Mobile QR Code QR CODE


P. Bose, N.K. Kasabov, L. Bruzzone, R.N. Hartono, “Spiking Neural Network for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series”, IEEE Transaction on Geoscience and Remote sensing, Vol. 54, No. 11 November 2016.URL
P.U. Diehl and M. Cook, “Unsupervised Learning of digit Recognition Using Spikes Timing Dependent Plasticity”, Frontiers in Computational Neuroscience, August 2015.DOI
G. Srinivasan, S. Roy, V. Raghunathan, K. Roy, “Spike Timing Dependent Plasticity Based Enhanced Self-Learning for Efficient Pattern Recognition in Spiking Neural Networks”, International Joint Conference on Neural Network (IJCNN), May 2017.URL
K. Kiani and E.M. Korayem, “Classification of Persian Handwritten Digits Using Spiking Neural Network”, International conference on Knowledge-Based Engineering and Inovation (KBEI), November 5-6, 2015.URL
A. Tahtirvancu and B. Yilmaz, “Classification of EEG Signals Using Spiking Neural Network”, International conference on Knowledge-Based Engineering and Inovation (KBEI), November 2015.URL
Z. G. Doborjeh, M. Doborjeh, N. Kasabov, “EEG Pattern Recognition Using brian-Inspired Spiking Neural Network for Modelling Human Decision Processes”, International Joint Conference on Neural Network (IJCNN), July 2018.URL
D. Querlioz, O. Bichler, P. Dollfus and C. Gamrat, "Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices," in IEEE Transactions on Nanotechnology, vol. 12, no. 3, pp. 288-295, May 2013.URL
M. Beyeler, N.D Dutt and J.L. Krichmar, “Categorization and Decision-making in A Neurobiologically Plausible Spiking Network Using a STDP-like Learning Rule”, Neural Network 48, 109-124. July 2013.DOI
P. Morella, J. Arthur, F. Akopyan, N. Imam, R. Manohar, D.S. Modha, “A Digital Neurosynaptic Core Using Embedded Crossbar Memory With 45pj Per Spike in 45 nm”, IEEE Custom Integrated Circuits Conference (CICC), September, Sept. 2011.URL
P. O’Connor, D. Neil, S.C. Liu, T. Delbruck, M. Pfeiffer, “Real Time Classification and Sensor Fusion with a Spiking Deep Belief Network”, Frontiers in Computational Neuroscience, October 2013.URL
S. Hussain, S.C. Liu, A. Basu, “Improved Margin Multi-Class Classification using dendritic neurons with morphological Learning”, in Circuits and Systems (ISCAS), IEEE International Symposium, June 2014.URL
M. C. Ergene, A. Durdu, H. Cetin “Imitation and Learning of human Hand Gesture Tasks of the 3D Printed Robotic Hand By Using Artificial Neural Networks”, International conference on Electronics, Computer and Artificial Intelligence (ECAI), July 2016.URL
N.K. Kasabov, “Evolving Connectionist Systems for Adaptive Learning and Knowledge Discovery: Trends and Directions”, Elseiver Knowledge-Based Systems. May 2015.URL
S.G. Wysoski, L. Benuskova, N. Kasabov, “Evolving Spiking Neural Networks for Audio Visual Information Processing”, Elseiver: Neural Network Vol. 23, No. 7, 2010.URL
S. Schliebs and N. Kasabov, “Evolving Spiking Neural Network - A survey”, Evolving Syst. Vol. 4, No. 2, February 2013.URL
N.K. Kasabov, K. Dhoble, N. Nuntalid, G. Indiveri, “Dynamic Evolving Spiking Neural Network for On-line Spatio and Spectro Temporal Pattern Recognition”, Elseiver: Neural Network, Vol. 41, May 2013.URL
A. Mohemmed, S. Schliebs, S. matsuda, N. Kasabov, “Training Spiking Neural Network to Associate Spatio-temporal Input-Output Spike Patterns”, Neurocomputing, Vol. 107, May 2013.DOI
K. Dhoble, N. Nuntalid, G. Indiveri, N. Kasabov, “Online Spatio-Temporal Pattern Recognition with Evolving Spiking Neural Networks Utilising Address Event Representation, Rank order, and Temporal Spike Learning”. IEEE WCCI, Brisbane. June 2012.URL
N. K. Kasabov, “NeuCube: A Spiking Neural Network Architecture For Mapping, Learning and Understanding of Spatio Temporal Brain Data”, Elseiver: Neural Networks, February 2013.DOI
C. Bensmail, V. Steuber, N. davey, B. Wrobel, “Evolving Spiking Neural Network To Control Animats for Temporal Pattern Recognition and Foraging”, IEEE Symposium Series on Computational Intelligence (SSCI), December 2017.URL
N.K. Kasabov, “Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence”, Springer Series on Bio and Neurosystems 7, August 2018.DOI
A. A. Abusnaina and R. Abdullah, “Spiking Neuron Models: A review”, International Journal of Digital Content Technology and its Applications (JDCTA), Vol. 8 No. 3, June 2014.URL
