Reactivation of behavioral activity during sharp waves: A computational model for two stage hippocampal dynamics

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.

Tau trajectory in Alzheimer's disease: Evidence from the connectome-based computational models

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an impairment of cognition and memory. Current research on connectomics have now related changes in the network organization in AD to the patterns of accumulation and spread of amyloid and tau, providing insights into the neurobiological mechanisms of the disease. In addition, network analysis and modeling focus on particular use of graphs to provide intuition into key organizational principles of brain structure, that stipulate how neural activity propagates along structural connections. The utility of connectome-based computational models aids in early predicting, tracking the progression of biomarker-directed AD neuropathology. In this article, we present a short review of tau trajectory, the connectome changes in tau pathology, and the dependent recent connectome-based computational modelling approaches for tau spreading, reproducing pragmatic findings, and developing significant novel tau targeted therapies.

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.

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.

Fast and flexible sequence induction in spiking neural networks via rapid excitability changes

Cognitive flexibility likely depends on modulation of the dynamics underlying how biological neural networks process information. While dynamics can be reshaped by gradually modifying connectivity, less is known about mechanisms operating on faster timescales. A compelling entrypoint to this problem is the observation that exploratory behaviors can rapidly cause selective hippocampal sequences to 'replay' during rest. Using a spiking network model, we asked whether simplified replay could arise from three biological components: fixed recurrent connectivity; stochastic 'gating' inputs; and rapid gating input scaling via long-term potentiation of intrinsic excitability (LTP-IE). Indeed, these enabled both forward and reverse replay of recent sensorimotor-evoked sequences, despite unchanged recurrent weights. LTP-IE 'tags' specific neurons with increased spiking probability under gating input, and ordering is reconstructed from recurrent connectivity. We further show how LTP-IE can implement temporary stimulus-response mappings. This elucidates a novel combination of mechanisms that might play a role in rapid cognitive flexibility.

Remembering rewarding futures: A simulation-selection model of the hippocampus

Despite tremendous progress, the neural circuit dynamics underlying hippocampal mnemonic processing remain poorly understood. We propose a new model for hippocampal function-the simulation-selection model-based on recent experimental findings and neuroecological considerations. Under this model, the mammalian hippocampus evolved to simulate and evaluate arbitrary navigation sequences. Specifically, we suggest that CA3 simulates unexperienced navigation sequences in addition to remembering experienced ones, and CA1 selects from among these CA3-generated sequences, reinforcing those that are likely to maximize reward during offline idling states. High-value sequences reinforced in CA1 may allow flexible navigation toward a potential rewarding location during subsequent navigation. We argue that the simulation-selection functions of the hippocampus have evolved in mammals mostly because of the unique navigational needs of land mammals. Our model may account for why the mammalian hippocampus has evolved not only to remember, but also to imagine episodes, and how this might be implemented in its neural circuits.

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.

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.

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

Hippocampal activity is fundamental for episodic memory formation and consolidation. During phases of rest and sleep, it exhibits sharp-wave/ripple (SPW/R) complexes, which are short episodes of increased activity with superimposed high-frequency oscillations. Simultaneously, spike sequences reflecting previous behavior, such as traversed trajectories in space, are replayed. Whereas these phenomena are thought to be crucial for the formation and consolidation of episodic memory, their neurophysiological mechanisms are not well understood. Here we present a unified model showing how experience may be stored and thereafter replayed in association with SPW/Rs. We propose that replay and SPW/Rs are tightly interconnected as they mutually generate and support each other. The underlying mechanism is based on the nonlinear dendritic computation attributable to dendritic sodium spikes that have been prominently found in the hippocampal regions CA1 and CA3, where SPW/Rs and replay are also generated. Besides assigning SPW/Rs a crucial role for replay and thus memory processing, the proposed mechanism also explains their characteristic features, such as the oscillation frequency and the overall wave form. The results shed a new light on the dynamical aspects of hippocampal circuit learning.

SIGNIFICANCE STATEMENT

During phases of rest and sleep, the hippocampus, the "memory center" of the brain, generates intermittent patterns of strongly increased overall activity with high-frequency oscillations, the so-called sharp-wave/ripples. We investigate their role in learning and memory processing. They occur together with replay of activity sequences reflecting previous behavior. Developing a unifying computational model, we propose that both phenomena are tightly linked, by mutually generating and supporting each other. The underlying mechanism depends on nonlinear amplification of synchronous inputs that has been prominently found in the hippocampus. Besides assigning sharp-wave/ripples a crucial role for replay generation and thus memory processing, the proposed mechanism also explains their characteristic features, such as the oscillation frequency and the overall wave form.

