Odelia Schwartz, 6/14/01
<br>
<br>
Spike-triggered average techniques are effective for
linear characterization of neural responses.  But sensory neurons
exhibit important nonlinear behaviors that are not captured by such
analyses.  Many of these nonlinear behaviors are consistent with gain
control.  We demonstrate an automated methodology for characterizing
a neuron with gain control.  We assume a  model in which the spike rate of
a neuron is proportional to the halfwave rectified response of a linear
kernel suppressively modulated by a weighted sum of rectified responses
of other linear kernels. First, we recover the linear kernel through spike
triggered averaging. Next, we compute the covariance matrix of the stimuli
eliciting spikes, and perform a principal components decomposition of
this matrix.  The principal axes (eigenvectors) associated with small
variance (eigenvalue) correspond to directions in which the response
of the neuron is modulated suppressively. Finally, we recover the
suppressive weights associated with these axes by maximizing the
likelihood of the spike data.  We demonstrate this method on a simulated
neuron and on salamander retinal ganglion cell data (Chichilnisky lab).
Analysis of physiological data reveals meaningful suppressive axes
and explains interesting nonlinearities. We believe this method will
be applicable to other sensory areas and modalities.