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

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

In the context of parameter estimation and model selection, it is only quite recently that a direct link between the Fisher information and information theoretic quantities has been exhibited. We give an interpretation of this link within the standard framework of information theory. We show that in the context of population coding, the mutual information between the activity of a large array of neurons and a stimulus to which the neurons are tuned is naturally related to the Fisher information. In the light of this result we consider the optimization of the tuning curves parameters in the case of neurons responding to a stimulus represented by an angular variable. To appear in Neural Computation Vol. 10, Issue 7, published by the MIT press. 1 Laboratory associated with C.N.R.S. (U.R.A. 1306), ENS, and Universities Paris VI and Paris VII 1 Introduction A natural framework to study how neurons communicate, or transmit information, in the nervous system is information theory (see e...

Diverse theories of animal navigation aim at explaining how to determine and maintain a course from one place to another in the environment, although each presents a particular perspective with its own terminologies. These vocabularies sometimes overlap, but unfortunately with different meanings. This paper attempts to precisely define the existing concepts and terminologies, so as to comprehensively describe the different theories and models within the same unifying framework. We present navigation strategies within a 4 level hierarchical framework based upon levels of complexity of required processing (Guidance, Place recognition-triggered Response, Topological navigation, Metric navigation). This classification is based upon what information is perceived, represented and processed. It contrasts with common distinctions based upon availability of certain sensors or cues and rather stresses the information structure and content of central processors. We then review computat...

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.<F3.733e+05> Head direction (HD) cells, abundant in the rat postsubiculum and anterior thalamic nuclei, fire maximally when the rat's head is facing a particular direction. The activity of a population of these cells forms a distributed representation of the animal's current heading. We describe a neural network model that creates a stable, distributed representation of head direction and updates that representation in response to angular velocity information. In contrast to earlier models, our model of the head direction system accurately tracks a series of actual rat head rotations, and, using biologically plausible neurons, it fits the single-cell tuning curves of real HD cells recorded from rats executing those same rotations. The model makes neurophysiological predictions that can be tested using current technologies.<F3.74e+05> Introduction<F3.733e+05> Head direction cells in the postsubiculum (PoS, also known as dorsal presubiculum) were first described by Ranck<F3.967e+05> et...

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...

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 ...

The ability to acquire a representation of the spatial environment and the ability to localize within it are essential for successful navigation in a-priori unknown environments. The hippocampal formation is believed to play a key role in spatial learning and navigation in animals. This paper briefly reviews the relevant neurobiological and cognitive data and their relation to computational models of spatial learning and localization used in mobile robots. It also describes a hippocampal model of spatial learning and navigation and analyzes it using Kalman filter based tools for information fusion from multiple uncertain sources. The resulting model allows a robot to learn a place-based, metric representation of space in a-priori unknown environments and to localize itself in a stochastically optimal manner. The paper also describes an algorithmic implementation of the model and results of several experiments that demonstrate its capabilities.

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