Name
Abstract | ||
|---|---|---|
Deterministic nonlinear dynamics has been observed in experimental electrophysiological recordings performed in several areas of the brain. However, little is known about the ability to transmit a complex temporally organized activity through different types of spiking neurons. This study investigates the response of a spiking neuron model representing three archetypical types (regular spiking, thalamocortical and resonator) to input spike trains composed of deterministic (chaotic) and stochastic processes with weak background activity. The comparison of the input and output spike trains allows to assess the transmission of information contained in the deterministic nonlinear dynamics. The pattern grouping algorithm (PGA) was applied to the output of the neuron to detect the dynamical attractor embedded in the original input spike train. The results show that the model of the thalamo-cortical neuron can be a better candidate than regular spiking and resonator type neurons in transmitting temporal information in a spatially organized neural network. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74690-4_94 | ICANN (1) |
Keywords | Field | DocType |
complex temporally organized activity,input spike,output spike train,original input spike train,resonator type neuron,regular spiking,thalamo-cortical neuron,deterministic nonlinear dynamic,spiking neuron model,spiking neuron,stochastic process,spatial organization,neural network,nonlinear dynamics | Attractor,Biological neuron model,Spike train,Computer science,Random neural network,Input/output,Artificial intelligence,Artificial neural network,Chaotic,Spiking neural network,Machine learning | Conference |
Volume | ISSN | ISBN |
4668 | 0302-9743 | 3-540-74689-7 |
Citations | PageRank | References |
1 | 0.38 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoshiyuki Asai | 1 | 30 | 7.56 |
| Takashi Yokoi | 2 | 9 | 1.43 |
| Alessandro E . P. Villa | 3 | 348 | 53.26 |