A Unified Dynamic Model for Learning, Replay, and Sharp-Wave/Ripples

Firing rate adaptation affords place cell theta sweeps, phase precession, and procession

Hippocampal place cells in freely moving rodents display both theta phase precession and procession, which is thought to play important roles in cognition, but the neural mechanism for producing theta phase shift remains largely unknown. Here, we show that firing rate adaptation within a continuous attractor neural network causes the neural activity bump to oscillate around the external input, resembling theta sweeps of decoded position during locomotion. These forward and backward sweeps naturally account for theta phase precession and procession of individual neurons, respectively. By tuning the adaptation strength, our model explains the difference between 'bimodal cells' showing interleaved phase precession and procession, and 'unimodal cells' in which phase precession predominates. Our model also explains the constant cycling of theta sweeps along different arms in a T-maze environment, the speed modulation of place cells' firing frequency, and the continued phase shift after transient silencing of the hippocampus. We hope that this study will aid an understanding of the neural mechanism supporting theta phase coding in the brain.

A neural network model for online one-shot storage of pattern sequences

Based on the CRISP theory (Content Representation, Intrinsic Sequences, and Pattern completion), we present a computational model of the hippocampus that allows for online one-shot storage of pattern sequences without the need for a consolidation process. In our model, CA3 provides a pre-trained sequence that is hetero-associated with the input sequence, rather than storing a sequence in CA3. That is, plasticity on a short timescale only occurs in the incoming and outgoing connections of CA3, not in its recurrent connections. We use a single learning rule named Hebbian descent to train all plastic synapses in the network. A forgetting mechanism in the learning rule allows the network to continuously store new patterns while forgetting those stored earlier. We find that a single cue pattern can reliably trigger the retrieval of sequences, even when cues are noisy or missing information. Furthermore, pattern separation in subregion DG is necessary when sequences contain correlated patterns. Besides artificially generated input sequences, the model works with sequences of handwritten digits and natural images. Notably, our model is capable of improving itself without external input, in a process that can be referred to as 'replay' or 'offline-learning', which helps in improving the associations and consolidating the learned patterns.

Intrinsic dynamics of randomly clustered networks generate place fields and preplay of novel environments

During both sleep and awake immobility, hippocampal place cells reactivate time-compressed versions of sequences representing recently experienced trajectories in a phenomenon known as replay. Intriguingly, spontaneous sequences can also correspond to forthcoming trajectories in novel environments experienced later, in a phenomenon known as preplay. Here, we present a model showing that sequences of spikes correlated with the place fields underlying spatial trajectories in both previously experienced and future novel environments can arise spontaneously in neural circuits with random, clustered connectivity rather than pre-configured spatial maps. Moreover, the realistic place fields themselves arise in the circuit from minimal, landmark-based inputs. We find that preplay quality depends on the network's balance of cluster isolation and overlap, with optimal preplay occurring in small-world regimes of high clustering yet short path lengths. We validate the results of our model by applying the same place field and preplay analyses to previously published rat hippocampal place cell data. Our results show that clustered recurrent connectivity can generate spontaneous preplay and immediate replay of novel environments. These findings support a framework whereby novel sensory experiences become associated with preexisting "pluripotent" internal neural activity patterns.

Differences in the consolidation by spontaneous and evoked ripples in the presence of active dendrites

Ripples are a typical form of neural activity in hippocampal neural networks associated with the replay of episodic memories during sleep as well as sleep-related plasticity and memory consolidation. The emergence of ripples has been observed both dependent as well as independent of input from other brain areas and often coincides with dendritic spikes. Yet, it is unclear how input-evoked and spontaneous ripples as well as dendritic excitability affect plasticity and consolidation. Here, we use mathematical modeling to compare these cases. We find that consolidation as well as the emergence of spontaneous ripples depends on a reliable propagation of activity in feed-forward structures which constitute memory representations. This propagation is facilitated by excitable dendrites, which entail that a few strong synapses are sufficient to trigger neuronal firing. In this situation, stimulation-evoked ripples lead to the potentiation of weak synapses within the feed-forward structure and, thus, to a consolidation of a more general sequence memory. However, spontaneous ripples that occur without stimulation, only consolidate a sparse backbone of the existing strong feed-forward structure. Based on this, we test a recently hypothesized scenario in which the excitability of dendrites is transiently enhanced after learning, and show that such a transient increase can strengthen, restructure and consolidate even weak hippocampal memories, which would be forgotten otherwise. Hence, a transient increase in dendritic excitability would indeed provide a mechanism for stabilizing memories.

CA3 Circuit Model Compressing Sequential Information in Theta Oscillation and Replay

The hippocampus plays a critical role in the compression and retrieval of sequential information. During wakefulness, it achieves this through theta phase precession and theta sequences. Subsequently, during periods of sleep or rest, the compressed information reactivates through sharp-wave ripple events, manifesting as memory replay. However, how these sequential neuronal activities are generated and how they store information about the external environment remain unknown. We developed a hippocampal cornu ammonis 3 (CA3) computational model based on anatomical and electrophysiological evidence from the biological CA3 circuit to address these questions. The model comprises theta rhythm inhibition, place input, and CA3-CA3 plastic recurrent connection. The model can compress the sequence of the external inputs, reproduce theta phase precession and replay, learn additional sequences, and reorganize previously learned sequences. A gradual increase in synaptic inputs, controlled by interactions between theta-paced inhibition and place inputs, explained the mechanism of sequence acquisition. This model highlights the crucial role of plasticity in the CA3 recurrent connection and theta oscillational dynamics and hypothesizes how the CA3 circuit acquires, compresses, and replays sequential information.

Intra-ripple frequency accommodation in an inhibitory network model for hippocampal ripple oscillations

Hippocampal ripple oscillations have been implicated in important cognitive functions such as memory consolidation and planning. Multiple computational models have been proposed to explain the emergence of ripple oscillations, relying either on excitation or inhibition as the main pacemaker. Nevertheless, the generating mechanism of ripples remains unclear. An interesting dynamical feature of experimentally measured ripples, which may advance model selection, is intra-ripple frequency accommodation (IFA): a decay of the instantaneous ripple frequency over the course of a ripple event. So far, only a feedback-based inhibition-first model, which relies on delayed inhibitory synaptic coupling, has been shown to reproduce IFA. Here we use an analytical mean-field approach and numerical simulations of a leaky integrate-and-fire spiking network to explain the mechanism of IFA. We develop a drift-based approximation for the oscillation dynamics of the population rate and the mean membrane potential of interneurons under strong excitatory drive and strong inhibitory coupling. For IFA, the speed at which the excitatory drive changes is critical. We demonstrate that IFA arises due to a speed-dependent hysteresis effect in the dynamics of the mean membrane potential, when the interneurons receive transient, sharp wave-associated excitation. We thus predict that the IFA asymmetry vanishes in the limit of slowly changing drive, but is otherwise a robust feature of the feedback-based inhibition-first ripple model.

