Pattern Classification by Spiking Neural Networks Combining Self-Organized and Reward-Related Spike-Timing-Dependent Plasticity

[1] M. I. Rabinovich, P. Varona, A. I. Selverston, and H. D. Abarbanel, Dynamical principles in neuro-science, Reviews of modern physics, vol. 78, no. 4, pp. 1213–1265, 2006.Search in Google Scholar

[2] R. Q. Quiroga and S. Panzeri, Principles of neural coding. CRC Press, 2013.10.1201/b14756Open DOISearch in Google Scholar

[3] S. Panzeri, J. H. Macke, J. Gross, and C. Kayser, Neural population coding: combining insights from microscopic and mass signals, Trends in cognitive sciences, vol. 19, no. 3, pp. 162–172, 2015.10.1016/j.tics.2015.01.002437938225670005Search in Google Scholar

[4] N. Schweighofer, K. Doya, H. Fukai, J. V. Chiron, T. Furukawa, and M. Kawato, Chaos may enhance information transmission in the inferior olive, Proceedings of the National Academy of Sciences, vol. 101, no. 13, pp. 4655–4660, 2004.Search in Google Scholar

[5] J. Mejias and A. Longtin, Optimal heterogeneity for coding in spiking neural networks, Physical Review Letters, vol. 108, no. 22, 228102, 2012.Search in Google Scholar

[6] N. Hiratani, J.-N. Teramae, and T. Fukai, Associative memory model with long-tail-distributed hebbian synaptic connections, Frontiers in computational neuroscience, vol. 6, 102, 2013.Search in Google Scholar

[7] S. Nobukawa and H. Nishimura, Chaotic resonance in coupled inferior olive neurons with the llinás approach neuron model, Neural computation, vol. 28, no. 11, pp. 2505–2532, 2016.Search in Google Scholar

[8] S. Nobukawa, H. Nishimura, and T. Yamanishi, Chaotic resonance in typical routes to chaos in the Izhikevich neuron model, Scientific reports, vol. 7, no. 1, 1331, 2017.Search in Google Scholar

[9] N. K. Kasabov, Neucube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data, Neural Networks, vol. 52, pp. 62–76, 2014.10.1016/j.neunet.2014.01.00624508754Search in Google Scholar

[10] J. H. Lee, T. Delbruck, and M. Pfeiffer, Training deep spiking neural networks using backpropagation, Frontiers in neuroscience, vol. 10, 508, 2016.Search in Google Scholar

[11] X. Lin, X. Wang, and Z. Hao, Supervised learning in multilayer spiking neural networks with inner products of spike trains, Neurocomputing, vol. 237, pp. 59–70, 2017.10.1016/j.neucom.2016.08.087Search in Google Scholar

[12] S. R. Kulkarni and B. Rajendran, Spiking neural networks for handwritten digit recognition–supervised learning and network optimization, Neural Networks, vol. 103, pp. 118–127, 2018.10.1016/j.neunet.2018.03.01929674234Search in Google Scholar

[13] S. R. Kheradpisheh, M. Ganjtabesh, S. J. Thorpe, and T. Masquelier, STDP-based spiking deep convolutional neural networks for object recognition, Neural Networks, vol. 99, pp. 56–67, 2018.10.1016/j.neunet.2017.12.00529328958Search in Google Scholar

[14] Z. Lin, D. Ma, J. Meng, and L. Chen, Relative ordering learning in spiking neural network for pattern recognition, Neurocomputing, vol. 275, pp. 94–106, 2018.10.1016/j.neucom.2017.05.009Search in Google Scholar

[15] A. Tavanaei, T. Masquelier, and A. Maida, Representation learning using event-based STDP, Neural Networks, vol. 105, pp. 294–303, 2018.10.1016/j.neunet.2018.05.01829894846Search in Google Scholar

[16] M. Mozafari, S. R. Kheradpisheh, T. Masquelier, A. Nowzari-Dalini, and M. Ganjtabesh, First-spike-based visual categorization using reward-modulated STDP, IEEE Transactions on Neural Networks and Learning Systems, vol. 99, pp. 1–13, 2018.10.1109/TNNLS.2018.282672129993898Search in Google Scholar

[17] A. Tavanaei, Z. Kirby, and A. S. Maida, Training spiking convnets by STDP and gradient descent, in Proceedings of 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018, pp. 1–8.10.1109/IJCNN.2018.8489104Search in Google Scholar

