The involvement of recurrent connections in area CA3 in establishing the properties of place fields: a model

State transitions in the statistically stable place cell population correspond to rate of perceptual change

The hippocampus occupies a central role in mammalian navigation and memory. Yet an understanding of the rules that govern the statistics and granularity of the spatial code, as well as its interactions with perceptual stimuli, is lacking. We analyzed CA1 place cell activity recorded while rats foraged in different large-scale environments. We found that place cell activity was subject to an unexpected but precise homeostasis—the distribution of activity in the population as a whole being constant at all locations within and between environments. Using a virtual reconstruction of the largest environment, we showed that the rate of transition through this statistically stable population matches the rate of change in the animals’ visual scene. Thus, place fields near boundaries were small but numerous, while in the environment’s interior, they were larger but more dispersed. These results indicate that hippocampal spatial activity is governed by a small number of simple laws and, in particular, suggest the presence of an information-theoretic bound imposed by perception on the fidelity of the spatial memory system.

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Neural activity in rodent CA1 place cell populations is homeostatically balanced

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Hippocampal place field size and frequency are governed by proximity to boundaries

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Transition rate through place cell population matches rate of change in visual scene

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.

Remembering to Forget: A Dual Role for Sleep Oscillations in Memory Consolidation and Forgetting

It has been known since the time of patient H. M. and Karl Lashley's equipotentiality studies that the hippocampus and cortex serve mnestic functions. Current memory models maintain that these two brain structures accomplish unique, but interactive, memory functions. Specifically, most modeling suggests that memories are rapidly acquired during waking experience by the hippocampus, before being later consolidated into the cortex for long-term storage. Sleep has been shown to be critical for the transfer and consolidation of memories in the cortex. Like memory consolidation, a role for sleep in adaptive forgetting has both historical precedent, as Francis Crick suggested in 1983 that sleep was for "reverse-learning," and recent empirical support. In this article I review the evidence indicating that the same brain activity involved in sleep replay associated memory consolidation is responsible for sleep-dependent forgetting. In reviewing the literature, it became clear that both a cellular mechanism for systems consolidation and an agreed upon general, as well as cellular, mechanism for sleep-dependent forgetting is seldom discussed or is lacking. I advocate here for a candidate cellular systems consolidation mechanism wherein changes in calcium kinetics and the activation of consolidative signaling cascades arise from the triple phase locking of non-rapid eye movement sleep (NREMS) slow oscillation, sleep spindle and sharp-wave ripple rhythms. I go on to speculatively consider several sleep stage specific forgetting mechanisms and conclude by discussing a notional function of NREM-rapid eye movement sleep (REMS) cycling. The discussed model argues that the cyclical organization of sleep functions to first lay down and edit and then stabilize and integrate engrams. All things considered, it is increasingly clear that hallmark sleep stage rhythms, including several NREMS oscillations and the REMS hippocampal theta rhythm, serve the dual function of enabling simultaneous memory consolidation and adaptive forgetting. Specifically, the same sleep rhythms that consolidate new memories, in the cortex and hippocampus, simultaneously organize the adaptive forgetting of older memories in these brain regions.

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.

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.

Hexagonal Grid Fields Optimally Encode Transitions in Spatiotemporal Sequences

Grid cells of the rodent entorhinal cortex are essential for spatial navigation. Although their function is commonly believed to be either path integration or localization, the origin or purpose of their hexagonal firing fields remains disputed. Here they are proposed to arise as an optimal encoding of transitions in sequences. First, storage requirements for transitions in general episodic sequences are examined using propositional logic and graph theory. Subsequently, transitions in complete metric spaces are considered under the assumption of an ideal sampling of an input space. It is shown that memory capacity of neurons that have to encode multiple feasible spatial transitions is maximized by a hexagonal pattern. Grid cells are proposed to encode spatial transitions in spatiotemporal sequences, with the entorhinal-hippocampal loop forming a multitransition system.

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.

Analysis of an Attractor Neural Network's Response to Conflicting External Inputs

The theory of attractor neural networks has been influential in our understanding of the neural processes underlying spatial, declarative, and episodic memory. Many theoretical studies focus on the inherent properties of an attractor, such as its structure and capacity. Relatively little is known about how an attractor neural network responds to external inputs, which often carry conflicting information about a stimulus. In this paper we analyze the behavior of an attractor neural network driven by two conflicting external inputs. Our focus is on analyzing the emergent properties of the megamap model, a quasi-continuous attractor network in which place cells are flexibly recombined to represent a large spatial environment. In this model, the system shows a sharp transition from the winner-take-all mode, which is characteristic of standard continuous attractor neural networks, to a combinatorial mode in which the equilibrium activity pattern combines embedded attractor states in response to conflicting external inputs. We derive a numerical test for determining the operational mode of the system a priori. We then derive a linear transformation from the full megamap model with thousands of neurons to a reduced 2-unit model that has similar qualitative behavior. Our analysis of the reduced model and explicit expressions relating the parameters of the reduced model to the megamap elucidate the conditions under which the combinatorial mode emerges and the dynamics in each mode given the relative strength of the attractor network and the relative strength of the two conflicting inputs. Although we focus on a particular attractor network model, we describe a set of conditions under which our analysis can be applied to more general attractor neural networks.

An Energy Model of Place Cell Network in Three Dimensional Space

Place cells are important elements in the spatial representation system of the brain. A considerable amount of experimental data and classical models are achieved in this area. However, an important question has not been addressed, which is how the three dimensional space is represented by the place cells. This question is preliminarily surveyed by energy coding method in this research. Energy coding method argues that neural information can be expressed by neural energy and it is convenient to model and compute for neural systems due to the global and linearly addable properties of neural energy. Nevertheless, the models of functional neural networks based on energy coding method have not been established. In this work, we construct a place cell network model to represent three dimensional space on an energy level. Then we define the place field and place field center and test the locating performance in three dimensional space. The results imply that the model successfully simulates the basic properties of place cells. The individual place cell obtains unique spatial selectivity. The place fields in three dimensional space vary in size and energy consumption. Furthermore, the locating error is limited to a certain level and the simulated place field agrees to the experimental results. In conclusion, this is an effective model to represent three dimensional space by energy method. The research verifies the energy efficiency principle of the brain during the neural coding for three dimensional spatial information. It is the first step to complete the three dimensional spatial representing system of the brain, and helps us further understand how the energy efficiency principle directs the locating, navigating, and path planning function of the brain.