Emergent structural and functional properties of hippocampal multi-cellular aggregates

Hippocampal neural networks are distinctly capable of integrating multi-modal sensory inputs to drive memory formation. Neuroscientific investigations using simplified in vitro models have greatly relied on planar (2D) neuronal cultures made from dissociated tissue. While these models have served as simple, cost-effective, and high-throughput tools for examining various morphological and electrophysiological characteristics of hippocampal networks, 2D cultures fail to reconstitute critical elements of the brain microenvironment that may be necessary for the emergence of sophisticated integrative network properties. To address this, we utilized a forced aggregation technique to generate high-density (>100,000 cells/mm³) multi-cellular three-dimensional aggregates using rodent embryonic hippocampal tissue. We contrasted the emergent structural and functional properties of aggregated (3D) and dissociated (2D) cultures over 28 days in vitro (DIV). Hippocampal aggregates displayed robust axonal fasciculation across large distances and significant neuronal polarization, i.e., spatial segregation of dendrites and axons, at earlier time points compared to dissociated cultures. Moreover, we found that astrocytes in aggregate cultures self-organized into non-overlapping quasi-domains and developed highly stellate morphologies resembling astrocyte structures in vivo. We maintained cultures on multi-electrode arrays (MEAs) to assess spontaneous electrophysiological activity for up to 28 DIV. We found that 3D networks of aggregated cultures developed highly synchronized networks and with high burstiness by 28 DIV. We also demonstrated that dual-aggregate networks became active by 7 DIV, in contrast to single-aggregate networks which became active and developed synchronous bursting activity with repeating motifs by 14 DIV. Taken together, our findings demonstrate that the high-density, multi-cellular, 3D microenvironment of hippocampal aggregates supports the recapitulation of emergent biofidelic morphological and functional properties. Our findings suggest that neural aggregates may be used as segregated, modular building blocks for the development of complex, multi-nodal neural network topologies.

How our understanding of memory replay evolves

Memory reactivations and replay, widely reported in the hippocampus and cortex across species, have been implicated in memory consolidation, planning, and spatial and skill learning. Technological advances in electrophysiology, calcium imaging, and human neuroimaging techniques have enabled neuroscientists to measure large-scale neural activity with increasing spatiotemporal resolution and have provided opportunities for developing robust analytic methods to identify memory replay. In this article, we first review a large body of historically important and representative memory replay studies from the animal and human literature. We then discuss our current understanding of memory replay functions in learning, planning, and memory consolidation and further discuss the progress in computational modeling that has contributed to these improvements. Next, we review past and present analytic methods for replay analyses and discuss their limitations and challenges. Finally, looking ahead, we discuss some promising analytic methods for detecting nonstereotypical, behaviorally nondecodable structures from large-scale neural recordings. We argue that seamless integration of multisite recordings, real-time replay decoding, and closed-loop manipulation experiments will be essential for delineating the role of memory replay in a wide range of cognitive and motor functions.

Computational models of Idling brain activity for memory processing

Studying the underlying neural mechanisms of cognitive functions of the brain is one of the central questions in modern biology. Moreover, it has significantly impacted the development of novel technologies in artificial intelligence. Spontaneous activity is a unique feature of the brain and is currently lacking in many artificially constructed intelligent machines. Spontaneous activity may represent the brain's idling states, which are internally driven by neuronal networks and possibly participate in offline processing during awake, sleep, and resting states. Evidence is accumulating that the brain's spontaneous activity is not mere noise but part of the mechanisms to process information about previous experiences. A bunch of literature has shown how previous sensory and behavioral experiences influence the subsequent patterns of brain activity with various methods in various animals. It seems, however, that the patterns of neural activity and their computational roles differ significantly from area to area and from function to function. In this article, I review the various forms of the brain's spontaneous activity, especially those observed during memory processing, and some attempts to model the generation mechanisms and computational roles of such activities.

Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity

Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal replay, which is critical for memory consolidation. The sudden and repeated occurrences of these burst states during ongoing neural activity suggest metastable neural circuit dynamics. As metastability has been attributed to noise and/or slow fatigue mechanisms, we propose a concise mesoscopic model which accounts for both. Crucially, our model is bottom-up: it is analytically derived from the dynamics of finite-size networks of Linear-Nonlinear Poisson neurons with short-term synaptic depression. As such, noise is explicitly linked to stochastic spiking and network size, and fatigue is explicitly linked to synaptic dynamics. To derive the mesoscopic model, we first consider a homogeneous spiking neural network and follow the temporal coarse-graining approach of Gillespie to obtain a "chemical Langevin equation", which can be naturally interpreted as a stochastic neural mass model. The Langevin equation is computationally inexpensive to simulate and enables a thorough study of metastable dynamics in classical setups (population spikes and Up-Down-states dynamics) by means of phase-plane analysis. An extension of the Langevin equation for small network sizes is also presented. The stochastic neural mass model constitutes the basic component of our mesoscopic model for replay. We show that the mesoscopic model faithfully captures the statistical structure of individual replayed trajectories in microscopic simulations and in previously reported experimental data. Moreover, compared to the deterministic Romani-Tsodyks model of place-cell dynamics, it exhibits a higher level of variability regarding order, direction and timing of replayed trajectories, which seems biologically more plausible and could be functionally desirable. This variability is the product of a new dynamical regime where metastability emerges from a complex interplay between finite-size fluctuations and local fatigue.

Dendritic axon origin enables information gating by perisomatic inhibition in pyramidal neurons

Information processing in neuronal networks involves the recruitment of selected neurons into coordinated spatiotemporal activity patterns. This sparse activation results from widespread synaptic inhibition in conjunction with neuron-specific synaptic excitation. We report the selective recruitment of hippocampal pyramidal cells into patterned network activity. During ripple oscillations in awake mice, spiking is much more likely in cells in which the axon originates from a basal dendrite rather than from the soma. High-resolution recordings in vitro and computer modeling indicate that these spikes are elicited by synaptic input to the axon-carrying dendrite and thus escape perisomatic inhibition. Pyramidal cells with somatic axon origin can be activated during ripple oscillations by blocking their somatic inhibition. The recruitment of neurons into active ensembles is thus determined by axonal morphological features.

Replay, the default mode network and the cascaded memory systems model

The spontaneous replay of patterns of activity related to past experiences and memories is a striking feature of brain activity, as is the coherent activation of sets of brain areas - particularly those comprising the default mode network (DMN) - during rest. We propose that these two phenomena are strongly intertwined and that their potential functions overlap. In the 'cascaded memory systems model' that we outline here, we hypothesize that the DMN forms the backbone for the propagation of replay, mediating interactions between the hippocampus and the neocortex that enable the consolidation of new memories. The DMN may also independently ignite replay cascades, which support reactivation of older memories or high-level semantic representations. We suggest that transient cortical activations, inducing long-range correlations across the neocortex, are a key mechanism supporting a hierarchy of representations that progresses from simple percepts to semantic representations of causes and, finally, to whole episodes.

