Latent attractors: a model for context-dependent place representations in the hippocampus

Cellular signaling pathways as plastic, proto-cognitive systems: Implications for biomedicine

Many aspects of health and disease are modeled using the abstraction of a "pathway"-a set of protein or other subcellular activities with specified functional linkages between them. This metaphor is a paradigmatic case of a deterministic, mechanistic framework that focuses biomedical intervention strategies on altering the members of this network or the up-/down-regulation links between them-rewiring the molecular hardware. However, protein pathways and transcriptional networks exhibit interesting and unexpected capabilities such as trainability (memory) and information processing in a context-sensitive manner. Specifically, they may be amenable to manipulation via their history of stimuli (equivalent to experiences in behavioral science). If true, this would enable a new class of biomedical interventions that target aspects of the dynamic physiological "software" implemented by pathways and gene-regulatory networks. Here, we briefly review clinical and laboratory data that show how high-level cognitive inputs and mechanistic pathway modulation interact to determine outcomes in vivo. Further, we propose an expanded view of pathways from the perspective of basal cognition and argue that a broader understanding of pathways and how they process contextual information across scales will catalyze progress in many areas of physiology and neurobiology. We argue that this fuller understanding of the functionality and tractability of pathways must go beyond a focus on the mechanistic details of protein and drug structure to encompass their physiological history as well as their embedding within higher levels of organization in the organism, with numerous implications for data science addressing health and disease. Exploiting tools and concepts from behavioral and cognitive sciences to explore a proto-cognitive metaphor for the pathways underlying health and disease is more than a philosophical stance on biochemical processes; at stake is a new roadmap for overcoming the limitations of today's pharmacological strategies and for inferring future therapeutic interventions for a wide range of disease states.

Modeling Contextual Modulation of Memory Associations in the Hippocampus

We present a computational model of how memories can be contextually acquired and recalled in the hippocampus. Our adaptive contextual memory model comprises the lateral entorhinal cortex (LEC), the dentate gyrus (DG) and areas CA3 and CA1 in the hippocampus, and assumes external inputs about context that originate in the prefrontal cortex (PFC). Specifically, we propose that there is a top-down bias on the excitability of cells in the DG of the hippocampus that recruits a sub-population of cells to differentiate contexts, independent of experienced stimuli, expanding the "pattern separation" role typically attributed to the DG. It has been demonstrated in rats that if PFC is inactivated, both acquisition and recall of memory associations are impaired. However, PFC inactivation during acquisition of one set of memory associations surprisingly leads to subsequent facilitation of the acquisition of a conflicting set of memory associations in the same context under normal PFC operation. We provide here the first computational and algorithmic account of how the absence or presence of the top-down contextual biases on the excitability of DG cells during different learning phases of these experiments explains these data. Our model simulates PFC inactivation as the loss of inhibitory control on DG, which leads to full or partial activation of DG cells related to conflicting memory associations previously acquired in different contexts. This causes context-inappropriate memory traces to become active in the CA3 recurrent network and thereby the output CA1 area within the hippocampus. We show that these incongruous memory patterns proactively interfere with and slow the acquisition of new memory associations. Further, we demonstrate that pattern completion within CA3 in response to a partial cue for the recall of previously acquired memories is also impaired by PFC inactivation for the same reason. Pre-training the model with interfering memories in contexts different from those used in the experiments, simulating a lifetime of experiences, was crucial to reproduce the rat behavioral data. Finally, we made several testable predictions based on the model that suggest future experiments to deepen our understanding of brain-wide memory processes.

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.

