ePoster

Bayesian causal inference predicts center-surround interactions in the middle temporal visual area (MT)

Gabor Lengyel, Sabyasachi Shivkumar, Ralf Haefner
FENS Forum 2024(2024)
Messe Wien Exhibition & Congress Center, Vienna, Austria

Conference

FENS Forum 2024

Messe Wien Exhibition & Congress Center, Vienna, Austria

Resources

Authors & Affiliations

Gabor Lengyel, Sabyasachi Shivkumar, Ralf Haefner

Abstract

Center-surround processing (CSP) is a ubiquitous feature of the brain, starting from early sensory processing (e.g., context-dependent retinal encoding) to representing abstract concepts (e.g., context-dependent object representation), manifesting itself in both behavior and neural responses. CPS in neural responses is typically modeled using normalization models or in purely descriptive terms. However, a normative model that could enhance our understanding of the function of CSP and link these neural effects to behavior is still lacking. Here, we build on a recently developed normative model for motion perception in which CSP emerges as the result of causal inference over different reference frames. We derive predictions for single neuron activities representing causal inference computations for the entire stimulus space of center and surround directions and center and surround speeds. These neural predictions show a highly idiosyncratic interaction of center and surround directions and speeds. Interestingly, these predictions are suggestive of the two main, previously described, classes of neurons in cortical motion area MT: those with antagonistic or integrative surround (Born and Bradley, 2005). Finally, we show how the neural predictions of this model differ from those of either the normalization or phenomenological models, paving the way for future neurophysiological studies to distinguish between these alternative models of CSP in motion perception.

Unique ID: fens-24/bayesian-causal-inference-predicts-center-surround-de58403d