Integrate-and-fire neuron modeled as a low-rate sparse time-encoding device

Neurons as Time Encoding Machines (TEMs) have been proposed to capture the information present in sensory stimuli and to encode it into spike trains [1], [2], [3]. These neurons, however, produce spikes at firing rates above Nyquist, which is usually much higher than the amount of information actually present in stimuli. We propose a low-rate spiking neuron which exploits the sparsity or compressibility present in natural signals to produce spikes at a firing rate proportional to the amount of information present in the signal rather than its duration. We consider the IAF (Integrate-and-Fire) neuron model, provide appropriate modifications to convert it into a low-rate encoder and develop an algorithm for reconstructing the input stimulus from the low-rate spike trains. Our simulations with frequency-sparse signals demonstrate the superior performance of the Low-Rate IAF neuron operating at a sub-Nyquist rate, when compared with IAF neurons proposed earlier, which operate at and above Nyquist rates.