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...

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A model of the hippocampus as a "cognitive graph" is proposed. It essentially considers the hippocampus as an heteroassociative network that learns temporal sequences of visited places and stores a topological representation of the environment. Using place cells, head-direction cells, and "goal cells", we propose a biologically plausible way of exploiting such a spatial representation for navigation, which does not require complicated graph search algorithms. Simulations show that the resulting animat is able to navigate in continuous environments that contain obstacles. Furthermore, we make experimental predictions on simultaneous recordings of multiple cells in the rat hippocampus. 1. Introduction The discovery of place cells in areas CA3 and CA1 of the rat hippocampus (O'Keefe and Dostrovsky, 1971) -- cells that discharge selectively when the rat is in restricted regions of the environment (their place fields) -- led to the idea that the hippocampus functions as a cognitive map of...

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Research in animal learning and behavioral neuroscience has distinguished between two forms of action control: a habit-based form, which relies on stored action values, and a goal-directed form, which forecasts and compares action outcomes based on a model of the environment. While habit-based control has been the subject of extensive computational research, the computational principles underlying goal-directed control in animals have so far received less attention. In the present paper, we advance a computational framework for goal-directed control in animals and humans. We take three empirically motivated points as founding premises: (1) Neurons in dorsolateral prefrontal cortex represent action policies, (2) Neurons in orbitofrontal cortex represent rewards, and (3) Neural computation, across domains, can be appropriately understood as performing structured probabilistic inference. On a purely computational level, the resulting account relates closely to previous work using Bayesian

This paper describes a hierarchy of four navigation strategies --- guidance, place recognition-triggered response, topological navigation and metric navigation. Such a hierarchy can be used to categorize models that are inspired by current knowledge about the way animals navigate in their environments. The main mechanisms implemented in each model are described, together with the basic adaptive capacities that the corresponding strategy affords. Because biomimetic models have seldom been implemented in real robots, it is premature to compare their merits with those of traditional engineering solutions to the navigation problem. Nevertheless, the methodological options that such implementations would entail are discussed in the text. 1 Introduction Animals are living proofs that any system, equipped with proper sensors, proper actuators, and a proper control architecture, can exhibit an adaptive behavior that allows it to survive in environments that can be quite unpredictable and chal...

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.

This paper describes how tracking and target selection are used in two behavior systems of the XT-1 vision architecture for mobile robots. The first system is concerned with active tracking of moving targets and the second is used for visually controlled spatial navigation. We overview the XT-1 architecture and describe the role of expectation-based template matching for both target tracking and navigation. The subsystems for low-level processing, attentional processing, single feature processing, spatial relations, and place/object-recognition are described and we present a number of behaviors that can make use of the different visual processing stages. The architecture, which is inspired by biology, has been successfully implemented in a number of robots which are also briefly described. 1.

This paper describes how an animat endowed with the MonaLysa control architecture can build a cognitive map that merges into a hierarchical framework not only topological links between landmarks, but also higher-level structures, control information, and metric distances and orientations. The paper also describes how the animat can use such a map to locate itself, even if it is endowed with noisy dead-reckoning capacities. MonaLysa's mapping and self-positioning capacities are illustrated by results obtained in three different environments and four noise-level conditions. These capacities appear to be gracefully degraded when the environment grows more challenging and when the noise level increases. In the discussion, the current approach is compared to others with similar objectives, and directions for future work are outlined. Keywords Hierarchical map. Topological information. Metric information. Landmarks. Self-positioning. Dead-reckoning. Robustness to noise. 1 Introduction In...