Abstract
This paper proposes a novel architecture for continuous spatio-temporal data modeling and pattern recognition utilizing evolving probabilistic spiking neural network ‘reservoirs’ (epSNNr). The paper demonstrates on a simple experimental data for moving object recognition that: (1) The epSNNr approach is more accurate and flexible than using standard SNN; (2) The use of probabilistic neuronal models is superior in several aspects when compared with the traditional deterministic SNN models, including a better performance on noisy data.
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Kasabov, N., Dhoble, K., Nuntalid, N., Mohemmed, A. (2011). Evolving Probabilistic Spiking Neural Networks for Spatio-temporal Pattern Recognition: A Preliminary Study on Moving Object Recognition. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_25
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DOI: https://doi.org/10.1007/978-3-642-24965-5_25
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