The head-direction (HD) cells found in the limbic system in freely moving rats represent the instantaneous head direction of the animal in the horizontal plane regardless of the location of the animal. The internal direction represented by these cells uses both self-motion information for inet-tially based updating and familiar visual landmarks for calibration. Here, a model of the dynamics of the HD cell ensemble is presented. The sta-bility of a localized static activity profile in the network and a dynamic shift mechanism are explained naturally by synaptic weight distribution components with even and odd symmetry, respectively. Under symmetric weights or symmetric reciprocal connections, a stable activity profile close to the known direc-tional tuning curves will emerge. By adding a slight asymmetry to the weights, the activity profile will shift continuously without 1

The firing rate maps of hippocampal place cells recorded in a freely moving rat are viewed as a set of approximate radial basis functions over the (2-D) environment of the rat. It is proposed that these firing fields are constructed during exploration from 'sensory inputs' (tuning curve responses to the distance of cues from the rat) and used by cells downstream to construct firing rate maps that approximate any desired surface over the environment. It is shown that, when a rat moves freely in an open field, the phase of firing of a place cell (with respect to the EEG 0 rhythm) contains information as to the relative position of its firing field from the rat. A model of hippocampal function is presented in which the firing rate maps of cells downstream of the hippocampus provide a 'population vector' encoding the instantaneous direction of the rat from a previously encountered reward site, enabling navigation to it. A neuronal simulation, involving reinforcement only at the goal location, provides good agreement with single cell recording from the hippocampal region, and can navigate to reward sites in open fields using sensory input from environmental cues. The system requires only brief exploration, performs latent learning, and can return to a goal location after encountering it only once.

Hippocampal ‘place ’ cells and the head-direction cells of the dorsal presubiculum and related neocortical and thalamic areas appear to be part of a preconfigured network that generates an abstract internal representation of two-dimensional space whose metric is self-motion. It appears that viewpoint-specific visual information (e.g. landmarks) becomes secondarily bound to this structure by associative learning. These associations between landmarks and the preconfigured path integrator serve to set the origin for path integration and to correct for cumulative

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ut, and place fields can form when the animal explores novel environments in the dark. Place cells also continue to fire when distal landmarks are removed, but permutation of landmarks causes the animal to behave as if it were in an unfamiliar environment. Finally, place cell firing may be dependent on head direction, at least under certain conditions. An acceptable model of place memory must allow the "current place" to be updated by non-visual means such as motor feedback, and must be both sensitive to visual cues and robust in their absence. We propose a computational theory of the core of rat navigation abilities, based on coupled mechanisms for path integration, place recognition, and maintenance of head direction. We assume the rat has a path integration system (see [Etienne 1987, Mittelstaedt & Mittelstaedt 1980]) that is able to keep track of its current position relative to selected reference points. We postulate that hippocampal pyramidal cells form place descriptions by lear

In this paper we describe a model that applies Marr's theory of hippocampal function to the problem of map based navigation. Like many others we attribute a spatial memory function to the hippocampus, but we suggest that the additional functional components required for map based navigation are located elsewhere in the brain. One of the key functional components in this model is an egocentric map of space, located in the neocortex, that is continuously updated using ideothetic (self motion) information. The hippocampus stores snapshots of this egocentric map. The modelled activity pattern of head direction cells is used to set the best egocentric map rotation to match the snapshots stored in the hippocampus, resulting in place cells with a non-directional firing pattern. We describe an evaluation of this model using a mobile robot, and demonstrate that with this model the robot can recognise an environment and find a hidden goal. This model is discussed in the context of prior experime...

We describe a computer model that reproduces many observed features of rat navigation behavior, including response properties of place cells and head direction cells. We discuss issues that arise when implementing models of this sort on a mobile robot. I. Rat Navigation As they navigate through their environment, rats appear to be employing several types of spatial representations. One type defines "places" based on the views they afford of distal landmarks [14]. Place cells in hippocampus, which fire when the rat is in a particular region of space, are known to be sensitive to visual cues (see [21] for a review). Rats' sense of place, as reflected in their navigation behavior, has also been shown to rotate in synchrony with the rotation of landmarks, but they fail to recognize the environment when landmarks are permuted [29]. This suggests that the animal's sense of place is not based on single landmarks but rather on landmark configurations. A number of computer models of visually-d...

A computer model of rodent navigation, based on coupled mechanisms for place recognition, path integration, and maintenance of head direction, offers a way to operationally combine constraints from neurophysiology and behavioral observation. We describe how one such model reproduces a variety of experiments by Collett, Cartwright, and Smith [6] in which gerbils learn to find a hidden food reward, guided by an array of visual landmarks in an open arena. We also describe some neurophysiological predictions of the model; these may soon be verified experimentally. Portions of the model have been implemented on a mobile robot. 1. Introduction Landmark-based navigation is a rich domain for exploring issues of visual and spatial cognition. At the behavioral level, there is a wealth of data on how animals use landmarks to locate food or return to their nests. At the neurophysiological level, hippocampal pyramidal cells called place cells have been discovered that fire when the animal is in a ...

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...