ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Detection of a dynamical system attractor from spike train analysis
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Which model to use for cortical spiking neurons?
IEEE Transactions on Neural Networks
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
| Hi-index | 0.00 |
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.