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You are here: Home Projects EvoSpike Publications

Publications

by Nikola Kasabov last modified Jun 22, 2012 05:33 PM
This folder contains publications related to the EvoSpike project (published before 2011) and resulted from it (published after 2011 or in preparation).

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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Book chapter on eSNN for STPR

by Nikola Kasabov last modified Jun 22, 2012 05:26 PM

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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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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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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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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Evolving spiking neural networks: A Survey

Schliebs, S. and N.Kasabov, Evolving spiking neural networks: A Survey, Evolving Systems, Special Issue on Applications of Evolving Connectionist Systems, M.Watts (ed), Springer, accepted, in print, 2012, http://www.springerlink.com/content/121561?MUD=MP

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Mapping, Learning and Mining of Spatiotemporal Brain Data with 3D Evolving Spiking Neurogenetic Models

Mapping, Learning and Mining of Spatiotemporal Brain Data with 3D Evolving Spiking Neurogenetic Models, Plenary talk, ICONIP 2012, Qatar, November 2012 (abstract of the presentation)

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Evolving Spiking Neural Networks for Spatio and Spectro-Temporal Pattern Recognition, Plenary talk IEEE IS

Kasabov, N. Evolving Spiking Neural Networks for Spatio and Spectro-Temporal Pattern Recognition, 2012 IEEE 6th International Conference ‘Intelligent Systems’, IEEE Press, 978-1-4673-2278-2/12/$31.00 ©2012, vol.1. 27-32, 2012 (plenary talk IEEE IS conference, Sofia, September 2012).

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Dynamic Evolving Spiking Neural Networks for On-line Spatio- and Spectro-Temporal Pattern Recognition

Kasabov, N. and Dhoble, K. and Nuntalid, N. and G. Indiveri, Dynamic Evolving Spiking Neural Networks for On-line Spatio- and Spectro-Temporal Pattern Recognition, Neural Networks, Special Issue on Autonomous Machine Learning, A.Roy (ed), preliminary accepted, 2012, http://ees.elsevier.com/neunet/

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Spiking neural network architecture for associative learning of spatio-temporal brain patterns

Kasabov, N., Spiking neural network architecture for associative learning of spatio-temporal brain patterns, Frontiers of Computational Neuroscience, 2012, abstract accepted

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Training Spiking Neural Networks to Associate Spatio-temporal Input-Output Spike Patterns

Mohemmed, A.and S. Schliebs and S. Matsuda and N. Kasabov, Training Spiking Neural Networks to Associate Spatio-temporal Input-Output Spike Patterns, Neurocomputing, accepted, in print, 2012, http://www.journals.elsevier.com/neurocomputing/

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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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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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Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications

N. Kasabov, N. Scott, E.Tu, S. Marks, N.Sengupta, E.Capecci, M.Othman,M. Doborjeh, N.Murli,R.Hartono, J.Espinosa-Ramos, L.Zhou, F.Alvi, G.Wang, D.Taylor, V. Feigin,S. Gulyaev, M.Mahmoudh, Z-G.Hou, J.Yang, Design methodology and selected applications of evolving spatio- temporal data machines in the NeuCube neuromorphic framework, Neural Networks, v.78, 1-14, 2016. http://dx.doi.org/10.1016/j.neunet.2015.09.011.

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Evolving, dynamic clustering of spatio/spectro-temporal data in 3D spiking neural network models and a case study on EEG data

MG Doborjeh, N Kasabov, ZG Doborjeh, Evolving, dynamic clustering of spatio/spectro-temporal data in 3D spiking neural network models and a case study on EEG data, Evolving Systems, 1-17, 2017.

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An evolving spatio-temporal approach for gender and age group classi cation with Spiking Neural Networks

F. B. Alvi, R. Pears, N. Kasabov An evolving spatio-temporal approach for gender and age group classification with Spiking Neural Networks, Evolving Systems, Springer, 2017.

