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Learning Midlevel Auditory Codes from Natural Sound Statistics

Author(s)
Mlynarski, Wiktor; McDermott, Joshua H.
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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Abstract
Interaction with the world requires an organism to transform sensory signals into representations in which behaviorally meaningful properties of the environment are made explicit. These representations are derived through cascades of neuronal processing stages in which neurons at each stage recode the output of preceding stages. Explanations of sensory coding may thus involve understanding how low-level patterns are combined into more complex structures. To gain insight into such midlevel representations for sound, we designed a hierarchical generative model of natural sounds that learns combinations of spectrotemporal features from natural stimulus statistics. In the first layer, the model forms a sparse convolutional code of spectrograms using a dictionary of learned spectrotemporal kernels. To generalize from specific kernel activation patterns, the second layer encodes patterns of time-varying magnitude of multiple first-layer coefficients. When trained on corpora of speech and environmental sounds, some second-layer units learned to group similar spectrotemporal features. Others instantiate opponency between distinct sets of features. Such groupings might be instantiated by neurons in the auditory cortex, providing a hypothesis for midlevel neuronal computation.
Date issued
2018-02
URI
http://hdl.handle.net/1721.1/114502
Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Journal
Neural Computation
Publisher
MIT Press
Citation
Młynarski, Wiktor, and Josh H. McDermott. “Learning Midlevel Auditory Codes from Natural Sound Statistics.” Neural Computation, vol. 30, no. 3, Mar. 2018, pp. 631–69. © 2018 Massachusetts Institute of Technology
Version: Final published version
ISSN
0899-7667
1530-888X

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