title: Central auditory neurons have composite receptive fields.
abstract: High-level neurons processing complex, behaviorally relevant signals are sensitive to conjunctions of features. Characterizing the receptive fields of such neurons is difficult with standard statistical tools, however, and the principles governing their organization remain poorly understood. Here, we demonstrate multiple distinct receptive-field features in individual high-level auditory neurons in a songbird, European starling, in response to natural vocal signals (songs). We then show that receptive fields with similar characteristics can be reproduced by an unsupervised neural network trained to represent starling songs with a single learning rule that enforces sparseness and divisive normalization. We conclude that central auditory neurons have composite receptive fields that can arise through a combination of sparseness and normalization in neural circuits. Our results, along with descriptions of random, discontinuous receptive fields in the central olfactory neurons in mammals and insects, suggest general principles of neural computation across sensory systems and animal classes.
authors.affiliation: Department of Bioengineering, Imperial College London, London, SW7 2AZ, United Kingdom; Department of Psychology, University of California, San Diego, La Jolla, CA 92093; a.kozlov@imperial.ac.uk.
authors.forename: Andrei S
authors.initials: AS
authors.lastname: Kozlov
authors.affiliation: Department of Psychology, University of California, San Diego, La Jolla, CA 92093; Section of Neurobiology, Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093; Neurosciences Program, University of California, San Diego, La Jolla, CA 92093; Kavli Institute for Brain and Mind, University of California, San Diego, La Jolla, CA 92093.
authors.forename: Timothy Q
authors.initials: TQ
authors.lastname: Gentner
mesh_terms: D000818:Animals; D001303:Auditory Cortex / Q000166:cytology / Q000502:physiology*; D009474:Neurons / Q000502:physiology*
keywords: auditory system; neural networks; receptive fields; sparseness; unsupervised learning