Collective Behavior of Place and Non-place Neurons in the Hippocampal Network

Discussions of the hippocampus often focus on place cells, but many neurons are not place cells in any given environment. Here we describe the collective activity in such mixed populations, treating place and non-place cells on the same footing. We start with optical imaging experiments on CA1 in mice as they run along a virtual linear track and use maximum entropy methods to approximate the distribution of patterns of activity in the population, matching the correlations between pairs of cells but otherwise assuming as little structure as possible. We find that these simple models accurately predict the activity of each neuron from the state of all the other neurons in the network, regardless of how well that neuron codes for position. Our results suggest that understanding the neural activity may require not only knowledge of the external variables modulating it but also of the internal network state.

From grid cells to place cells with realistic field sizes

While grid cells in the medial entorhinal cortex (MEC) of rodents have multiple, regularly arranged firing fields, place cells in the cornu ammonis (CA) regions of the hippocampus mostly have single spatial firing fields. Since there are extensive projections from MEC to the CA regions, many models have suggested that a feedforward network can transform grid cell firing into robust place cell firing. However, these models generate place fields that are consistently too small compared to those recorded in experiments. Here, we argue that it is implausible that grid cell activity alone can be transformed into place cells with robust place fields of realistic size in a feedforward network. We propose two solutions to this problem. Firstly, weakly spatially modulated cells, which are abundant throughout EC, provide input to downstream place cells along with grid cells. This simple model reproduces many place cell characteristics as well as results from lesion studies. Secondly, the recurrent connections between place cells in the CA3 network generate robust and realistic place fields. Both mechanisms could work in parallel in the hippocampal formation and this redundancy might account for the robustness of place cell responses to a range of disruptions of the hippocampal circuitry.

Megamap: flexible representation of a large space embedded with nonspatial information by a hippocampal attractor network

The problem of how the hippocampus encodes both spatial and nonspatial information at the cellular network level remains largely unresolved. Spatial memory is widely modeled through the theoretical framework of attractor networks, but standard computational models can only represent spaces that are much smaller than the natural habitat of an animal. We propose that hippocampal networks are built on a basic unit called a "megamap," or a cognitive attractor map in which place cells are flexibly recombined to represent a large space. Its inherent flexibility gives the megamap a huge representational capacity and enables the hippocampus to simultaneously represent multiple learned memories and naturally carry nonspatial information at no additional cost. On the other hand, the megamap is dynamically stable, because the underlying network of place cells robustly encodes any location in a large environment given a weak or incomplete input signal from the upstream entorhinal cortex. Our results suggest a general computational strategy by which a hippocampal network enjoys the stability of attractor dynamics without sacrificing the flexibility needed to represent a complex, changing world.

Noncanonical connections between the subiculum and hippocampal CA1

The hippocampal formation is traditionally viewed as having a feedforward, unidirectional circuit organization that promotes propagation of excitatory processes. While the substantial forward projection from hippocampal CA1 to the subiculum has been very well established, accumulating evidence supports the existence of a significant backprojection pathway comprised of both excitatory and inhibitory elements from the subiculum to CA1. Based on these recently updated anatomical connections, such a backprojection could serve to modulate information processing in hippocampal CA1. Here we review the published anatomical and physiological studies on the subiculum to CA1 backprojection, and present recent conclusive anatomical evidence for the presence of noncanonical subicular projections to CA1. New insights into this understudied pathway will improve our understanding of reciprocal CA1-subicular connections and guide future studies on how the subiculum interacts with CA1 to regulate hippocampal circuit activity and learning and memory behaviors. J. Comp. Neurol. 524:3666-3673, 2016. © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.

Neural Activity Patterns Underlying Spatial Coding in the Hippocampus

The hippocampus is well known as a central site for memory processing-critical for storing and later retrieving the experiences events of daily life so they can be used to shape future behavior. Much of what we know about the physiology underlying hippocampal function comes from spatial navigation studies in rodents, which have allowed great strides in understanding how the hippocampus represents experience at the cellular level. However, it remains a challenge to reconcile our knowledge of spatial encoding in the hippocampus with its demonstrated role in memory-dependent tasks in both humans and other animals. Moreover, our understanding of how networks of neurons coordinate their activity within and across hippocampal subregions to enable the encoding, consolidation, and retrieval of memories is incomplete. In this chapter, we explore how information may be represented at the cellular level and processed via coordinated patterns of activity throughout the subregions of the hippocampal network.

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.

Effects of increasing CREB-dependent transcription on the storage and recall processes in a hippocampal CA1 microcircuit

The involvement of the hippocampus in learning processes and major brain diseases makes it an ideal candidate to investigate possible ways to devise effective therapies for memory-related pathologies like Alzheimer's Disease (AD). It has been previously reported that augmenting CREB activity increases the synaptic Long-Term Potentiation (LTP) magnitude in CA1 pyramidal neurons and their intrinsic excitability in healthy rodents. It has also been suggested that hippocampal CREB signaling is likely to be down-regulated during AD, possibly degrading memory functions. Therefore, the concept of CREB-based memory enhancers, i.e. drugs that would boost memory by activation of CREB, has emerged. Here, using a model of a CA1 microcircuit, we investigate whether hippocampal CA1 pyramidal neuron properties altered by increasing CREB activity may contribute to improve memory storage and recall. With a set of patterns presented to a network, we find that the pattern recall quality under AD-like conditions is significantly better when boosting CREB function with respect to control. The results are robust and consistent upon increasing the synaptic damage expected by AD progression, supporting the idea that the use of CREB-based therapies could provide a new approach to treat AD.