Memory trace replay: the shaping of memory consolidation by neuromodulation

The consolidation of memories for places and events is thought to rely, at the network level, on the replay of spatially tuned neuronal firing patterns representing discrete places and spatial trajectories. This occurs in the hippocampal-entorhinal circuit during sharp wave ripple events (SWRs) that occur during sleep or rest. Here, we review theoretical models of lingering place cell excitability and behaviorally induced synaptic plasticity within cell assemblies to explain which sequences or places are replayed. We further provide new insights into how fluctuations in cholinergic tone during different behavioral states might shape the direction of replay and how dopaminergic release in response to novelty or reward can modulate which cell assemblies are replayed.

Transition between encoding and consolidation/replay dynamics via cholinergic modulation of CAN current: A modeling study

Hippocampal place cells that are activated sequentially during active waking get reactivated in a temporally compressed (5-20 times) manner during slow-wave-sleep and quiet waking. The two-stage model of the hippocampus suggests that neural activity during awaking supports encoding function while temporally compressed reactivation (replay) supports consolidation. However, the mechanisms supporting different neural activity with different temporal scales during encoding and consolidation remain unclear. Based on the idea that acetylcholine modulates functional transition between encoding and consolidation, we tested whether the cholinergic modulation may adjust intrinsic network dynamics to support different temporal scales for these two modes of operation. Simulations demonstrate that cholinergic modulation of the calcium activated non-specific cationic (CAN) current and the synaptic transmission may be sufficient to switch the network dynamics between encoding and consolidation modes. When the CAN current is active and the synaptic transmission is suppressed, mimicking the high acetylcholine condition during active waking, a slow propagation of multiple spikes is evident. This activity resembles the firing pattern of place cells and time cells during active waking. On the other hand, when CAN current is suppressed and the synaptic transmission is intact, mimicking the low acetylcholine condition during slow-wave-sleep, a time compressed fast (∼10 times) activity propagation of the same set of cells is evident. This activity resembles the time compressed firing pattern of place cells during replay and pre-play, achieving a temporal compression factor in the range observed in vivo (5-20 times). These observations suggest that cholinergic system could adjust intrinsic network dynamics suitable for encoding and consolidation through the modulation of the CAN current and synaptic conductance in the hippocampus.

Hippocampal replay captures the unique topological structure of a novel environment

Hippocampal place-cell replay has been proposed as a fundamental mechanism of learning and memory, which might support navigational learning and planning. An important hypothesis of relevance to these proposed functions is that the information encoded in replay should reflect the topological structure of experienced environments; that is, which places in the environment are connected with which others. Here we report several attributes of replay observed in rats exploring a novel forked environment that support the hypothesis. First, we observed that overlapping replays depicting divergent trajectories through the fork recruited the same population of cells with the same firing rates to represent the common portion of the trajectories. Second, replay tended to be directional and to flip the represented direction at the fork. Third, replay-associated sharp-wave-ripple events in the local field potential exhibited substructure that mapped onto the maze topology. Thus, the spatial complexity of our recording environment was accurately captured by replay: the underlying neuronal activities reflected the bifurcating shape, and both directionality and associated ripple structure reflected the segmentation of the maze. Finally, we observed that replays occurred rapidly after small numbers of experiences. Our results suggest that hippocampal replay captures learned information about environmental topology to support a role in navigation.

Synaptic gating at axonal branches, and sharp-wave ripples with replay: a simulation study

Mechanisms of place cell replay occurring during sharp-wave ripples (SPW-Rs) remain obscure due to the fact that ripples in vitro depend on non-synaptic mechanisms, presumably via axo-axonal gap junctions between pyramidal cells. We suggest a model of in vivo SPW-Rs in which synaptic excitatory post-synaptic potentials (EPSPs) control the axonal spiking of cells in SPW-Rs: ripple activity remains hidden in the network of axonal collaterals (connected by gap junctions) due to conduction failures, unless there is a sufficient dendritic EPSP. The EPSP brings the axonal branching point to threshold, and action potentials from the collateral start to propagate to the soma and to the distal axon. The model coherently explains multiple experimental data on SPW-Rs, both in vitro and in vivo. The mechanism of synaptic gating leads to the following implication: a sequence of pyramidal cells can be replayed at ripple frequency by the superposition of subthreshold dendritic EPSPs and ripple activity in the axonal plexus. Replay is demonstrated in both forward and reverse directions. We discuss several testable predictions. In general, the mechanism of synaptic gating suggests that pyramidal cells under certain conditions can act like a transistor.