Place cell rate remapping by CA3 recurrent collaterals

Episodic-like memory is thought to be supported by attractor dynamics in the hippocampus. A possible neural substrate for this memory mechanism is rate remapping, in which the spatial map of place cells encodes contextual information through firing rate variability. To test whether memories are stored as multimodal attractors in populations of place cells, recent experiments morphed one familiar context into another while observing the responses of CA3 cell ensembles. Average population activity in CA3 was reported to transition gradually rather than abruptly from one familiar context to the next, suggesting a lack of attractive forces associated with the two stored representations. On the other hand, individual CA3 cells showed a mix of gradual and abrupt transitions at different points along the morph sequence, and some displayed hysteresis which is a signature of attractor dynamics. To understand whether these seemingly conflicting results are commensurate with attractor network theory, we developed a neural network model of the CA3 with attractors for both position and discrete contexts. We found that for memories stored in overlapping neural ensembles within a single spatial map, position-dependent context attractors made transitions at different points along the morph sequence. Smooth transition curves arose from averaging across the population, while a heterogeneous set of responses was observed on the single unit level. In contrast, orthogonal memories led to abrupt and coherent transitions on both population and single unit levels as experimentally observed when remapping between two independent spatial maps. Strong recurrent feedback entailed a hysteretic effect on the network which diminished with the amount of overlap in the stored memories. These results suggest that context-dependent memory can be supported by overlapping local attractors within a spatial map of CA3 place cells. Similar mechanisms for context-dependent memory may also be found in other regions of the cerebral cortex.

The activity of ‘place cells’ in hippocampal area CA3 systematically changes as a function of the animal's position in an arena as well as contextual variables like the color or shape of enclosing walls. Large changes to the local environment, e.g. moving the animal to a different room, can induce a complete reorganization of place-cell firing locations. Such ‘global remapping’ reveals that memory for different environments is encoded as separate spatial maps. Smaller changes to features within an environment can induce a modulation of place cell firing rates without affecting their firing locations. This kind of ‘rate remapping’ is still poorly understood. In this paper we describe a computational model in which discrete memories for contextual features were stored locally within a spatial map of place cells. This network structure supports retrieval of both positional and contextual information from an arbitrary cue, as required by an episodic memory structure. The activity of the network qualitatively matches empirical data from rate remapping experiments, both on the population level and the level of single place cells. The results support the idea that CA3 rate remapping reflects content-addressable memories stored as multimodal attractor states in the hippocampus.

An attempt to construct a (general) mathematical framework to model biological "context-dependence"

Context-dependent nature of biological phenomena is well documented in every branch of biology. While there have been few previous attempts to (implicitly) model various (particular) facets of biological context-dependence, a formal and general mathematical construct to model the wide spectrum of context-dependence, eludes the students of biology. Such an objective model, from both 'bottom-up' as well as 'top-down' perspective, is proposed here to serve as the template to describe the various kinds of context-dependence that we encounter in different branches of biology. Interactions between biological contexts was found to be transitive but non-commutative. It is found that a hierarchical nature of dependence among the biological contexts models the emergent biological properties efficiently. Reasons for these findings are provided in a general model to describe biological reality. Scheme to algorithmically implement the hierarchic structure of organization of biological contexts was proposed with a construct named 'Context tree'. A 'Context tree' based analysis of context interactions among biophysical factors influencing protein structure was performed.

Place cells, grid cells, attractors, and remapping

Place and grid cells are thought to use a mixture of external sensory information and internal attractor dynamics to organize their activity. Attractor dynamics may explain both why neurons react coherently following sufficiently large changes to the environment (discrete attractors) and how firing patterns move smoothly from one representation to the next as an animal moves through space (continuous attractors). However, some features of place cell behavior, such as the sometimes independent responsiveness of place cells to environmental change (called “remapping”), seem hard to reconcile with attractor dynamics. This paper suggests that the explanation may be found in an anatomical separation of the two attractor systems coupled with a dynamic contextual modulation of the connection matrix between the two systems, with new learning being back-propagated into the matrix. Such a scheme could explain how place cells sometimes behave coherently and sometimes independently.

Phase precession and variable spatial scaling in a periodic attractor map model of medial entorhinal grid cells with realistic after-spike dynamics

We present a model that describes the generation of the spatial (grid fields) and temporal (phase precession) properties of medial entorhinal cortical (MEC) neurons by combining network and intrinsic cellular properties. The model incorporates network architecture derived from earlier attractor map models, and is implemented in 1D for simplicity. Periodic driving of conjunctive (position × head-direction) layer-III MEC cells at theta frequency with intensity proportional to the rat's speed, moves an 'activity bump' forward in network space at a corresponding speed. The addition of prolonged excitatory currents and simple after-spike dynamics resembling those observed in MEC stellate cells (for which new data are presented) accounts for both phase precession and the change in scale of grid fields along the dorso-ventral axis of MEC. Phase precession in the model depends on both synaptic connectivity and intrinsic currents, each of which drive neural spiking either during entry into, or during exit out of a grid field. Thus, the model predicts that the slope of phase precession changes between entry into and exit out of the field. The model also exhibits independent variation in grid spatial period and grid field size, which suggests possible experimental tests of the model.

