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EvoSpike Publications

by Giacomo Indiveri last modified Jun 22, 2012 05:31 PM

Book chapter on eSNN for STPR

N. Kasabov, Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition, Springer-Verlag Berlin Heidelberg 2012, J. Liu et al. (Eds.): IEEE WCCI 2012, LNCS 7311, pp. 234–260, 2012.

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WCCI 2012 paper on a new algorithm - deSNN

Dhoble, K., N. Nuntalid, G. Indivery and N.Kasabov, On-line Spatiotemporal Pattern Recognition with Evolving Spiking Neural Networks utilising Address Event Representation, Rank Oder- and Temporal Spike Learning, WCCI 2012 IEEE World Congress on Computational Intelligence, June, 10-15, 2012 - Brisbane, Australia, 554-560

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Incremental SPAN learning algorithm presented at WCCI 2012

Mohemmed, A. and N.Kasabov, Incremental learning algorithm for spike pattern classification, WCCI 2012 IEEE World Congress on Computational Intelligence, June, 10-15, 2012 - Brisbane, Australia, 1227- 1232

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N. Kasabov, M. Doborjeh, Z. Doborjeh, Mapping, learning, visualisation, classification and understanding of fMRI data in the NeuCube Spatio Temporal Data Machine

IEEE Transactions of Neural Networks and Learning Systems, vol. 28,4, 887-899, 2017, DOI: 10.1109/TNNLS.2016.2612890

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NeuCube

Kasabov, N., NeuCube EvoSpike Architecture for Spatio-Temporal Modelling and Pattern Recognition of Brain Signals, in: Mana, Schwenker and Trentin (Eds) ANNPR, Springer LNAI 7477, 2012, 225-243. http://www.springer.com/computer/ai/book/978-3-642-33211-1

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Modelling the Effect of Genes

Kasabov, N. and Schliebs, S. and Mohemmed, A. Modeling the Effect of Genes on the Dynamics of Probabilistic Spiking Neural Networks for Computational Neurogenetic Modeling, 8th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Gargnano-Lago di Garda, Italy, 30 June, 2011, LNBI 7548, 1-9,2012

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Method for Training a Spiking Neuron to Associate Input-Output Spike Trains

Mohemmed, A. and Schliebs, S. and Matsuda, S. and Kasabov, N. Method for training a spiking neuron to associate input-output spike trains, Proceedings of the EANN/AIAI 2011, Part I, IFIP AICT 363, 219-228, 2011 (http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-23956-4?changeHeader)

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Reservoir-based evolving spiking neural network for spatio-temporal pattern recognition

Schliebs, S. and Hamed, H. N. A. and Kasabov, N. A reservoir-based evolving spiking neural network for on-line spatio-temporal pattern learning and recognition, Neural Information Processing, Proceedings of the 18th International Conference on Neural Information Processing, ICONIP, 2011, Shanghai, China, Springer, Heidelberg, LNCS vol. 7063, pp.160-168 (http://www.springer.com/generic/search/results?SGWID=5-40109-24-653415-0&sortOrder=relevance&searchType=EASY_CDA&searchScope=editions&queryText=iconip+2011)

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Evolving probabilistic spiking neural networks for spatio-temporal pattern recognition

Kasabov, N. and Dhoble, K. and Nuntalid, N. and Mohemmed, A. Evolving probabilistic spiking neural networks for spatio-temporal pattern recognition: A preliminary study on moving object recognition, Neural Information Processing, Proceedings of the 18th International Conference on Neural Information Processing, ICONIP, 2011, Shanghai, China, Springer, Heidelberg, LNCS vol. 7064, 230-239. http://www.springer.com/generic/search/results?SGWID=5-40109-24-653415-0&sortOrder=relevance&searchType=EASY_CDA&searchScope=editions&queryText=iconip+2011

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EEG Classification with BSA Spike Encoding Algorithm and Evolving Probabilistic Spiking Neural Network

Nuntalid, N. and Dhoble, K. and Kasabov, N. EEG Classification with BSA Spike Encoding Algorithm and Evolving Probabilistic Spiking Neural Network, Neural Information Processing, Proceedings of the 18th International Conference on Neural Information Processing, ICONIP, 2011, Shanghai, China, Springer, Heidelberg, LNCS vol. 7062, 451-460. http://www.springer.com/generic/search/results?SGWID=5-40109-24-653415-0&sortOrder=relevance&searchType=EASY_CDA&searchScope=editions&queryText=iconip+2011

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Probabilistic Computational Neurogenetic modeling: From Cognitive Systems to Alzheimer’s Disease

Kasabov, N. R.Schliebs, H.Kojima Probabilistic Computational Neurogenetic Framework: From Modelling Cognitive Systems to Alzheimer’s Disease, IEEE Transactions of Autonomous Mental Development, 3:(4) 300-3011, 2011 http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=6097099&punumber=4563672

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Springer Handbook of Bio-/Neuro-Informatics

This Springer Handbook of Bio-/Neuroinformatics is the first published book in one volume that explains together the basics and the state-of-the-art of two major science disciplines in their interaction and mutual relationship, namely: bioinformatics and neuroinformatics. The text is organized in three groups of parts: foundations, bioinformatics and neuroinformatics. Each group consists of three parts: introduction to the subject area; presentation of methods and systems and advanced science and technology. Informatics is the science of information. Informatics methods and techniques include methods of statistical learning, data mining, machine learning, knowledge engineering, neural networks, evolutionary computation, chaos theory, quantum computation, and many more. These methods have been widely used in bioinformatics and neuroinformatics studies and technological developments. Bioinformatics is the area of science that is concerned with the information processes in biology and the development and applications of methods, tools and systems for storing and processing of biological information in order to facilitate new knowledge discovery. Neuroinformatics is concerned with the information processes in the brain and the nervous system and consequently with the development of methods and system for storing and processing such information, ultimately leading to a better understanding, modeling and curing the brain and the nervous system.

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SPAN: Spike Pattern Association Neuron for Learning Spatio-Temporal Sequences

A.Mohemmed,S.Schliebs,S.Matsuda,Kasabov, SPAN: Spike Pattern Association Neuron for Learning Spatio-Temporal Sequences, Int. J. Neural Systems, vol.22, 4, 2012.

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Kasabov, N. et al, (2014). Evolving Spiking Neural Networks for Personalised Modelling of Spatio-Temporal Data and Early Prediction of Events: A Case Study on Stroke. Neurocomputing, vol .134, 269-279, 2014

Kasabov, N., Liang, L., Krishnamurthi, R., Feigin, V., Othman, M., Hou, Z.,. Parmar, P. (2014). Evolving Spiking Neural Networks for Personalised Modelling of Spatio-Temporal Data and Early Prediction of Events: A Case Study on Stroke. Neurocomputing, vol .134, 269-279, 2014

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SPAN incremental Learning for Handwritten Digit Recognition

Mohemmed, A. and Guoyu Lu and N. Kasabov, Evaluating SPAN incremental Learning for Handwritten Digit Recognition, T. Huang et al. (Eds.): ICONIP 2012, Part III, LNCS 7665, pp. 670–677, 2012, Springer-Verlag Berlin Heidelberg 2012

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Constructing Robust Liquid State Machines to Process Highly Variable Data Streams

Schliebs S and Fiasché M and Kasabov N, Constructing Robust Liquid State Machines to Process Highly Variable Data Streams. Proceedings Editors: Villa AEP, Duch W, Érdi P, Masulli F, Palm G. ICANN (1). Springer, LNCS 7552, 604-611, 2012, http://www.springerlink.com/content/k4u5316x55401156/

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