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

We analyze a model of navigational map formation based on correlation-based, temporally asymmetric potentiation and depression of synapses between hippocampal place cells. We show that synaptic modification during random exploration of an environment shifts the location encoded by place cell activity in such a way that it indicates the direction from any location to a fixed target avoiding walls and other obstacles. Multiple maps to different targets can be simultaneously stored if we introduce target-dependent modulation of place cell activity. Once maps to a number of target locations in a given environment have been stored, novel maps to previously unknown target locations are automatically constructed by interpolation between existing maps.

In the last decade the outlines of the neural structures subserving the sense of direction have begun to emerge. Several investigations have shed light on the effects of vestibular input and visual input on the head direction representation. In this paper, a model is formulated of the neural mechanisms underlying the head direction system. The model is built out of simple ingredients, depending on nothing more complicated than connectional specificity, attractor dynamics, Hebbian learning, and sigmoidal nonlinearities, but it behaves in a sophisticated way and is consistent with most of the observed properties of real head direction cells. In addition it makes a number of predictions that ought to be testable by reasonably straightforward experiments. 1 Head Direction Cells in the Rat There is quite a bit of behavioral evidence for an intrinsic sense of direction in many species of mammals, including rats and humans (e.g., Gallistel, 1990). The first specific information regarding the...

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

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 ...

A model based on coupled mechanisms for place recognition, path integration, and maintenance of head direction in rodents replicates a variety of neurophysiological and behavioral data. Here we consider a task described in [Collett et al. 1986] in which gerbils were trained to find food equidistant from three identical landmarks arranged in an equilateral triangle. In probe trials with various manipulations of the landmark array, the model produces behaviors similar to those of the animals. We discuss computer simulations and an implementation of portions of the model on a mobile robot. Introduction We have developed a model of rodent navigation based on tightly coupled mechanisms for place coding,path integration, and maintenance of head direction. Our theory reproduces a variety of behavioral and neurophysiological phenomena. 1 In this paper, we model behavioral data from [Collett et al. 1986] in which gerbils find a food reward among local landmarks in an otherwise cue impoveris...

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The least-angle strategy is a common wayfinding method that can be applied in unknown environments if the target direction is known. The strategy is based on the navigator's heuristic to select the street segment at an intersection which is most in line with the target direction. To use this strategy, the navigator needs to know the angles between the target direction and the street segments leading out from the intersection. If the direct view to the target is blocked and the target vector cannot be perceived, the target direction that is needed for the decision process is based on the agent's believed position and orientation (estimated through path integration). The agent's believed position and target direction are distorted by human errors in estimation of distances and directions, mainly affecting the path integration process. In this paper we examine how human estimation errors of distance and rotation influence the decision behavior in the wayfinding process in an unknown street environment.

O'Keefe (1991) has proposed that spatial information in rats might be represented as phasors: phase and amplitude of a sine wave encoding angle and distance to a landmark. We describe computer simulations showing that operations on phasors can be efficiently realized by arrays of spiking neurons that re-code the temporal dimension of the sine wave spatially. Some cells in motor and parietal cortex exhibit response properties compatible with this proposal. 1 Address all correspondence to the first author. Electronic mail address: dst@cs.cmu.edu. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Fujitsu Corporation, the National Science Foundation, or the U.S. government. Keywords: Neural Modelling, Spatial Reasoning, Parietal Cortex, Sinusoidal Arrays 1. Introduction Any vector in polar coordinates ~v = (r; OE) can be represented as a sine wave f(t) =...

Abstract goes here ii This page is left intentionally blank iii Table of Contents 1 Introduction ...................................................................................................................1 1.1 Spatial Navigation......................................................................................................1 1.2 A General Cognitive Model of Spatial Navigation......................................................3 1.3 Philosophy and Motivation.........................................................................................5 1.4 Overview ...................................................................................................................6 2 Brains for Rats ...............................................................................................................7 2.1 The Rat Brain in Perspective ......................................................................................7 2.2 Neurophysiology of Rodent Navigation .......