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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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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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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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