A Hodgkin-Huxley Type Neuron Model That Learns Slow Non-Spike Oscillation

Part of Advances in Neural Information Processing Systems 6 (NIPS 1993)

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Kenji Doya, Allen Selverston, Peter Rowat


A gradient descent algorithm for parameter estimation which is similar to those used for continuous-time recurrent neural networks was derived for Hodgkin-Huxley type neuron models. Using mem(cid:173) brane potential trajectories as targets, the parameters (maximal conductances, thresholds and slopes of activation curves, time con(cid:173) stants) were successfully estimated. The algorithm was applied to modeling slow non-spike oscillation of an identified neuron in the lobster stomatogastric ganglion. A model with three ionic currents was trained with experimental data. It revealed a novel role of A-current for slow oscillation below -50 mY.