- Conference Paper
As the number of VLSI implementations of spike-based neural networks is steadily increasing, and the development of spike-based multi-chip systems is becoming more popular it is important to design spike-based learning algorithms and circuits, compatible with existing solutions, that endow these systems with adaptation and classification capabilities. We propose a spike-based learning algorithm that is highly effective in classifying complex patterns in semi-supervised fashion, and present neuromorphic circuits that support its VLSI implementation. We describe the architecture of a spike-based learning neural network, the analog circuits that implement the synaptic learning mechanism, and present results from a prototype VLSI chip comprising a full network of integrate-and-fire neurons and plastic synapses. We demonstrate how the VLSI circuits proposed reproduce the learning model's properties and fulfil its basic requirements for classifying complex patterns of mean firing rates. Show more
Book title2007 IEEE International Symposium on Circuits and Systems
Pages / Article No.
Organisational unit03453 - Douglas, Rodney J.
03701 - Fusi, Stefano (SNF-Professur)
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