Interference of Neural Waves in Distributed Inhibition-stabilized Networks

TL;DR

This paper investigates how neural waves in inhibition-stabilized networks create interference patterns that influence visual perception, revealing a unified mechanism for diverse neural interactions.

Contribution

It demonstrates that neural-wave interference in canonical circuits can explain various visual processing phenomena traditionally attributed to different neural circuits.

Findings

Neural activation propagates as spatiotemporal waves in inhibition-stabilized networks.

Interference patterns confer stimulus selectivity based on frequency and velocity.

Network nonlinearity causes intrinsic tuning to stimulus intensity and contrast.

Abstract

To gain insight into the neural events responsible for visual perception of static and dynamic optical patterns, we study how neural activation spreads in arrays of inhibition-stabilized neural networks with nearest-neighbor coupling. The activation generated in such networks by local stimuli propagates between locations, forming spatiotemporal waves that affect the dynamics of activation generated by stimuli separated spatially and temporally, and by stimuli with complex spatiotemporal structure. These interactions form characteristic interference patterns that make the network intrinsically selective for certain stimuli, such as modulations of luminance at specific spatial and temporal frequencies and specific velocities of visual motion. Due to the inherent nonlinearity of the network, its intrinsic tuning depends on stimulus intensity and contrast. The interference patterns have multiple features of "lateral" interactions between stimuli, well known in physiological and behavioral studies of visual systems. The diverse phenomena have been previously attributed to distinct neural circuits. Our results demonstrate how the canonical circuit can perform the diverse operations in a manner predicted by neural-wave interference.