Optimal filtering in biological neural networks

Understanding how a population of biological neurons encode and decode signals, is a primary task in biological control problems. This enables one to understand how the sensory organs detect and process a signal which finally results in generating a motor command. 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 start with some known analog signals and encode them using a population of biological neurons., Then using a set of optimal filters we in fact try to recover the original signal.