Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke

Authors:
Nikola Kasabov;Valery Feigin;Zeng-Guang Hou;Yixiong Chen;Linda Liang;Rita Krishnamurthi;Muhaini Othman;Priya Parmar
Affiliations:
-;-;-;-;-;-;-;-
Venue:
Neurocomputing
Year:
2014

Citing 13
Cited 0

Rank order coding

CNS '97 Proceedings of the sixth annual conference on Computational neuroscience : trends in research, 1998: trends in research, 1998
Polychronization: Computation with Spikes

Neural Computation
Advances in Design and Application of Spiking Neural Networks

Soft Computing - A Fusion of Foundations, Methodologies and Applications - Fuzzy-neural computation and robotics
GSA: A Gravitational Search Algorithm

Information Sciences: an International Journal
2009 Special Issue: Integrated feature and parameter optimization for an evolving spiking neural network: Exploring heterogeneous probabilistic models

Neural Networks
A decade of Kasabov's evolving connectionist systems: a review

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Neural networks letter: To spike or not to spike: A probabilistic spiking neuron model

Neural Networks
Quantum-inspired evolutionary algorithm: a multimodel EDA

IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Evolving spiking neural networks for audiovisual information processing

Neural Networks
Which model to use for cortical spiking neurons?

IEEE Transactions on Neural Networks
Training spiking neural networks to associate spatio-temporal input-output spike patterns

Neurocomputing
2013 Special Issue: Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition

Neural Networks
NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data

Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

The paper presents a novel method and system for personalised (individualised) modelling of spatio/spectro-temporal data (SSTD) and prediction of events. A novel evolving spiking neural network reservoir system (eSNNr) is proposed for the purpose. The system consists of spike-time encoding module of continuous value input information into spike trains; a recurrent 3D SNNr; eSNN as an evolving output classifier. Such system is generated for every new individual, using existing data of similar individuals. Subject to proper training and parameter optimisation, the system is capable of accurate spatio-temporal pattern recognition (STPR) and of early prediction of individual events. The method and the system are generic, applicable to various SSTD and classification and prediction problems. As a case study, the method is applied for early prediction of occurrence of stroke on an individual basis. Preliminary experiments demonstrated a significant improvement in accuracy and time of event prediction when using the proposed method when compared with standard machine learning methods, such as MLR, SVM, and MLP. Future development and applications are discussed.