A Hodgkin-Huxley Type Neuron Model That Learns Slow Non-Spike Oscillation
Abstract
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.
Cite
Text
Doya et al. "A Hodgkin-Huxley Type Neuron Model That Learns Slow Non-Spike Oscillation." Neural Information Processing Systems, 1993.Markdown
[Doya et al. "A Hodgkin-Huxley Type Neuron Model That Learns Slow Non-Spike Oscillation." Neural Information Processing Systems, 1993.](https://mlanthology.org/neurips/1993/doya1993neurips-hodgkinhuxley/)BibTeX
@inproceedings{doya1993neurips-hodgkinhuxley,
title = {{A Hodgkin-Huxley Type Neuron Model That Learns Slow Non-Spike Oscillation}},
author = {Doya, Kenji and Selverston, Allen I. and Rowat, Peter F.},
booktitle = {Neural Information Processing Systems},
year = {1993},
pages = {566-573},
url = {https://mlanthology.org/neurips/1993/doya1993neurips-hodgkinhuxley/}
}