Controlling Synfire Chain by Inhibitory Synaptic Input(Cross-disciplinary physics and related areas of science and technology)
スポンサーリンク
概要
- 論文の詳細を見る
The propagation of highly synchronous firings across neuronal networks, called the synfire chain, has been actively studied both theoretically and experimentally. The temporal accuracy and remarkable stability of the propagation have been repeatedly examined in previous studies. However, for such a mode of signal transduction to play a major role in processing information in the brain, the propagation should also be controlled dynamically and flexibly. Here, we show that inhibitory but not excitatory input can bidirectionally modulate the propagation, i.e., enhance or suppress the synchronous firings depending on the timing of the input. Our simulations based on the Hodgkin-Huxley neuron model demonstrate this bidirectional modulation and suggest that it should be achieved with any biologically inspired modeling. Our finding may help describe a concrete scenario of how multiple synfire chains lying in a neuronal network are appropriately controlled to perform significant information processing.
- 社団法人日本物理学会の論文
- 2007-04-15
著者
-
Okada Masato
Riken Brain Sci. Inst. Saitama
-
Cateau Hideyuki
Riken Brain Science Institute
-
Okada Masato
Riken Brain Science Institute:graduate School Of Frontier Sciences University Of Tokyo
-
SHINOZAKI Takashi
RIKEN Brain Science Institute
-
URAKUBO Hidetoshi
Graduate School of Science, University of Tokyo
-
Urakubo Hidetoshi
Graduate School Of Science University Of Tokyo
関連論文
- Statistical Mechanics for Neural Spike Data Analysis Using Log-Linear Model
- Analysis of Ensemble Learning Using Simple Perceptrons Based on Online Learning Theory
- Correlation of Firing in Layered Associative Neural Networks(Condensed Matter : Structure, Mechanical and Thermal Properties)
- Controlling Synfire Chain by Inhibitory Synaptic Input(Cross-disciplinary physics and related areas of science and technology)
- Statistical Mechanics for Neural Spike Data Analysis Using Log-Linear Model