An Adaptive Structure Learning Algorithm for Multi-Layer Spiking Neural Networks

2019 ◽  
Author(s):  
Doudou Wu ◽  
Xianghong Lin ◽  
Pangao Du
Keyword(s):  
Neural Networks ◽  
Structure Learning ◽  
Learning Algorithm ◽  
Spiking Neural Networks ◽  
Adaptive Structure

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

Effective Transfer Learning Algorithm in Spiking Neural Networks

2021 ◽  
pp. 1-13
Author(s):  
Qiugang Zhan ◽  
Guisong Liu ◽  
Xiurui Xie ◽  
Guolin Sun ◽  
Huajin Tang
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Spiking Neural Networks ◽  
Effective Transfer

scholarly journals Supervised Learning Algorithm for Multilayer Spiking Neural Networks with Long-Term Memory Spike Response Model

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Xianghong Lin ◽  
Mengwei Zhang ◽  
Xiangwen Wang
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Spike Trains ◽  
Spiking Neural Networks ◽  
Long Term Memory ◽  
Response Model ◽  
Spike Response ◽  
Spike Response Model

As a new brain-inspired computational model of artificial neural networks, spiking neural networks transmit and process information via precisely timed spike trains. Constructing efficient learning methods is a significant research field in spiking neural networks. In this paper, we present a supervised learning algorithm for multilayer feedforward spiking neural networks; all neurons can fire multiple spikes in all layers. The feedforward network consists of spiking neurons governed by biologically plausible long-term memory spike response model, in which the effect of earlier spikes on the refractoriness is not neglected to incorporate adaptation effects. The gradient descent method is employed to derive synaptic weight updating rule for learning spike trains. The proposed algorithm is tested and verified on spatiotemporal pattern learning problems, including a set of spike train learning tasks and nonlinear pattern classification problems on four UCI datasets. Simulation results indicate that the proposed algorithm can improve learning accuracy in comparison with other supervised learning algorithms.

A Hybrid Higher Order Neural Structure for Pattern Recognition

2013 ◽  
pp. 364-387 ◽  
Author(s):  
Mehdi Fallahnezhad ◽  
Salman Zaferanlouei
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Structure Learning ◽  
Learning Algorithm ◽  
Hybrid Structure ◽  
Higher Order ◽  
Superior Performance ◽  
Data Sets ◽  
Neural Structure ◽  
Higher Order Neural Networks

Considering high order correlations of selected features next to the raw features of input can facilitate target pattern recognition. In artificial intelligence, this is being addressed by Higher Order Neural Networks (HONNs). In general, HONN structures provide superior specifications (e.g. resolving the dilemma of choosing the number of neurons and layers of networks, better fitting specs, quicker, and open-box specificity) to traditional neural networks. This chapter introduces a hybrid structure of higher order neural networks, which can be generally applied in various branches of pattern recognition. Structure, learning algorithm, and network configuration are introduced, and structure is applied either as classifier (where is called HHONC) to different benchmark statistical data sets or as functional behavior approximation (where is called HHONN) to a heat and mass transfer dilemma. In each structure, results are compared with previous studies, which show its superior performance next to other mentioned advantages.

scholarly journals Supervised Learning in Multilayer Spiking Neural Networks

2013 ◽  
Vol 25 (2) ◽  
pp. 473-509 ◽  
Author(s):  
Ioana Sporea ◽  
André Grüning
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Neuron Model ◽  
Learning Algorithm ◽  
Spiking Neural Networks ◽  
Reservoir Computing ◽  
Data Set ◽  
Coding Schemes ◽  
Xor Problem ◽  
Iris Data

We introduce a supervised learning algorithm for multilayer spiking neural networks. The algorithm overcomes a limitation of existing learning algorithms: it can be applied to neurons firing multiple spikes in artificial neural networks with hidden layers. It can also, in principle, be used with any linearizable neuron model and allows different coding schemes of spike train patterns. The algorithm is applied successfully to classic linearly nonseparable benchmarks such as the XOR problem and the Iris data set, as well as to more complex classification and mapping problems. The algorithm has been successfully tested in the presence of noise, requires smaller networks than reservoir computing, and results in faster convergence than existing algorithms for similar tasks such as SpikeProp.

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