Crosstalk and transitions between multiple spatial maps in an attractor neural network model of the hippocampus: collective motion of the activity

The dynamics of a neural model for hippocampal place cells storing spatial maps is studied. In the absence of external input, depending on the number of cells and on the values of control parameters (number of environments stored, level of neural noise, average level of activity, connectivity of place cells), a "clump" of spatially localized activity can diffuse or remains pinned due to crosstalk between the environments. In the single-environment case, the macroscopic coefficient of diffusion of the clump and its effective mobility are calculated analytically from first principles and corroborated by numerical simulations. In the multienvironment case the heights and the widths of the pinning barriers are analytically characterized with the replica method; diffusion within one map is then in competition with transitions between different maps. Possible mechanisms enhancing mobility are proposed and tested.

Forward and backward inference in spatial cognition

This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of 'lower-level' computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus.

Mechanism of highly synchronized bilateral hippocampal activity

In vivo studies of epileptiform discharges in the hippocampi of rodents have shown that bilateral seizure activity can sometimes be synchronized with very small delays (<2 ms). This observed small time delay of epileptiform activity between the left and right CA3 regions is unexpected given the physiological propagation time across the hemispheres (>6 ms). The goal of this study is to determine the mechanisms of this tight synchronization with in-vitro electrophysiology techniques and computer simulations. The hypothesis of a common source was first eliminated by using an in-vitro preparation containing both hippocampi with a functional ventral hippocampal commissure (VHC) and no other tissue. Next, the hypothesis that a noisy baseline could mask the underlying synchronous activity between the two hemispheres was ruled out by low noise in-vivo recordings and computer simulation of the noisy environment. Then we built a novel bilateral CA3 model to test the hypothesis that the phenomenon of very small left-to-right propagation delay of seizure activity is a product of epileptic cell network dynamics. We found that the commissural tract connectivity could decrease the delay between seizure events recorded from two sides while the activity propagated longitudinally along the CA3 layer thereby yielding delays much smaller than the propagation time between the two sides. The modeling results indicate that both recurrent and feedforward inhibition were required for shortening the bilateral propagation delay and depended critically on the length of the commissural fiber tract as well as the number of cells involved in seizure generation. These combined modeling/experimental studies indicate that it is possible to explain near perfect synchronization between the two hemispheres by taking into account the structure of the hippocampal network.

A cognitive map model based on spatial and goal-oriented mental exploration in rodents

The rodent hippocampus has been used to represent the spatial environment as a cognitive map. Classical theories suggest that the cognitive map is a consequence of assignment of different spatial regions to variant cell populations in the framework of rate coding. The current study constructs a novel computational neural model of the cognitive map based on firing rate coding, as widely appears in associative memory, thus providing an explanation for formation and function of the two types of cognitive maps: the spatial vector map, responsible for self localization and simultaneous updating of detailed information; and the goal-oriented vector map, important in route finding. A proposed intermediate between these two map types was constructed by combining the spatial vector and goal-orientation maps to form an effective and efficient path finding mechanism. Application of such novel cognitive map based path finding methods to a mental exploration model was explored. With adaptation as a driving force, the basic knowledge of the location relationships in the spatial cognitive map was reformed and sent to the goal-oriented cognitive map, thus solving a series of new path problems through mental exploration. This method allows for rapid identification of suitable paths under variant conditions, thus providing a simpler and safer resource for path finding. Additionally, this method also provides an improved basis for potential robotic path finding applications.

The hippocampo-cortical loop: spatio-temporal learning and goal-oriented planning in navigation

We present a neural network model where the spatial and temporal components of a task are merged and learned in the hippocampus as chains of associations between sensory events. The prefrontal cortex integrates this information to build a cognitive map representing the environment. The cognitive map can be used after latent learning to select optimal actions to fulfill the goals of the animal. A simulation of the architecture is made and applied to learning and solving tasks that involve both spatial and temporal knowledge. We show how this model can be used to solve the continuous place navigation task, where a rat has to navigate to an unmarked goal and wait for 2 seconds without moving to receive a reward. The results emphasize the role of the hippocampus for both spatial and timing prediction, and the prefrontal cortex in the learning of goals related to the task.

Attractor dynamics of spatially correlated neural activity in the limbic system

Attractor networks are a popular computational construct used to model different brain systems. These networks allow elegant computations that are thought to represent a number of aspects of brain function. Although there is good reason to believe that the brain displays attractor dynamics, it has proven difficult to test experimentally whether any particular attractor architecture resides in any particular brain circuit. We review models and experimental evidence for three systems in the rat brain that are presumed to be components of the rat's navigational and memory system. Head-direction cells have been modeled as a ring attractor, grid cells as a plane attractor, and place cells both as a plane attractor and as a point attractor. Whereas the models have proven to be extremely useful conceptual tools, the experimental evidence in their favor, although intriguing, is still mostly circumstantial.

Self-organization of multiple spatial and context memories in the hippocampus

One obstacle to understanding the exact processes unfolding inside the hippocampus is that it is still difficult to clearly define what the hippocampus actually does, at the system level. Associated for a long time with the formation of episodic and semantic memories, and with their temporary storage, the hippocampus is also regarded as a structure involved in spatial navigation. These two independent perspectives on the hippocampus are not necessarily exclusive: proposals have been put forward to make them fit into the same conceptual frame. We review both approaches and argue that three critical developments need consideration: (a) recordings of neuronal activity in rodents, revealing beautiful spatial codes expressed in entorhinal cortex, upstream of the hippocampus; (b) comparative behavioral results suggesting, in an evolutionary perspective, qualitative similarity of function across homologous structures with a distinct internal organization; (c) quantitative measures of information, shifting the focus from who does what to how much each neuronal population expresses each code. These developments take the hippocampus away from philosophical discussions of all-or-none cause-effect relations, and into the quantitative mainstream of modern neural science.

