Abstract
An extension of an evolving spiking neural network (eSNN) is proposed that enables the method to process spatio-temporal information. In this extension, an additional layer is added to the network architecture that transforms a spatio-temporal input pattern into a single intermediate high-dimensional network state which in turn is mapped into a desired class label using a fast one-pass learning algorithm. The intermediate state is represented by a novel probabilistic reservoir computing approach in which a stochastic neural model introduces a non-deterministic component into a liquid state machine. A proof of concept is presented demonstrating an improved separation capability of the reservoir and consequently its suitability for an eSNN extension.
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References
Bohte, S.M., Kok, J.N., Poutré, J.A.L.: Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48(1-4), 17–37 (2002)
Clopath, C., Jolivet, R., Rauch, A., Lüscher, H.R., Gerstner, W.: Predicting neuronal activity with simple models of the threshold type: Adaptive exponential integrate-and-fire model with two compartments. Neurocomput. 70(10-12), 1668–1673 (2007)
Destexhe, A., Contreras, D.: Neuronal computations with stochastic network states. Science 314(5796), 85–90 (2006)
Gerstner, W., Kistler, W.M.: Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge (2002)
Grzyb, B.J., Chinellato, E., Wojcik, G.M., Kaminski, W.A.: Which model to use for the liquid state machine? In: IJCNN 2009: Proceedings of the 2009 international joint conference on Neural Networks, pp. 1692–1698. IEEE Press, Piscataway (2009)
van Kampen, N.G.: Stochastic Processes in Physics and Chemistry. North-Holland, Amsterdam (2007)
Kasabov, N.: The ECOS framework and the ECO learning method for evolving connectionist systems. JACIII 2(6), 195–202 (1998)
Kasabov, N.: To spike or not to spike: A probabilistic spiking neuron model. Neural Networks 23(1), 16–19 (2010)
Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14(11), 2531–2560 (2002)
Maass, W., Zador, A.: Dynamic stochastic synapses as computational units. In: Advances in Neural Information Processing Systems, pp. 903–917. MIT Press, Cambridge (1998)
Schliebs, S., Defoin-Platel, M., Kasabov, N.: Analyzing the dynamics of the simultaneous feature and parameter optimization of an evolving spiking neural network. In: International Joint Conference on Neural Networks, IEEE - INNS - ENNS. IEEE Computer Society Press, Barcelona (2010)
Schliebs, S., Defoin-Platel, M., Worner, S., Kasabov, N.: Integrated feature and parameter optimization for an evolving spiking neural network: Exploring heterogeneous probabilistic models. Neural Networks 22(5-6), 623–632 (2009)
Schrauwen, B., Verstraeten, D., Campenhout, J.V.: An overview of reservoir computing: theory, applications and implementations. In: Proceedings of the 15th European Symposium on Artificial Neural Networks, pp. 471–482 (2007)
Thorpe, S.J.: How can the human visual system process a natural scene in under 150ms? On the role of asynchronous spike propagation. In: ESANN. D-Facto public (1997)
Verstraeten, D., Schrauwen, B., D’Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Networks 20(3), 391–403 (2007)
Wysoski, S.G., Benuskova, L., Kasabov, N.K.: Adaptive learning procedure for a network of spiking neurons and visual pattern recognition. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2006. LNCS, vol. 4179, pp. 1133–1142. Springer, Heidelberg (2006)
Yamazaki, T., Tanaka, S.: 2007 special issue: The cerebellum as a liquid state machine. Neural Netw. 20(3), 290–297 (2007)
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Schliebs, S., Nuntalid, N., Kasabov, N. (2010). Towards Spatio-Temporal Pattern Recognition Using Evolving Spiking Neural Networks. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_21
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DOI: https://doi.org/10.1007/978-3-642-17537-4_21
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