TY - GEN
T1 - Self-consistent neuronal population under spike inputs and unbalanced conditions
AU - Gutierrez, Carlos E.
AU - Doya, Kenji
AU - Yoshimoto, Junichiro
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/3/22
Y1 - 2016/3/22
N2 - A single neuron gain function can predict the population activity of homogeneous neurons under strong limitations, such as the stationary state and balanced conditions of the total input. In this work, we propose a modification to the self-consistency model when balanced conditions are not fully satisfied. We present a scaling factor to modify the excitatory weights in a Brunel network. It allows using the self-consistency model in more realistic cases. The approach is used and analyzed for different network features.
AB - A single neuron gain function can predict the population activity of homogeneous neurons under strong limitations, such as the stationary state and balanced conditions of the total input. In this work, we propose a modification to the self-consistency model when balanced conditions are not fully satisfied. We present a scaling factor to modify the excitatory weights in a Brunel network. It allows using the self-consistency model in more realistic cases. The approach is used and analyzed for different network features.
UR - https://www.scopus.com/pages/publications/84966662566
UR - https://www.scopus.com/pages/publications/84966662566#tab=citedBy
U2 - 10.1109/ICIIBMS.2015.7439532
DO - 10.1109/ICIIBMS.2015.7439532
M3 - Conference contribution
AN - SCOPUS:84966662566
T3 - ICIIBMS 2015 - International Conference on Intelligent Informatics and Biomedical Sciences
SP - 309
EP - 312
BT - ICIIBMS 2015 - International Conference on Intelligent Informatics and Biomedical Sciences
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2015
Y2 - 28 November 2015 through 30 November 2015
ER -