TY - GEN
T1 - Nonlinear effect on phase response curve of neuron model
AU - Iida, Munenori
AU - Omori, Toshiaki
AU - Aonishi, Toru
AU - Okada, Masato
PY - 2011
Y1 - 2011
N2 - One of the more useful tools for better understanding population dynamics is the phase response curve (PRC). Recent physiological experiments on the PRCs using real neurons showed that different shapes of the PRCs are generated depending on the perturbation, which has a finite amplitude. In order to clarify the origin of the nonlinear response of the PRCs, we analytically derived the PRCs from single neurons by using a spike response model. We clarified the relation between the subthreshold membrane response property and the PRC. Furthermore, we performed numerical simulations using the Hodgkin-Huxley model and their results have shown that a nonlinear change of the PRCs is generated. Our theory and numerical results imply that the nonlinear change of PRCs is due to the nonlinear element in spike time shift of firing neurons induced by the finite amplitude of the perturbation stimuli.
AB - One of the more useful tools for better understanding population dynamics is the phase response curve (PRC). Recent physiological experiments on the PRCs using real neurons showed that different shapes of the PRCs are generated depending on the perturbation, which has a finite amplitude. In order to clarify the origin of the nonlinear response of the PRCs, we analytically derived the PRCs from single neurons by using a spike response model. We clarified the relation between the subthreshold membrane response property and the PRC. Furthermore, we performed numerical simulations using the Hodgkin-Huxley model and their results have shown that a nonlinear change of the PRCs is generated. Our theory and numerical results imply that the nonlinear change of PRCs is due to the nonlinear element in spike time shift of firing neurons induced by the finite amplitude of the perturbation stimuli.
KW - neuron model
KW - phase response curve
KW - spike response model
UR - http://www.scopus.com/inward/record.url?scp=81855177301&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=81855177301&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24965-5_26
DO - 10.1007/978-3-642-24965-5_26
M3 - Conference contribution
AN - SCOPUS:81855177301
SN - 9783642249648
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 240
EP - 250
BT - Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings
T2 - 18th International Conference on Neural Information Processing, ICONIP 2011
Y2 - 13 November 2011 through 17 November 2011
ER -