Reorganization of CA1 dendritic dynamics by hippocampal sharp-wave ripples during learning

The hippocampus plays a critical role in memory consolidation, mediated by coordinated network activity during sharp-wave ripple (SWR) events. Despite the link between SWRs and hippocampal plasticity, little is known about how network state affects information processing in dendrites, the primary sites of synaptic input integration and plasticity. Here, we monitored somatic and basal dendritic activity in CA1 pyramidal cells in behaving mice using longitudinal two-photon calcium imaging integrated with simultaneous local field potential recordings. We found immobility was associated with an increase in dendritic activity concentrated during SWRs. Coincident dendritic and somatic activity during SWRs predicted increased coupling during subsequent exploration of a novel environment. In contrast, somatic-dendritic coupling and SWR recruitment varied with cells' tuning distance to reward location during a goal-learning task. Our results connect SWRs with the stabilization of information processing within CA1 neurons and suggest that these mechanisms may be dynamically biased by behavioral demands.

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.

Hippocampal sharp wave-ripples and the associated sequence replay emerge from structured synaptic interactions in a network model of area CA3

Hippocampal place cells are activated sequentially as an animal explores its environment. These activity sequences are internally recreated (‘replayed’), either in the same or reversed order, during bursts of activity (sharp wave-ripples [SWRs]) that occur in sleep and awake rest. SWR-associated replay is thought to be critical for the creation and maintenance of long-term memory. In order to identify the cellular and network mechanisms of SWRs and replay, we constructed and simulated a data-driven model of area CA3 of the hippocampus. Our results show that the chain-like structure of recurrent excitatory interactions established during learning not only determines the content of replay, but is essential for the generation of the SWRs as well. We find that bidirectional replay requires the interplay of the experimentally confirmed, temporally symmetric plasticity rule, and cellular adaptation. Our model provides a unifying framework for diverse phenomena involving hippocampal plasticity, representations, and dynamics, and suggests that the structured neural codes induced by learning may have greater influence over cortical network states than previously appreciated.

Enriched Environment Modulates Sharp Wave-Ripple (SPW-R) Activity in Hippocampal Slices

Behavioral flexibility depends on neuronal plasticity which forms and adapts the central nervous system in an experience-dependent manner. Thus, plasticity depends on interactions between the organism and its environment. A key experimental paradigm for studying this concept is the exposure of rodents to an enriched environment (EE), followed by studying differences to control animals kept under standard conditions (SC). While multiple changes induced by EE have been found at the cellular-molecular and cognitive-behavioral levels, little is known about EE-dependent alterations at the intermediate level of network activity. We, therefore, studied spontaneous network activity in hippocampal slices from mice which had previously experienced EE for 10–15 days. Compared to control animals from standard conditions (SC) and mice with enhanced motor activity (MC) we found several differences in sharp wave-ripple complexes (SPW-R), a memory-related activity pattern. Sharp wave amplitude, unit firing during sharp waves, and the number of superimposed ripple cycles were increased in tissue from the EE group. On the other hand, spiking precision with respect to the ripple oscillations was reduced. Recordings from single pyramidal cells revealed a reduction in synaptic inhibition during SPW-R together with a reduced inhibition-excitation ratio. The number of inhibitory neurons, including parvalbumin-positive interneurons, was unchanged. Altered activation or efficacy of synaptic inhibition may thus underlie changes in memory-related network activity patterns which, in turn, may be important for the cognitive-behavioral effects of EE exposure.

Distributed Phase Oscillatory Excitation Efficiently Produces Attractors Using Spike-Timing-Dependent Plasticity

The brain is thought to represent information in the form of activity in distributed groups of neurons known as attractors. We show here that in a randomly connected network of simulated spiking neurons, periodic stimulation of neurons with distributed phase offsets, along with standard spike-timing-dependent plasticity (STDP), efficiently creates distributed attractors. These attractors may have a consistent ordered firing pattern or become irregular, depending on the conditions. We also show that when two such attractors are stimulated in sequence, the same STDP mechanism can create a directed association between them, forming the basis of an associative network. We find that for an STDP time constant of 20 ms, the dependence of the efficiency of attractor creation on the driving frequency has a broad peak centered around 8 Hz. Upon restimulation, the attractors self-oscillate, but with an oscillation frequency that is higher than the driving frequency, ranging from 10 to 100 Hz.

A Model of the CA1 Field Rhythms

We propose a model of the main rhythms in the hippocampal CA1 field: theta rhythm; slow, middle, and fast gamma rhythms; and ripple oscillations. We have based this on data obtained from animals behaving freely. We have considered the modes of neuronal discharges and the occurrence of local field potential oscillations in the theta and non-theta states at different inputs from the CA3 field, the medial entorhinal cortex, and the medial septum. In our work, we tried to reproduce the main experimental phenomena about rhythms in the CA1 field: the coupling of neurons to the phase of rhythms, cross-rhythm phase–phase coupling, and phase–amplitude coupling. Using computational experiments, we have proved the hypothesis that the descending phase of the theta rhythm in the CA1 field is formed by the input from the CA3 field via the Shaffer collaterals, and the ascending phase of the theta rhythm is formed by the IPSPs from CCK basket cells. The slow gamma rhythm is coupled to the descending phase of the theta rhythm, since it also depends on the arrival of the signal via the Shaffer collaterals. The middle gamma rhythm is formed by the EPSPs of the principal neurons of the third layer of the entorhinal cortex, corresponds to experimental data. We were able to unite in a single mathematical model several theoretical ideas about the mechanisms of rhythmic processes in the CA1 field of the hippocampus.

Spiking Neural Networks and Hippocampal Function: A Web-Accessible Survey of Simulations, Modeling Methods, and Underlying Theories

Computational modeling has contributed to hippocampal research in a wide variety of ways and through a large diversity of approaches, reflecting the many advanced cognitive roles of this brain region. The intensively studied neuron type circuitry of the hippocampus is a particularly conducive substrate for spiking neural models. Here we present an online knowledge base of spiking neural network simulations of hippocampal functions. First, we overview theories involving the hippocampal formation in subjects such as spatial representation, learning, and memory. Then we describe an original literature mining process to organize published reports in various key aspects, including: (i) subject area (e.g., navigation, pattern completion, epilepsy); (ii) level of modeling detail (Hodgkin-Huxley, integrate-and-fire, etc.); and (iii) theoretical framework (attractor dynamics, oscillatory interference, self-organizing maps, and others). Moreover, every peer-reviewed publication is also annotated to indicate the specific neuron types represented in the network simulation, establishing a direct link with the Hippocampome.org portal. The web interface of the knowledge base enables dynamic content browsing and advanced searches, and consistently presents evidence supporting every annotation. Moreover, users are given access to several types of statistical reports about the collection, a selection of which is summarized in this paper. This open access resource thus provides an interactive platform to survey spiking neural network models of hippocampal functions, compare available computational methods, and foster ideas for suitable new directions of research.

Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks

The synaptic-tagging-and-capture (STC) hypothesis formulates that at each synapse the concurrence of a tag with protein synthesis yields the maintenance of changes induced by synaptic plasticity. This hypothesis provides a biological principle underlying the synaptic consolidation of memories that is not verified for recurrent neural circuits. We developed a theoretical model integrating the mechanisms underlying the STC hypothesis with calcium-based synaptic plasticity in a recurrent spiking neural network. In the model, calcium-based synaptic plasticity yields the formation of strongly interconnected cell assemblies encoding memories, followed by consolidation through the STC mechanisms. Furthermore, we show for the first time that STC mechanisms modify the storage of memories such that after several hours memory recall is significantly improved. We identify two contributing processes: a merely time-dependent passive improvement, and an active improvement during recall. The described characteristics can provide a new principle for storing information in biological and artificial neural circuits.

Synchronicity of excitatory inputs drives hippocampal networks to distinct oscillatory patterns

The rodent hippocampus expresses a variety of neuronal network oscillations depending on the behavioral state of the animal. Locomotion and active exploration are accompanied by theta-nested gamma oscillations while resting states and slow-wave sleep are dominated by intermittent sharp wave-ripple complexes. It is believed that gamma rhythms create a framework for efficient acquisition of information whereas sharp wave-ripples are thought to be involved in consolidation and retrieval of memory. While not strictly mutually exclusive, one of the two patterns usually dominates in a given behavioral state. Here we explore how different input patterns induce either of the two network states, using an optogenetic stimulation approach in hippocampal brain slices of mice. We report that the pattern of the evoked oscillation depends strongly on the initial synchrony of activation of excitatory cells within CA3. Short, synchronous activation favors the emergence of sharp wave-ripple complexes while persistent but less synchronous activity-as typical for sensory input during exploratory behavior-supports the generation of gamma oscillations. This dichotomy is reflected by different degrees of synchrony of excitatory and inhibitory synaptic currents within these two states. Importantly, the induction of these two fundamental network patterns does not depend on the presence of any neuromodulatory transmitter like acetylcholine, but is merely based on a different synchrony in the initial activation pattern.

Modeling awake hippocampal reactivations with model-based bidirectional search

Hippocampal offline reactivations during reward-based learning, usually categorized as replay events, have been found to be important for performance improvement over time and for memory consolidation. Recent computational work has linked these phenomena to the need to transform reward information into state-action values for decision making and to propagate it to all relevant states of the environment. Nevertheless, it is still unclear whether an integrated reinforcement learning mechanism could account for the variety of awake hippocampal reactivations, including variety in order (forward and reverse reactivated trajectories) and variety in the location where they occur (reward site or decision-point). Here, we present a model-based bidirectional search model which accounts for a variety of hippocampal reactivations. The model combines forward trajectory sampling from current position and backward sampling through prioritized sweeping from states associated with large reward prediction errors until the two trajectories connect. This is repeated until stabilization of state-action values (convergence), which could explain why hippocampal reactivations drastically diminish when the animal's performance stabilizes. Simulations in a multiple T-maze task show that forward reactivations are prominently found at decision-points while backward reactivations are exclusively generated at reward sites. Finally, the model can generate imaginary trajectories that are not allowed to the agent during task performance. We raise some experimental predictions and implications for future studies of the role of the hippocampo-prefronto-striatal network in learning.

Somatodendritic consistency check for temporal feature segmentation

The brain identifies potentially salient features within continuous information streams to process hierarchical temporal events. This requires the compression of information streams, for which effective computational principles are yet to be explored. Backpropagating action potentials can induce synaptic plasticity in the dendrites of cortical pyramidal neurons. By analogy with this effect, we model a self-supervising process that increases the similarity between dendritic and somatic activities where the somatic activity is normalized by a running average. We further show that a family of networks composed of the two-compartment neurons performs a surprisingly wide variety of complex unsupervised learning tasks, including chunking of temporal sequences and the source separation of mixed correlated signals. Common methods applicable to these temporal feature analyses were previously unknown. Our results suggest the powerful ability of neural networks with dendrites to analyze temporal features. This simple neuron model may also be potentially useful in neural engineering applications.

The authors propose a learning rule for a neuron model with dendrite. In their model, somatodendritic interaction implements self-supervised learning applicable to a wide range of sequence learning tasks, including spike pattern detection, chunking temporal input and blind source separation.

Unsupervised Learning of Persistent and Sequential Activity

Two strikingly distinct types of activity have been observed in various brain structures during delay periods of delayed response tasks: Persistent activity (PA), in which a sub-population of neurons maintains an elevated firing rate throughout an entire delay period; and Sequential activity (SA), in which sub-populations of neurons are activated sequentially in time. It has been hypothesized that both types of dynamics can be “learned” by the relevant networks from the statistics of their inputs, thanks to mechanisms of synaptic plasticity. However, the necessary conditions for a synaptic plasticity rule and input statistics to learn these two types of dynamics in a stable fashion are still unclear. In particular, it is unclear whether a single learning rule is able to learn both types of activity patterns, depending on the statistics of the inputs driving the network. Here, we first characterize the complete bifurcation diagram of a firing rate model of multiple excitatory populations with an inhibitory mechanism, as a function of the parameters characterizing its connectivity. We then investigate how an unsupervised temporally asymmetric Hebbian plasticity rule shapes the dynamics of the network. Consistent with previous studies, we find that for stable learning of PA and SA, an additional stabilization mechanism is necessary. We show that a generalized version of the standard multiplicative homeostatic plasticity (Renart et al., 2003; Toyoizumi et al., 2014) stabilizes learning by effectively masking excitatory connections during stimulation and unmasking those connections during retrieval. Using the bifurcation diagram derived for fixed connectivity, we study analytically the temporal evolution and the steady state of the learned recurrent architecture as a function of parameters characterizing the external inputs. Slow changing stimuli lead to PA, while fast changing stimuli lead to SA. Our network model shows how a network with plastic synapses can stably and flexibly learn PA and SA in an unsupervised manner.

