Adaptive Synaptogenesis Constructs Neural Codes That Benefit Discrimination
Intelligent organisms face a variety of tasks requiring the acquisition of expertise within a specific domain, including the ability to discriminate between a large number of similar patterns. From an energy-efficiency perspective, effective discrimination requires a prudent allocation of neural res...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Public Library of Science
2015
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| Online Access: | http://hdl.handle.net/1721.1/98118 https://orcid.org/0000-0001-6071-3190 |
| _version_ | 1826188736508985344 |
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| author | Thomas, Blake T. Levy, William B. Blalock, Davis |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Thomas, Blake T. Levy, William B. Blalock, Davis |
| author_sort | Thomas, Blake T. |
| collection | MIT |
| description | Intelligent organisms face a variety of tasks requiring the acquisition of expertise within a specific domain, including the ability to discriminate between a large number of similar patterns. From an energy-efficiency perspective, effective discrimination requires a prudent allocation of neural resources with more frequent patterns and their variants being represented with greater precision. In this work, we demonstrate a biologically plausible means of constructing a single-layer neural network that adaptively (i.e., without supervision) meets this criterion. Specifically, the adaptive algorithm includes synaptogenesis, synaptic shedding, and bi-directional synaptic weight modification to produce a network with outputs (i.e. neural codes) that represent input patterns proportional to the frequency of related patterns. In addition to pattern frequency, the correlational structure of the input environment also affects allocation of neural resources. The combined synaptic modification mechanisms provide an explanation of neuron allocation in the case of self-taught experts. |
| first_indexed | 2024-09-23T08:03:56Z |
| format | Article |
| id | mit-1721.1/98118 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:03:56Z |
| publishDate | 2015 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/981182022-09-30T07:14:26Z Adaptive Synaptogenesis Constructs Neural Codes That Benefit Discrimination Thomas, Blake T. Levy, William B. Blalock, Davis Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Blalock, Davis Intelligent organisms face a variety of tasks requiring the acquisition of expertise within a specific domain, including the ability to discriminate between a large number of similar patterns. From an energy-efficiency perspective, effective discrimination requires a prudent allocation of neural resources with more frequent patterns and their variants being represented with greater precision. In this work, we demonstrate a biologically plausible means of constructing a single-layer neural network that adaptively (i.e., without supervision) meets this criterion. Specifically, the adaptive algorithm includes synaptogenesis, synaptic shedding, and bi-directional synaptic weight modification to produce a network with outputs (i.e. neural codes) that represent input patterns proportional to the frequency of related patterns. In addition to pattern frequency, the correlational structure of the input environment also affects allocation of neural resources. The combined synaptic modification mechanisms provide an explanation of neuron allocation in the case of self-taught experts. 2015-08-20T15:44:00Z 2015-08-20T15:44:00Z 2015-07 2014-10 Article http://purl.org/eprint/type/JournalArticle 1553-7358 1553-734X http://hdl.handle.net/1721.1/98118 Thomas, Blake T., Davis W. Blalock, and William B. Levy. “Adaptive Synaptogenesis Constructs Neural Codes That Benefit Discrimination.” Edited by Richard Granger. PLoS Comput Biol 11, no. 7 (July 15, 2015): e1004299. https://orcid.org/0000-0001-6071-3190 en_US http://dx.doi.org/10.1371/journal.pcbi.1004299 PLOS Computational Biology Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ application/pdf Public Library of Science Public Library of Science |
| spellingShingle | Thomas, Blake T. Levy, William B. Blalock, Davis Adaptive Synaptogenesis Constructs Neural Codes That Benefit Discrimination |
| title | Adaptive Synaptogenesis Constructs Neural Codes That Benefit Discrimination |
| title_full | Adaptive Synaptogenesis Constructs Neural Codes That Benefit Discrimination |
| title_fullStr | Adaptive Synaptogenesis Constructs Neural Codes That Benefit Discrimination |
| title_full_unstemmed | Adaptive Synaptogenesis Constructs Neural Codes That Benefit Discrimination |
| title_short | Adaptive Synaptogenesis Constructs Neural Codes That Benefit Discrimination |
| title_sort | adaptive synaptogenesis constructs neural codes that benefit discrimination |
| url | http://hdl.handle.net/1721.1/98118 https://orcid.org/0000-0001-6071-3190 |
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