Structured random receptive fields enable informative sensory encodings
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 parameterized distributions and demonstrate this model in two sensory modalities using data from insect mechanosensors and mammalian primary visual cortex. Our approach leads to a significant theoretical connection between the foundational concepts of receptive fields and random features, a leading theory for understanding artificial neural networks. The modeled neurons perform a randomized wavelet transform on inputs, which removes high frequency noise and boosts the signal. 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 10, 2022
- Source ID
- 10.1371/journal.pcbi.1010484
Entities
People
- Bing W. Brunton
- Biraj Pandey
- Kameron Decker Harris
- Marius PachiĊ£ariu
Organizations
- Achievement Rewards for College Scientists Foundation
- Air Force Office of Scientific Research
- Howard Hughes Medical Institute
- National Science Foundation
- University of Washington
- Washington Research Foundation
- Western Washington University