A sequential learning algorithm for a spiking neural classifier

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

doi:10.1016/j.asoc.2015.06.062

Cited by 13 publications

(4 citation statements)

References 50 publications

(80 reference statements)

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“…If ( t ≠ c t or E t ≥ β a ) and  c (x t ) ≤ β c , then allocate a new hidden neurons in the cognitive component. The width and center of newly added hidden neuron are determined based on the intra and inter class distances of nearest neurons using (18), (19), (20) and (21). The…”

Section: B Neuron Growth Strategymentioning

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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Section: B Neuron Growth Strategymentioning

confidence: 99%

Parkinson’s Disease Classification using Various Advanced Neural Network Classifiers

Devi*,

Sivachitra,

Sivachitra

et al. 2019

IJRTE

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show abstract

“…Broadly, the existing spiking neural network learning algorithms for pattern classification problems can be classified into three major categories, namely, the gradient-descent based learning algorithms [4,14], the rank order based learning algorithms [19,10,37,8,18] and the Spike Timing Dependent Plasticity (STDP) [23,7] based algorithms [25,30,35,34].…”

Section: Related Workmentioning

confidence: 99%

“…Rank order coding [28] is another coding strategy that encodes information using the order of the spikes. Due to the ability of rank order coding for faster information transmission [33] several learning algorithms have been developed for SNNs using rank order coding [19,9,10]. It is a non-local encoding technique, but it allows the network to learn the knowledge present in the spike patterns in one-shot.…”

Section: Related Workmentioning

confidence: 99%

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

Dora

Sundaram

Sundararajan

2017

Information Sciences

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This paper presents a new spiking neural network architecture with a metaneuron which envelopes all the pre-and postsynaptic neurons in the network.The concept of the meta-neuron is inspired by the role of astrocytes in modulating synaptic plasticity in biological neural networks. The meta-neuron utilizes the global information stored in the network (synaptic weights) and the local information present in the input spike pattern to determine a weight sensitivity modulation factor for a given synapse. Based on the weight sensitivity modulation factor and the postsynaptic potential of a neuron, the meta-neuron based learning rule updates the synaptic weights in the network to produce precise shifts in the spike times of the postsynaptic neurons. Using this learning rule, an Online Meta-neuron based Learning Algorithm (OMLA) is presented for an evolving spiking neural classifier. The learning algorithm employs heuristic learning strategies for learning each input spike pattern. It can choose to add a neuron, update the network parameters or delete a spike pattern depending on the spike times of the output neurons. OMLA employs a meta-neuron with memory that stores only those spike patterns which are used to add a neuron to the network. These spike patterns (spike patterns in meta-neuron memory) are used as representative of past information stored in the network during subsequent neuron additions. The performance of OMLA has been compared with both the existing online learning and batch learning algorithms for spiking

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“…The important challenge that still needs to be addressed is the feasibility in hardware implementation of these algorithms into algorithms, circuits, devices and systems, based on the nature of the primary contribution. The reader can view this figure in the supplementary material (Additional file 1) provided with the manuscript to click on the hyperlinks and navigate to the corresponding reference in the bibliography section [141][142][143][144][145][146][147] and approaches, that is eventually critical for realizing practical applications, such as on embedded platforms. The storage and computational capacity available on such platforms is limited, hence the algorithms need to have a low computational complexity, that translates to low-power requirements in hardware.…”

Section: Algorithmsmentioning

confidence: 99%

Recent trends in neuromorphic engineering

Soman

Jayadeva

2016

Big Data Anal

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Neuromorphic Engineering has emerged as an exciting research area, primarily owing to the paradigm shift from conventional computing architectures to data-driven, cognitive computing. There is a diversity of work in the literature pertaining to neuromorphic systems, devices and circuits. This review looks at recent trends in neuromorphic engineering and its sub-domains, with an attempt to identify key research directions that would assume significance in the future. We hope that this review would serve as a handy reference to both beginners and experts, and provide a glimpse into the broad spectrum of applications of neuromorphic hardware and algorithms. Our survey indicates that neuromorphic engineering holds a promising future, particularly with growing data volumes, and the imminent need for intelligent, versatile computing.

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A sequential learning algorithm for a spiking neural classifier

Cited by 13 publications

References 50 publications

Parkinson’s Disease Classification using Various Advanced Neural Network Classifiers

Parkinson’s Disease Classification using Various Advanced Neural Network Classifiers

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

Recent trends in neuromorphic engineering

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