The structure of networks that produce the transformation from grid cells to place cells

Since grid cells were discovered in the medial entorhinal cortex, several models have been proposed for the transformation from periodic grids to the punctate place fields of hippocampal place cells. These prior studies have each focused primarily on a particular model structure. By contrast, the goal of this study is to understand the general nature of the solutions that generate the grids-to-places transformation, and to exploit this insight to solve problems that were previously unsolved. First, we derive a family of feedforward networks that generate the grids-to-places transformations. These networks have in common an inverse relationship between the synaptic weights and a grid property that we call the normalized offset. Second, we analyze the solutions of prior models in terms of this novel measure and found to our surprise that almost all prior models yield solutions that can be described by this family of networks. The one exception is a model that is unrealistically sensitive to noise. Third, with this insight into the structure of the solutions, we then construct explicitly solutions for the grids-to-places transformation with multiple spatial maps, that is, with place fields in arbitrary locations either within the same (multiple place fields) or in different (global remapping) enclosures. These multiple maps are possible because the weights are learned or assigned in such a way that a group of weights contributes to spatial specificity in one context but remains spatially unstructured in another context. Fourth, we find parameters such that global remapping solutions can be found by synaptic learning in spiking neurons, despite previous suggestions that this might not be possible. In conclusion, our results demonstrate the power of understanding the structure of the solutions and suggest that we may have identified the structure that is common to all robust solutions of the grids-to-places transformation.

Complementary spatial firing in place cell-interneuron pairs

The hippocampal formation plays a pivotal role in representing the physical environment. While CA1 pyramidal cells display sharply tuned location-specific firing, the activity of many interneurons show weaker but significant spatial modulation. Although hippocampal interneurons were proposed to participate in the representation of space, their interplay with pyramidal cells in formation of spatial maps is not well understood. In this study, we investigated the spatial correlation between CA1 pyramidal cells and putative interneurons recorded concurrently in awake rats. Positively and negatively correlated pairs were found to be simultaneously present in the CA1 region. While pyramidal cell-interneuron pairs with positive spatial correlation showed similar firing maps, negative spatial correlation was often accompanied by complementary place maps, which could occur even in the presence of a monosynaptic excitation between the cells. Thus, location-specific firing increase of hippocampal interneurons is not necessarily a simple product of excitation by a pyramidal cell with a similarly positioned firing field. Based on our observation of pyramidal cells firing selectively in the low firing regions of interneurons, we speculate that the location-specific firing of place cells is partly determined by the location-specific decrease of interneuron activity that can release place cells from inhibition.

A computer model of unitary responses from associational/commissural and perforant path synapses in hippocampal CA3 pyramidal cells

Despite the central position of CA3 pyramidal cells in the hippocampal circuit, the experimental investigation of their synaptic properties has been limited. Recent slice experiments from adult rats characterized AMPA and NMDA receptor unitary synaptic responses in CA3b pyramidal cells. Here, excitatory synaptic activation is modeled to infer biophysical parameters, aid analysis interpretation, explore mechanisms, and formulate predictions by contrasting simulated somatic recordings with experimental data. Reconstructed CA3b pyramidal cells from the public repository NeuroMorpho.Org were used to allow for cell-specific morphological variation. For each cell, synaptic responses were simulated for perforant pathway and associational/commissural synapses. Means and variability for peak amplitude, time-to-peak, and half-height width in these responses were compared with equivalent statistics from experimental recordings. Synaptic responses mediated by AMPA receptors are best fit with properties typical of previously characterized glutamatergic receptors where perforant path synapses have conductances twice that of associational/commissural synapses (0.9 vs. 0.5 nS) and more rapid peak times (1.0 vs. 3.3 ms). Reanalysis of passive-cell experimental traces using the model shows no evidence of a CA1-like increase of associational/commissural AMPA receptor conductance with increasing distance from the soma. Synaptic responses mediated by NMDA receptors are best fit with rapid kinetics, suggestive of NR2A subunits as expected in mature animals. Predictions were made for passive-cell current clamp recordings, combined AMPA and NMDA receptor responses, and local dendritic depolarization in response to unitary stimulations. Models of synaptic responses in active cells suggest altered axial resistivity and the presence of synaptically activated potassium channels in spines.

Distance coding strategies based on the entorhinal grid cell system

Estimating and keeping track of the distance from salient points of the environment are important constituents of the spatial awareness and navigation. In rodents, the majority of principal cells in the hippocampus are known to be correlated with the position of the animal. However, the lack of topography in the hippocampal cognitive map does not support the assumption that connections between these cells are able to store and recall distances between coded positions. In contrast, the firing fields of the grid cells in the medial entorhinal cortex form triangular grids and are organized on metrical principles. We suggest a model in which a hypothesized 'distance cell' population is able to extract metrics from the activity of grid cells. We show that storing the momentary activity pattern of the grid cell system in a freely chosen position by one-shot learning and comparing it to the actual grid activity at other positions results in a distance dependent activity of these cells. The actual distance of the animal from the origin can be decoded directly by selecting the distance cell receiving the largest excitation or indirectly via transmission of local interneurons. We found that direct decoding works up to the longest grid spacing, but fails on smaller scales, while the indirect way provides precise distance determination up to the half of the longest grid spacing. In both cases, simulated distance cells have a multi-peaked, patchy spatial activity pattern consistent with the experimentally observed behavior of granule cells in the dentate gyrus.