Coincidence detection of place and temporal context in a network model of spiking hippocampal neurons

Recent advances in single-neuron biophysics have enhanced our understanding of information processing on the cellular level, but how the detailed properties of individual neurons give rise to large-scale behavior remains unclear. Here, we present a model of the hippocampal network based on observed biophysical properties of hippocampal and entorhinal cortical neurons. We assembled our model to simulate spatial alternation, a task that requires memory of the previous path through the environment for correct selection of the current path to a reward site. The convergence of inputs from entorhinal cortex and hippocampal region CA3 onto CA1 pyramidal cells make them potentially important for integrating information about place and temporal context on the network level. Our model shows how place and temporal context information might be combined in CA1 pyramidal neurons to give rise to splitter cells, which fire selectively based on a combination of place and temporal context. The model leads to a number of experimentally testable predictions that may lead to a better understanding of the biophysical basis of information processing in the hippocampus.

Understanding how behavior is connected to cellular and network processes is one of the most important challenges in neuroscience, and computational modeling allows one to directly formulate hypotheses regarding the interactions between these scales. We present a model of the hippocampal network, an area of the brain important for spatial navigation and episodic memory, memory of “what, when, and where.” We show how the model, which consists of neurons and connections based on biophysical properties known from experiments, can guide a virtual rat through the spatial alternation task by storing a memory of the previous path through an environment. Our model shows how neurons that fire selectively based on both the current location and past trajectory of the animal (dubbed “splitter cells”) might emerge from a newly discovered biophysical interaction in these cells. Our model is not intended to be comprehensive, but rather to contain just enough detail to achieve performance of the behavioral task. Goals of this approach are to present a scenario by which the gap between biophysics and behavior can be bridged and to provide a framework for the formulation of experimentally testable hypotheses.

Context learning in the rodent hippocampus

We present a Bayesian statistical theory of context learning in the rodent hippocampus. While context is often defined in an experimental setting in relation to specific background cues or task demands, we advance a single, more general notion of context that suffices for a variety of learning phenomena. Specifically, a context is defined as a statistically stationary distribution of experiences, and context learning is defined as the problem of how to form contexts out of groups of experiences that cluster together in time. The challenge of context learning is solving the model selection problem: How many contexts make up the rodent's world? Solving this problem requires balancing two opposing goals: minimize the variability of the distribution of experiences within a context and minimize the likelihood of transitioning between contexts. The theory provides an understanding of why hippocampal place cell remapping sometimes develops gradually over many days of experience and why even consistent landmark differences may need to be relearned after other environmental changes. The theory provides an explanation for progressive performance improvements in serial reversal learning, based on a clear dissociation between the incremental process of context learning and the relatively abrupt context selection process. The impact of partial reinforcement on reversal learning is also addressed. Finally, the theory explains why alternating sequence learning does not consistently result in unique context-dependent sequence representations in hippocampus.

Space, time and learning in the hippocampus: how fine spatial and temporal scales are expanded into population codes for behavioral control

The hippocampus participates in multiple functions, including spatial navigation, adaptive timing and declarative (notably, episodic) memory. How does it carry out these particular functions? The present article proposes that hippocampal spatial and temporal processing are carried out by parallel circuits within entorhinal cortex, dentate gyrus and CA3 that are variations of the same circuit design. In particular, interactions between these brain regions transform fine spatial and temporal scales into population codes that are capable of representing the much larger spatial and temporal scales that are needed to control adaptive behaviors. Previous models of adaptively timed learning propose how a spectrum of cells tuned to brief but different delays are combined and modulated by learning to create a population code for controlling goal-oriented behaviors that span hundreds of milliseconds or even seconds. Here it is proposed how projections from entorhinal grid cells can undergo a similar learning process to create hippocampal place cells that can cover a space of many meters that are needed to control navigational behaviors. The suggested homology between spatial and temporal processing may clarify how spatial and temporal information may be integrated into an episodic memory. The model proposes how a path integration process activates a spatial map of grid cells. Path integration has a limited spatial capacity, and must be reset periodically, leading to the observed grid cell periodicity. Integration-to-map transformations have been proposed to exist in other brain systems. These include cortical mechanisms for numerical representation in the parietal cortex. As in the grid-to-place cell spatial expansion, the analog representation of number is extended by additional mechanisms to represent much larger numbers. The model also suggests how visual landmarks may influence grid cell activities via feedback projections from hippocampal place cells to the entorhinal cortex.

