Abstract
Polychronization has been proposed as a possible way to investigate the notion of cell assemblies and to understand their role as memory supports for information coding. In a spiking neuron network, polychronous groups (PGs) are small subsets of neurons that can be activated in a chain reaction according to a specific time-locked pattern. PGs can be detected in a neural network with known connection delays and visualized on a spike raster plot. In this paper, we specify the definition of PGs, making a distinction between structural and dynamical polychronous groups. We propose two algortihms to scan for structural PGs supported by a given network topology, one based on the distribution of connection delays and the other taking into account the synaptic weight values. At last, we propose a third algorithm to scan for the PGs that are actually activated in the network dynamics during a given time window.
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References
Abeles, M.: Corticonics: Neural Circuits of the Cerebral Cortex. Cambridge Press, New York (1991)
Gerstner, W., Kistler, W.: Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge (2002)
Gourévitch, B., Eggermont, J.: Maximum decoding abilities of temporal patterns and synchronized firings. In: NeuroComp 2008 (2008) hal-00331583
Gross, C.G.: Genealogy of the ”grandmother cell”. The Neuroscientist (2002)
Hebb, D.O.: The Organization of Behaviour. Wiley, New York (1949)
Izhikevich, E.M.: http://vesicle.nsi.edu/users/izhikevich/publications/spnet.htm (2006)
Izhikevich, E.M.: Polychronization: Computation with spikes. Neural Computation 18(2), 245–282 (2006)
Maier, W.L., Miller, B.N.: A minimal model for the study of polychronous groups. arXiv:0806.1070v1 [cond-mat.dis-nn] (2008); (presented at the TS4CF08 Meeting of The American Physical Society)
Martinez, R., Paugam-Moisy, H.: Les groupes polychrones pour capturer l’aspect spatio-temporel de la mémorisation. In: NeuroComp. 2008 (2008) hal-00331613
Paugam-Moisy, H., Martinez, R., Bengio, S.: Delay learning and polychronization for reservoir computing. Neurocomputing 71(7-9), 1143–1158 (2008)
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© 2009 Springer-Verlag Berlin Heidelberg
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Martinez, R., Paugam-Moisy, H. (2009). Algorithms for Structural and Dynamical Polychronous Groups Detection. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_8
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DOI: https://doi.org/10.1007/978-3-642-04277-5_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04276-8
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