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

TL;DR

This paper introduces a new algorithm for MAP inference in linear models that uses sparse connectivity, inspired by the mouse olfactory bulb, and is applicable to sensory processing tasks with sparse variables.

Contribution

The authors propose a principled, sparse connectivity algorithm for MAP inference, overcoming the all-to-all connectivity requirement of previous methods.

Findings

Algorithm provably reaches MAP inference solution

Applicable to various sensory modalities

Potential extension to nonlinear models

Abstract

Sensory processing is hard because the variables of interest are encoded in spike trains in a relatively complex way. A major goal in 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 usually require all-to-all connectivity, inconsistent with the sparse connectivity seen in the brain. Here we propose a principled algorithm that provably reaches the MAP inference solution but using sparse connectivity. Our algorithm is inspired by the mouse olfactory bulb, but our approach is general enough to apply to other modalities; in addition, it should be possible to extend it to nonlinear encoding models.