Heading-vector navigation based on head-direction cells and path integration

Insect navigation is guided by heading vectors that are computed by path integration. Mammalian navigation models, on the other hand, are typically based on map-like place representations provided by hippocampal place cells. Such models compute optimal routes as a continuous series of locations that connect the current location to a goal. We propose a "heading-vector" model in which head-direction cells or their derivatives serve both as key elements in constructing the optimal route and as the straight-line guidance during route execution. The model is based on a memory structure termed the "shortcut matrix," which is constructed during the initial exploration of an environment when a set of shortcut vectors between sequential pairs of visited waypoint locations is stored. A mechanism is proposed for calculating and storing these vectors that relies on a hypothesized cell type termed an "accumulating head-direction cell." Following exploration, shortcut vectors connecting all pairs of waypoint locations are computed by vector arithmetic and stored in the shortcut matrix. On re-entry, when local view or place representations query the shortcut matrix with a current waypoint and goal, a shortcut trajectory is retrieved. Since the trajectory direction is in head-direction compass coordinates, navigation is accomplished by tracking the firing of head-direction cells that are tuned to the heading angle. Section 1 of the manuscript describes the properties of accumulating head-direction cells. It then shows how accumulating head-direction cells can store local vectors and perform vector arithmetic to perform path-integration-based homing. Section 2 describes the construction and use of the shortcut matrix for computing direct paths between any pair of locations that have been registered in the shortcut matrix. In the discussion, we analyze the advantages of heading-based navigation over map-based navigation. Finally, we survey behavioral evidence that nonhippocampal, heading-based navigation is used in small mammals and humans.

A principle for learning egocentric-allocentric transformation

Numerous single-unit recording studies have found mammalian hippocampal neurons that fire selectively for the animal's location in space, independent of its orientation. The population of such neurons, commonly known as place cells, is thought to maintain an allocentric, or orientation-independent, internal representation of the animal's location in space, as well as mediating long-term storage of spatial memories. The fact that spatial information from the environment must reach the brain via sensory receptors in an inherently egocentric, or viewpoint-dependent, fashion leads to the question of how the brain learns to transform egocentric sensory representations into allocentric ones for long-term memory storage. Additionally, if these long-term memory representations of space are to be useful in guiding motor behavior, then the reverse transformation, from allocentric to egocentric coordinates, must also be learned. We propose that orientation-invariant representations can be learned by neural circuits that follow two learning principles: minimization of reconstruction error and maximization of representational temporal inertia. Two different neural network models are presented that adhere to these learning principles, the first by direct optimization through gradient descent and the second using a more biologically realistic circuit based on the restricted Boltzmann machine (Hinton, 2002; Smolensky, 1986). Both models lead to orientation-invariant representations, with the latter demonstrating place-cell-like responses when trained on a linear track environment.

Path-finding in real and simulated rats: assessing the influence of path characteristics on navigation learning

A large body of experimental evidence suggests that the hippocampal place field system is involved in reward based navigation learning in rodents. Reinforcement learning (RL) mechanisms have been used to model this, associating the state space in an RL-algorithm to the place-field map in a rat. The convergence properties of RL-algorithms are affected by the exploration patterns of the learner. Therefore, we first analyzed the path characteristics of freely exploring rats in a test arena. We found that straight path segments with mean length 23 cm up to a maximal length of 80 cm take up a significant proportion of the total paths. Thus, rat paths are biased as compared to random exploration. Next we designed a RL system that reproduces these specific path characteristics. Our model arena is covered by overlapping, probabilistically firing place fields (PF) of realistic size and coverage. Because convergence of RL-algorithms is also influenced by the state space characteristics, different PF-sizes and densities, leading to a different degree of overlap, were also investigated. The model rat learns finding a reward opposite to its starting point. We observed that the combination of biased straight exploration, overlapping coverage and probabilistic firing will strongly impair the convergence of learning. When the degree of randomness in the exploration is increased, convergence improves, but the distribution of straight path segments becomes unrealistic and paths become 'wiggly'. To mend this situation without affecting the path characteristic two additional mechanisms are implemented: a gradual drop of the learned weights (weight decay) and path length limitation, which prevents learning if the reward is not found after some expected time. Both mechanisms limit the memory of the system and thereby counteract effects of getting trapped on a wrong path. When using these strategies individually divergent cases get substantially reduced and for some parameter settings no divergence was found anymore at all. Using weight decay and path length limitation at the same time, convergence is not much improved but instead time to convergence increases as the memory limiting effect is getting too strong. The degree of improvement relies also on the size and degree of overlap (coverage density) in the place field system. The used combination of these two parameters leads to a trade-off between convergence and speed to convergence. Thus, this study suggests that the role of the PF-system in navigation learning cannot be considered independently from the animals' exploration pattern.

Learning in a geometric model of place cell firing

Following Hartley et al. (Hartley et al. (2000) Hippocampus 10:369-379), we present a simple feed-forward model of place cell (PC) firing predicated on neocortical information regarding the environmental geometry surrounding the animal. Incorporating the idea of boundaries with distinct sensory qualities, we show that synaptic plasticity mediated by a BCM-like rule (Bienenstock et al. (1982) J Neurosci 2:32-48) produces PCs that encode position relative to specific extended landmarks. In an unchanging environment the model is shown to undergo an initial phase of learning, resulting in the formation of stable place fields. In familiar environments, perturbation of environmental cues produces graded changes in the firing rate and position of place fields. Model simulations are compared favorably with three sets of experimental data: (1) Results published by Barry et al. (Barry et al. (2006) Rev Neurosci 17:71-97) showing the slow disappearance of duplicate place fields produced when a barrier is placed into a familiar environment. (2) Rivard et al.'s (Rivard et al. (2004) J Gen Physiol 124:9-25) study showing a graded response in PC firing such that fields near to a centrally placed object encode space relative to the object, whereas more distant fields respond to the surrounding environment. (3) Fenton et al.'s (Fenton et al. (2000a) J Gen Physiol 116:191-209) observation that inconsistent rotation of cue cards produces parametric changes in place field positions. The merits of the model are discussed in terms of its extensibility and biological plausibility.

