||Johannes Schemmel, Andreas Gruebl, Karlheinz Meier, Eilif Mueller
||Implementing Synaptic Plasticity in a VLSI Spiking Neural Network Model
||Proceedings of the 2006 International Joint Conference on Neural Networks (IJCNN 2006), 1-6, IEEE Press (2006)
||This paper describes an area-efficient mixed-signal implementation of synapse-based long term plasticity realized in a VLSI model of a spiking neural network. The artificial synapses are based on an implementation of spike time dependent plasticity (STDP). In the biological specimen, STDP is a mechanism acting locally in each synapse. The presented electronic implementation succeeds in maintaining this high level of parallelism and simultaneously achieves a synapse density of more than 9k synapses per mm^2 in a 180 nm technology. This allows the construction of neural micro-circuits close to the biological specimen while maintaining a speed several orders of magnitude faster than biological real time. The large acceleration factor enhances the possibilities to investigate key aspects of plasticity, e.g. by performing extensive parameter searches.