Generation and reshaping of sequences in neural systems

The generation of informational sequences and their reorganization or reshaping is one of the most intriguing subjects for both neuroscience and the theory of autonomous intelligent systems. In spite of the diversity of sequential activities of sensory, motor, and cognitive neural systems, they have many similarities from the dynamical point of view. In this review we discus the ideas, models, and mathematical image of sequence generation and reshaping on different levels of the neural hierarchy, i.e., the role of a sensory network dynamics in the generation of a motor program (hunting swimming of marine mollusk Clione), olfactory dynamical coding, and sequential learning and decision making. Analysis of these phenomena is based on the winnerless competition principle. The considered models can be a basis for the design of biologically inspired autonomous intelligent systems.

Hippocampal mechanisms for the context-dependent retrieval of episodes

Behaviors ranging from delivering newspapers to waiting tables depend on remembering previous episodes to avoid incorrect repetition. Physiologically, this requires mechanisms for long-term storage and selective retrieval of episodes based on the time of occurrence, despite variable intervals and similarity of events in a familiar environment. Here, this process has been modeled based on the physiological properties of the hippocampal formation, including mechanisms for sustained activity in entorhinal cortex and theta rhythm oscillations in hippocampal subregions. The model simulates the context-sensitive firing properties of hippocampal neurons including trial-specific firing during spatial alternation and trial by trial changes in theta phase precession on a linear track. This activity is used to guide behavior, and lesions of the hippocampal network impair memory-guided behavior. The model links data at the cellular level to behavior at the systems level, describing a physiologically plausible mechanism for the brain to recall a given episode which occurred at a specific place and time.

Hippocampal replay contributes to within session learning in a temporal difference reinforcement learning model

Temporal difference reinforcement learning (TDRL) algorithms, hypothesized to partially explain basal ganglia functionality, learn more slowly than real animals. Modified TDRL algorithms (e.g. the Dyna-Q family) learn faster than standard TDRL by practicing experienced sequences offline. We suggest that the replay phenomenon, in which ensembles of hippocampal neurons replay previously experienced firing sequences during subsequent rest and sleep, may provide practice sequences to improve the speed of TDRL learning, even within a single session. We test the plausibility of this hypothesis in a computational model of a multiple-T choice-task. Rats show two learning rates on this task: a fast decrease in errors and a slow development of a stereotyped path. Adding developing replay to the model accelerates learning the correct path, but slows down the stereotyping of that path. These models provide testable predictions relating the effects of hippocampal inactivation as well as hippocampal replay on this task.

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 simple neural network model of the hippocampus suggesting its pathfinding role in episodic memory retrieval

The goal of this work is to extend the theoretical understanding of the relationship between hippocampal spatial and memory functions to the level of neurophysiological mechanisms underlying spatial navigation and episodic memory retrieval. The proposed unifying theory describes both phenomena within a unique framework, as based on one and the same pathfinding function of the hippocampus. We propose a mechanism of reconstruction of the context of experience involving a search for a nearly shortest path in the space of remembered contexts. To analyze this concept in detail, we define a simple connectionist model consistent with available rodent and human neurophysiological data. Numerical study of the model begins with the spatial domain as a simple analogy for more complex phenomena. It is demonstrated how a nearly shortest path is quickly found in a familiar environment. We prove numerically that associative learning during sharp waves can account for the necessary properties of hippocampal place cells. Computational study of the model is extended to other cognitive paradigms, with the main focus on episodic memory retrieval. We show that the ability to find a correct path may be vital for successful retrieval. The model robustly exhibits the pathfinding capacity within a wide range of several factors, including its memory load (up to 30,000 abstract contexts), the number of episodes that become associated with potential target contexts, and the level of dynamical noise. We offer several testable critical predictions in both spatial and memory domains to validate the theory. Our results suggest that (1) the pathfinding function of the hippocampus, in addition to its associative and memory indexing functions, may be vital for retrieval of certain episodic memories, and (2) the hippocampal spatial navigation function could be a precursor of its memory function.

