Journal of Neuroscience, Vol 12, 3568-3581, Copyright © 1992 by Society for Neuroscience
Predicting responses of nonlinear neurons in monkey striate cortex to complex patterns
SR Lehky, TJ Sejnowski and R Desimone
Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda, Maryland 20892.
The overwhelming majority of neurons in primate visual cortex are
nonlinear. For those cells, the techniques of linear system analysis, used
with some success to model retinal ganglion cells and striate simple cells,
are of limited applicability. As a start toward understanding the
properties of nonlinear visual neurons, we have recorded responses of
striate complex cells to hundreds of images, including both simple stimuli
(bars and sinusoids) as well as complex stimuli (random textures and 3-D
shaded surfaces). The latter set tended to give the strongest response. We
created a neural network model for each neuron using an iterative
optimization algorithm. The recorded responses to some stimulus patterns
(the training set) were used to create the model, while responses to other
patterns were reserved for testing the networks. The networks predicted
recorded responses to training set patterns with a median correlation of
0.95. They were able to predict responses to test stimuli not in the
training set with a correlation of 0.78 overall, and a correlation of 0.65
for complex stimuli considered alone. Thus, they were able to capture much
of the input/output transfer function of the neurons, even for complex
patterns. Examining connection strengths within each network, different
parts of the network appeared to handle information at different spatial
scales. To gain further insights, the network models were inverted to
construct "optimal" stimuli for each cell, and their receptive fields were
mapped with high-resolution spots. The receptive field properties of
complex cells could not be reduced to any simpler mathematical formulation
than the network models themselves.
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