations. Finally, we apply this ACDC network to show how it can learn the famous “Thunderstruck” song intro and then flexibly play it in a “bossa nova” rhythm without further training.

How do humans flexibly adapt action sequences? For instance, musicians can learn a song and quickly speed up or slow down the tempo, or even play the song following a completely different rhythm (e.g., a rock song using a bossa nova rhythm). In this work, we build a biologically plausible network of cortico-basal ganglia interactions that explains how this temporal flexibility may emerge in the brain. Crucially, our model factorizes sequence order and action timing, respectively represented in cortical and basal ganglia dynamics. This factorization allows full temporal flexibility, i.e. the timing of a learned action sequence can be recomposed without interfering with the order of the sequence. As such, our model is capable of learning asynchronous action sequences, and flexibly shift, rescale, and recompose them, while accounting for biological data.

Neurons learn by predicting future activity

Understanding how the brain learns may lead to machines with human-like intellectual capacities. It was previously proposed that the brain may operate on the principle of predictive coding. However, it is still not well understood how a predictive system could be implemented in the brain. Here we demonstrate that the ability of a single neuron to predict its future activity may provide an effective learning mechanism. Interestingly, this predictive learning rule can be derived from a metabolic principle, where neurons need to minimize their own synaptic activity (cost), while maximizing their impact on local blood supply by recruiting other neurons. We show how this mathematically derived learning rule can provide a theoretical connection between diverse types of brain-inspired algorithms, thus, offering a step toward development of a general theory of neuronal learning. We tested this predictive learning rule in neural network simulations and in data recorded from awake animals. Our results also suggest that spontaneous brain activity provides “training data” for neurons to learn to predict cortical dynamics. Thus, the ability of a single neuron to minimize surprise: i.e. the difference between actual and expected activity, could be an important missing element to understand computation in the brain.

Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies

In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains unclear, although it has been hypothesized to be implemented by the connectivity of the sampled neurons. We test this idea and explore the interactions between local synaptic connectivity and its ability to encode information in a lower dimensional manifold through simulations of a detailed microcircuit model with realistic sources of noise. We confirm that even in isolation such a model can encode the identity of different stimuli in a lower-dimensional space. We then demonstrate that the reliability of the encoding depends on the connectivity between the sampled neurons by specifically sampling populations whose connectivity maximizes certain topological metrics. Finally, we developed an alternative method for determining stimulus identity from the activity of neurons by combining their spike trains with their recurrent connectivity. We found that this method performs better for sampled groups of neurons that perform worse under the classical approach, predicting the possibility of two separate encoding strategies in a single microcircuit.

A Platform for Spatiotemporal "Matrix" Stimulation in Brain Networks Reveals Novel Forms of Circuit Plasticity

Here we demonstrate a facile method by which to deliver complex spatiotemporal stimulation to neural networks in fast patterns, to trigger interesting forms of circuit-level plasticity in cortical areas. We present a complete platform by which patterns of electricity can be arbitrarily defined and distributed across a brain circuit, either simultaneously, asynchronously, or in complex patterns that can be easily designed and orchestrated with precise timing. Interfacing with acute slices of mouse cortex, we show that our system can be used to activate neurons at many locations and drive synaptic transmission in distributed patterns, and that this elicits new forms of plasticity that may not be observable via traditional methods, including interesting measurements of associational and sequence plasticity. Finally, we introduce an automated "network assay" for imaging activation and plasticity across a circuit. Spatiotemporal stimulation opens the door for high-throughput explorations of plasticity at the circuit level, and may provide a basis for new types of adaptive neural prosthetics.

Sequential Attractors in Combinatorial Threshold-Linear Networks

Sequences of neural activity arise in many brain areas, including cortex, hippocampus, and central pattern generator circuits that underlie rhythmic behaviors like locomotion. While network architectures supporting sequence generation vary considerably, a common feature is an abundance of inhibition. In this work, we focus on architectures that support sequential activity in recurrently connected networks with inhibition-dominated dynamics. Specifically, we study emergent sequences in a special family of threshold-linear networks, called combinatorial threshold-linear networks (CTLNs), whose connectivity matrices are defined from directed graphs. Such networks naturally give rise to an abundance of sequences whose dynamics are tightly connected to the underlying graph. We find that architectures based on generalizations of cycle graphs produce limit cycle attractors that can be activated to generate transient or persistent (repeating) sequences. Each architecture type gives rise to an infinite family of graphs that can be built from arbitrary component subgraphs. Moreover, we prove a number of graph rules for the corresponding CTLNs in each family. The graph rules allow us to strongly constrain, and in some cases fully determine, the fixed points of the network in terms of the fixed points of the component subnetworks. Finally, we also show how the structure of certain architectures gives insight into the sequential dynamics of the corresponding attractor.

