reverse correlation and visual receptive fields

[blank] correlation analysis can be applied to the visual system, including the retina, lateral geniculate nucleus of the thalamus, and the primary visual cortex

reverse

reverse correlation analysis would involve averaging the [blank] that precedes activity

stimuli

linear filters, Wiener kernels, and static nonlinearity create models of the [blank] of dynamic stimuli to time-dependent firing rates

transformation

neighborhood relationships in the retina are transformed into the cortex through a complex [blank] map

logarithmic

[blank] sampling is used in the retina and Gabor functions for separable and non-separable visual fields

nyquist

nyquist sampling involves separating a signal into its [blank] parts if the sampling frequency is twice the highest frequency component of the signal

discrete

static nonlinearities of a neuron means that the neuron generator (receptor) potential is fed into a memoryless, nonlinear function and is transformed into its [blank] rate

firing

[blank] functions are products of a Gaussian envelope and a sinusoidal wave and model the receptive fields of simple and complex cells in our primary visual cortex

Gabor

retinal ganglion cell and LGN responses were modeled using a [blank] kernel

difference-of-Gaussian

difference-of-Gaussian [blanks] highlight areas of high contrast and edge features in the original image

kernels