期刊
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS
卷 6, 期 4, 页码 385-398出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBCAS.2012.2184285
关键词
Hebbian learning; neural learning; neural networks; neuromorphic VLSI; Spike-Timing-Dependent Plasticity (STDP); synaptic learning rule; weight dependence
资金
- EPSRC
- MRC via the University of Edinburgh Doctoral Training Centre for Neuroinformatics
- EC [216809]
Analogue and mixed-signal VLSI implementations of Spike-Timing-Dependent Plasticity (STDP) are reviewed. A circuit is presented with a compact implementation of STDP suitable for parallel integration in large synaptic arrays. In contrast to previously published circuits, it uses the limitations of the silicon substrate to achieve various forms and degrees of weight dependence of STDP. It also uses reverse-biased transistors to reduce leakage from a capacitance representing weight. Chip results are presented showing: various ways in which the learning rule may be shaped; how synaptic weights may retain some indication of their learned values over periods of minutes; and how distributions of weights for synapses convergent on single neurons may shift between more or less extreme bimodality according to the strength of correlational cues in their inputs.
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