Interneuronal gap junctions increase synchrony and robustness of hippocampal ripple oscillations

Sharp wave-ripples (SWRs) are important for memory consolidation. Their signature in the hippocampal extracellular field potential can be decomposed into a ≈100 ms long sharp wave superimposed by ≈200 Hz ripple oscillations. How ripple oscillations are generated is currently not well understood. A promising model for the genesis of ripple oscillations is based on recurrent interneuronal networks (INT-INT). According to this hypothesis, the INT-INT network in CA1 receives a burst of excitation from CA3 that generates the sharp wave, and recurrent inhibition leads to an ultrafast synchronization of the CA1 network causing the ripple oscillations; fast-spiking parvalbumin-positive basket cells (PV⁺  BCs) may constitute the ripple-generating interneuronal network. PV⁺  BCs are also coupled by gap junctions (GJs) but the function of GJs for ripple oscillations has not been quantified. Using simulations of CA1 hippocampal networks of PV⁺  BCs, we show that GJs promote synchrony beyond a level that could be obtained by only inhibition. GJs also increase the neuronal firing rate of the interneuronal ensemble, while they affect the ripple frequency only mildly. The promoting effect of GJs on ripple oscillations depends on fast GJ transmission ( ≲ 0.5 ms), which requires proximal GJ coupling ( ≲ 100 μm from soma), but is robust to variability in the delay and the amplitude of GJ coupling.

The Brain-Derived Neurotrophic Factor: Missing Link Between Sleep Deprivation, Insomnia, and Depression

The brain-derived neurotrophic factor (BDNF) mediates the plasticity-related changes that associate with memory processing during sleep. Sleep deprivation and chronic stress are associated with propensity to depression, anxiety, and insomnia. We propose a model by which explain alterations in the CNS and serum expression of BDNF associated with chronic sleep deprivation, depression, and insomnia. Mild sleep deprivation activates the cerebral cortex and brainstem to generate the physiologic drive for non-rapid eye movement (NREM) and rapid eye movement (REM) sleep drive respectively, associated with BDNF upregulation in these regions. This physiological response loses effectiveness with longer episodes or during chronic of total or selective REM sleep loss, which are associated with impaired hippocampal BDNF expression, impaired memory and cognition. Chronic sleep deprivation and insomnia can act as an external stressors and result in depression, characterized by hippocampal BDNF downregulation along with disrupted frontal cortical BDNF expression, as well as reduced levels and impaired diurnal alterations in serum BDNF expression. Acute REM sleep deprivation breaks the cycle by restoration of hippocampal, and possibly restoration of cortical and serum expression of BDNF. The BDNF Val66Met polymorphism alters susceptibility to depression, anxiety, and insomnia by altering availability and expression of BDNF in brain and blood. The proposed model is testable and implies that low levels and low variability in serum BDNF are associated with poor response to anti-depressive medications, electroconvulsive therapy, and REM sleep deprivation, in patients with depression. Our mode is also backed up by the existing clinical evidence but is yet to be investigated.

Hippocampal-Cortical Memory Trace Transfer and Reactivation Through Cell-Specific Stimulus and Spontaneous Background Noise

The hippocampus plays important roles in memory formation and retrieval through sharp-wave-ripples. Recent studies have shown that certain neuron populations in the prefrontal cortex (PFC) exhibit coordinated reactivations during awake ripple events. These experimental findings suggest that the awake ripple is an important biomarker, through which the hippocampus interacts with the neocortex to assist memory formation and retrieval. However, the computational mechanisms of this ripple based hippocampal-cortical coordination are still not clear due to the lack of unified models that include both the hippocampal and cortical networks. In this work, using a coupled biophysical model of both CA1 and PFC, we investigate possible mechanisms of hippocampal-cortical memory trace transfer and the conditions that assist reactivation of the transferred memory traces in the PFC. To validate our model, we first show that the local field potentials generated in the hippocampus and PFC exhibit ripple range activities that are consistent with the recent experimental studies. Then we demonstrate that during ripples, sequence replays can successfully transfer the information stored in the hippocampus to the PFC recurrent networks. We investigate possible mechanisms of memory retrieval in PFC networks. Our results suggest that the stored memory traces in the PFC network can be retrieved through two different mechanisms, namely the cell-specific input representing external stimuli and non-specific spontaneous background noise representing spontaneous memory recall events. Importantly, in both cases, the memory reactivation quality is robust to network connection loss. Finally, we find out that the quality of sequence reactivations is enhanced by both increased number of SWRs and an optimal background noise intensity, which tunes the excitability of neurons to a proper level. Our study presents a mechanistic explanation for the memory trace transfer from the hippocampus to neocortex through ripple coupling in awake states and reports two different mechanisms by which the stored memory traces can be successfully retrieved.

Chaotic dynamics as a mechanism of rapid transition of hippocampal local field activity between theta and non-theta states

We propose a dynamical model of the local hippocampal circuit realizing the transition between the theta and non-theta states. We model the interaction between hippocampal local rhythm generators and the external periodic input from the medial septum and diagonal band of Broca (MS-DBB). With our model, bifurcation of the nonlinear dynamics serves as a mechanism that realizes two distinctive oscillations in the hippocampus, where the amplitude of the oscillatory input from the MS-DBB works as a bifurcation parameter. We model the network of the hippocampal interneurons with a network of simple class 1 neuron models connected mutually with gap junctions. The model neurons exhibit highly synchronous periodic oscillations under the existence of an external force from the MS-DBB, just as the real hippocampus shows theta oscillation under the rhythmic input from the MS-DBB. The model shows diffusion-induced chaotic dynamics under an aperiodic MS-DBB activity, just as the large amplitude irregular activity appears following the disappearance of the rhythmicity of the MS-DBB neurons in the real brain. The model is consistent with both previous experimental findings reporting the existence of local rhythm generators in the hippocampus and the executive role of the MS-DBB in synchronizing theta oscillation in vivo. Our model also replicates the traveling waves of theta oscillations in two-dimensionally coupled networks.

A Hippocampal Model for Behavioral Time Acquisition and Fast Bidirectional Replay of Spatio-Temporal Memory Sequences

The hippocampus is known to play a crucial role in the formation of long-term memory. For this, fast replays of previously experienced activities during sleep or after reward experiences are believed to be crucial. But how such replays are generated is still completely unclear. In this paper we propose a possible mechanism for this: we present a model that can store experienced trajectories on a behavioral timescale after a single run, and can subsequently bidirectionally replay such trajectories, thereby omitting any specifics of the previous behavior like speed, etc, but allowing repetitions of events, even with different subsequent events. Our solution builds on well-known concepts, one-shot learning and synfire chains, enhancing them by additional mechanisms using global inhibition and disinhibition. For replays our approach relies on dendritic spikes and cholinergic modulation, as supported by experimental data. We also hypothesize a functional role of disinhibition as a pacemaker during behavioral time.

