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- K. Wimmer, M. Stimberg,
R. Martin, L. Schwabe, J. Mariño, J. Schummers, D. C. Lyon, M. Sur, and
K. Obermayer. Dependence of Orientation Tuning on Recurrent Excitation and
Inhibition in a Network Model of V1.
.
In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances
in Neural Information Processing Systems 21, pages 1769-1776. MIT
Press, 2009.
(PDF)
The computational role of the local recurrent network in primary
visual cortex is still a matter of debate. To address this issue, we analyze
intracellular recording data of cat V1, which combine measuring the tuning of
a range of neuronal properties with a precise localization of the recording
sites in the orientation preference map. For the analysis, we consider a
network model of Hodgkin-Huxley type neurons arranged according to a
biologically plausible two-dimensional topographic orientation preference
map. We then systematically vary the strength of the recurrent excitation and
inhibition relative to the strength of the afferent input. Each
parametrization gives rise to a different model instance for which the tuning
of model neurons at different locations of the orientation map is compared to
the experimentally measured orientation tuning of membrane potential, spike
output, excitatory, and inhibitory conductances. A quantitative analysis
shows that the data provides strong evidence for a network model in which the
afferent input is dominated by strong, balanced contributions of recurrent
excitation and inhibition. This recurrent regime is close to a regime of
“instability”, where strong, self-sustained activity of the network
occurs. The firing rate of neurons in the best-fitting network is
particularly sensitive to small modulations of model parameters, which could
be one of the functional benefits of a network operating in this particular
regime.
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