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
References:
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]