Time-series prediction with single integrate-and-fire neuron

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

doi:10.1016/j.asoc.2006.02.004

Cited by 15 publications

(9 citation statements)

References 13 publications

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“…This model has been referred to, repeatedly, as the linear model. The data set has since been used by many researchers including [35,36].…”

Section: Modelmentioning

confidence: 99%

A Hybrid GMDH and Least Squares Support Vector Machines in Time Series Forecasting

Samsudin¹,

Saad²,

Shabri³

2011

NNW

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Nowadays, remote sensing technology is being used as an essential tool for monitoring and detecting oil spills to take precautions and to prevent the damages to the marine environment. As an important branch of remote sensing, satellite based synthetic aperture radar imagery (SAR) is the most effective way to accomplish these tasks. Since a marine surface with oil spill seems as a dark object because of much lower backscattered energy, the main problem is to recognize and differentiate the dark objects of oil spills from others to be formed by oceanographic and atmospheric conditions. In this study, Radarsat-1 images covering Lebanese coasts were employed for oil spill detection. For this purpose, a powerful classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) was used. As the original contribution of the paper, the network was trained by a novel heuristic optimization algorithm known as Artificial Bee Colony (ABC) method besides the conventional Backpropagation (BP) and Levenberg-Marquardt (LM) learning algorithms. A comparison and evaluation of different network training algorithms regarding reliability of detection and robustness show that for this problem best result is achieved with the Artificial Bee Colony algorithm (ABC).

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“…This model has been referred to, repeatedly, as the linear model. The data set has since been used by many researchers including [35,36].…”

Section: Modelmentioning

confidence: 99%

A Hybrid GMDH and Least Squares Support Vector Machines in Time Series Forecasting

Samsudin¹,

Saad²,

Shabri³

2011

NNW

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“…Although there are more developed neuron models and Lapique neuron model is being the simplest neuron model of them all, even today, Lapicque's neuron model is still one of the most used neuron models in neuroscience. It has found application areas in neural and cellular networks studies and also in computational neurology due to its simplicity [2,[12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Synchronization of two cardiomyocyte with Integrate and fire neuron model model is examined in [16].…”

Section: Introductionmentioning

confidence: 99%

A Simple Lapicque Neuron Emulator

Tulumbaci¹,

Eryildiz²,

Mutlu³

2020

World Congress on Electrical Engineering and Computer Systems and Science

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Neuron models such as Leaky Integrate and Fire (Lapicque Neuron) model, Hodgkin Huxley Model, Fitzhugh-Nagumo Model, and Izhikevich neuron model are commonly used in neuronal studies. Nobel laureate Hudgkin-Hugsley neuron model is quite complex despite of being accurate. That's why simplified neuron models such as Fitzhugh-Nagumo neuron model is commonly used in studies. Lapicque Neuron is the first neuron model suggested in Literature. It had been reported by Lapicque in 1907, it is easy to understand, and it is still commonly used for neural studies for its simplicity. Neuron emulator circuits are used for education and research purposes. They can be made using individual electronics components or VLSI circuits. In this study, a Lapicque Neuron emulator circuit topology is introduced. It is simple enough to be made by students and it can be made using cheap off-the shelves components in a short time. Its circuit analysis is also given. It is experimentally shown that the emulator is able to mimic activation potentials well. It is suggested that the neuron emulator can be used for not only educational purposes in Biomedical Engineering Courses but also neuronal studies to show and prove concepts.

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“…The regression models include state-space approaches, such as hidden Markov models (MacDonald and Zucchini, 1997), generalized linear models (Green and Silverman, 1994), (McCullagh and Nelder, 1989) and logistic regression models (Liang and Zeger, 1989). Machine learning techniques, such as Support Vector Machines (SVM) and different types of neural networks, are also increasingly applied for time series regressions (Yadav et al, 2007), (Zhang and Wan, 2007), (Zainuddin and Pauline, 2011), also in the field of reliability prediction where they have proved to be powerful tools (Xu et al, 2003), (Pai, 2006), (Chatterjee and Bandopadhyay, 2012). However, the most commonly used neural network model, multilayer perceptrons (MLP), generally, suffer from drawbacks which include obtaining sub-optimal solutions due to local minima and requiring long computation time.…”

Section: Introductionmentioning

confidence: 99%

Predicting time series of railway speed restrictions with time-dependent machine learning techniques

Fink¹,

Zio²,

Weidmann³

2013

Expert Systems with Applications

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International audienceIn this paper, a hybrid approach to combine conditional restricted Boltzmann machines (CRBM) and echo state networks (ESN) for binary time series prediction is proposed. Both methods have demonstrated their ability to extract complex dynamic patterns from time-dependent data in several applications and benchmark studies. To the authors' knowledge, it is the first time that the proposed combination of algorithms is applied for reliability prediction. The proposed approach is verified on a case study predicting the occurrence of railway operation disruptions based on discrete-event data, which is represented by a binary time series. The case study concerns speed restrictions a ecting railway operations, caused by failures of tilting systems of railway vehicles. The overall prediction accuracy of the algorithm is 99.93%; the prediction accuracy for occurrence of speed restrictions within the foresight period is 98% (which corresponds to the sensitivity of the algorithm). The prediction results of the case study are compared to the prediction with a MLP trained with a Newton conjugate gradient algorithm. The proposed approach proves to be superior to MLP

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Time-series prediction with single integrate-and-fire neuron

Cited by 15 publications

References 13 publications

A Hybrid GMDH and Least Squares Support Vector Machines in Time Series Forecasting

A Hybrid GMDH and Least Squares Support Vector Machines in Time Series Forecasting

A Simple Lapicque Neuron Emulator

Predicting time series of railway speed restrictions with time-dependent machine learning techniques

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