Online Meta-neuron based Learning Algorithm for a spiking neural classifier

Dora, Shirin; Sundaram, Suresh; Sundararajan, N.

doi:10.1016/j.ins.2017.05.050

Cited by 16 publications

(18 citation statements)

References 31 publications

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“…The real valued data from the data sets has been encoded into the spike patterns using the well known population coding [5], [26], [14] scheme. As in [27], the overlap constant for population coding is fixed at 0.7 and six receptive fields are used to encode a single feature for all problems. An encoding interval of 3 ms is used for population coding.…”

Section: Performance Evaluation Of the Tmm-snnmentioning

confidence: 99%

An Interclass Margin Maximization Learning Algorithm for Evolving Spiking Neural Network

Dora

Sundaram

Sundararajan

2019

IEEE Trans. Cybern.

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This paper presents a new learning algorithm developed for a three layered spiking neural network for pattern classification problems. The learning algorithm maximizes the interclass margin and is referred to as the two stage margin maximization spiking neural network (TMM-SNN). In the structure learning stage, the learning algorithm completely evolves the hidden layer neurons in the first epoch. Further, TMM-SNN updates the weights of the hidden neurons for multiple epochs using the newly developed normalized membrane potential learning rule such that the interclass margins (based on the response of hidden neurons) are maximized. The normalized membrane potential learning rule considers both the local information in the spike train generated by a presynaptic neuron and the existing knowledge (synaptic weights) stored in the network to update the synaptic weights. After the first stage, the number of hidden neurons and their parameters are not updated. In the output weights learning stage, TMM-SNN updates the weights of the output layer neurons for multiple epochs to maximize the interclass margins (based on the response of output neurons). Performance of TMM-SNN is evaluated using ten benchmark data sets from the UCI machine learning repository. Statistical performance comparison of TMM-SNN with other existing learning algorithms for SNNs is conducted using the nonparametric Friedman test followed by a pairwise comparison using the Fisher's least significant difference method. The results clearly indicate that TMM-SNN achieves better generalization performance in comparison to other algorithms.

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Section: Performance Evaluation Of the Tmm-snnmentioning

confidence: 99%

An Interclass Margin Maximization Learning Algorithm for Evolving Spiking Neural Network

Dora

Sundaram

Sundararajan

2019

IEEE Trans. Cybern.

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“…Further, the meta-neuron can access the global information stored in the network as synaptic weights of the postsynaptic neuron. The concept of the meta-neuron is inspired by the hetero synaptic plasticity induced by astrocyte cells in biological systems [3].…”

Section: A Online Meta Neuron Based Learning Algorithm (Omla)mentioning

confidence: 99%

“…In this paper, Online Meta-neuron based spiking neural network have been applied to classify parkinson"s disease. The meta-neuron based learning rule uses both local and global information present in the network to perform one shot weight updation of the synapses [3].…”

Section: Introductionmentioning

confidence: 99%

Parkinson’s Disease Classification using Various Advanced Neural Network Classifiers

Devi*,

Sivachitra,

Sivachitra

et al. 2019

IJRTE

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This paper proposes the application of Online Meta-neuron Based Learning Algorithm (OMLA), Self adaptive Resource Allocation Network (SRAN) and Projection Based Learning Meta-cognitive Radial Basis Functional Network (PBL-McRBFN) for Parkinson’s disease classification. This is the first journal paper to apply the concept of OMLA, SRAN and PBL-McRBFN for Parkinson’s disease classification. Online Meta-neuron based Learning Algorithm (OMLA) is a newly evolved network applied for Parkinson’s disease classification. This classifier make use of both global and local information of the network. Self Adaptive Resource Allocation Network (SRAN) consists of self adaptive control parameters that changes training data sequence, develop network architecture and learns network parameters. Also, repeated learning samples are removed with the help of this algorithm, hence training time and over flow problems can be minimized. The Projection Based Learning algorithm determines the output parameters of the network such that the energy function is minimum. The result shows the comparison of efficiency for the three networks in classification of Parkinson’s disease

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“…Based on the use of these techniques, the method has been named SRPSO‐SVM classifier. SRPSO leverages the principles of self regulation 29,30 for faster optimization but was originally developed for an optimization problem with continuous variables. Here, it was extended to discrete optimization problems for selection of voxels that provide information useful for the classification problem.…”

Section: Introductionmentioning

confidence: 99%

Identifying brain regions contributing to Alzheimer's disease using self regulating particle swarm optimization

Ganotra

Dora

Gupta

2020

Int J Imaging Syst Tech

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In this article, we developed an approach for detecting brain regions that contribute to Alzheimer's disease (AD) using support vector machine (SVM) classifiers and the recently developed self regulating particle swarm optimization (SRPSO) algorithm. SRPSO employs strategies inspired by the principles of learning in humans to achieve faster and better optimization results. The classifiers for distinguishing subjects into AD patients and cognitively normal (CN) individuals were built using grey matter (GM) and white matter (WM) volumetric features extracted from structural magnetic resonance (MR) images. It could be observed from results that the classifier built using both GM and WM features provided accuracy of 89.26% which is better than the performance of classifiers built using either GM or WM features only. Moreover, consideration of clinical features in addition to volumetric features improves the accuracy further to 94.63% which is better than the performance reported by recent works in literature. In order to identify the brain regions that are important for AD vs CN classification problem, we used SRPSO to extract GM and WM features that yield better classification performance. Using 50 features identified by SRPSO, an accuracy of 89.39% was obtained which is close to the accuracy based on all features. The features identified by SRPSO were mapped back to the brain to identify brain regions that exhibit degeneration in AD. In addition to identifying areas known to be involved in AD like cerebellum, hippocampus, this helped in finding newer areas that might contribute towards AD.

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Online Meta-neuron based Learning Algorithm for a spiking neural classifier

Cited by 16 publications

References 31 publications

An Interclass Margin Maximization Learning Algorithm for Evolving Spiking Neural Network

An Interclass Margin Maximization Learning Algorithm for Evolving Spiking Neural Network

Parkinson’s Disease Classification using Various Advanced Neural Network Classifiers

Identifying brain regions contributing to Alzheimer's disease using self regulating particle swarm optimization

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