Filtering signals in models of neurons and neural networks

Understanding how populations of artificial neurons in neuron models encode and decode signals, is a primary task in control problems. Since the neurons use spiky signals, it is first necessary to understand what these signals mean in terms of carrying a sensory input. Also, to apply the concepts in control theory, we prefer analog form of these signals. In this work, we try to find an optimal filter which would help decoding the spiky signals to obtain an analog equivalent. We use some known analog signals and encode and decode them using a population of neurons.