Abstract
Izhikevich [6] has proposed that certain strongly connected groups of neurons known as polychronous neural groups (or PNGs) might provide the neural basis for representation and memory. Polychronous groups exist in large numbers within the connection graph of a spiking neural network, providing a large repertoire of structures that can potentially match an external stimulus [6,8]. In this paper we examine some of the requirements of a representational system and test the idea of PNGs as the underlying mechanism against one of these requirements, the requirement for consistency in the neural response to stimuli. The results provide preliminary evidence for consistency of PNG activation in response to known stimuli, although these results are limited by problems with the current methods for detecting PNG activation.
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References
Abraham, W.C.: Metaplasticity: tuning synapses and networks for plasticity. Nature Reviews Neuroscience 9(5), 387–387 (2008)
Caporale, N., Dan, Y.: Spike timing-dependent plasticity: a hebbian learning rule. Annu. Rev. Neurosci. 31, 25–46 (2008)
Caroni, P., Donato, F., Muller, D.: Structural plasticity upon learning: regulation and functions. Nature Reviews Neuroscience 13(7), 478–490 (2012)
Guise, M., Knott, A., Benuskova, L.: Consistency of polychronous neural group activation supports a role as an underlying mechanism for representation and memory: detailed methods and results. Tech. rep., Dept of Computer Science, University of Otago, Dunedin (2013)
Hoffmann, H., Howard, M.D., Daily, M.J.: Fast pattern matching with time-delay neural networks. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 2424–2429. IEEE (2011)
Izhikevich, E.M.: Polychronization: computation with spikes. Neural Computation 18(2), 245–282 (2006)
Izhikevich, E.M.: Reference software implementation for the Izhikevich model: minimal spiking network that can polychronize (2006), http://www.izhikevich.org/publications/spnet.htm
Izhikevich, E.M., Gally, J.A., Edelman, G.M.: Spike-timing dynamics of neuronal groups. Cerebral Cortex 14(8), 933–944 (2004)
Martin, S., Grimwood, P., Morris, R.: Synaptic plasticity and memory: an evaluation of the hypothesis. Annual Review of Neuroscience 23(1), 649–711 (2000)
Martinez, R., Paugam-Moisy, H.: Algorithms for structural and dynamical polychronous groups detection. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009, Part II. LNCS, vol. 5769, pp. 75–84. Springer, Heidelberg (2009)
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Guise, M., Knott, A., Benuskova, L. (2013). Evidence for Response Consistency Supports Polychronous Neural Groups as an Underlying Mechanism for Representation and Memory. In: Cranefield, S., Nayak, A. (eds) AI 2013: Advances in Artificial Intelligence. AI 2013. Lecture Notes in Computer Science(), vol 8272. Springer, Cham. https://doi.org/10.1007/978-3-319-03680-9_10
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DOI: https://doi.org/10.1007/978-3-319-03680-9_10
Publisher Name: Springer, Cham
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