Encoding Chaos in Neural Spike Trains

Recently, it has been shown that interspike interval (ISI) series from driven model neurons can be used to discriminate between chaotic and stochastic inputs. Here we extend this work to in vitro experimental studies with rat cutaneous mechanoreceptors. For each of the neurons tested, we show that a chaotically driven ISI series can be distinguished from a stochastically driven ISI series on the basis of a nonlinear prediction measure. This work demonstrates that dynamical information can be preserved when an analog chaotic signal is converted into a spike train by a sensory neuron.