Transmission of distributed deterministic telporal information through a diverging/converging three-layers neural network

Yoshiyuki Asai 1 Alessandro Villa 2, *
* Auteur correspondant
2 INSERM U836, équipe 7, Nanomédecine et cerveau
GIN - Grenoble Institut des Neurosciences, Neuroheuristic Research Group
Abstract : This study investigates the ability of a diverging/converging neural network to transmit and integrate a complex temporally organized activity embedded in afferent spike trains. The temporal information is originally generated by a deterministic nonlinear dynamical system whose parameters determine a chaotic attractor. We present the simulations obtained with a network formed by simple spiking neurons (SSN) and a network formed by a multiple-timescale adaptive threshold neurons (MAT). The assessment of the temporal structure embedded in the spike trains is carried out by sorting the preferred firing sequences detected by the pattern grouping algorithm (PGA). The results suggest that adaptive threshold neurons are much more efficient in maintaining a specific temporal structure distributed across multiple spike trains throughout the layers of a feed-forward network.
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Communication dans un congrès
20th International Conference on Artificial Neural Networks, Sep 2011, Thessaloniki, Greece. 6352, pp.145-154, 2010
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Yoshiyuki Asai, Alessandro Villa. Transmission of distributed deterministic telporal information through a diverging/converging three-layers neural network. 20th International Conference on Artificial Neural Networks, Sep 2011, Thessaloniki, Greece. 6352, pp.145-154, 2010. 〈inserm-00624053〉

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