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      Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet

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          Abstract

          Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy model in which invariant representations can be built by self-organizing learning based on the temporal and spatial statistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associative synaptic learning rule with a short-term memory trace, and/or it can use spatial continuity in continuous spatial transformation learning which does not require a temporal trace. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in, for example, spatial and object search tasks. The approach has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene. The approach has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus.

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          Most cited references212

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          Neural networks and physical systems with emergent collective computational abilities.

          J Hopfield (1982)
          Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
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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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              Neuronal synchrony: a versatile code for the definition of relations?

              W. Singer (1999)
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                Author and article information

                Journal
                Front Comput Neurosci
                Front Comput Neurosci
                Front. Comput. Neurosci.
                Frontiers in Computational Neuroscience
                Frontiers Research Foundation
                1662-5188
                19 June 2012
                2012
                : 6
                : 35
                Affiliations
                [1] 1simpleOxford Centre for Computational Neuroscience Oxford, UK
                [2] 2simpleDepartment of Computer Science, University of Warwick Coventry, UK
                Author notes

                Edited by: Evgeniy Bart, Palo Alto Research Center, USA

                Reviewed by: Alexander G. Dimitrov, Washington State University Vancouver, USA; Jay Hegdé, Georgia Health Sciences University, USA

                *Correspondence: Edmund T. Rolls, Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK. e-mail: edmund.rolls@ 123456oxcns.org
                Article
                10.3389/fncom.2012.00035
                3378046
                22723777
                d275440b-a88a-43b8-a7f8-1febbedf394f
                Copyright © 2012 Rolls.

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.

                History
                : 01 November 2011
                : 23 May 2012
                Page count
                Figures: 44, Tables: 6, Equations: 46, References: 301, Pages: 70, Words: 63292
                Categories
                Neuroscience
                Review Article

                Neurosciences
                trace learning rule,inferior temporal visual cortex,hippocampus,object recognition,spatial scene representation,invariance,face recognition,visnet

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