Central auditory neurons have composite receptive fields
File(s)
Accepted version
Published version
Author(s)
Kozlov, A
Gentner, T
Type
Journal Article
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.
Date Issued
2016-02-02
Date Acceptance
2015-12-24
Citation
Proceedings of the National Academy of Sciences of the United States of America, 2016, 113 (5), pp.1441-1446
ISSN
0027-8424
Publisher
National Academy of Sciences
Start Page
1441
End Page
1446
Journal / Book Title
Proceedings of the National Academy of Sciences of the United States of America
Volume
113
Issue
5
Copyright Statement
Freely available online through the PNAS open access option.
Identifier
https://www.pnas.org/content/113/5/1441
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
auditory system
neural networks
receptive fields
sparseness
unsupervised learning
NEURAL RESPONSES
SELECTIVITY
SPARSE
SONG
REPRESENTATIONS
EMERGENCE
CODE
auditory system
neural networks
receptive fields
sparseness
unsupervised learning
Animals
Auditory Cortex
Neurons
Auditory Cortex
Neurons
Animals
Publication Status
Published
Date Publish Online
2016-01-19