Generalization through the recurrent interaction of episodic memories: a model of the hippocampal system

In this article, we present a perspective on the role of the hippocampal system in generalization, instantiated in a computational model called REMERGE (recurrency and episodic memory results in generalization). We expose a fundamental, but neglected, tension between prevailing computational theories that emphasize the function of the hippocampus in pattern separation (Marr, 1971; McClelland, McNaughton, & O'Reilly, 1995), and empirical support for its role in generalization and flexible relational memory (Cohen & Eichenbaum, 1993; Eichenbaum, 1999). Our account provides a means by which to resolve this conflict, by demonstrating that the basic representational scheme envisioned by complementary learning systems theory (McClelland et al., 1995), which relies upon orthogonalized codes in the hippocampus, is compatible with efficient generalization-as long as there is recurrence rather than unidirectional flow within the hippocampal circuit or, more widely, between the hippocampus and neocortex. We propose that recurrent similarity computation, a process that facilitates the discovery of higher-order relationships between a set of related experiences, expands the scope of classical exemplar-based models of memory (e.g., Nosofsky, 1984) and allows the hippocampus to support generalization through interactions that unfold within a dynamically created memory space.

The balance of forward and backward hippocampal sequences shifts across behavioral states

Place cell firing patterns in the rat hippocampus are often organized as sequences. Sequences falling within cycles of the theta (6-10 Hz) local field potential (LFP) oscillation represent segments of ongoing behavioral trajectories. Sequences expressed during sharp wave ripple (SWR) complexes represent spatial trajectories through the environment, in both the same direction as actual trajectories (forward sequences) and in an ordering opposite that of behavior (backward sequences). Although hippocampal sequences could fulfill unique functional roles depending on the direction of the sequence and the animal's state when the sequence occurs, quantitative comparisons of sequence direction across behavioral and physiological states within the same experiment, employing consistent methodology, are lacking. Here, we used cross-correlation and Bayesian decoding to measure the direction of hippocampal sequences in rats during active behavior, awake rest and slow-wave sleep. During pretask sleep, few sequences were detected in either direction. Sequences within theta cycles during active behavior were overwhelmingly forward. Sequences during quiescent moments of behavior were both forward and backward, in equal proportion. During postbehavior sleep, sequences were again expressed in both directions, but significantly more forward than backward sequences were detected. The shift in the balance of sequence direction could reflect changing functional demands on the hippocampal network across behavioral and physiological states.

Cholinergic modulation of the CAN current may adjust neural dynamics for active memory maintenance, spatial navigation and time-compressed replay

Suppression of cholinergic receptors and inactivation of the septum impair short-term memory, and disrupt place cell and grid cell activity in the medial temporal lobe (MTL). Location-dependent hippocampal place cell firing during active waking, when the acetylcholine level is high, switches to time-compressed replay activity during quiet waking and slow-wave-sleep (SWS), when the acetylcholine level is low. However, it remains largely unknown how acetylcholine supports short-term memory, spatial navigation, and the functional switch to replay mode in the MTL. In this paper, we focus on the role of the calcium-activated non-specific cationic (CAN) current which is activated by acetylcholine. The CAN current is known to underlie persistent firing, which could serve as a memory trace in many neurons in the MTL. Here, we review the CAN current and discuss possible roles of the CAN current in short-term memory and spatial navigation. We further propose a novel theoretical model where the CAN current switches the hippocampal place cell activity between real-time and time-compressed sequential activity during encoding and consolidation, respectively.

