PMID: 11909116Mar 23, 2002Paper

Initial state randomness improves sequence learning in a model hippocampal network

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
A P ShonW B Levy

Abstract

Randomness can be a useful component of computation. Using a computationally minimal, but still biologically based model of the hippocampus, we evaluate the effects of initial state randomization on learning a cognitive problem that requires this brain structure. Greater randomness of initial states leads to more robust performance in simulations of the cognitive task called transverse patterning, a context-dependent discrimination task that we code as a sequence prediction problem. At the conclusion of training, greater initial randomness during training trials also correlates with increased, repetitive firing of select individual neurons, previously named local context neurons. In essence, such repetitively firing neurons recognize subsequences, and previously their presence has been correlated with solving the transverse patterning problem. A more detailed analysis of the simulations across training trials reveals more about initial state randomization. The beneficial effects of initial state randomization derive from enhanced variation, across training trials, of the sequential states of a network. This greater variation is not uniformly present during training; it is largely restricted to the beginning of training and when...Continue Reading

References

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Citations

Mar 17, 2004·Network : Computation in Neural Systems·D W Sullivan, W B Levy
Nov 5, 2005·Neural Networks : the Official Journal of the International Neural Network Society·William B LevyXiangbao Wu

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