Computation in a single neuron: Hodgkin and Huxley revisited

摘要: A spiking neuron ``computes'' by transforming a complex dynamical input into train of action potentials, or spikes. The computation performed the can be formulated as dimensional reduction, feature detection, followed nonlinear decision function over low space. Generalizations reverse correlation technique with white noise provide numerical strategy for extracting relevant features from experimental data, and information theory used to evaluate quality low--dimensional approximation. We apply these methods analyze simplest biophysically realistic model neuron, Hodgkin--Huxley model, using this system illustrate general methodological issues. focus on in stimulus that trigger spike, explicitly eliminating effects interactions between One approximate triggering ``feature space'' two linear subspace high--dimensional space histories, capturing way substantial fraction mutual inputs spike time. find an even better approximation, however, is describe dimensional, but curved; we capture 90% at high time resolution. Our analysis provides new understanding computational properties model. While it common neural behavior ``integrate fire,'' HH not integrator nor well described single threshold.