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.

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...

Strong constraints on the neural mechanisms underlying the formation of place fields in the rodent hippocampus come from the systematic changes in spatial activity patterns that are consequent on systematic environmental manipulations. We describe an attractor network model of area CA3 in which local, recurrent, excitatory, and inhibitory interactions generate appropriate place cell representations from location- and directionspecific activity in the entorhinal cortex. In the model, familiarity with the environment, as reflected by activity in neuromodulatory systems, influences the efficacy and plasticity of the recurrent and feedforward inputs to CA3. In unfamiliar, novel, environments, mossy fiber inputs impose activity patterns on CA3, and the recurrent collaterals and the perforant path inputs are subject to graded Hebbian plasticity. The hippocampus is known to be involved in spatial learning and memory in rodents. Some of the most convincing evidence for this is the presence of place cells in areas CA3 and CA1 of the hippocampus (O’Keefe and Dostrovsky, 1971; O’Keefe, 1976) and of many other types of spatially selective cells in neighboring areas

For a robot, an animal, and even for man, to be able to use an internal representation of the spatial layout of its environment to position itself is a very complex task, which raises numerous issues of perception, categorization and motor control that must all be solved in an integrated manner to promote survival. This point is illustrated here, within the framework of a review of localization strategies in mobile robots. The allothetic and idiothetic sensors that may be used by these robots to build internal representations of their environment, and the maps in which these representations may be instantiated, are first described. Then map-based navigation systems are categorized according to a 3-level hierarchy of localization strategies, which respectively call upon direct position inference, single-hypothesis tracking, and multiple-hypothesis tracking. The advantages and drawbacks of these strategies, notably with respect to the limitations of the sensors on which they rely, are discussed throughout the text.

This article reviews map-learning and path-planning strategies within the context of map-based navigation in mobile robots. Concerning map-learning, it distinguishes metric maps from topological maps and describes procedures that help maintain the coherency of these maps. Concerning path-planning, it distinguishes continuous from discretized spaces and describes procedures applicable when the execution of a plan fails. It insists on the need for an integrated conception of such procedures, that must be tightly tailored to the specific robot that is used - notably to the capacities and limitations of its sensory-motor equipment - and to the specific environment that is experienced. A hierarchy of navigation strategies is outlined in the discussion, together with the sort of adaptive capacities each affords to cope with unexpected obstacles or dangers encountered on an animat or robot's way to its goal.

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...

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

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 ...