Spike-based learning in VLSI networks of integrate-and-fire neurons
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Date
2007
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Conference Paper
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Abstract
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.
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published
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2007 IEEE International Symposium on Circuits and Systems
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Pages / Article No.
3371 - 3374
Publisher
IEEE
Event
2007 IEEE International Symposium on Circuits and Systems (ISCAS 2007)
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03453 - Douglas, Rodney J. (emeritus)
03701 - Fusi, Stefano (SNF-Professur) (ehem.)