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

TL;DR

This paper introduces a sinc wavelet model for motion cortex neurons that naturally generates the temporal frequency filtering gradient observed in the visual processing stream, offering a more physiologically consistent representation.

Contribution

The sinc wavelet model replaces the sine wave with a sinc function, inherently producing the TFFG property, and is simpler or equal in complexity compared to existing models.

Findings

The sinc wavelet model naturally produces TFFG.

It is more physiologically consistent with motion cortex responses.

The model has fewer or equal parameters compared to previous models.

Abstract

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.