July 2013
Volume 13, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   July 2013
MT Motion integration can be explained by the spatiotemporal frequency content of V1 surround suppression
Author Affiliations
  • Maria-Jose Escobar
    Universidad Técnica Federico Santa María, Department of Electronics Engineering, Valparaíso, Chile
  • Pedro F. Toledo
    Universidad Técnica Federico Santa María, Department of Electronics Engineering, Valparaíso, Chile
  • Guillaume S. Masson
    Team InViBe, Institut de Neurosciences de la Timone, Marseille, France
  • Pierre Kornprobst
    Neuromathcomp Project Team, Inria Sophia Antipolis, France
Journal of Vision July 2013, Vol.13, 362. doi:10.1167/13.9.362
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      Maria-Jose Escobar, Pedro F. Toledo, Guillaume S. Masson, Pierre Kornprobst; MT Motion integration can be explained by the spatiotemporal frequency content of V1 surround suppression. Journal of Vision 2013;13(9):362. doi: 10.1167/13.9.362.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Hidden formatting deleted. Delete this text! inter-ideograph">Most of the complex properties computed by high-order layers can be explained by the properties of their inputs: For example, MT response depends on the organization of V1 surround suppression [1]. More precisely, it was shown that the spatiotemporal frequency (STF) content of V1 surround has a similar but broader bandwidth than the classical receptive field (CRF), with a spatial frequency (SF) smaller than the CRF [2,3]. Interestingly, the recent study of [4] showed that higher SFs increase the strength of the surround suppression instead of decreasing it, thus interrogating the classical view of surround configuration. Hidden formatting deleted. Delete this text! inter-ideograph">In this work, we investigate through simulations the role of the STF content of suppressive V1 surrounds to reproduce desired MT population responses. To do so, we propose a V1-MT feedforward model. V1 neurons are implemented as entities defined by STF and orientation selectivity (9 STF, 36 orientations). Suppressive surround is spatially isotropic and it only depends on its STF content given by neighboring V1 neurons. Then, MT neurons response is classically obtained by pooling the activity of V1 neurons in the spatial and orientation domains (8 orientations). The connection weights of the neighboring V1 neurons are learned using a genetic algorithm in order to obtain an expected response at the level of MT. Hidden formatting deleted. Delete this text! inter-ideograph">The system was tested using a plaid type II where the expected MT response is the IOC, and with plaids type I where pattern or component responses are expected in MT. From these simulations, the results show that a variety of population response at the level of MT can be explained by the STF surround suppression at the level of V1.

Meeting abstract presented at VSS 2013

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