Self-Organizing Maps of Spiking Neurons Using Temporal Coding

Abstract: The basic idea of self-organizing maps (SOM) introduced by Kohonen, namely to map similar input patterns to contiguous locations in the output space, is not only of importance to artificial but also to biological systems, e.g. in the visual cortex. However, the standard formulation of the SOM and the corresponding learning rule are not suitable for biological systems. Here we show how networks of spiking neurons can be used to implement a variation of the SOM in temporal coding, which has the same characteristic behavior. In contrast to the standard formulation of the SOM our construction has the additional advantage that the winner among the competing neurons can be determined fast and locally.

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