Learning rules and network repair in spike-timing-based computation networks

Hopfield, J. J., Brody, C. D. (January 2004) Learning rules and network repair in spike-timing-based computation networks. Proceedings of the National Academy of Sciences of the United States of America, 101 (1). pp. 337-342. ISSN 0027-8424

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URL: https://www.ncbi.nlm.nih.gov/pubmed/14694191
DOI: 10.1073/pnas.2536316100


Plasticity in connections between neurons allows learning and adaptation, but it also allows noise to degrade the function of a network. Ongoing network self-repair is thus necessary. We describe a method to derive spike-timing-dependent plasticity rules for self-repair, based on the firing patterns of a functioning network. These plasticity rules for self-repair also provide the basis for unsupervised learning of new tasks. The particular plasticity rule derived for a network depends on the network and task. Here, self-repair is illustrated for a model of the mammalian olfactory system in which the computational task is that of odor recognition. In this olfactory example, the derived rule has qualitative similarity with experimental results seen in spike-timing-dependent plasticity. Unsupervised learning of new tasks by using the derived self-repair rule is demonstrated by learning to recognize new odors.

Item Type: Paper
Uncontrolled Keywords: SYNAPTIC PLASTICITY synaptic plasticity ADULT CORTEX adult cortex HIPPOCAMPUS hippocampus EXPERIENCE experience NEURONS neurons
Subjects: organs, tissues, organelles, cell types and functions > cell types and functions > cell functions > neural plasticity
organism description > animal behavior > olfactory
CSHL Authors:
Communities: CSHL labs > Brody lab
Depositing User: CSHL Librarian
Date: January 2004
Date Deposited: 03 Feb 2012 14:37
Last Modified: 11 Jan 2018 21:19
PMCID: PMC314186
Related URLs:
URI: https://repository.cshl.edu/id/eprint/22391

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