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 behavior 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 toward a more realistic description of unsupervised learning in biological neural systems. Furthermore, it may provide a basis for fast implementations in pulsed VLSI.