Hippocampal place cells are not controlled by visual input during the small irregular activity state in the rat

In the actively foraging rat, hippocampal pyramidal cells have strong spatial correlates. Each "place cell" fires rapidly only when the rat enters a particular delimited portion of its environment, called the "place field" of that cell. Hippocampal pyramidal cells also exhibit spatial selectivity during a physiological state that occurs during sleep, termed "small irregular activity" (SIA), because of the appearance of the hippocampal EEG. It is not known whether rats determine their current location in space during SIA using current visual information or whether they recall the location in which they fell asleep. To address this question, we recorded spikes from ensembles of CA1 pyramidal cells and hippocampal EEG while rats slept along the edge of a large circular recording arena with minimal local features in a room with prominent distal visual cues. To move the rats to a new location in the room while they were sleeping, we slowly rotated the recording arena on which they slept to a new orientation in the room. Hippocampal place cell activity in subsequent SIA episodes reflected the location in the room in which the rats fell asleep, rather than the location to which they were moved, although the alignment of the rats' spatial map was governed by the room cues in the subsequent active foraging session. Thus, the hippocampal population activity during SIA does not result from the processing of current visual information but instead probably reflects a memory for the location in which the rat fell asleep.

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.

Configural representations in transverse patterning with a hippocampal model

The hippocampus is necessary in both humans and rats for learning configural representations in tasks such as transverse patterning. The transverse patterning task, (A+B-, B+C-, C+A-), requires representing individual stimuli in the context of other stimuli. This paper extends a previous application to rat data [INNS World Congress on Neural Networks, 1995; Biol Cybern 6 (1998a) 203] by applying a model of the CA3 region of the hippocampus to human data. A decision function is also added that enables the system to choose among training items. Analysis of the simulations show that configural representations are formed by unique neural codes that depend on temporal and stimuli context. Based on the simulations, we hypothesize that configural representations in biological networks depend on a proper balance of input and context representations. Furthermore, the division of labor between functions in the model is a specific working hypothesis that in learning this task the hippocampus specializes in sequence prediction and the decision function evaluates the predictions.

Heterogeneous modulation of place cell firing by changes in context

Hippocampal place cells show spatially localized activity that can be modulated by both geometric information (e.g., the distances and directions of features in the environment) and nongeometric information (e.g., colors, odors, and possibly behaviors). Nongeometric information may allow the discrimination of different spatial contexts. Understanding how nongeometric (or contextual) information affects hippocampal activity is important in light of proposals that the hippocampus may play a role in constructing a representation of spatial context. We investigated the contextual modulation of place cell activity by recording hippocampal place cells while rats foraged in compound contexts comprising black or white color paired with lemon or vanilla odor. Some cells responded to the color or odor changes alone, but most responded to varying combinations of both. Thus, we demonstrate, for the first time, that there is a heterogeneous input by contextual inputs into the hippocampus. We propose a model of contextual remapping of place cells in which the geometric inputs are selectively activated by subsets of contextual stimuli. Because it appears that different place cells are affected by different subsets of contextual stimuli, the representation of the entire context would require activity at the population level, supporting a role for the hippocampus in constructing a representation of spatial context.

Dynamic interactions between local surface cues, distal landmarks, and intrinsic circuitry in hippocampal place cells

A number of computational models of hippocampal place cells incorporate attractor neural network architecture to simulate key findings in the place cell literature, including the properties of pattern completion, firing in the absence of visual input, and nonlinear responses to environmental manipulations. To test for evidence of attractor dynamics, ensembles of place cells were recorded using multiple-tetrode techniques. After many days of experience in an environment with salient local surface cues on a circular track and salient distal landmarks on the wall, the local surface cues were rotated as a set in opposition to the distal landmarks. The amount of mismatch between the local and distal sets of cues varied from 45 to 180 degrees. If place cells were parts of strong attractors, then their place fields should follow either the local cues or the distal cues as an integrated ensemble. Instead, in single recording sessions, some place cells were controlled by the distal landmarks, other cells were controlled by the local cues, and other cells became silent or gained new fields. In some cases, individual place fields split in half, following both the local and distal cues. If place cells are indeed parts of attractor networks in the hippocampus, then the attractors may be weak relative to the inputs from external sources, such as representations of the sensory environment and representations of heading direction, in a familiar, well explored environment.

