Decoding of firing intervals in a temporal-coded spike train using a topographically mapped neural network

It is shown that firing intervals in a temporal-coded spike train can be decoded by a multilayered time-delayed neural network. The serially coded firing intervals of a spike train can be converted into a spatially distributed topographical map from which the interspike-interval and bandwidth information can be extracted. This network can be used to decode multiplexed pulse-coded signals embedded serially in the incoming spike train into parallel-distributed topographically mapped channels. The two-dimensionally distributed output neuron array can also be used to extract the variance (or inaccuracy) tolerance of the incoming firing interspike intervals. This mapping can also be used to characterize the underlying stochastic processes in the firing of the incoming spike train. Thus, the proposed network represents an implementation of a signal-processing scheme for code conversion using time for computing and coding that does not require learning