Theta-modulation drives the emergence of connectivity patterns underlying replay in a network model of place cells

Place cells of the rodent hippocampus fire action potentials when the animal traverses a particular spatial location in any environment. Therefore for any given trajectory one observes a repeatable sequence of place cell activations. When the animal is quiescent or sleeping, one can observe similar sequences of activation known as replay, which underlie the process of memory consolidation. However, it remains unclear how replay is generated. Here we show how a temporally asymmetric plasticity rule during spatial exploration gives rise to spontaneous replay in a model network by shaping the recurrent connectivity to reflect the topology of the learned environment. Crucially, the rate of this encoding is strongly modulated by ongoing rhythms. Oscillations in the theta range optimize learning by generating repeated pre-post pairings on a time-scale commensurate with the window for plasticity, while lower and higher frequencies generate learning rates which are lower by orders of magnitude.

Computational models of memory consolidation and long-term synaptic plasticity during sleep

The brain stores memories by persistently changing the connectivity between neurons. Sleep is known to be critical for these changes to endure. Research on the neurobiology of sleep and the mechanisms of long-term synaptic plasticity has provided data in support of various theories of how brain activity during sleep affects long-term synaptic plasticity. The experimental findings - and therefore the theories - are apparently quite contradictory, with some evidence pointing to a role of sleep in the forgetting of irrelevant memories, whereas other results indicate that sleep supports the reinforcement of the most valuable recollections. A unified theoretical framework is in need. Computational modeling and simulation provide grounds for the quantitative testing and comparison of theoretical predictions and observed data, and might serve as a strategy to organize the rather complicated and diverse pool of data and methodologies used in sleep research. This review article outlines the emerging progress in the computational modeling and simulation of the main theories on the role of sleep in memory consolidation.

Dendritic processing of spontaneous neuronal sequences for single-trial learning

Spontaneous firing sequences are ubiquitous in cortical networks, but their roles in cellular and network-level computations remain unexplored. In the hippocampus, such sequences, conventionally called preplay, have been hypothesized to participate in learning and memory. Here, we present a computational model for encoding input sequence patterns into internal network states based on the propagation of preplay sequences in recurrent neuronal networks. The model instantiates two synaptic pathways in cortical neurons, one for proximal dendrite-somatic interactions to generate intrinsic preplay sequences and the other for distal dendritic processing of extrinsic signals. The core dendritic computation is the maximization of matching between patterned activities in the two compartments through nonlinear spike generation. The model performs robust single-trial learning with long-term stability and independence that are modulated by the plasticity of dendrite-targeted inhibition. Our results demonstrate that dendritic computation enables somatic spontaneous firing sequences to act as templates for rapid and stable memory formation.

Storage fidelity for sequence memory in the hippocampal circuit

Episodic memories have been suggested to be represented by neuronal sequences, which are stored and retrieved from the hippocampal circuit. A special difficulty is that realistic neuronal sequences are strongly correlated with each other since computational memory models generally perform poorly when correlated patterns are stored. Here, we study in a computational model under which conditions the hippocampal circuit can perform this function robustly. During memory encoding, CA3 sequences in our model are driven by intrinsic dynamics, entorhinal inputs, or a combination of both. These CA3 sequences are hetero-associated with the input sequences, so that the network can retrieve entire sequences based on a single cue pattern. We find that overall memory performance depends on two factors: the robustness of sequence retrieval from CA3 and the circuit's ability to perform pattern completion through the feedforward connectivity, including CA3, CA1 and EC. The two factors, in turn, depend on the relative contribution of the external inputs and recurrent drive on CA3 activity. In conclusion, memory performance in our network model critically depends on the network architecture and dynamics in CA3.

Recurrent network model for learning goal-directed sequences through reverse replay

Reverse replay of hippocampal place cells occurs frequently at rewarded locations, suggesting its contribution to goal-directed path learning. Symmetric spike-timing dependent plasticity (STDP) in CA3 likely potentiates recurrent synapses for both forward (start to goal) and reverse (goal to start) replays during sequential activation of place cells. However, how reverse replay selectively strengthens forward synaptic pathway is unclear. Here, we show computationally that firing sequences bias synaptic transmissions to the opposite direction of propagation under symmetric STDP in the co-presence of short-term synaptic depression or afterdepolarization. We demonstrate that significant biases are created in biologically realistic simulation settings, and this bias enables reverse replay to enhance goal-directed spatial memory on a W-maze. Further, we show that essentially the same mechanism works in a two-dimensional open field. Our model for the first time provides the mechanistic account for the way reverse replay contributes to hippocampal sequence learning for reward-seeking spatial navigation.

Bridging structure and function: A model of sequence learning and prediction in primary visual cortex

Recent experiments have demonstrated that visual cortex engages in spatio-temporal sequence learning and prediction. The cellular basis of this learning remains unclear, however. Here we present a spiking neural network model that explains a recent study on sequence learning in the primary visual cortex of rats. The model posits that the sequence learning and prediction abilities of cortical circuits result from the interaction of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. It also reproduces changes in stimulus-evoked multi-unit activity during learning. Furthermore, it makes precise predictions regarding how training shapes network connectivity to establish its prediction ability. Finally, it predicts that the adapted connectivity gives rise to systematic changes in spontaneous network activity. Taken together, our model establishes a new conceptual bridge between the structure and function of cortical circuits in the context of sequence learning and prediction.

A central goal of Neuroscience is to understand the relationship between the structure and function of brain networks. Of particular interest are the circuits of the neocortex, the seat of our highest cognitive abilities. Here we provide a new link between the structure and function of neocortical circuits in the context of sequence learning. We study a spiking neural network model that self-organizes its connectivity and activity via a combination of different plasticity mechanisms known to operate in cortical circuits. We use this model to explain various findings from a recent experimental study on sequence learning and prediction in rat visual cortex. Our model reproduces the changes in activity patterns as the animal learns the sequential pattern of visual stimulation. In addition, the model predicts what stimulation-induced structural changes underlie this sequence learning ability. Finally, the model also predicts how the adapted network structure influences spontaneous network activity when there is no visual stimulation. Hence, our model provides new insights about the relationship between structure and function of cortical circuits.

Circuit mechanisms of hippocampal reactivation during sleep

The hippocampus is important for memory and learning, being a brain site where initial memories are formed and where sharp wave - ripples (SWR) are found, which are responsible for mapping recent memories to long-term storage during sleep-related memory replay. While this conceptual schema is well established, specific intrinsic and network-level mechanisms driving spatio-temporal patterns of hippocampal activity during sleep, and specifically controlling off-line memory reactivation are unknown. In this study, we discuss a model of hippocampal CA1-CA3 network generating spontaneous characteristic SWR activity. Our study predicts the properties of CA3 input which are necessary for successful CA1 ripple generation and the role of synaptic interactions and intrinsic excitability in spike sequence replay during SWRs. Specifically, we found that excitatory synaptic connections promote reactivation in both CA3 and CA1, but the different dynamics of sharp waves in CA3 and ripples in CA1 result in a differential role for synaptic inhibition in modulating replay: promoting spike sequence specificity in CA3 but not in CA1 areas. Finally, we describe how awake learning of spatial trajectories leads to synaptic changes sufficient to drive hippocampal cells' reactivation during sleep, as required for sleep-related memory consolidation.

