Kavli Affiliate: Henry Abarbanel
| Authors: Randall E Clark, Lawson Fuller, Jason A Platt and Henry Abarbanel
| Summary:
Using methods from nonlinear dynamics and interpolation techniques from applied mathematics, we show how to use data alone to construct discrete time dynamical rules that forecast observed neuron properties. These data may come from from simulations of a Hodgkin-Huxley (HH) neuron model or from laboratory current clamp experiments. In each case the reduced dimension data driven forecasting (DDF) models are shown to predict accurately for times after the training period.