Storage, recall, and novelty detection of sequences by the hippocampus: elaborating on the SOCRATIC model to account for normal and aberrant effects of dopamine

In order to understand how the molecular or cellular defects that underlie a disease of the nervous system lead to the observable symptoms, it is necessary to develop a large-scale neural model. Such a model must specify how specific molecular processes contribute to neuronal function, how neurons contribute to network function, and how networks interact to produce behavior. This is a challenging undertaking, but some limited progress has been made in understanding the memory functions of the hippocampus with this degree of detail. There is increasing evidence that the hippocampus has a special role in the learning of sequences and the linkage of specific memories to context. In the first part of this paper, we review a model (the SOCRATIC model) that describes how the dentate and CA3 hippocampal regions could store and recall memory sequences in context. A major line of evidence for sequence recall is the "phase precession" of hippocampal place cells. In the second part of the paper, we review the evidence for theta-gamma phase coding. According to a framework that incorporates this form of coding, the phase precession is interpreted as cued recall of a discrete sequence of items from long-term memory. The third part of the paper deals with the issue of how the hippocampus could learn memory sequences. We show that if multiple items can be active within a theta cycle through the action of a short-term "buffer," NMDA-dependent plasticity can lead to the learning of sequences presented at realistic item separation intervals. The evidence for such a buffer function is reviewed. An important underlying issue is whether the hippocampal circuitry is configured differently for learning and recall. We argue that there are indeed separate states for learning and recall, but that both involve theta oscillations, albeit in possibly different forms. This raises the question of how neuromodulatory input might switch the hippocampus between learning and recall states and more generally how different neuromodulatory inputs reconfigure the hippocampus for different functions. In the fifth part of this paper we review our studies of dopamine and dopamine/NMDA interactions in the control of synaptic function. Our results show that dopamine dramatically reduces the direct cortical input to CA1 (the perforant path input), while having little effect on the input from CA3. In order to interpret the functional consequences of this pathway-specific modulation, it is necessary to understand the function of CA1 and the role of dopaminergic input from the ventral tegmental area (VTA). In the sixth part of this paper we consider several possibilities and address the issue of how dopamine hyperfunction or NMDA hypofunction, abnormalities that may underlie schizophrenia, might lead to the symptoms of the disease. Relevant to this issue is the demonstrated role of the hippocampus in novelty detection, a function that is likely to depend on sequence recall by the hippocampus. Novelty signals are generated when reality does not match the expectations generated by sequence recall. One possible site for computing mismatch is CA1, since it receives predictions from CA3 and sensory "reality" via the perforant path. Our data suggest that disruption of this comparison would be expected under conditions of dopamine hyperfunction or NMDA hypofunction. Also relevant is the fact that the VTA, which fires in response to novelty, may both depend on hippocampal-dependent novelty detection processes and, in turn, affect hippocampal function. Through large-scale modeling that considers both the processes performed by the hippocampus and the neuromodulatory loops in which the hippocampus is embedded, it is becoming possible to generate working hypotheses that relate synaptic function and malfunction to behavior.

Spatial processing in the brain: the activity of hippocampal place cells

The startling discovery by O'Keefe & Dostrovsky (Brain Res. 1971; 34: 171-75) that hippocampal neurons fire selectively in different regions or "place fields" of an environment and the subsequent development of the comprehensive theory by O'Keefe & Nadel (The Hippocampus as a Cognitive Map. Oxford, UK: Clarendon, 1978) that the hippocampus serves as a cognitive map have stimulated a substantial body of literature on the characteristics of hippocampal "place cells" and their relevance for our understanding of the mechanisms by which the brain processes spatial information. This paper reviews the major dimensions of the empirical research on place-cell activity and the development of computational models to explain various characteristics of place fields.