Sparse connectivity for MAP inference in linear models using sister mitral cells.

ABSTRACT: Sensory processing is hard because the variables of interest are encoded in spike trains in a relatively complex way. A major goal in studies of sensory processing is to understand how the brain extracts those variables. Here we revisit a common encoding model in which variables are encoded linearly. Although there are typically more variables than neurons, this problem is still solvable because only a small number of variables appear at any one time (sparse prior). However, previous solutions require all-to-all connectivity, inconsistent with the sparse connectivity seen in the brain. Here we propose an algorithm that provably reaches the MAP (maximum a posteriori) inference solution, but does so using sparse connectivity. Our algorithm is inspired by the circuit of the mouse olfactory bu

SUBMITTER: Tootoonian S 

PROVIDER: S-EPMC8830798 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature