An Optimal Power-Law for Synchrony and Lognormally Synaptic Weighted Hub Networks
Yasuomi D. Sato**
Department of Brain Science and Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4, Hibikino, Wakamatsu, Kitakyushu, 808-0196, Japan Frankfurt Institute for Advanced Studies (FIAS), Johann Wolfgang Goethe University, Ruth-Moufang-Str.1, D60438, Frankfurt am Main, Germany
Abstract：Details about the structure of a network model are revealed at the spontaneous spike activity level, in which the power-law of synchrony is optimized to that observed in the CA3 hippocampal slice cultures. The network model is subject to spike noise with exponentially distributed interspike intervals. The excitatory (E) and/or inhibitory (I) neurons interact through synapses whose weights show a log-normal distribution. The spike behavior observed in the network model with the appropriate log-normal distributed synaptic weights fits best to that observed in the experiment. The best-fit is then achieved with high activities of I neurons having a hub-like structure, in which the I neurons, subject to optimized spike noise, are intensively projected from low active E neurons.
2013, Vol. 30 