Long term potentiation, but not depression, in interlamellar hippocampus CA1

Synaptic plasticity in the lamellar CA3 to CA1 circuitry has been extensively studied while interlamellar CA1 to CA1 connections have not yet received much attention. One of our earlier studies demonstrated that axons of CA1 pyramidal neurons project to neighboring CA1 neurons, implicating information transfer along a longitudinal interlamellar network. Still, it remains unclear whether long-term synaptic plasticity is present within this longitudinal CA1 network. Here, we investigate long-term synaptic plasticity between CA1 pyramidal cells, using in vitro and in vivo extracellular recordings and 3D holography glutamate uncaging. We found that the CA1-CA1 network exhibits NMDA receptor-dependent long-term potentiation (LTP) without direction or layer selectivity. By contrast, we find no significant long-term depression (LTD) under various LTD induction protocols. These results implicate unique synaptic properties in the longitudinal projection suggesting that the interlamellar CA1 network could be a promising structure for hippocampus-related information processing and brain diseases.

Pacing Hippocampal Sharp-Wave Ripples With Weak Electric Stimulation

Sharp-wave ripples (SWRs) are spontaneous neuronal population events that occur in the hippocampus during sleep and quiet restfulness, and are thought to play a critical role in the consolidation of episodic memory. SWRs occur at a rate of 30-200 events per minute. Their overall abundance may, however, be reduced with aging and neurodegenerative disease. Here we report that the abundance of SWR within murine hippocampal slices can be increased by paced administration of a weak electrical stimulus, especially when the spontaneously occurring rate is low or compromised. Resultant SWRs have large variations in amplitude and ripple patterns, which are morphologically indistinguishable from those of spontaneous SWRs, despite identical stimulus parameters which presumably activate the same CA3 neurons surrounding the electrode. The stimulus intensity for reliably pacing SWRs is weaker than that required for inducing detectable evoked field potentials in CA1. Moreover, repetitive ~1 Hz stimuli with low intensity can reliably evoke thousands of SWRs without detectable LTD or "habituation." Our results suggest that weak stimuli may facilitate the spontaneous emergence of SWRs without significantly altering their characteristics. Pacing SWRs with weak electric stimuli could potentially be useful for restoring their abundance in the damaged hippocampus.

Dendrites, deep learning, and sequences in the hippocampus

The hippocampus places us both in time and space. It does so over remarkably large spans: milliseconds to years, and centimeters to kilometers. This works for sensory representations, for memory, and for behavioral context. How does it fit in such wide ranges of time and space scales, and keep order among the many dimensions of stimulus context? A key organizing principle for a wide sweep of scales and stimulus dimensions is that of order in time, or sequences. Sequences of neuronal activity are ubiquitous in sensory processing, in motor control, in planning actions, and in memory. Against this strong evidence for the phenomenon, there are currently more models than definite experiments about how the brain generates ordered activity. The flip side of sequence generation is discrimination. Discrimination of sequences has been extensively studied at the behavioral, systems, and modeling level, but again physiological mechanisms are fewer. It is against this backdrop that I discuss two recent developments in neural sequence computation, that at face value share little beyond the label "neural." These are dendritic sequence discrimination, and deep learning. One derives from channel physiology and molecular signaling, the other from applied neural network theory - apparently extreme ends of the spectrum of neural circuit detail. I suggest that each of these topics has deep lessons about the possible mechanisms, scales, and capabilities of hippocampal sequence computation.

Predictive Place-Cell Sequences for Goal-Finding Emerge from Goal Memory and the Cognitive Map: A Computational Model

Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, we propose a model of spatial learning and sequence generation as interdependent processes, integrating cortical contextual coding, synaptic plasticity and neuromodulatory mechanisms into a map-based approach. Following goal learning, sequential activity emerges from continuous attractor network dynamics biased by goal memory inputs. We apply Bayesian decoding on the resulting spike trains, allowing a direct comparison with experimental data. Simulations show that this model (1) explains the generation of never-experienced sequence trajectories in familiar environments, without requiring virtual self-motion signals, (2) accounts for the bias in place-cell sequences toward goal locations, (3) highlights their utility in flexible route planning, and (4) provides specific testable predictions.

Modelling plasticity in dendrites: from single cells to networks

One of the key questions in neuroscience is how our brain self-organises to efficiently process information. To answer this question, we need to understand the underlying mechanisms of plasticity and their role in shaping synaptic connectivity. Theoretical neuroscience typically investigates plasticity on the level of neural networks. Neural network models often consist of point neurons, completely neglecting neuronal morphology for reasons of simplicity. However, during the past decades it became increasingly clear that inputs are locally processed in the dendrites before they reach the cell body. Dendritic properties enable local interactions between synapses and location-dependent modulations of inputs, rendering the position of synapses on dendrites highly important. These insights changed our view of neurons, such that we now think of them as small networks of nearly independent subunits instead of a simple point. Here, we propose that understanding how the brain processes information strongly requires that we consider the following properties: which plasticity mechanisms are present in the dendrites and how do they enable the self-organisation of synapses across the dendritic tree for efficient information processing? Ultimately, dendritic plasticity mechanisms can be studied in networks of neurons with dendrites, possibly uncovering unknown mechanisms that shape the connectivity in our brains.

Neural Representation. A Survey-Based Analysis of the Notion

The word representation (as in “neural representation”), and many of its related terms, such as to represent, representational and the like, play a central explanatory role in neuroscience literature. For instance, in “place cell” literature, place cells are extensively associated with their role in “the representation of space.” In spite of its extended use, we still lack a clear, universal and widely accepted view on what it means for a nervous system to represent something, on what makes a neural activity a representation, and on what is re-presented. The lack of a theoretical foundation and definition of the notion has not hindered actual research. My aim here is to identify how active scientists use the notion of neural representation, and eventually to list a set of criteria, based on actual use, that can help in distinguishing between genuine or non-genuine neural-representation candidates. In order to attain this objective, I present first the results of a survey of authors within two domains, place-cell and multivariate pattern analysis (MVPA) research. Based on the authors’ replies, and on a review of neuroscientific research, I outline a set of common properties that an account of neural representation seems to require. I then apply these properties to assess the use of the notion in two domains of the survey, place-cell and MVPA studies. I conclude by exploring a shift in the notion of representation suggested by recent literature.

