Inproceedings: an article in a conference proceedings.
Transmission of Distributed Deterministic Temporal Information through a Diverging/Converging Three-Layers Neural Network
Title of the conference
Artificial Neural Networks – ICANN 2010
Springer Berlin Heidelberg
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.
Spiking neural networks, synfire chains, adaptive threshold neurons, computational neuroscience, preferred firing sequences
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