Self-Organization of Spiking Neurons Using Action Potential Timing
We propose a mechanism for unsupervised learning in networks of spiking
neurons which is based on the timing of single firing events. Our results
show that a topology preserving behaviour quite similar to that of Kohonen's
self-organizing map can be achieved using temporal coding. In contrast
to previous approaches, which use rate coding, the winner among competing
neurons can be determined fast and locally. Our model is a further step
towards a more realistic description of unsupervised learning in biological
neural systems. Furthermore, it may provide a basis for fast implementations
in pulsed VLSI.