The malleable brain: plasticity of neural circuits and behavior - a review from students to students

One of the most intriguing features of the brain is its ability to be malleable, allowing it to adapt continually to changes in the environment. Specific neuronal activity patterns drive long-lasting increases or decreases in the strength of synaptic connections, referred to as long-term potentiation and long-term depression, respectively. Such phenomena have been described in a variety of model organisms, which are used to study molecular, structural, and functional aspects of synaptic plasticity. This review originated from the first International Society for Neurochemistry (ISN) and Journal of Neurochemistry (JNC) Flagship School held in Alpbach, Austria (Sep 2016), and will use its curriculum and discussions as a framework to review some of the current knowledge in the field of synaptic plasticity. First, we describe the role of plasticity during development and the persistent changes of neural circuitry occurring when sensory input is altered during critical developmental stages. We then outline the signaling cascades resulting in the synthesis of new plasticity-related proteins, which ultimately enable sustained changes in synaptic strength. Going beyond the traditional understanding of synaptic plasticity conceptualized by long-term potentiation and long-term depression, we discuss system-wide modifications and recently unveiled homeostatic mechanisms, such as synaptic scaling. Finally, we describe the neural circuits and synaptic plasticity mechanisms driving associative memory and motor learning. Evidence summarized in this review provides a current view of synaptic plasticity in its various forms, offers new insights into the underlying mechanisms and behavioral relevance, and provides directions for future research in the field of synaptic plasticity. Read the Editorial Highlight for this article on page 788. Cover Image for this issue: doi: 10.1111/jnc.13815.

Memory replay in balanced recurrent networks

Complex patterns of neural activity appear during up-states in the neocortex and sharp waves in the hippocampus, including sequences that resemble those during prior behavioral experience. The mechanisms underlying this replay are not well understood. How can small synaptic footprints engraved by experience control large-scale network activity during memory retrieval and consolidation? We hypothesize that sparse and weak synaptic connectivity between Hebbian assemblies are boosted by pre-existing recurrent connectivity within them. To investigate this idea, we connect sequences of assemblies in randomly connected spiking neuronal networks with a balance of excitation and inhibition. Simulations and analytical calculations show that recurrent connections within assemblies allow for a fast amplification of signals that indeed reduces the required number of inter-assembly connections. Replay can be evoked by small sensory-like cues or emerge spontaneously by activity fluctuations. Global-potentially neuromodulatory-alterations of neuronal excitability can switch between network states that favor retrieval and consolidation.

Effects of Calcium Spikes in the Layer 5 Pyramidal Neuron on Coincidence Detection and Activity Propagation

The role of dendritic spiking mechanisms in neural processing is so far poorly understood. To investigate the role of calcium spikes in the functional properties of the single neuron and recurrent networks, we investigated a three compartment neuron model of the layer 5 pyramidal neuron with calcium dynamics in the distal compartment. By performing single neuron simulations with noisy synaptic input and occasional large coincident input at either just the distal compartment or at both somatic and distal compartments, we show that the presence of calcium spikes confers a substantial advantage for coincidence detection in the former case and a lesser advantage in the latter. We further show that the experimentally observed critical frequency phenomenon, in which action potentials triggered by stimuli near the soma above a certain frequency trigger a calcium spike at distal dendrites, leading to further somatic depolarization, is not exhibited by a neuron receiving realistically noisy synaptic input, and so is unlikely to be a necessary component of coincidence detection. We next investigate the effect of calcium spikes in propagation of spiking activities in a feed-forward network (FFN) embedded in a balanced recurrent network. The excitatory neurons in the network are again connected to either just the distal, or both somatic and distal compartments. With purely distal connectivity, activity propagation is stable and distinguishable for a large range of recurrent synaptic strengths if the feed-forward connections are sufficiently strong, but propagation does not occur in the absence of calcium spikes. When connections are made to both the somatic and the distal compartments, activity propagation is achieved for neurons with active calcium dynamics at a much smaller number of neurons per pool, compared to a network of passive neurons, but quickly becomes unstable as the strength of recurrent synapses increases. Activity propagation at higher scaling factors can be stabilized by increasing network inhibition or introducing short term depression in the excitatory synapses, but the signal to noise ratio remains low. Our results demonstrate that the interaction of synchrony with dendritic spiking mechanisms can have profound consequences for the dynamics on the single neuron and network level.

Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex

Pyramidal neurons represent the majority of excitatory neurons in the neocortex. Each pyramidal neuron receives input from thousands of excitatory synapses that are segregated onto dendritic branches. The dendrites themselves are segregated into apical, basal, and proximal integration zones, which have different properties. It is a mystery how pyramidal neurons integrate the input from thousands of synapses, what role the different dendrites play in this integration, and what kind of network behavior this enables in cortical tissue. It has been previously proposed that non-linear properties of dendrites enable cortical neurons to recognize multiple independent patterns. In this paper we extend this idea in multiple ways. First we show that a neuron with several thousand synapses segregated on active dendrites can recognize hundreds of independent patterns of cellular activity even in the presence of large amounts of noise and pattern variation. We then propose a neuron model where patterns detected on proximal dendrites lead to action potentials, defining the classic receptive field of the neuron, and patterns detected on basal and apical dendrites act as predictions by slightly depolarizing the neuron without generating an action potential. By this mechanism, a neuron can predict its activation in hundreds of independent contexts. We then present a network model based on neurons with these properties that learns time-based sequences. The network relies on fast local inhibition to preferentially activate neurons that are slightly depolarized. Through simulation we show that the network scales well and operates robustly over a wide range of parameters as long as the network uses a sparse distributed code of cellular activations. We contrast the properties of the new network model with several other neural network models to illustrate the relative capabilities of each. We conclude that pyramidal neurons with thousands of synapses, active dendrites, and multiple integration zones create a robust and powerful sequence memory. Given the prevalence and similarity of excitatory neurons throughout the neocortex and the importance of sequence memory in inference and behavior, we propose that this form of sequence memory may be a universal property of neocortical tissue.

When less is more: non-monotonic spike sequence processing in neurons

Fundamental response properties of neurons centrally underly the computational capabilities of both individual nerve cells and neural networks. Most studies on neuronal input-output relations have focused on continuous-time inputs such as constant or noisy sinusoidal currents. Yet, most neurons communicate via exchanging action potentials (spikes) at discrete times. Here, we systematically analyze the stationary spiking response to regular spiking inputs and reveal that it is generically non-monotonic. Our theoretical analysis shows that the underlying mechanism relies solely on a combination of the discrete nature of the communication by spikes, the capability of locking output to input spikes and limited resources required for spike processing. Numerical simulations of mathematically idealized and biophysically detailed models, as well as neurophysiological experiments confirm and illustrate our theoretical predictions.