Structured random receptive fields enable informative sensory encodings

Biraj Pandey, Marius Pachitariu, Bingni W. Brunton, Kameron Decker Harris

Abstract: Author summary Evolution has ensured that animal brains are dedicated to extracting useful information from raw sensory stimuli while discarding everything else. Models of sensory neurons are a key part of our theories of how the brain represents the world. In this work, we model the tuning properties of sensory neurons in a way that incorporates randomness and builds a bridge to a leading mathematical theory for understanding how artificial neural networks learn. Our models capture important properties of large populations of real neurons presented with varying stimuli. Moreover, we give a precise mathematical formula for how sensory neurons in two distinct areas, one involving a gyroscopic organ in insects and the other visual processing center in mammals, transform their inputs. We also find that artificial models imbued with properties from real neurons learn more efficiently, with shorter training time and fewer examples, and our mathematical theory explains some of these findings. This work expands our understanding of sensory representation in large networks with benefits for both the neuroscience and machine learning communities.