Statistical and Dynamical Interpretation of ISIH Data from Periodically Stimulated Sensory Neurons

Part of Advances in Neural Information Processing Systems 5 (NIPS 1992)

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John K. Douglass, Frank Moss, André Longtin


We interpret the time interval data obtained from periodically stimulated sensory neurons in terms of two simple dynamical systems driven by noise with an embedded weak periodic function called the signal: 1) a bistable system defined by two potential wells separated by a barrier, and 2) a Fit(cid:173) zHugh-Nagumo system. The implementation is by analog simulation: elec(cid:173) tronic circuits which mimic the dynamics. For a given signal frequency, our simulators have only two adjustable parameters, the signal and noise intensi(cid:173) ties. We show that experimental data obtained from the periodically stimu(cid:173) lated mechanoreceptor in the crayfish tail fan can be accurately approximated by these simulations. Finally, we discuss stochastic resonance in the two models.