Adaptive Synaptogenesis Constructs Neural Codes That Benefit Discrimination
Author(s)Thomas, Blake T.; Levy, William B.; Blalock, Davis
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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.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
PLOS Computational Biology
Public Library of Science
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.
Final published version