Stochastic Transitions between States of Neural Activity

Paul Miller and Donald B. Katz

in The Dynamic Brain

Published in print January 2011 | ISBN: 9780195393798
Published online September 2011 | e-ISBN: 9780199897049 | DOI:
Stochastic Transitions between States of Neural Activity

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This chapter shows, using hidden Markov modeling, how the trial—to—trial variability of neural activity in gustatory cortex during taste processing can be described in terms of coherent sequences of states, with variability arising from differences in the timing of transitions between those states. Computer simulations of models, in which neural activity follows noise-induced transitions—or jumps—between attractor states, reproduce the observed neural dynamics in gustatory cortex. In other contexts such models can reproduce the observed behavioral variability in time estimation. Finally, decision-making can be improved if the initial “undecided” state remains an attractor and the decision is made when neural activity “jumps” via a stochastic fluctuation to a favorable attractor state. The requirement of sufficient fluctuations to produce such noise-induced decision-making is an example of stochastic resonance.

Keywords: attractor; transitions; hidden Markov modeling; taste processing; Gustatory Cortex; decision-making; timing; stochastic resonance

Chapter.  6501 words.  Illustrated.

Subjects: Neuroscience

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