Theta phase precession emerges from a hybrid computational model of a CA3 place cell

The origins and functional significance of theta phase precession in the hippocampus remain obscure, in part, because of the difficulty of reproducing hippocampal place cell firing in experimental settings where the biophysical underpinnings can be examined in detail. The present study concerns a neurobiologically based computational model of the emergence of theta phase precession in which the responses of a single model CA3 pyramidal cell are examined in the context of stimulation by realistic afferent spike trains including those of place cells in entorhinal cortex, dentate gyrus, and other CA3 pyramidal cells. Spike-timing dependent plasticity in the model CA3 pyramidal cell leads to a spatially correlated associational synaptic drive that subsequently creates a spatially asymmetric expansion of the model cell's place field. Following an initial training period, theta phase precession can be seen in the firing patterns of the model CA3 pyramidal cell. Through selective manipulations of the model it is possible to decompose theta phase precession in CA3 into the separate contributing factors of inheritance from upstream afferents in the dentate gyrus and entorhinal cortex, the interaction of synaptically controlled increasing afferent drive with phasic inhibition, and the theta phase difference between dentate gyrus granule cell and CA3 pyramidal cell activity. In the context of a single CA3 pyramidal cell, the model shows that each of these factors plays a role in theta phase precession within CA3 and suggests that no one single factor offers a complete explanation of the phenomenon. The model also shows parallels between theta phase encoding and pattern completion within the CA3 autoassociative network.

Neural dynamics of the cognitive map in the hippocampus

The rodent hippocampus has been thought to represent the spatial environment as a cognitive map. In the classical theory, the cognitive map has been explained as a consequence of the fact that different spatial regions are assigned to different cell populations in the framework of rate coding. Recently, the relation between place cell firing and local field oscillation theta in terms of theta phase precession was experimentally discovered and suggested as a temporal coding mechanism leading to memory formation of behavioral sequences accompanied with asymmetric Hebbian plasticity. The cognitive map theory is apparently outside of the sequence memory view. Therefore, theoretical analysis is necessary to consider the biological neural dynamics for the sequence encoding of the memory of behavioral sequences, providing the cognitive map formation. In this article, we summarize the theoretical neural dynamics of the real-time sequence encoding by theta phase precession, called theta phase coding, and review a series of theoretical models with the theta phase coding that we previously reported. With respect to memory encoding functions, instantaneous memory formation of one-time experience was first demonstrated, and then the ability of integration of memories of behavioral sequences into a network of the cognitive map was shown. In terms of memory retrieval functions, theta phase coding enables the hippocampus to represent the spatial location in the current behavioral context even with ambiguous sensory input when multiple sequences were coded. Finally, for utilization, retrieved temporal sequences in the hippocampus can be available for action selection, through the process of reverting theta rhythm-dependent activities to information in the behavioral time scale. This theoretical approach allows us to investigate how the behavioral sequences are encoded, updated, retrieved and used in the hippocampus, as the real-time interaction with the external environment. It may indeed be the bridge to the episodic memory function in human hippocampus.

Plastic processes in the dentate gyrus: a computational perspective

The dentate gyrus has the capacity for numerous types of synaptic plasticity that use diverse mechanisms and are thought essential for the storage of information in the hippocampus. Here we review the various forms of synaptic plasticity that involve afferents and efferents of the dentate gyrus, and, from a computational perspective, relate how these plastic processes might contribute to sparse, orthogonal encoding, and the selective recall of information within the hippocampus.

Models, structure, function: the transformation of cortical signals in the dentate gyrus

Our central question is why the hippocampal CA3 region is the only cortical area capable of forming interference-free representations of complex environmental events (episodes), given that apparently all cortical regions have recurrent excitatory circuits with modifiable synapses, the basic substrate for autoassociative memory networks. We review evidence for the radical (but classic) view that a unique transformation of incoming cortical signals by the dentate gyrus and the subsequent faithful transfer of the resulting code by the mossy fibers are absolutely critical for the appropriate association of memory items by CA3 and, in general, for hippocampal function. In particular, at the gate of the hippocampal formation, the dentate gyrus possesses a set of unusual properties, which selectively evolved for the task of code transformation between cortical afferents and the hippocampus. These evolutionarily conserved anatomical features enable the dentate gyrus to translate the noisy signal of the upstream cortical areas into the sparse and specific code of hippocampal formation, which is indispensable for the efficient storage and recall of multiple, multidimensional memory items. To achieve this goal the mossy fiber pathway maximally utilizes the opportunity to differentially regulate its postsynaptic partners. Selective innervation of CA3 pyramidal cells and interneurons by distinct terminal types creates a favorable condition to differentially regulate the short-term and long-term plasticity and the motility of various mossy terminal types. The utility of this highly dynamic system appears to be the frequency-dependent fine-tuning the excitation and inhibition evoked by the large and the small mossy terminals respectively. This will determine exactly which CA3 cell population is active and induces permanent modification in the autoassociational network of the CA3 region.

Rearing on hind legs, environmental novelty, and the hippocampal formation

Many mammals spontaneously rear on their hind legs in response to novelty. The current paper is the first review of rearing behaviour, and is intended to collate findings from different perspectives that are not usually brought together. We suggest that rearing is a useful marker of environmental novelty, that the hippocampal formation is a crucial component of the system controlling rearing in novel environments, and that rearing is one of several ethological measures that can profitably be used to assess hippocampal learning and memory. Consideration is given to the following topics: the possible functions of rearing in information-gathering and escape behaviour; the modulation of rearing by various factors such as anxiety/ fear emotionality; comparative perspectives on rearing; neuroanatomical circuits involved in rearing with particular reference to the hippocampal formation and its afferents and efferents; and the role of the hippocampal formation in uncharted and mismatch environmental novelty. The review concludes with testable predictions about rearing, environmental novelty and the hippocampus.