E.M. Izhikevich, “Simple Model of Spiking Neurons”, IEEE Transactions on Neural Networks Vol. 14 Issue. 6 November 2003.URL
R. Vazquez, “Izhikevich Neuron Model and Its Application in Pattern Recognition”, Neurodynamics: Australian Journal of Intelligent Information Processing System, Vol. 11, No. 1, 2010.URL
M. Lu, J.L. Wang, J. Wen, X.W. Dong, “Implementation of Hodgkin-Huxley Neuron Model in FPGAs”, 7th Asia Pacific International Symposium on Electromagnetic Compatibility, May 2016.URL
M. Dimopoulou, E. Doutsi, M. Antonini “A Retina-Inspired Encoder: An Innovative Step on Image Coding Using Leaky Integrate and Fire Neurons”, IEEE International Conference on Image Processing (ICIP), October 2018.URL
B. Sengupta, S. B. Laughlin, J. E. Niven, “Consequences of converting graded to action potentials upon neural information coding and energy efficiency”, PLoS Comput. Biol., Vol. 10, No. 1, Jan 2014.DOI
D. Auge, J. Hille, E. Mueller, A. Knoll, “A Survey of Encoding Techniques for signal Processing in Spiking Neural Networks”, Neural Process Lett., Vol 53, pp. 4693-4710, July 2021.DOI
E. D. Adrian, Y. Zotterman, “The impulses produced by sensory nerve-endings: Part II. The response of a Single End-Organ”, J Physiol, Vol. 61(2), pp. 151-171, April 1926.DOI
J. Gautrais, S. Thorpe, “Rate coding versus temporal order coding: a theoretical approach”, Biosystems,Vol. 48, pp. 57-65, November 1998.DOI
S. J. Thorpe, “Spike arrival times: A highly efficient coding scheme for neural networks”, Parallel Processing in Neural Systems and Computers, pp. 91-94, January 1990.URL
R. B. Stein, E. R. Gossen, K. E. Jones, “Neuronal variability: noise or part of the signal?”, Nature Reviews Neuroscience, Vol. 6, pp. 389-397, May 2005.DOI
F. Theunissen, J. P. Miller, “Temporal encoding in nervous systems: A rigorous definition”, J comput Neurosci, Vol 2, pp. 149-162, November 1994.DOI
C.F. Stevens, A. Zador, “Neural Coding: The enigma of the brain”, Current Biology, Vol 5, No 12, pp. 1370-1371, December 1995.DOI
A. Dey, “Machine Learning Algorithms: A Review”, International Journal of Computer Science and Information Technology, Vol. 7(3), 2016.URL
M. Varone, D. Mayer, A. Melegari et al, “What is Machine Learning? A definition”,URL
S.B Hiregoudar, “A Survey: Research Summary on Neural Network ”, International journal of Research in Engineering and Technology, ISSN: 2319 1163, Vol. 03, S.I. 03, pages: 385-389, May 2014.URL
J. Xin and M.J. Embrechts, “Supervised Learning with Spiking Neural Network”, International Joint Conference on Neural Networks, July 2001.URL
L. Guo, Z. Wang, M. Adjouadi, “A Supervised Learning Rule for Classification of Spatiotemporal Spike Patterns” 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), August 2016.URL
S.M. Bohte, J.N. Kok, H. La Poutre, “Unsupervised Classification of complex Clusters in networks of Spiking Neurons”. Proceedings of the IEEE - INNS - ENNS International Joint Conference on Neural Networks IJCNN, July 2000.URL
H. Hazan, D. Saunders, D. T. Sanghavi, H. Siegelmann and R. Kozma, "Unsupervised Learning with Self-Organizing Spiking Neural Networks," 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, July 2018.URL
D.J. Saunders, H.T. Siegelmann, R. Kozma, M. ruszinko, “STDP Learning of Image Features with Spiking Neural networks”, IEEE/ INNS IJCNN, July 2018.URL
Y. Dorogyy and V. Kolisnichenko, "Unsupervised Pre-Training with Spiking Neural Networks in Semi-Supervised Learning," 2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC), Kiev, October 2018.URL
R. A. Koene and M. E. Hasselmo, “An Integrate and Fire Model of Prefrontal Cortex Provides a biological Implementation of Action Selection in Reinforcement Learning Theory that Reuse Known Representations”, IEEE International Joint conference on Neural Network 2005.URL
M. Spüler, S. Nagel and W. Rosenstiel, "A spiking neuronal model learning a motor control task by reinforcement learning and structural synaptic plasticity," 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, July 2015.URL
I. Garg, S. S. Chowdhury, K. Roy, “DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural Networks”, arXiv preprint arXiv: 2010.01795, October 2020.DOI