A computational model of the hippocampus that represents environmental structure and goal location, and guides movement

Hippocampal place cells (PCs) are believed to represent environmental structure. However, it is unclear how and which brain regions represent goals and guide movements. Recently, another type of cells that fire around a goal was found in rat hippocampus (we designate these cells as goal place cells, GPCs). This suggests that the hippocampus is also involved in goal representation. Assuming that the activities of GPCs depend on the distance to a goal, we propose an adaptive navigation model. By monitoring the population activity of GPCs, the model navigates to shorten the distance to the goal. To achieve the distance-dependent activities of GPCs, plastic connections are assumed between PCs and GPCs, which are modified depending on two reward-triggered activities: activity propagation through PC-PC network representing the topological environmental structure, and the activity of GPCs with different durations. The former activity propagation is regarded as a computational interpretation of "reverse replay" phenomenon found in rat hippocampus. Simulation results confirm that after reaching a goal only once, the model can navigate to the goal along almost the shortest path from arbitrary places in the environment. This indicates that the hippocampus might play a primary role in the representation of not only the environmental structure but also the goal, in addition to guiding the movement. This navigation strategy using the population activity of GPCs is equivalent to the taxis strategy, the simplest and most basic for biological systems. Our model is unique because this simple strategy allows the model to follow the shortest path in the topological map of the environment.

Spike-timing dependent plasticity and the cognitive map

Since the discovery of place cells – single pyramidal neurons that encode spatial location – it has been hypothesized that the hippocampus may act as a cognitive map of known environments. This putative function has been extensively modeled using auto-associative networks, which utilize rate-coded synaptic plasticity rules in order to generate strong bi-directional connections between concurrently active place cells that encode for neighboring place fields. However, empirical studies using hippocampal cultures have demonstrated that the magnitude and direction of changes in synaptic strength can also be dictated by the relative timing of pre- and post-synaptic firing according to a spike-timing dependent plasticity (STDP) rule. Furthermore, electrophysiology studies have identified persistent “theta-coded” temporal correlations in place cell activity in vivo, characterized by phase precession of firing as the corresponding place field is traversed. It is not yet clear if STDP and theta-coded neural dynamics are compatible with cognitive map theory and previous rate-coded models of spatial learning in the hippocampus. Here, we demonstrate that an STDP rule based on empirical data obtained from the hippocampus can mediate rate-coded Hebbian learning when pre- and post-synaptic activity is stochastic and has no persistent sequence bias. We subsequently demonstrate that a spiking recurrent neural network that utilizes this STDP rule, alongside theta-coded neural activity, allows the rapid development of a cognitive map during directed or random exploration of an environment of overlapping place fields. Hence, we establish that STDP and phase precession are compatible with rate-coded models of cognitive map development.

Dual coding with STDP in a spiking recurrent neural network model of the hippocampus

The firing rate of single neurons in the mammalian hippocampus has been demonstrated to encode for a range of spatial and non-spatial stimuli. It has also been demonstrated that phase of firing, with respect to the theta oscillation that dominates the hippocampal EEG during stereotype learning behaviour, correlates with an animal's spatial location. These findings have led to the hypothesis that the hippocampus operates using a dual (rate and temporal) coding system. To investigate the phenomenon of dual coding in the hippocampus, we examine a spiking recurrent network model with theta coded neural dynamics and an STDP rule that mediates rate-coded Hebbian learning when pre- and post-synaptic firing is stochastic. We demonstrate that this plasticity rule can generate both symmetric and asymmetric connections between neurons that fire at concurrent or successive theta phase, respectively, and subsequently produce both pattern completion and sequence prediction from partial cues. This unifies previously disparate auto- and hetero-associative network models of hippocampal function and provides them with a firmer basis in modern neurobiology. Furthermore, the encoding and reactivation of activity in mutually exciting Hebbian cell assemblies demonstrated here is believed to represent a fundamental mechanism of cognitive processing in the brain.

Changes in the strength of synaptic connections between neurons are believed to mediate processes of learning and memory in the brain. A computational theory of this synaptic plasticity was first provided by Donald Hebb within the context of a more general neural coding mechanism, whereby phase sequences of activity directed by ongoing external and internal dynamics propagate in mutually exciting ensembles of neurons. Empirical evidence for this cell assembly model has been obtained in the hippocampus, where neuronal ensembles encoding for spatial location repeatedly fire in sequence at different phases of the ongoing theta oscillation. To investigate the encoding and reactivation of these dual coded activity patterns, we examine a biologically inspired spiking neural network model of the hippocampus with a novel synaptic plasticity rule. We demonstrate that this allows the rapid development of both symmetric and asymmetric connections between neurons that fire at concurrent or consecutive theta phase respectively. Recall activity, corresponding to both pattern completion and sequence prediction, can subsequently be produced by partial external cues. This allows the reconciliation of two previously disparate classes of hippocampal model and provides a framework for further examination of cell assembly dynamics in spiking neural networks.

Play it again: reactivation of waking experience and memory

Episodic and spatial memories each involve the encoding of complex associations in hippocampal neuronal circuits. Such memory traces could be stabilised from short- to long-term forms by consolidation processes involving the 'reactivation' of the original network firing patterns during sleep and rest. Waking experience can be replayed in many different brain areas, but an important role for the hippocampus lies in the organisation of the 'reactivation' process. Emerging evidence suggests that sharp wave/ripple (SWR) events in the hippocampus could coordinate the reactivation of memory traces and direct their reinstatement in cortical circuits. Although the mechanisms remain uncertain, there is a growing consensus that such SWR-directed reactivation of brain-wide memory traces could underlie memory consolidation.

Early deficits in spatial memory and theta rhythm in experimental temporal lobe epilepsy

Patients with temporal lobe epilepsy (TLE), the most common form of epilepsy in adults, often display cognitive deficits. The time course and underlying mechanisms of cognitive decline remain unknown during epileptogenesis (the process leading to epilepsy). Using the rat pilocarpine model of TLE, we performed a longitudinal study to assess spatial and nonspatial cognitive performance during epileptogenesis. In parallel, we monitored interictal-like activity (ILA) in the hippocampal CA1 region, as well as theta oscillations, a brain rhythm central to numerous cognitive processes. Here, we report that spatial memory was altered soon after pilocarpine-induced status epilepticus, i.e., already during the seizure-free, latent period. Spatial deficits correlated with a decrease in the power of theta oscillations but not with the frequency of ILA. Spatial deficits persisted when animals had spontaneous seizures (chronic stage) without further modification. In contrast, nonspatial memory performances remained unaffected throughout. We conclude that the reorganization of hippocampal circuitry that immediately follows the initial insult can affect theta oscillation mechanisms, in turn, resulting in deficits in hippocampus-dependent memory tasks. These deficits may be dissociated from the process that leads to epilepsy itself but could instead constitute, as ILA, early markers in at-risk patients and/or provide beneficial therapeutic targets.

Working memory dynamics and spontaneous activity in a flip-flop oscillations network model with a Milnor attractor

Many cognitive tasks require the ability to maintain and manipulate simultaneously several chunks of information. Numerous neurobiological observations have reported that this ability, known as the working memory, is associated with both a slow oscillation (leading to the up and down states) and the presence of the theta rhythm. Furthermore, during resting state, the spontaneous activity of the cortex exhibits exquisite spatiotemporal patterns sharing similar features with the ones observed during specific memory tasks. Here to enlighten neural implication of working memory under these complicated dynamics, we propose a phenomenological network model with biologically plausible neural dynamics and recurrent connections. Each unit embeds an internal oscillation at the theta rhythm which can be triggered during up-state of the membrane potential. As a result, the resting state of a single unit is no longer a classical fixed point attractor but rather the Milnor attractor, and multiple oscillations appear in the dynamics of a coupled system. In conclusion, the interplay between the up and down states and theta rhythm endows high potential in working memory operation associated with complexity in spontaneous activities.

The engram formation and the global oscillations of CA3

The investigation on the conditions which cause global population oscillatory activities in neural fields, originated some years ago with reference to a kinetic theory of neural systems, as been further deepened in this paper. In particular, the genesis of sharp waves and of some rhythmic activities, such as theta and gamma rhythms, of the hippocampal CA3 field, behaviorally important for their links to learning and memory, has been analyzed with more details. To this aim, the modeling-computational framework previously devised for the study of activities in large neural fields, has been enhanced in such a way that a greater number of biological features, extended dendritic trees-in particular, could be taken into account. By using that methodology, a two-dimensional model of the entire CA3 field has been described and its activity, as it results from the several external inputs impinging on it, has been simulated. As a consequence of these investigations, some hypotheses have been elaborated about the possible function of global oscillatory activities of neural populations of Hippocampus in the engram formation.

