2017/09/05 by Sina Tootoonian, Peter E. Latham, Tootoonian, Sina +1 · 1 citation
Engineering · Neuroscience · #Advanced Chemical Sensor Technologies #FOS: Biological sciences #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Olfactory and Sensory Function Studies
paper · pdf · doi:10.48550/arxiv.1709.01437
openalex publication_date 2017/09/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.