Learning with single integrate-and-fire neuron

Yadav, Abhishek; Mishra, Deepak; Yadav, Ram Narayan; Ray, Sudipta; Kalra, Prem Kumar

doi:10.1109/ijcnn.2005.1556234

Cited by 6 publications

(6 citation statements)

References 15 publications

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“…Because, the interspike interval of the proposed neuron model is significant and it is biologically plausible. The timing of spikes is significant for exchange of information among neurons . The interspike interval of the proposed neuron model is derived and utilized as an aggregation function in ELM and it is named as spiking ELM.…”

Section: Spiking Extreme Learning Machinementioning

confidence: 99%

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Credit score classification using spiking extreme learning machine

Kuppili

Tripathi

Edla

2019

Computational Intelligence

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Credit score classification is a prominent research problem in the banking or financial industry, and its predictive performance is responsible for the profitability of financial industry. This paper addresses how Spiking Extreme Learning Machine (SELM) can be effectively used for credit score classification. A novel spike‐generating function is proposed in Leaky Nonlinear Integrate and Fire Model (LNIF). Its interspike period is computed and utilized in the extreme learning machine (ELM) for credit score classification. The proposed model is named as SELM and is validated on five real‐world credit scoring datasets namely: Australian, German‐categorical, German‐numerical, Japanese, and Bankruptcy. Further, results obtained by SELM are compared with back propagation, probabilistic neural network, ELM, voting‐based Q‐generalized extreme learning machine, Radial basis neural network and ELM with some existing spiking neuron models in terms of classification accuracy, Area under curve (AUC), H‐measure and computational time. From the experimental results, it has been noticed that improvement in accuracy and execution time for the proposed SELM is highly statistically important for all aforementioned credit scoring datasets. Thus, integrating a biological spiking function with ELM makes it more efficient for categorization.

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Section: Spiking Extreme Learning Machinementioning

confidence: 99%

“…The second layer contains number of neurons equal to number of records of the dataset, that is, one neuron for each record P j in the input data. Equation shows a multiplication neuron model, in this model gradient descent method is used to train the architecture shown in Figure .…”

Section: Spiking Extreme Learning Machinementioning

confidence: 99%

Credit score classification using spiking extreme learning machine

Kuppili

Tripathi

Edla

2019

Computational Intelligence

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“…© 1996-2020 an activation function in a prediction model for malaria using data engineering, for the southern regions of India, and addressed the problems of scalability and time complexity for traditional machine learning algorithms. For classification, a feedforward multilayer perceptron (MLP) is a widely used neuron model that uses backpropagation (BP) as a learning method with weight updating (13). Feedforward nets are efficient in terms of classification, but are timeconsuming.…”

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confidence: 99%

“…The interspike interval (ISI) is a key factor affecting the passing of information from one neuron to another. Yadav et al (13) observed that ANNs are efficient in performing pattern classification when the biological properties of the neuron are included. The authors used an MLP with an ISI derived from IFN, and this yielded better accuracy and lower time complexity.…”

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confidence: 99%

A new backpropagation neural network classification model for prediction of incidence of malaria

Verma

Kuppili

Srivastava³

et al. 2020

Front Biosci

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“…In our previous work [24], we have used interspike interval relationship (equation 7) of integrate and fire neuron model for the learning purpose. In our present work we are mainly concentrating on the timing of spikes in Spiking neuron model inorder to derive an aggregation function for the learning of a neural network.…”

Section: Spiking Neuron Modelsmentioning

confidence: 99%

A Neural Network Using Single Multiplicative Spiking Neuron for Function Approximation and Classification

Mishra¹,

Yadav²,

Dwivedi³

et al.

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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In this paper, learning algorithm for a single multiplicative spiking neuron (MSN) is proposed and tested for various applications where a multilayer perceptron (MLP) neural network is conventionally used. It is found that a single MSN is sufficient for the applications that require a number of neurons in different hidden layers of a conventional neural network. Several benchmark and real-life problems of classification and function-approximation are illustrated. It has been observed that the inclusion of few more biological phenomenon in artificial neural networks can make them more prevailing.

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Learning with single integrate-and-fire neuron

Cited by 6 publications

References 15 publications

Credit score classification using spiking extreme learning machine

Credit score classification using spiking extreme learning machine

A new backpropagation neural network classification model for prediction of incidence of malaria

A Neural Network Using Single Multiplicative Spiking Neuron for Function Approximation and Classification

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