Structured random receptive fields enable informative sensory encodings

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bioRxiv. 2022 Feb 13;. doi: 10.1101/2021.09.09.459651

Structured random receptive fields enable informative sensory encodings Pachitariu Lab

Pandey Biraj, Pachitariu Marius, Brunton Bingni, Harris Kameron

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Abstract

Brains must represent the outside world so that animals survive and thrive. In early sensory systems, neural populations have diverse receptive fields structured to detect important features in inputs, yet significant variability has been ignored in classical models of sensory neurons. We model neuronal receptive fields as random, variable samples from parametrized distributions in two sensory modalities, using data from insect mechanosensors and neurons of mammalian primary visual cortex. We show that these random feature neurons perform a randomized wavelet transform on inputs which removes high frequency noise and boosts the signal. Our result makes a significant theoretical connection between the foundational concepts of receptive fields in neuroscience and random features in artificial neural networks. Further, these random feature neurons enable learning from fewer training samples and with smaller networks in artificial tasks. This structured random model of receptive fields provides a unifying, mathematically tractable framework to understand sensory encodings across both spatial and temporal domains.

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Structured random receptive fields enable informative sensory encodings

Abstract

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