Spatiotemporal structure of sensory-evoked and spontaneous activity revealed by mesoscale imaging in anesthetized and awake mice

Stimuli-evoked and spontaneous brain activity propagates across the cortex in diverse spatiotemporal patterns. Despite extensive studies, the relationship between spontaneous and evoked activity is poorly understood. We investigate this relationship by comparing the amplitude, speed, direction, and complexity of propagation trajectories of spontaneous and evoked activity elicited with visual, auditory, and tactile stimuli using mesoscale wide-field imaging in mice. For both spontaneous and evoked activity, the speed and direction of propagation is modulated by the amplitude. However, spontaneous activity has a higher complexity of the propagation trajectories. For low stimulus strengths, evoked activity amplitude and speed is similar to that of spontaneous activity but becomes dissimilar at higher stimulus strengths. These findings are consistent with observations that primary sensory areas receive widespread inputs from other cortical regions, and during rest, the cortex tends to reactivate traces of complex multisensory experiences that might have occurred in exhibition of different behaviors.

Neuronal Cascades Shape Whole-Brain Functional Dynamics at Rest

At rest, mammalian brains display remarkable spatiotemporal complexity, evolving through recurrent functional connectivity (FC) states on a slow timescale of the order of tens of seconds. While the phenomenology of the resting state dynamics is valuable in distinguishing healthy and pathologic brains, little is known about its underlying mechanisms. Here, we identify neuronal cascades as a potential mechanism. Using full-brain network modeling, we show that neuronal populations, coupled via a detailed structural connectome, give rise to large-scale cascades of firing rate fluctuations evolving at the same time scale of resting-state networks (RSNs). The ignition and subsequent propagation of cascades depend on the brain state and connectivity of each region. The largest cascades produce bursts of blood oxygen level-dependent (BOLD) co-fluctuations at pairs of regions across the brain, which shape the simulated RSN dynamics. We experimentally confirm these theoretical predictions. We demonstrate the existence and stability of intermittent epochs of FC comprising BOLD co-activation (CA) bursts in mice and human functional magnetic resonance imaging (fMRI). We then provide evidence for the existence and leading role of the neuronal cascades in humans with simultaneous EEG/fMRI recordings. These results show that neuronal cascades are a major determinant of spontaneous fluctuations in brain dynamics at rest.

Stimulus Feature-Specific Control of Layer 2/3 Subthreshold Whisker Responses by Layer 4 in the Mouse Primary Somatosensory Cortex

In the barrel field of the rodent primary somatosensory cortex (S1bf), excitatory cells in layer 2/3 (L2/3) display sparse firing but reliable subthreshold response during whisker stimulation. Subthreshold responses encode specific features of the sensory stimulus, for example, the direction of whisker deflection. According to the canonical model for the flow of sensory information across cortical layers, activity in L2/3 is driven by layer 4 (L4). However, L2/3 cells receive excitatory inputs from other regions, raising the possibility that L4 partially drives L2/3 during whisker stimulation. To test this hypothesis, we combined patch-clamp recordings from L2/3 pyramidal neurons in S1bf with selective optogenetic inhibition of L4 during passive whisker stimulation in both anesthetized and awake head-restrained mice. We found that L4 optogenetic inhibition did not abolish the subthreshold whisker-evoked response nor it affected spontaneous membrane potential fluctuations of L2/3 neurons. However, L4 optogenetic inhibition decreased L2/3 subthreshold responses to whisker deflections in the preferred direction, and it increased L2/3 responses to stimuli in the nonpreferred direction, leading to a change in the direction tuning. Our results contribute to reveal the circuit mechanisms underlying the processing of sensory information in the rodent S1bf.

Mutual Interactions between Brain States and Alzheimer's Disease Pathology: A Focus on Gamma and Slow Oscillations

Brain state varies from moment to moment. While brain state can be defined by ongoing neuronal population activity, such as neuronal oscillations, this is tightly coupled with certain behavioural or vigilant states. In recent decades, abnormalities in brain state have been recognised as biomarkers of various brain diseases and disorders. Intriguingly, accumulating evidence also demonstrates mutual interactions between brain states and disease pathologies: while abnormalities in brain state arise during disease progression, manipulations of brain state can modify disease pathology, suggesting a therapeutic potential. In this review, by focusing on Alzheimer's disease (AD), the most common form of dementia, we provide an overview of how brain states change in AD patients and mouse models, and how controlling brain states can modify AD pathology. Specifically, we summarise the relationship between AD and changes in gamma and slow oscillations. As pathological changes in these oscillations correlate with AD pathology, manipulations of either gamma or slow oscillations can modify AD pathology in mouse models. We argue that neuromodulation approaches to target brain states are a promising non-pharmacological intervention for neurodegenerative diseases.

