  Visual perception results from a systematic transformation of the information
flowing through the visual system. In the neuronal hierarchy, the response
properties of single neurons are determined by neurons located one level below,
and in turn, determine the responses of neurons located one level above.
Therefore in modeling receptive fields, it is essential to ensure that the
response properties of neurons in a given level can be generated by combining
the response models of neurons in its input levels. However, existing response
models of neurons in the motion cortex do not inherently yield the temporal
frequency filtering gradient (TFFG) property that is known to emerge along the
primary visual cortex (V1) to middle temporal (MT) motion processing stream.
TFFG is the change from predominantly lowpass to predominantly bandpass
temporal frequency filtering character along the V1 to MT pathway (Foster et al
1985; DeAngelis et al 1993; Hawken et al 1996). We devised a new model, the
sinc wavelet model (Odaibo, 2014), which logically and efficiently generates
the TFFG. The model replaces the Gabor function's sine wave carrier with a sinc
(sin(x)/x) function, and has the same or fewer number of parameters as existing
models. Because of its logical consistency with the emergent network property
of TFFG, we conclude that the sinc wavelet is a better model for the receptive
fields of motion cortex neurons. This model will provide new physiological
insights into how the brain represents visual information.
