Automatic decoding of input sinusoidal signal in a neuron model: Improved SNR spectrum by low-pass homomorphic filtering

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. It exhibited very high performance, in terms of sensitivity and precision, in automatically decoding the input information even when using a relatively small number of model runs (≈ 200). This could provide a fast and valid procedure to understand the encoding mechanisms of low-level sinusoidal stimuli used by different types of neurons.