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A theoretical account of cue averaging in the rodent head direction system
Authors:Hector J I Page  Daniel M Walters  Rebecca Knight  Caitlin E Piette  Kathryn J Jeffery  Simon M Stringer
Institution:1.Oxford Centre for Theoretical Neuroscience and Artificial Intelligence, Departmental of Experimental Psychology, University of Oxford, South Parks Road, Oxford OX1 3UD, UK;2.Division of Psychology and Language Sciences, Department of Cognitive, Perceptual and Brain Sciences, Institute of Behavioural Neuroscience, University College London, 26 Bedford Way, London WC1H 0AP, UK
Abstract:Head direction (HD) cell responses are thought to be derived from a combination of internal (or idiothetic) and external (or allothetic) sources of information. Recent work from the Jeffery laboratory shows that the relative influence of visual versus vestibular inputs upon the HD cell response depends on the disparity between these sources. In this paper, we present simulation results from a model designed to explain these observations. The model accurately replicates the Knight et al. data. We suggest that cue conflict resolution is critically dependent on plastic remapping of visual information onto the HD cell layer. This remap results in a shift in preferred directions of a subset of HD cells, which is then inherited by the rest of the cells during path integration. Thus, we demonstrate how, over a period of several minutes, a visual landmark may gain cue control. Furthermore, simulation results show that weaker visual landmarks fail to gain cue control as readily. We therefore suggest a second longer term plasticity in visual projections onto HD cell areas, through which landmarks with an inconsistent relationship to idiothetic information are made less salient, significantly hindering their ability to gain cue control. Our results provide a mechanism for reliability-weighted cue averaging that may pertain to other neural systems in addition to the HD system.
Keywords:path integration  head direction cells  isomapping  sensory cue integration  attractor dynamics  neural networks
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