Spike-triggered characterization of excitatory and
suppressive stimulus dimensions in monkey V1
directionally selective neurons
Nicole C Rust, Odelia Schwartz, J Anthony Movshon, and
Eero P Simoncelli
Presented (as a talk) at:
Annual Meeting, Computational Neuroscience (CNS*03), Alicante Spain, 5-9 July 2003.
Published in:
Neurocomputing
vol 58-60C, pp 793-799, 2004.
© Elsevier publishers.
Neurons in primary visual cortex are commonly characterized using
linear models, or simple extensions of linear models. Specifically, V1
simple cell responses are often characterized using a rectified linear
receptive field, and complex cell responses are often described as the
sum of squared responses of two linear subunits. We examined this
class of model directly by applying spike-triggered covariance
analysis to responses of monkey V1 neurons under binary white noise
stimulation. The analysis extracts a low-dimensional subspace of the
full stimulus space that is primarily responsible for generation of
the neural response, including both excitatory and suppressive
components. We found no fewer than two excitatory dimensions in simple
cells, and as many as seven dimensions in complex cells. For all
cells, we also found suppressive dimensions that were at least equal
in number to the excitatory dimensions. These results suggest that
extensions to standard models are required to fully describe the
response properties of cells in V1.
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