Reversed and forward buffering of behavioral spike sequences enables retrospective and prospective retrieval in hippocampal regions CA3 and CA1

We propose a mechanism to explain both retrospective and prospective recall activity found in experimental data from hippocampal regions CA3 and CA1. Our model of temporal context dependent episodic memory replicates reverse recall in CA1, as recently recorded and published [Foster, D., & Wilson, M. (2006). Reverse replay of behavioural sequences in hippocampal place cells during the awake state. Nature, 440, 680-683], as well as the prospective and retrospective activity recorded in region CA3 during spatial tasks [Johnson, A., & Redish, A. (2006). Neural ensembles in ca3 transiently encode paths forward of the animal at a decision point: a possible mechanism for the consideration of alternatives. In 2006 neuroscience meeting planner. Atlanta, GA: Society for Neuroscience. (Program no. 574.2)]. We suppose that CA3 encodes episodic memory of both forward and reversed sequences of perforant path spikes representing place input. Using a persistent firing buffer mechanism in layer II of entorhinal cortex, simulated episodic learning involves dentate gyrus, layer III of entorhinal cortex, and hippocampal regions CA3 and CA1. Associations are formed between buffered episodic cues, unique temporal context specific representations in dentate gyrus, and episodic memory in the CA3 recurrent network.

The role of protein synthesis in memory consolidation: progress amid decades of debate

A major component of consolidation theory holds that protein synthesis is required to produce the synaptic modification needed for long-term memory storage. Protein synthesis inhibitors have played a pivotal role in the development of this theory. However, these commonly used drugs have unintended effects that have prompted some to reevaluate the role of protein synthesis in memory consolidation. Here we review the role of protein synthesis in memory formation as proposed by consolidation theory calling special attention to the controversy involving the non-specific effects of a group of protein synthesis inhibitors commonly used to study memory formation in vivo. We argue that molecular and genetic approaches that were subsequently applied to the problem of memory formation confirm the results of less selective pharmacological studies. Thus, to a certain extent, the debate over the role of protein synthesis in memory based on interpretational difficulties inherent to the use of protein synthesis inhibitors may be somewhat moot. We conclude by presenting avenues of research we believe will best provide answers to both long-standing and more recent questions facing field of learning and memory.

A model of late long-term potentiation simulates aspects of memory maintenance

Late long-term potentiation (L-LTP) denotes long-lasting strengthening of synapses between neurons. L-LTP appears essential for the formation of long-term memory, with memories at least partly encoded by patterns of strengthened synapses. How memories are preserved for months or years, despite molecular turnover, is not well understood. Ongoing recurrent neuronal activity, during memory recall or during sleep, has been hypothesized to preferentially potentiate strong synapses, preserving memories. This hypothesis has not been evaluated in the context of a mathematical model representing ongoing activity and biochemical pathways important for L-LTP. In this study, ongoing activity was incorporated into two such models – a reduced model that represents some of the essential biochemical processes, and a more detailed published model. The reduced model represents synaptic tagging and gene induction simply and intuitively, and the detailed model adds activation of essential kinases by Ca²⁺. Ongoing activity was modeled as continual brief elevations of Ca²⁺. In each model, two stable states of synaptic strength/weight resulted. Positive feedback between synaptic weight and the amplitude of ongoing Ca²⁺ transients underlies this bistability. A tetanic or theta-burst stimulus switches a model synapse from a low basal weight to a high weight that is stabilized by ongoing activity. Bistability was robust to parameter variations in both models. Simulations illustrated that prolonged periods of decreased activity reset synaptic strengths to low values, suggesting a plausible forgetting mechanism. However, episodic activity with shorter inactive intervals maintained strong synapses. Both models support experimental predictions. Tests of these predictions are expected to further understanding of how neuronal activity is coupled to maintenance of synaptic strength. Further investigations that examine the dynamics of activity and synaptic maintenance can be expected to help in understanding how memories are preserved for up to a lifetime in animals including humans.

A unified view of theta-phase coding in the entorhinal–hippocampal system

The discovery of theta-rhythm-dependent firing of rodent hippocampal neurons highlighted the functional significance of temporal encoding in hippocampal memory. However, earlier theoretical studies on this topic seem divergent and experimental implications are invariably complicated. To obtain a unified understanding of neural dynamics in the hippocampal memory, we here review recent developments in computational models and experimental discoveries on the 'theta-phase precession' of hippocampal place cells and entorhinal grid cells. We identify a theoretical hypothesis that is well supported by experimental facts; this model reveals a significant contribution of theta-phase coding to the on-line real-time operation of episodic events, through highly parallel representation of spatiotemporal information.