Modulation of orientation selectivity by orientation precision

Abstract

The primary visual cortex (V1) processes complex mixtures of orientations to build neural representations of our everyday visual environment. It remains unclear how V1 adapts to the highly volatile distributions of orientations found in natural images. We used naturalistic stimuli and measured the response of V1 neurons to orientation distributions of varying bandwidth. Although broad distributions decreased single neuron tuning, a neurally plausible decoder could robustly retrieve the orientations of stimuli from the population activity at all bandwidths. This decoder demonstrates that V1 population co-encodes orientation and its precision, enhancing population decoding performances compared to sole orientation decoding. This internal representation is mediated by temporally distinct neural dynamics and supports a precision-weighted description of neuronal message passing in the visual cortex, in line with predictive processing theories.

Hugo Ladret

Phd in Computational Neuroscience

My PhD subject focused on the role of precision in natural and artificial neural networks.

Frédéric Chavane

Laurent U Perrinet

Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.