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IJCNN
2006
IEEE

Implementing Synaptic Plasticity in a VLSI Spiking Neural Network Model

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Implementing Synaptic Plasticity in a VLSI Spiking Neural Network Model
— This paper describes an area-efficient mixed-signal implementation of synapse-based long term plasticity realized in a VLSI1 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 mm2 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.
Johannes Schemmel, Andreas Grübl, Karlheinz M
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where IJCNN
Authors Johannes Schemmel, Andreas Grübl, Karlheinz Meier, Eilif Mueller
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