Statistical moments of the interspike intervals for a neuron model driven by trichotomous noise

The influence of a colored three-level input noise (trichotomous noise) on the spike generation of a perfect integrate-and-fire (PIF) model of neurons is studied. Using a first-passage-time formulation, exact expressions for the Laplace transform of the output interspike interval (ISI) density and for the statistical moments of the ISIs (such as the coefficient of variation, the skewness, the serial correlation coefficient, and the Fano factor) are derived. To model the anomalous subdiffusion that can arise from, e.g., the trapping properties of dendritic spines, the model is extended by including a random operational time in the form of an inverse strictly increasing Lévy-type subordinator, and exact formulas for ISI statistics are given for this case as well. Particularly, it is shown that at some parameter regimes, the ISI density exhibits a three-modal structure. The results for the extended model show that the ISI serial correlation coefficient and the Fano factor are nonmonotonic with respect to the input current, which indicates that at an intermediate value of the input current the variability of the output spike trains is minimal. Similarities and differences between the behavior of the presented models and the previously investigated PIF models driven by dichotomous noise are also discussed.

Atypical electrophysiological and behavioral responses to diazepam in a leading mouse model of Down syndrome

Mounting evidence implicates dysfunctional GABA_AR-mediated neurotransmission as one of the underlying causes of learning and memory deficits observed in the Ts65Dn mouse model of Down syndrome (DS). The specific origin and nature of such dysfunction is still under investigation, which is an issue with practical consequences to preclinical and clinical research, as well as to the care of individuals with DS and anxiety disorder or those experiencing seizures in emergency room settings. Here, we investigated the effects of GABA_AR positive allosteric modulation (PAM) by diazepam on brain activity, synaptic plasticity, and behavior in Ts65Dn mice. We found Ts65Dn mice to be less sensitive to diazepam, as assessed by electroencephalography, long-term potentiation, and elevated plus-maze. Still, diazepam pre-treatment displayed typical effectiveness in reducing susceptibility and severity to picrotoxin-induced seizures in Ts65Dn mice. These findings fill an important gap in the understanding of GABAergic function in a key model of DS.

Cortical astrocytes independently regulate sleep depth and duration via separate GPCR pathways

Non-rapid eye movement (NREM) sleep, characterized by slow-wave electrophysiological activity, underlies several critical functions, including learning and memory. However, NREM sleep is heterogeneous, varying in duration, depth, and spatially across the cortex. While these NREM sleep features are thought to be largely independently regulated, there is also evidence that they are mechanistically coupled. To investigate how cortical NREM sleep features are controlled, we examined the astrocytic network, comprising a cortex-wide syncytium that influences population-level neuronal activity. We quantified endogenous astrocyte activity in mice over natural sleep and wake, then manipulated specific astrocytic G-protein-coupled receptor (GPCR) signaling pathways in vivo. We find that astrocytic Gi- and Gq-coupled GPCR signaling separately control NREM sleep depth and duration, respectively, and that astrocytic signaling causes differential changes in local and remote cortex. These data support a model in which the cortical astrocyte network serves as a hub for regulating distinct NREM sleep features.

Sleep has many roles, from strengthening new memories to regulating mood and appetite. While we might instinctively think of sleep as a uniform state of reduced brain activity, the reality is more complex. First, over the course of the night, we cycle between a number of different sleep stages, which reflect different levels of sleep depth. Second, the amount of sleep depth is not necessarily even across the brain but can vary between regions.

These sleep stages consist of either rapid eye movement (REM) sleep or non-REM (NREM) sleep. REM sleep is when most dreaming occurs, whereas NREM sleep is particularly important for learning and memory and can vary in duration and depth. During NREM sleep, large groups of neurons synchronize their firing to create rhythmic waves of activity known as slow waves. The more synchronous the activity, the deeper the sleep.

Vaidyanathan et al. now show that brain cells called astrocytes help regulate NREM sleep. Astrocytes are not neurons but belong to a group of specialized cells called glia. They are the largest glia cell type in the brain and display an array of proteins on their surfaces called G-protein-coupled receptors (GPCRs). These enable them to sense sleep-wake signals from other parts of the brain and to generate their own signals. In fact, each astrocyte can communicate with thousands of neurons at once. They are therefore well-poised to coordinate brain activity during NREM sleep.

Using innovative tools, Vaidyanathan et al. visualized astrocyte activity in mice as the animals woke up or fell asleep. The results showed that astrocytes change their activity just before each sleep–wake transition. They also revealed that astrocytes control both the depth and duration of NREM sleep via two different types of GPCR signals. Increasing one of these signals (Gi-GPCR) made the mice sleep more deeply but did not change sleep duration. Decreasing the other (Gq-GPCR) made the mice sleep for longer but did not affect sleep depth.

Sleep problems affect many people at some point in their lives, and often co-exist with other conditions such as mental health disorders. Understanding how the brain regulates different features of sleep could help us develop better – and perhaps more specific – treatments for sleep disorders. The current study suggests that manipulating GPCRs on astrocytes might increase sleep depth, for example. But before work to test this idea can begin, we must first determine whether findings from sleeping mice also apply to people.