We propose a computational model of the CA3 region of the rat hippocampus that is able to reproduce the available experimental data concerning the dependence of directional selectivity of the place cell discharge on the environment and on the spatial task. The main feature of our model is a continuous, unsupervised Hebbian learning dynamics of recurrent connections, which is driven by the neuronal activities imposed upon the network by the environment-dependent external input. In our simulations, the environment and the movements of the rat are chosen to mimic those commonly observed in neurophysiological experiments. The environment is represented as local views that depend on both the position and the heading direction of the rat. We hypothesize that place cells are intrinsically directional, that is, they respond to local views. We show that the synaptic dynamics in the recurrent neural network rapidly modify the discharge correlates of the place cells: cells tend to be...

this paper with describing linked temporal sequences of events.) The hippocampus is also implicated in spatial memory. For example, damage to the hippocampal system in monkeys produces de# cits in learning about where objects are and where responses must be made (Rolls 1996; Gaffan 1998), and in rats produces spatial learning de# cits (Martin et al. 2000). Neurophysiologically, hippocampal neurons in rats respond to the place where the animal is located (O'Keefe 1990; Kubie & Muller 1991; Wilson & McNaughton 1993), and in primates to the place being viewed (Rolls et al. 1997; Rolls 1999). It has thus been a long-standing question about whether the hippocampus and nearby temporal lobe structures are involved in episodic memory or spatial function. In this paper we show that this question can be resolved by revealing that a single neural network can implement both episodic and spatial memory

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This paper presents a method for a mobile robot to construct and localize relative to a "cognitive map", where the cognitive map is assumed to be a representational structure that encodes both spatial and behavioral information. The localization is performed by applying a generic Bayes filter. The cognitive map was implemented within a behavior-based robotic system, providing a new behavior that allows the robot to anticipate future events using the cognitive map. One of the prominent advantages of this approach is elimination of the pose sensor usage (e.g., shaft encoder, compass, GPS, etc.), which is known for its limitations and proneness to various errors. A preliminary experiment was conducted in simulation and its promising results are discussed.

INTRODUCTION The hippocampus is the most-studied part of the brain, attracting interest due to its position many synapses removed from sensory transducers or motor-e#ectors, its role in human amnesia and Alzheimer's disease, and the discovery of long term potentiation (LTP, see SYNAPTIC PLASTICITY) and of spatially coded cell firing. Bilateral damage to the hippocampus and nearby structures in patient H.M., as treatment for epilepsy, produced a profound retrograde and anterograde amnesia, prompting extensive cross-species research to uncover the specific memory deficit that results from hippocampal damage (the most prominent of which, in the rat, appears to be a deficit in spatial navigation). In short, the hippocampus has become the primary region in the mammalian brain for the study of the synaptic basis of memory and learning. Structurally, it is the simplest form of cortex. It contains one projection cell type, whose cell bodies are confined to a single layer, and receives inputs

Abstract. 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 noconnections 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.

First, the information represented in the primate hippocampus, and what is computed by the primate hippocampus, are considered.

Spatial orientation can take place in three separate scales: scenes within an individual's visual field, surrounds including information to the front, side, and rear, and neighborhoods, that contain points not visible from the current location. When asked to orient in a surround people are especially sensitive to information to their fronts and backs. However if the surround has been experienced by viewing a map time to access information about a point increases with the angle between the forward direction and the queried point. As people become familiar with neighborhoods they first notice landmarks, then paths between landmarks, and finally develop configurational knowledge of the key locations. The last stage is not always reached, even after years of experience. On the average, people can orient themselves toward an unseen point in a neighborhood with an accuracy of about twenty degrees. However there are very large individual differences in orienting ability. People can acquire or...

ABSTRACT: 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 3 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. VC 2011 Wiley-Liss, Inc. KEY WORDS: path integration; dead reckoning; continuous attractor neural network; place cells; entorhinal stellate cells