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


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[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, 2006.10.1103/RevModPhys.78.1213Search 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, 2004.10.1073/pnas.0305966101Search 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, 2012.10.1103/PhysRevLett.108.228102Search 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, 2013.10.3389/fncom.2012.00102Search 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, 2016.10.1162/NECO_a_00894Search 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, 2017.10.1038/s41598-017-01511-ySearch 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, 2016.10.3389/fnins.2016.00508Search 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, 2009.10.4304/jcp.4.11.1182-1186Search 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, 2015.10.1109/TNNLS.2015.2388544Search 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, 2016.10.3389/fncom.2016.00094Search 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, 2007.10.1093/cercor/bhl152Search in Google Scholar

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