Fast unsupervised learning for visual pattern recognition using spike timing dependent plasticity

Liu, Daqi and Yue, Shigang (2017) Fast unsupervised learning for visual pattern recognition using spike timing dependent plasticity. Neurocomputing, 249 . pp. 212-224. ISSN 0925-2312

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Abstract

Real-time learning needs algorithms operating in a fast speed comparable to human or animal, however this is a huge challenge in processing visual inputs. Research shows a biological brain can process complicated real-life recognition scenarios at milliseconds scale. Inspired by biological system, in this paper, we proposed a novel real-time learning method by combing the spike timing-based feed-forward spiking neural network (SNN) and the fast unsupervised spike timing dependent plasticity learning method with dynamic post-synaptic thresholds. Fast cross-validated experiments using MNIST database showed the high e�ciency of the proposed method at an acceptable accuracy.

Keywords:Spiking neural network(SNN), rank order coding, unsupervised, STDP, visual pattern recognition, fast learning
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G730 Neural Computing
Divisions:College of Science > School of Computer Science
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ID Code:26922
Deposited On:07 Apr 2017 09:14

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