2014/01/22 by Stephen G. Odaibo, Odaibo, Stephen G.
Biochemistry, Genetics and Molecular Biology · Neuroscience · #Advanced Fluorescence Microscopy Techniques #Biological Physics (physics.bio-ph) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Visual perception and processing mechanisms
paper · pdf · doi:10.48550/arxiv.1401.5589
openalex publication_date 2014/01/22 · openalex created_date 2025/10/24 · openalex updated_date 2026/07/28
Our visual system is astonishingly efficient at detecting moving objects.\nThis process is mediated by the neurons which connect the primary visual cortex\n(V1) to the middle temporal (MT) area. Interestingly, since Kuffler's\npioneering experiments on retinal ganglion cells, mathematical models have been\nvital for advancing our understanding of the receptive fields of visual\nneurons. However, existing models were not designed to describe the most\nsalient attributes of the highly specialized neurons in the V1 to MT motion\nprocessing stream; and they have not been able to do so. Here, we introduce the\nGabor-Einstein wavelet, a new family of functions for representing the\nreceptive fields of V1 to MT neurons. We show that the way space and time are\nmixed in the visual cortex is analogous to the way they are mixed in the\nspecial theory of relativity (STR). Hence we constrained the Gabor-Einstein\nmodel by requiring: (i) relativistic-invariance of the wave carrier, and (ii)\nthe minimum possible number of parameters. From these two constraints, the sinc\nfunction emerged as a natural descriptor of the wave carrier. The particular\ndistribution of lowpass to bandpass temporal frequency filtering properties of\nV1 to MT neurons (Foster et al 1985; DeAngelis et al 1993b; Hawken et al 1996)\nis clearly explained by the Gabor-Einstein basis. Furthermore, it does so in a\nmanner innately representative of the motion-processing stream's neuronal\nhierarchy. Our analysis and computer simulations show that the distribution of\ntemporal frequency filtering properties along the motion processing stream is a\ndirect effect of the way the brain jointly encodes space and time. We uncovered\nthis fundamental link by demonstrating that analogous mathematical structures\nunderlie STR and joint cortical spacetime encoding. This link will provide new\nphysiological insights into how the brain represents visual information.\n