Place from time: Reconstructing position from a distributed representation of temporal context

The temporal context model (TCM) [. A distributed representation of temporal context. Journal of Mathematical Psychology, 46(3), 269-299] was proposed to describe recency and associative effects observed in episodic recall. Episodic recall depends on an intact medial temporal lobe, a region of the brain that also supports a place code. Howard, Fotedar, Datey, and Hasselmo [. The temporal context model in spatial navigation and relational learning: Toward a common explanation of medial temporal lobe function across domains. Psychological Review, 112(1), 75-116] demonstrated that the leaky integrator that supports a gradually changing representation of temporal context in TCM is sufficient to describe properties of cells observed in ventromedial entorhinal cortex during spatial navigation if it is provided with input about the animal's current velocity. This representation of temporal context generates noisy place cells in the open field, unlike the clearly defined place cells observed in the hippocampus. Here we demonstrate that a reasonably accurate spatial representation can be extracted from temporal context with as few as eight cells, suggesting that the spatial precision observed in the place code in the hippocampus is not inconsistent with the input from a representation of temporal-spatial context in entorhinal cortex.

A computational principle for hippocampal learning and neurogenesis

In the three decades since Marr put forward his computational theory of hippocampal coding, many computational models have been built on the same key principles proposed by Marr: sparse representations, rapid Hebbian storage, associative recall and consolidation. Most of these models have focused on either the CA3 or CA1 fields, using "off-the-shelf" learning algorithms such as competitive learning or Hebbian pattern association. Here, we propose a novel coding principle that is common to all hippocampal regions, and from this one principal, we derive learning rules for each of the major pathways within the hippocampus. The learning rules turn out to have much in common with several models of CA3 and CA1 in the literature, and provide a unifying framework in which to view these models. Simulations of the complete circuit confirm that both recognition memory and recall are superior relative to a hippocampally lesioned model, consistent with human data. Further, we propose a functional role for neurogenesis in the dentate gyrus (DG), namely, to create distinct memory traces for highly similar items. Our simulation results support our prediction that memory capacity increases with the number of dentate granule cells, while neuronal turnover with a fixed dentate layer size improves recall, by minimizing interference between highly similar items.

Requirement of NMDA receptor reactivation for consolidation and storage of nondeclarative taste memory revealed by inducible NR1 knockout

We employed an inducible, reversible and region-specific gene knockout technique to investigate the requirements for cortical NMDA receptors (NMDAR) during the various stages (acquisition, consolidation and storage, and retrieval) of nondeclarative, hippocampal-independent memory in mice using a conditioned taste aversion memory paradigm. Here we show that temporary knockout of the cortical NMDAR during either the learning or postlearning consolidation stage, but not during the retrieval stage, causes severe performance deficits in the 1-month taste memory retention tests. More importantly, we found that the consolidation and storage of the long-term nondeclarative taste memories requires cortical NMDAR reactivation. Thus, the dynamic engagement of the NMDAR during the postlearning stage leads us to postulate that NMDAR reactivation-mediated synaptic re-entry reinforcement is crucial for overcoming the destabilizing effects intrinsic to synaptic protein turnover and for achieving consolidation and storage of nondeclarative memories in the brain.

Effects of seizures on brain development: lessons from the laboratory

Both clinical and laboratory studies demonstrate that seizures early in life can result in permanent behavioral abnormalities and enhance epileptogenicity. In experimental rodent models, the consequences of seizures are dependent upon age, etiology, seizure duration, and frequency. Recurrent seizures in immature rats result in long-term adverse effects on learning and memory. These behavioral changes are paralleled by changes in brain connectivity, dendritic morphology, excitatory and inhibitory receptor subunits, ion channels, and neurogenesis. These changes can occur in the absence of cell loss. Although impaired cognitive function and brain changes have been well documented after early onset seizures, the mechanisms of seizure-induced injury remain unclear. Recent studies have demonstrated abnormalities in single cell function that parallel behavioral changes.

Modelling brain emergent behaviours through coevolution of neural agents

Recently, many research efforts focus on modelling partial brain areas with the long-term goal to support cognitive abilities of artificial organisms. Existing models usually suffer from heterogeneity, which constitutes their integration very difficult. The present work introduces a computational framework to address brain modelling tasks, emphasizing on the integrative performance of substructures. Moreover, implemented models are embedded in a robotic platform to support its behavioural capabilities. We follow an agent-based approach in the design of substructures to support the autonomy of partial brain structures. Agents are formulated to allow the emergence of a desired behaviour after a certain amount of interaction with the environment. An appropriate collaborative coevolutionary algorithm, able to emphasize both the speciality of brain areas and their cooperative performance, is employed to support design specification of agent structures. The effectiveness of the proposed approach is illustrated through the implementation of computational models for motor cortex and hippocampus, which are successfully tested on a simulated mobile robot.

Deforming the hippocampal map

To investigate conjoint stimulus control over place cells, Fenton et al. (J Gen Physiol 116:191-209, 2000a) recorded while rats foraged in a cylinder with 45 degrees black and white cue cards on the wall. Card centers were 135 degrees apart. In probe trials, the cards were rotated together or apart by 25 degrees . Firing field centers shifted during these trials, stretching and shrinking the cognitive map. Fenton et al. (2000b) described this deformation with an ad hoc vector field equation. We consider what sorts of neural network mechanisms might be capable of accounting for their observations. In an abstract, maximum likelihood formulation, the rat's location is estimated by a conjoint probability density function of landmark positions. In an attractor neural network model, recurrent connections produce a bump of activity over a two-dimensional array of cells; the bump's position is influenced by landmark features such as distances or bearings. If features are chosen with appropriate care, the attractor network and maximum likelihood models yield similar results, in accord with previous demonstrations that recurrent neural networks can efficiently implement maximum likelihood computations (Pouget et al. Neural Comput 10:373-401, 1998; Deneve et al. Nat Neurosci 4:826-831, 2001).

