RT Journal Article
SR Electronic
T1 Neural computations combine low- and high-order motion cues similarly, in dragonfly and monkey
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 240101
DO 10.1101/240101
A1 Eyal I. Nitzany
A1 Gil Menda
A1 Paul S. Shamble
A1 James R. Golden
A1 Qin Hu
A1 Ron R. Hoy
A1 Jonathan D. Victor
YR 2017
UL http://biorxiv.org/content/early/2017/12/28/240101.abstract
AB Visual motion analysis is fundamental to survival across the animal kingdom. In insects, our understanding of the underlying computations has centered on the Hassenstein-Reichardt motion detector, which computes two-point cross-correlation via multiplication; in mammalian cortex, it is postulated that a similar signal is computed by comparing matched squaring operations. Both of these operations are difficult to implement biophysically in a precise fashion; moreover, they fail to detect the more complex multipoint local motion cues present in the visual environment. Here, via single-unit recordings in two visual specialists, dragonfly "(Odonata)" and macaque, and via model simulations, we show that neuronal computations are not simply approximations to idealized behaviors forced by biological constraints, but rather, are signatures of a common computational strategy to capture multiple local motion cues. The similarity of motion computations at the neuronal level in the brains of two extremely dissimilar animals, with evolutionary divergence of over 700 Myr1, suggests convergence on a common computational scheme for detecting visual motion.