Simplified Spike-timing Dependent Plasticity Learning Rule of Spiking Neural Networks for Unsupervised Clustering

2019 ◽  
Author(s):  
Hongyu Sun ◽  
Xining Cui ◽  
Yinjing Guo ◽  
Anqing Ding
Keyword(s):  
Neural Networks ◽  
Learning Rule ◽  
Spike Timing ◽  
Unsupervised Clustering ◽  
Spiking Neural Networks ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity

scholarly journals Computing of temporal information in spiking neural networks with ReRAM synapses

2019 ◽  
Vol 213 ◽  
pp. 453-469 ◽  
Author(s):  
W. Wang ◽  
G. Pedretti ◽  
V. Milo ◽  
R. Carboni ◽  
A. Calderoni ◽  
...  
Keyword(s):  
Neural Networks ◽  
Spike Timing ◽  
Temporal Information ◽  
Spiking Neural Networks ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Neural Spikes

This work addresses the methodology and implementation of a neuromorphic SNN system to compute the temporal information among neural spikes using ReRAM synapses capable of spike-timing dependent plasticity (STDP).

Soft-Reward Based Reinforcement Learning by Spiking Neural Networks

2011 ◽  
Vol 219-220 ◽  
pp. 770-773
Author(s):  
Wei Ya Shi
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Synaptic Weight ◽  
Spike Timing ◽  
Benchmark Dataset ◽  
Spiking Neural Networks ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Winner Take All ◽  
Take All

In this paper, we propose algorithm based reinforcement learning for spiking neural networks. The algorithm simulates biological adaptability and uses the soft-reward from environment to modulate the synaptic weight, which combines spike-timing-dependent plasticity (STDP), winner-take-all mechanism. The algorithm is tested to classify a number of standard benchmark dataset. The obtained results show the effectiveness of the proposed algorithm.

scholarly journals Pattern Classification by Spiking Neural Networks Combining Self-Organized and Reward-Related Spike-Timing-Dependent Plasticity

2019 ◽  
Vol 9 (4) ◽  
pp. 283-291 ◽  
Author(s):  
Sou Nobukawa ◽  
Haruhiko Nishimura ◽  
Teruya Yamanishi
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Supervised Learning ◽  
Pattern Classification ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Learning Problems ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Self Organized

Abstract Many recent studies have applied to spike neural networks with spike-timing-dependent plasticity (STDP) to machine learning problems. The learning abilities of dopamine-modulated STDP (DA-STDP) for reward-related synaptic plasticity have also been gathering attention. Following these studies, we hypothesize that a network structure combining self-organized STDP and reward-related DA-STDP can solve the machine learning problem of pattern classification. Therefore, we studied the ability of a network in which recurrent spiking neural networks are combined with STDP for non-supervised learning, with an output layer joined by DA-STDP for supervised learning, to perform pattern classification. We confirmed that this network could perform pattern classification using the STDP effect for emphasizing features of the input spike pattern and DA-STDP supervised learning. Therefore, our proposed spiking neural network may prove to be a useful approach for machine learning problems.

Spike timing-dependent plasticity in sparse recurrent neural networks

2014 ◽  
Vol 1 ◽  
pp. 485-488
Author(s):  
Hideyuki Kato ◽  
Tohru Ikeguchi
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity

scholarly journals Biologically Plausible Sequence Learning with Spiking Neural Networks

2020 ◽  
Vol 34 (02) ◽  
pp. 1316-1323
Author(s):  
Zuozhu Liu ◽  
Thiparat Chotibut ◽  
Christopher Hillar ◽  
Shaowei Lin
Keyword(s):  
Neural Networks ◽  
Sequence Learning ◽  
Nervous Activity ◽  
Learning Rule ◽  
Generative Models ◽  
Spike Timing ◽  
Hopfield Network ◽  
Spiking Neural Networks ◽  
Theoretical Ground ◽  
Time Model

Motivated by the celebrated discrete-time model of nervous activity outlined by McCulloch and Pitts in 1943, we propose a novel continuous-time model, the McCulloch-Pitts network (MPN), for sequence learning in spiking neural networks. Our model has a local learning rule, such that the synaptic weight updates depend only on the information directly accessible by the synapse. By exploiting asymmetry in the connections between binary neurons, we show that MPN can be trained to robustly memorize multiple spatiotemporal patterns of binary vectors, generalizing the ability of the symmetric Hopfield network to memorize static spatial patterns. In addition, we demonstrate that the model can efficiently learn sequences of binary pictures as well as generative models for experimental neural spike-train data. Our learning rule is consistent with spike-timing-dependent plasticity (STDP), thus providing a theoretical ground for the systematic design of biologically inspired networks with large and robust long-range sequence storage capacity.

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