August 2012
Volume 12, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2012
Space-based and Feature-based Attention in a Realistic Layered-microcircuit Model of Visual Cortex
Author Affiliations
  • Nobuhiko Wagatsuma
    RIKEN Brain Science Institute\nResearch Fellow of the Japan Society for the Promotion of Science
  • Tobias Potjans
    Research Center Juelich
  • Markus Diesmann
    Research Center Juelich
  • Ko Sakai
    University of Tsukuba
  • Tomoki Fukai
    RIKEN Brain Science Institute
Journal of Vision August 2012, Vol.12, 662. doi:10.1167/12.9.662
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      Nobuhiko Wagatsuma, Tobias Potjans, Markus Diesmann, Ko Sakai, Tomoki Fukai; Space-based and Feature-based Attention in a Realistic Layered-microcircuit Model of Visual Cortex. Journal of Vision 2012;12(9):662. doi: 10.1167/12.9.662.

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

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Abstract

Attention towards space and feature modulates various levels of neural responses and perceptions. However, spatial and feature-based attention differently affect visual processing and perception via their gain and tuning properties (McAdams and Maunsell, 1999; Martinez-Trujillo and Treue, 2004; Ling, Liu and Carrasco, 2009). We examined computationally a mechanism of the type-specific attention modulation through a layered visual cortical microcircuit model based on current knowledge of cortical neurobiology. The proposed microcircuit model consists of eight orientation columnar circuits in V1, each sharing their receptive fields. A column is based on about 20,000 integrate-and-fire neurons and represents layers 2/3, 4, 5 and 6 (Potjans and Diesmann, 2011). These columns primarily interact via lateral inhibitions from layer 2/3 excitatory neurons in one column to layer 2/3 inhibitory in others (Wagatsuma, Potjans, Diesmann and Fukai, 2011). We introduced additional inter-columnar connections between excitatory neurons residing in columns of similar selectivity. Eight columns receive different preferred bottom-up visual stimuli at layers 4 and 6 as well as selective top-down feature-based attention at layers 2/3 and 5. In contrast, top-down spatial attention is homogeneously projected to all columns without the dependence on their selectivity. Our model quantitatively reproduced the type-specific attention modulations reported from physiological studies: spatial attention indicated a multiplicative scaling of the responses of all orientations, whereas feature-based attention both increased the gain and sharpened the tuning curve. Furthermore, the simulations of the model with various levels of external noise showed good agreement with psychophysical observations: spatial attention increased discriminability only when the low external noise, whereas feature-based attention boosted the performance at both low and high level of noise. These simulation results suggested that the allocation of top-down signals and the inter-columnar synaptic connection within the subpopulation of the visual cortex underlie the type-dependent attention modulations of neuronal responses and visual perception.

Meeting abstract presented at VSS 2012

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