Switching circuit for optimal context integration during static + moving contexts (Voina et al 2022)

The brain processes information at all times and much of that information is context-dependent.The visual system presents an important example: processing is ongoing, but the context changes dramatically when an animal is still vs. running. How is context-dependent information processing achieved? We take inspiration from recent neurophysiology studies on the role of distinct cell types in primary visual cortex (V1). We find that relatively few “switching units” — akin to the VIP neuron type in V1 in that they turn on and off in the running vs. still context and have connections to and from the main population — are sufficient to drive context dependent image processing. We demonstrate this in a model of feature integration and in a test of image denoising. The underlying circuit architecture illustrates a concrete computational role for the multiple cell types under increasing study across the brain, and may inspire more flexible neurally inspired computing architectures.

Region(s) or Organism(s): Visual cortex

Model Concept(s): Receptive field; Connectivity matrix; Direction Selectivity; Orientation selectivity; Stimulus selectivity; Context integration

Simulation Environment: Python; MATLAB

Implementer(s): Voina, Doris


Voina D, Recanatesi S, Hu B, Shea-Brown E, Mihalas S. (2022). Single Circuit in V1 Capable of Switching Contexts During Movement Using an Inhibitory Population as a Switch Neural computation. 34 [PubMed]

This website requires cookies and limited processing of your personal data in order to function. By continuing to browse or otherwise use this site, you are agreeing to this use. See our Privacy policy and how to cite and terms of use.