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New Algorithms for Encoding, Learning and Classification of fMRI Data in a Spiking Neural Network Architecture: A Case on Modelling and Understanding of Dynamic Cognitive Processes

N. Kasabov, L. Zhou, M. Gholami Doborjeh, J. Yang, “New Algorithms for Encoding, Learning and Classification of fMRI Data in a Spiking Neural Network Architecture: A Case on Modelling and Understanding of Dynamic Cognitive Processes”, IEEE Transaction on Cognitive and Developmental Systems, 2017, DOI: 10.1109/TCDS.2016.2636291.

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NeuCube for obstacle avoidance in simulated prosthetic vision

C Ge, N Kasabov, J Yang, A spiking neural network model for obstacle avoidance in simulated prosthetic vision, Information Sciences 399, 30-42, 2017.

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Spiking Neural Networks for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series

P. Bose; N. Kasabov; L. Bruzzone; R. Hartono. Spiking Neural Networks for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series IEEE Transactions on Geoscience and Remote Sensing, Year: 2016, Volume: 54, Issue: 11, Pages: 6563 - 6573, DOI: 10.1109/TGRS.2016.2586602

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Adaptive Cow Movement Detection using Evolving Spiking Neural Network Models

T. Gao, N. Kasabov, Adaptive Cow Movement Detection using Evolving Spiking Neural Network Models, Evolving Systems, Springer, vol.7, No.4, 277-285, 2016.

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Analysis of connectivity in a NeuCube spiking neural network trained on EEG data for the understanding and prediction of functional changes in the brain: A case study on opiate dependence treatment

E. Capecci, G.Wang , N. Kasabov, Analysis of connectivity in a NeuCube spiking neural network trained on EEG data for the understanding and prediction of functional changes in the brain: A case study on opiate dependence treatment, Neural Networks, vol.68, 62-77, 2015.

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Longitudinal Study of Alzheimer's Disease Degeneration through EEG Data Analysis with a NeuCube Spiking Neural Network Model

Capecci, E., Doborjeh, Z. G.,Mammone, N., Foresta, F. L., Morabito F. C., and Kasabov, N. , (2016), Longitudinal Study of Alzheimer's Disease Degeneration through EEG Data Analysis with a NeuCube Spiking Neural Network Model, IEEE, International Joint Conference on Neural Networks (IJCNN),1360-1366, DOI:10.1109/IJCNN.2016.7727356, 2016.

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Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes

14 Kasabov, N., E.Capecci, Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes, Information Sciences, 294, 565-575, 2015, DOI: 10.1016/j.ins.2014.06.028, 2014.

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FROM MULTILAYER PERCEPTRONS AND NEURO-FUZZY SYSTEMS TO DEEP LEARNING MACHINES: WHICH METHOD TO USE? - A SURVEY

Nikola Kasabov, FROM MULTILAYER PERCEPTRONS AND NEURO-FUZZY SYSTEMS TO DEEP LEARNING MACHINES: WHICH METHOD TO USE? - A SURVEY, International Journal on Information Technologies & Security, No 2 (vol. 9), 3-24, 2017.

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An improved collaborative representation based classification with regularized least square (CRC–RLS) method for robust face recognition

Cheng, Y., Jin, Z., Gao, T., Chen, H., and Kasabov, N. (2016), An improved collaborative representation based classification with regularized least square (CRC–RLS) method for robust face recognition, Neurocomputing, Vol. 215, 250-259, 2016.

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Training spiking neural networks to associate spatio-temporal input–output spike patterns

Mohemmeda, A., Schliebs, S., Matsuda, S., Kasabov, N., (2013), Training spiking neural networks to associate spatio-temporal input–output spike patterns, Neurocomputing, Vol. 107, 3-10, 2013.

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A Spiking Neural Network Methodology and System for Learning and Comparative Analysis of EEG Data from Healthy versus Addiction Treated versus Addiction Not Treated Subjects

Doborjeh, M. G., Wangb, ,G. Y., Kasabova, N., Kyddd, R., Russell, B., (2015), A Spiking Neural Network Methodology and System for Learning and Comparative Analysis of EEG Data from Healthy versus Addiction Treated versus Addiction Not Treated Subjects, DOI:10.1109/TBME.2015.2503400, IEEE Transactions on Biomedical Engineering, 1830 - 1841, 2015.

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