Configuring Spiking Neural Networks for Given Spatio-Temporal Patterns

We have developed a general framework to configure a spiking neuronal network so that it can precisely generate a desired spatio-temporal pattern of spikes. The unit of spiking neuronal networks employed here is a leaky integrate-and-fire model with a connection delay. We have shown that the required network configuration, and its existence, can be characterized by a set of linear inequalities constructed from the given pattern. The stability of the configured spiking neuronal network is discussed, which lead us to applying some routine methods in linear-programming to solve the set of inequalities, and yields the desirable spiking neural network configuration. To demonstrate the application of our approach, numerical examples with randomly generated patterns were explored and included.