Suppressive Traveling Waves Shape Representations of Illusory Motion in Primary Visual Cortex of Awake Primate

Abstract

How does the brain link visual stimuli across space and time? Visual illusions provide an experimental paradigm to study these processes. When two stationary dots are flashed in close spatial and temporal succession, human observers experience a percept of apparent motion. Large spatiotemporal separation challenges the visual system to keep track of object identity along the apparent motion path, the so-called "correspondence problem." Here, we use voltage-sensitive dye imaging in primary visual cortex (V1) of awake monkeys to show that intracortical connections within V1 can solve this issue by shaping cortical dynamics to represent the illusory motion. We find that the appearance of the second stimulus in V1 creates a systematic suppressive wave traveling toward the retinotopic representation of the first. Using a computational model, we show that the suppressive wave is the emergent property of a recurrent gain control fed by the intracortical network. This suppressive wave acts to explain away ambiguous correspondence problems and contributes to precisely encode the expected motion velocity at the surface of V1. Together, these results demonstrate that the nonlinear dynamics within retinotopic maps can shape cortical representations of illusory motion. Understanding these dynamics will shed light on how the brain links sensory stimuli across space and time, by preformatting population responses for a straightforward read-out by downstream areas.

Scene Representations Conveyed by Cortical Feedback to Early Visual Cortex Can Be Described by Line Drawings

“…The conservative mapping and large cortical area of occluded ROIs in this study minimizes this second possibility. Thus, activity patterns in occluded V1 and V2 are related to internal model predictions transmitted to the early visual cortex via cortical feedback with only a minimal contribution coming from lateral connections Chemla et al (2019). To determine whether scene category and depth were related to internal models, we first attempted to decode these two scene characteristics from occluded V1 and V2 responses.…”

“…It also suggests that the signals we are examining are indeed from cortical feedback and are not because of signal spillover. Instead, these responses could be explained by feedback signals alone, or by interactions between cortical feedback and lateral processing (Erlhagen, 2003; Liang et al, 2017; Chemla et al, 2019).…”

Cholinergic switch between two types of slow waves in cerebral cortex

“…In particular, inhibitory cells were endowed with enhanced gain to simulate excitatory and inhbitory firing rates at the onset of UP and DOWN states under anesthesia [Jercog et al, 2017]. Additionally, a gain difference between excitatory and inhbitory cells was also needed to model network dynamics and responsiveness to multiple interacting stimuli in the awake state [Chemla et al, 2019].…”

A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin–Huxley models

“…On the other hand, they enable a direct comparison with imaging studies where the spatial resolution implies that the recorded field represents the average over a large population of neurons (i.e. a mean-field) (Capone et al, 2017;Chemla et al, 2019).…”