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Learning by structural remodeling in a class of single cell models

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Abstract

Changes in neural connectivity are thought to underlie the most permanent forms of memory in the brain. We consider two models, derived from the clusteron (Mel, Adv Neural Inf Process Syst 4:35–42, 1992), to study this method of learning. The models show a direct relationship between the speed of memory acquisition and the probability of forming appropriate synaptic connections. Moreover, the strength of learned associations grows with the number of fibers that have taken part in the learning process. We provide simple and intuitive explanations of these two results by analyzing the distribution of synaptic activations. The obtained insights are then used to extend the model to perform novel tasks: feature detection, and learning spatio-temporal patterns. We also provide an analytically tractable approximation to the model to put these observations on a firm basis. The behavior of both the numerical and analytical models correlate well with experimental results of learning tasks which are thought to require a reorganization of neuronal networks.

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Notes

  1. The basic implementations of our unbranched and branched models, as well as the mathematical model, are available online at http://www.math.uh.edu/~josic/research/papers/clusteron/.

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Acknowledgements

KK and CC was supported by grants NIH AG 027577 and NIH NS 038310. KJ was supported by grant NSF-0604429. we thank Steven Coombes for his comments and suggestions.

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Authors and Affiliations

  1. Department of Biology and Biochemistry, University of Houston, Houston, TX, 77204-5001, USA

    K. J. Kelleher & C. M. Colbert

  2. Facebook, 156 University Ave., Palo Alto, CA, 94301, USA

    V. Hajdik

  3. Evolved Machines Inc., 470 Ramona St., Palo Alto, CA, 94301, USA

    C. M. Colbert

  4. Department of Mathematics, University of Houston, Houston, TX, 77204-3008, USA

    V. Hajdik & K. Josić

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  1. K. J. Kelleher
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  4. K. Josić
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Correspondence to K. J. Kelleher.

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Kelleher, K.J., Hajdik, V., Colbert, C.M. et al. Learning by structural remodeling in a class of single cell models. J Comput Neurosci 25, 282–295 (2008). https://doi.org/10.1007/s10827-008-0078-6

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  • DOI: https://doi.org/10.1007/s10827-008-0078-6

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