Low-power neuromorphic circuits for unsupervised spike based learning

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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Bibliographic Details
Main Author: He, Tong
Other Authors: Arindam Basu
Format: Final Year Project (FYP)
Language:English
Published: 2016
Subjects:
DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Biometrics
Online Access:http://hdl.handle.net/10356/68169

Internet

http://hdl.handle.net/10356/68169

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