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Articles, Systems/Circuits

Inhibitory Interneurons Decorrelate Excitatory Cells to Drive Sparse Code Formation in a Spiking Model of V1

Paul D. King, Joel Zylberberg and Michael R. DeWeese
Journal of Neuroscience 27 March 2013, 33 (13) 5475-5485; https://doi.org/10.1523/JNEUROSCI.4188-12.2013
Paul D. King
1Redwood Center for Theoretical Neuroscience,
2Helen Wills Neuroscience Institute, and
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Joel Zylberberg
1Redwood Center for Theoretical Neuroscience,
3Department of Physics, University of California, Berkeley, California 94720
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Michael R. DeWeese
1Redwood Center for Theoretical Neuroscience,
2Helen Wills Neuroscience Institute, and
3Department of Physics, University of California, Berkeley, California 94720
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Abstract

Sparse coding models of natural scenes can account for several physiological properties of primary visual cortex (V1), including the shapes of simple cell receptive fields (RFs) and the highly kurtotic firing rates of V1 neurons. Current spiking network models of pattern learning and sparse coding require direct inhibitory connections between the excitatory simple cells, in conflict with the physiological distinction between excitatory (glutamatergic) and inhibitory (GABAergic) neurons (Dale's Law). At the same time, the computational role of inhibitory neurons in cortical microcircuit function has yet to be fully explained. Here we show that adding a separate population of inhibitory neurons to a spiking model of V1 provides conformance to Dale's Law, proposes a computational role for at least one class of interneurons, and accounts for certain observed physiological properties in V1. When trained on natural images, this excitatory–inhibitory spiking circuit learns a sparse code with Gabor-like RFs as found in V1 using only local synaptic plasticity rules. The inhibitory neurons enable sparse code formation by suppressing predictable spikes, which actively decorrelates the excitatory population. The model predicts that only a small number of inhibitory cells is required relative to excitatory cells and that excitatory and inhibitory input should be correlated, in agreement with experimental findings in visual cortex. We also introduce a novel local learning rule that measures stimulus-dependent correlations between neurons to support “explaining away” mechanisms in neural coding.

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The Journal of Neuroscience: 33 (13)
Journal of Neuroscience
Vol. 33, Issue 13
27 Mar 2013
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Inhibitory Interneurons Decorrelate Excitatory Cells to Drive Sparse Code Formation in a Spiking Model of V1
Paul D. King, Joel Zylberberg, Michael R. DeWeese
Journal of Neuroscience 27 March 2013, 33 (13) 5475-5485; DOI: 10.1523/JNEUROSCI.4188-12.2013

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Inhibitory Interneurons Decorrelate Excitatory Cells to Drive Sparse Code Formation in a Spiking Model of V1
Paul D. King, Joel Zylberberg, Michael R. DeWeese
Journal of Neuroscience 27 March 2013, 33 (13) 5475-5485; DOI: 10.1523/JNEUROSCI.4188-12.2013
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