A model of V1 for visual working memory using cortical and interlaminar feedback
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1
Justus Liebig University, Department of General Psychology, Germany
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2
University of Ulm, Institute of Neural Information Processing, Germany
Early visual areas can store specific information about visual features held in working memory for many seconds in the absence of a physical stimulus (Harrison & Tong 2009, Nature 458 632-635). We have developed a model of V1 using recurrent long-range interaction that enhances coherent contours (Hansen & Neumann 2008, Journal of Vision 8(8):8 1-25) and robustly extracts corners and junctions points (Hansen & Neumann 2004, Neural Computation 16(5) 1013-1037). Here we extend this model by incorporating an orientation selective feedback signal from a higher cortical area. The feedback signal is nonlinearly compressed and multiplied with the feedforward signal. The compression increases the gain for decreasing input, such that the selection of the orientation to be memorized is realized by a selective decrease of feedback for this orientation. As a consequence, the model predicts that the overall activity in the network should decrease with the number of orientations to be memorized. Model simulations reveal that the feedback results in sustained activity of the orientation to be memorized over many recurrent cycles after stimulus removal. The pattern of activity is robust against an intervening, irrelevant orthogonal orientation shown after the orientation to be memorized. We suggest that the prolonged activation for sustained working memory in V1 shares similarities with the finding that different processing stages map onto different temporal episodes of V1 activation in figure-ground segregation (Roelfsema, Tolboom, & Khayat 2007, Neuron 56 785-792). Unlike previous approaches that have modeled working memory with a dedicated circuit, we show that a model of recurrent interactions in a sensory area such as V1 can be extended to memorize visual features by incorporating a feedback signal from a higher area.
Conference:
Bernstein Conference on Computational Neuroscience, Frankfurt am Main, Germany, 30 Sep - 2 Oct, 2009.
Presentation Type:
Poster Presentation
Topic:
Dynamical systems and recurrent networks
Citation:
Hansen
T and
Neumann
H
(2009). A model of V1 for visual working memory using cortical and interlaminar feedback.
Front. Comput. Neurosci.
Conference Abstract:
Bernstein Conference on Computational Neuroscience.
doi: 10.3389/conf.neuro.10.2009.14.055
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Received:
26 Aug 2009;
Published Online:
26 Aug 2009.
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Correspondence:
Thorsten Hansen, Justus Liebig University, Department of General Psychology, Giessen, Germany, thorsten.hansen@psychol.uni-giessen.de