Ricerc@Sapienza

The principles on how neurons encode and process information from low-level stimuli are still open questions in neuroscience. Neuron models represent useful tools to answer this question but a sensitive method is needed to decode the input information embedded in the neuron spike sequence. In this work, we developed an automatic decoding procedure based on the SNR spectrum improved by low-pass homomorphic filtering. The procedure was applied to a stochastic Hodgkin Huxley neuron model forced by a low-level sinusoidal signal in the range 50 Hz-300 Hz.

A processing technique for decoding the information transferred from a sinusoidal input to the output spike sequence of a neuron model is a desirable tool for understanding the encoding principles of neuronal systems. An automatic decoding procedure, already proposed by the authors, is based on an improved version of the Signal to Noise Ratio (SNR) calculation and requires a knowledge of both spontaneous (in absence of input signal) and stimulated (in presence of input signal) neuronal activities.