[18] Y. Wu, L. Deng, G. Li, J. Zhu, and L. Shi, Spatio-temporal backpropagation for training high-performance spiking neural networks, Frontiers in neuroscience, vol. 12, 331, 2018.10.3389/fnins.2018.00331597421529875621Open DOISearch in Google Scholar

[19] M. Bernardo, C. Budd, A. R. Champneys, and P. Kowalczyk, Piecewise-smooth dynamical systems: theory and applications. Springer Science & Business Media, 2008, vol. 163.Search in Google Scholar

[20] N. Kasabov, Neucube evospike architecture for spatio-temporal modelling and pattern recognition of brain signals, in Proceedings of IAPR Workshop on Artificial Neural Networks in Pattern Recognition. Springer, 2012, pp. 225–243.10.1007/978-3-642-33212-8_21Search in Google Scholar

[21] N. Kasabov and E. Capecci, Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes, Information Sciences, vol. 294, pp. 565–575, 2015.10.1016/j.ins.2014.06.028Search in Google Scholar

[22] C. Ge, N. Kasabov, Z. Liu, and J. Yang, A spiking neural network model for obstacle avoidance in simulated prosthetic vision, Information Sciences, vol. 399, pp. 30–42, 2017.10.1016/j.ins.2017.03.006Search in Google Scholar

[23] D. Verstraeten, B. Schrauwen, D. Stroobandt, and J. Van Campenhout, Isolated word recognition with the liquid state machine: a case study, Information Processing Letters, vol. 95, no. 6, pp. 521–528, 2005.10.1016/j.ipl.2005.05.019Open DOISearch in Google Scholar

[24] A. Ghani, T. M. McGinnity, L. P. Maguire, and J. Harkin, Neuro-inspired speech recognition with recurrent spiking neurons, in Proceedings of International Conference on Artificial Neural Networks. Springer, 2008, pp. 513–522.10.1007/978-3-540-87536-9_53Search in Google Scholar

[25] Z. Yanduo and W. Kun, The application of liquid state machines in robot path planning, Journal of Computers, vol. 4, no. 11, pp. 1183–1186, 2009.Search in Google Scholar

[26] Y. Zhang, P. Li, Y. Jin, and Y. Choe, A digital liquid state machine with biologically inspired learning and its application to speech recognition, IEEE transactions on neural networks and learning systems, vol. 26, no. 11, pp. 2635–2649, 2015.Search in Google Scholar

[27] Y. Jin and P. Li, Calcium-modulated supervised spike-timing-dependent plasticity for readout training and sparsification of the liquid state machine, in Proceedings of 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017, pp. 2007–2014.10.1109/IJCNN.2017.7966097Search in Google Scholar

[28] R. V. Florian, Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity, Neural Computation, vol. 19, no. 6, pp. 1468–1502, 2007.10.1162/neco.2007.19.6.146817444757Open DOISearch in Google Scholar

[29] N. Frémaux, H. Sprekeler, and W. Gerstner, Functional requirements for reward-modulated spike-timing-dependent plasticity, Journal of Neuro-science, vol. 30, no. 40, pp. 13 326–13 337, 2010.10.1523/JNEUROSCI.6249-09.2010663472220926659Search in Google Scholar

[30] T.-S. Chou, L. D. Bucci, and J. L. Krichmar, Learning touch preferences with a tactile robot using dopamine modulated STDP in a model of insular cortex, Frontiers in neurorobotics, vol. 9, p. 6, 2015.10.3389/fnbot.2015.00006451077626257639Search in Google Scholar

[31] A. H. Marblestone, G. Wayne, and K. P. Kording, Toward an integration of deep learning and neuroscience, Frontiers in computational neuroscience, vol. 10, 94, 2016.Search in Google Scholar

[32] A. S. Warlaumont and M. K. Finnegan, Learning to produce syllabic speech sounds via reward-modulated neural plasticity, PloS one, vol. 11, no. 1, e0145096, 2016.10.1371/journal.pone.0145096472662326808148Search in Google Scholar

[33] Y. Kawai, T. Takimoto, J. Park, and M. Asada, Efficient reward-based learning through body representation in a spiking neural network, in Proceedings of the 8th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics. IEEE, 2018, pp. 198–203.10.1109/DEVLRN.2018.8761011Search in Google Scholar

[34] E. M. Izhikevich, Polychronization: computation with spikes, Neural computation, vol. 18, no. 2, pp. 245–282, 2006.10.1162/08997660677509388216378515Search in Google Scholar

[35] E. M. Izhikevich, Solving the distal reward problem through linkage of STDP and dopamine signaling, Cerebral cortex, vol. 17, no. 10, pp. 2443–2452, 2007.Search in Google Scholar