Low-power neuromorphic circuits for unsupervised spike based learning
This article introduces a novel multi-layer Winner- Take-All (ML-WTA) spiking neural network (SNN) architecture using neurons with nonlinear dendrites and binary synapses. The network is trained by an unsupervised spike based learning rule that modifies the network connections. Inspired by the multi...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2016
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| Online Access: | http://hdl.handle.net/10356/68169 |