Experimental Demonstration of Learning Properties of a New Supervised Learning Method for the Spiking Neural Networks

2005 ◽  
pp. 145-152 ◽  
Author(s):  
Andrzej Kasinski ◽  
Filip Ponulak
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Spiking Neural Networks ◽  
Learning Method ◽  
Experimental Demonstration

An online supervised learning method for spiking neural networks with adaptive structure

2014 ◽  
Vol 144 ◽  
pp. 526-536 ◽  
Author(s):  
Jinling Wang ◽  
Ammar Belatreche ◽  
Liam Maguire ◽  
Thomas Martin McGinnity
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Spiking Neural Networks ◽  
Learning Method ◽  
Adaptive Structure

scholarly journals Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
S. R. Nandakumar ◽  
Irem Boybat ◽  
Manuel Le Gallo ◽  
Evangelos Eleftheriou ◽  
Abu Sebastian ◽  
...  
Keyword(s):  
Neural Networks ◽  
Phase Change ◽  
Supervised Learning ◽  
Phase Change Memory ◽  
Spiking Neural Networks ◽  
Experimental Demonstration ◽  
Change Memory

scholarly journals A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule

2020 ◽  
Vol 121 ◽  
pp. 387-395 ◽  
Author(s):  
Yunzhe Hao ◽  
Xuhui Huang ◽  
Meng Dong ◽  
Bo Xu
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Spiking Neural Networks ◽  
Learning Method ◽  
Stdp Rule

Area-efficient memristor spiking neural networks and supervised learning method

2019 ◽  
Vol 62 (9) ◽  
Author(s):  
Errui Zhou ◽  
Liang Fang ◽  
Rulin Liu ◽  
Zhensen Tang
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Spiking Neural Networks ◽  
Learning Method ◽  
Area Efficient

scholarly journals Memristive Spiking Neural Networks Trained with Unsupervised STDP

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 396 ◽  
Author(s):  
Errui Zhou ◽  
Liang Fang ◽  
Binbin Yang
Keyword(s):  
Neural Networks ◽  
Feature Learning ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Learning Method ◽  
Hardware Complexity ◽  
Neuromorphic Computing ◽  
Computing Systems ◽  
Von Neumann ◽  
Complex Architectures

Neuromorphic computing systems are promising alternatives in the fields of pattern recognition, image processing, etc. especially when conventional von Neumann architectures face several bottlenecks. Memristors play vital roles in neuromorphic computing systems and are usually used as synaptic devices. Memristive spiking neural networks (MSNNs) are considered to be more efficient and biologically plausible than other systems due to their spike-based working mechanism. In contrast to previous SNNs with complex architectures, we propose a hardware-friendly architecture and an unsupervised spike-timing dependent plasticity (STDP) learning method for MSNNs in this paper. The architecture, which is friendly to hardware implementation, includes an input layer, a feature learning layer and a voting circuit. To reduce hardware complexity, some constraints are enforced: the proposed architecture has no lateral inhibition and is purely feedforward; it uses the voting circuit as a classifier and does not use additional classifiers; all neurons can generate at most one spike and do not need to consider firing rates and refractory periods; all neurons have the same fixed threshold voltage for classification. The presented unsupervised STDP learning method is time-dependent and uses no homeostatic mechanism. The MNIST dataset is used to demonstrate our proposed architecture and learning method. Simulation results show that our proposed architecture with the learning method achieves a classification accuracy of 94.6%, which outperforms other unsupervised SNNs that use time-based encoding schemes.

scholarly journals A Supervised Learning Algorithm for Learning Precise Timing of Multiple Spikes in Multilayer Spiking Neural Networks

2018 ◽  
Vol 29 (11) ◽  
pp. 5394-5407 ◽  
Author(s):  
Aboozar Taherkhani ◽  
Ammar Belatreche ◽  
Yuhua Li ◽  
Liam P. Maguire
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Spiking Neural Networks ◽  
Precise Timing
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