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      Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells

      research-article
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      PLoS Computational Biology
      Public Library of Science

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          Abstract

          We present a model for the self-organized formation of place cells, head-direction cells, and spatial-view cells in the hippocampal formation based on unsupervised learning on quasi-natural visual stimuli. The model comprises a hierarchy of Slow Feature Analysis (SFA) nodes, which were recently shown to reproduce many properties of complex cells in the early visual system [ 1]. The system extracts a distributed grid-like representation of position and orientation, which is transcoded into a localized place-field, head-direction, or view representation, by sparse coding. The type of cells that develops depends solely on the relevant input statistics, i.e., the movement pattern of the simulated animal. The numerical simulations are complemented by a mathematical analysis that allows us to accurately predict the output of the top SFA layer.

          Author Summary

          Rats excel at navigating through complex environments. In order to find their way, they need to answer two basic questions. Where am I? In which direction am I heading? As the brain has no direct access to information about its position in space, it has to rely on sensory signals—from eyes and ears for example—to answer these questions. Information about its position and orientation is typically present in the information it gathers from its senses, but unfortunately it is encoded in a way that is not obvious to decode. Three major types of cells in the brain whose firing directly reflects spatial information are place, head-direction, and view cells. Place cells, for example, fire when the animal is at a particular location independent of the direction the animal is looking in. In this study, we present a self-organizational model that develops these three representation types by learning on naturalistic videos mimicking the visual input of a rat. Although the model works on complex visual stimuli, a rigorous mathematical description of the system is given as well.

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          Most cited references47

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          Microstructure of a spatial map in the entorhinal cortex.

          The ability to find one's way depends on neural algorithms that integrate information about place, distance and direction, but the implementation of these operations in cortical microcircuits is poorly understood. Here we show that the dorsocaudal medial entorhinal cortex (dMEC) contains a directionally oriented, topographically organized neural map of the spatial environment. Its key unit is the 'grid cell', which is activated whenever the animal's position coincides with any vertex of a regular grid of equilateral triangles spanning the surface of the environment. Grids of neighbouring cells share a common orientation and spacing, but their vertex locations (their phases) differ. The spacing and size of individual fields increase from dorsal to ventral dMEC. The map is anchored to external landmarks, but persists in their absence, suggesting that grid cells may be part of a generalized, path-integration-based map of the spatial environment.
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            Path integration and the neural basis of the 'cognitive map'.

            The hippocampal formation can encode relative spatial location, without reference to external cues, by the integration of linear and angular self-motion (path integration). Theoretical studies, in conjunction with recent empirical discoveries, suggest that the medial entorhinal cortex (MEC) might perform some of the essential underlying computations by means of a unique, periodic synaptic matrix that could be self-organized in early development through a simple, symmetry-breaking operation. The scale at which space is represented increases systematically along the dorsoventral axis in both the hippocampus and the MEC, apparently because of systematic variation in the gain of a movement-speed signal. Convergence of spatially periodic input at multiple scales, from so-called grid cells in the entorhinal cortex, might result in non-periodic spatial firing patterns (place fields) in the hippocampus.
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              • Article: not found

              The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat.

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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                pcbi
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                August 2007
                31 August 2007
                : 3
                : 8
                : e166
                Affiliations
                [1]Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
                University College London, United Kingdom
                Author notes
                * To whom correspondence should be addressed. E-mail: m.franzius@ 123456biologie.hu-berlin.de
                Article
                07-PLCB-RA-0240R2 plcb-03-08-18
                10.1371/journal.pcbi.0030166
                1963505
                17784780
                0c9e7b89-922a-479d-bbb8-79e57ed3ca7c
                Copyright: © 2007 Franzius et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 1 May 2007
                : 5 July 2007
                Page count
                Pages: 18
                Categories
                Research Article
                Computational Biology
                Neuroscience
                Rattus (Rat)
                Primates
                Custom metadata
                Franzius M, Sprekeler H, Wiskott L (2007) Slowness and sparseness lead to place, head-direction, and spatial-view cells. PLoS Comput Biol 3(8): e166. doi: 10.1371/journal.pcbi.0030166

                Quantitative & Systems biology
                Quantitative & Systems biology

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