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Visual perception results from a systematic transformation of the informationflowing through the visual system. In the neuronal hierarchy, the responseproperties 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 theresponse properties of neurons in a given level can be generated by combiningthe response models of neurons in its input levels. However, existing responsemodels of neurons in the motion cortex do not inherently yield the temporalfrequency filtering gradient (TFFG) property that is known to emerge along theprimary visual cortex (V1) to middle temporal (MT) motion processing stream.TFFG is the change from predominantly lowpass to predominantly bandpasstemporal frequency filtering character along the V1 to MT pathway (Foster et al1985; DeAngelis et al 1993; Hawken et al 1996). We devised a new model, thesinc wavelet model (Odaibo, 2014), which logically and efficiently generatesthe 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 existingmodels. Because of its logical consistency with the emergent network propertyof TFFG, we conclude that the sinc wavelet is a better model for the receptivefields of motion cortex neurons. This model will provide new physiologicalinsights into how the brain represents visual information.

A Sinc Wavelet Describes the Receptive Fields of Neurons in the Motion Cortex