A continuous attractor network model without recurrent excitation: maintenance and integration in the head direction cell system

Motivated by experimental observations of the head direction system, we study a three population network model that operates as a continuous attractor network. This network is able to store in a short-term memory an angular variable (the head direction) as a spatial profile of activity across neurons in the absence of selective external inputs, and to accurately update this variable on the basis of angular velocity inputs. The network is composed of one excitatory population and two inhibitory populations, with inter-connections between populations but no connections within the neurons of a same population. In particular, there are no excitatory-to-excitatory connections. Angular velocity signals are represented as inputs in one inhibitory population (clockwise turns) or the other (counterclockwise turns). The system is studied using a combination of analytical and numerical methods. Analysis of a simplified model composed of threshold-linear neurons gives the conditions on the connectivity for (i) the emergence of the spatially selective profile, (ii) reliable integration of angular velocity inputs, and (iii) the range of angular velocities that can be accurately integrated by the model. Numerical simulations allow us to study the proposed scenario in a large network of spiking neurons and compare their dynamics with that of head direction cells recorded in the rat limbic system. In particular, we show that the directional representation encoded by the attractor network can be rapidly updated by external cues, consistent with the very short update latencies observed experimentally by Zugaro et al. (2003) in thalamic head direction cells.

Cognitive map formation through sequence encoding by theta phase precession

The rodent hippocampus has been thought to represent the spatial environment as a cognitive map. The associative connections in the hippocampus imply that a neural entity represents the map as a geometrical network of hippocampal cells in terms of a chart. According to recent experimental observations, the cells fire successively relative to the theta oscillation of the local field potential, called theta phase precession, when the animal is running. This observation suggests the learning of temporal sequences with asymmetric connections in the hippocampus, but it also gives rather inconsistent implications on the formation of the chart that should consist of symmetric connections for space coding. In this study, we hypothesize that the chart is generated with theta phase coding through the integration of asymmetric connections. Our computer experiments use a hippocampal network model to demonstrate that a geometrical network is formed through running experiences in a few minutes. Asymmetric connections are found to remain and distribute heterogeneously in the network. The obtained network exhibits the spatial localization of activities at each instance as the chart does and their propagation that represents behavioral motions with multidirectional properties. We conclude that theta phase precession and the Hebbian rule with a time delay can provide the neural principles for learning the cognitive map.

The temporal context model in spatial navigation and relational learning: toward a common explanation of medial temporal lobe function across domains

The medial temporal lobe (MTL) has been studied extensively at all levels of analysis, yet its function remains unclear. Theory regarding the cognitive function of the MTL has centered along 3 themes. Different authors have emphasized the role of the MTL in episodic recall, spatial navigation, or relational memory. Starting with the temporal context model (M. W. Howard & M. J. Kahana, 2002a), a distributed memory model that has been applied to benchmark data from episodic recall tasks, the authors propose that the entorhinal cortex supports a gradually changing representation of temporal context and the hippocampus proper enables retrieval of these contextual states. Simulation studies show this hypothesis explains the firing of place cells in the entorhinal cortex and the behavioral effects of hippocampal lesion in relational memory tasks. These results constitute a first step toward a unified computational theory of MTL function that integrates neurophysiological, neuropsychological, and cognitive findings.

Hippocampal CA3-region is crucial for acquisition and memory consolidation in Morris water maze task in mice

This experiment investigated the involvement of the dorsal hippocampal CA3-region in the different phases of learning and memory in spatial and non-spatial tasks. To do so, we temporarily inactivated the CA3-subfield by a focal injection of diethyldithiocarbamate (DDC) which chelates most of the heavy metals present in this region. The effects of temporary inactivation of the CA3-region were examined in an associative task, the Morris water maze (MWM). To study the different phase of memory we used a new behavioural massed-procedure founded on four massed training sessions in the spatial and the non-spatial (cue) version of this task. In the spatial version, we showed that a bilateral injection of DDC into the CA3-region impairs the acquisition but not the recall of spatial information. The main result of this study is that the same injection performed immediately after the training session also perturbed memory consolidation. In the cue version of the MWM, we found no difference between the DDC-injected mice and their controls in acquisition or memory consolidation of non-spatial information. These results suggest that the hippocampal CA3-region is essential for spatial memory processes and specifically in memory consolidation of spatial information.

A double dissociation between hippocampal subfields: differential time course of CA3 and CA1 place cells for processing changed environments

Computational theories have suggested different functions for the hippocampal subfields (e.g., CA1 and CA3) in memory. However, it has been difficult to find dissociations relevant to these hypothesized functions in investigations of the hippocampal correlates of space ("place fields") in freely behaving animals. The current study demonstrates a double dissociation between the shifts in the center of mass (COM) of the place fields that were simultaneously recorded in CA1 and CA3 when familiar cue configurations were dynamically changed over days. The COM of CA3 place fields shifted backward in the first experience of the cue-changed environment, whereas the COM of CA1 place fields did not display the backward shift until the next day. These results support the hypothesis that CA3 plays a key role in the rapid formation of representations of new spatiotemporal sequences, whereas CA1 may be more important for comparing currently experienced sequence information with stored sequences in the CA3 network.

Comparison of population coherence of place cells in hippocampal subfields CA1 and CA3

The hippocampus, a critical brain structure for navigation, context-dependent learning and episodic memory, is composed of anatomically heterogeneous subregions. These regions differ in their anatomical inputs as well as in their internal circuitry. A major feature of the CA3 region is its recurrent collateral circuitry, by which the CA3 pyramidal cells make excitatory synaptic contacts on each other. In contrast, pyramidal cells in the CA1 region are not extensively interconnected. Although these differences have inspired numerous theoretical models of differential processing capacities of these two regions, there have been few reports of robust differences in the firing properties of CA1 and CA3 neurons in behaving animals. The most extensively studied of these properties is the spatially selective firing of hippocampal 'place cells'. Here we report that in a dynamically changing environment, in which familiar landmarks on the behavioural track and along the wall are rotated relative to each other, the population representation of the environment is more coherent between the original and cue-altered environments in CA3 than in CA1. These results demonstrate a functional heterogeneity between the place cells of CA3 and CA1 at the level of neural population representations.