Automatic decoding of input sinusoidal signal in a neuron model: High pass homomorphic filtering

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. In this work, an automatic decoding procedure based on high-pass homomorphic filtering is developed that provides performances comparable or better than that obtained with the improved SNR. The advantages of not requiring the neuronal spontaneous activities, as most SNR methods do, are a procedure simplification, a reduction of the amount of data needed to decode the information, and the possibility of application to contexts where the neuronal spontaneous activity is not available.