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Algorithms for structural and dynamical polychronous groups detection

Regis Martinez 1, * Hélène Paugam-Moisy 2 
* Corresponding author
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
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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Conference papers
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Contributor : Hélène Paugam-Moisy Connect in order to contact the contributor
Submitted on : Saturday, December 11, 2010 - 12:10:56 AM
Last modification on : Tuesday, October 25, 2022 - 4:24:38 PM
Long-term archiving on: : Monday, November 5, 2012 - 1:15:57 PM


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  • HAL Id : inria-00425514, version 1


Regis Martinez, Hélène Paugam-Moisy. Algorithms for structural and dynamical polychronous groups detection. ICANN'2009, International Conference on Artificial Neural Networks, IEEE - INNS, Sep 2009, Limassol, Cyprus. pp.75-84. ⟨inria-00425514⟩



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