Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4947475 | Neurocomputing | 2017 | 40 Pages |
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 efficiency of the proposed method at an acceptable accuracy.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Daqi Liu, Shigang Yue,