Input response of neural network model with lognormally distributed synaptic weights

Yoshihiro Nagano, Ryo Karakida, Norifumi Watanabe, Atsushi Aoyama, Masato Okada

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Neural assemblies in the cortical microcircuit can sustain irregular spiking activity without external inputs. On the other hand, neurons exhibit rich evoked activities driven by sensory stimulus, and both activities are reported to contribute to cognitive functions. We studied the external input response of the neural network model with lognormally distributed synaptic weights. We show that the model can achieve irregular spontaneous activity and population oscillation depending on the presence of external input. The firing rate distribution was maintained for the external input, and the order of firing rates in evoked activity reflected that in spontaneous activity. Moreover, there were bistable regions in the inhibitory input parameter space. The bimodal membrane potential distribution, which is a characteristic feature of the up-down state, was obtained under such conditions. From these results, we can conclude that the model displays various evoked activities due to the external input and is biologically plausible.

Original languageEnglish
Article number074001
JournalJournal of the Physical Society of Japan
Issue number7
Publication statusPublished - 2016 Jul 15

ASJC Scopus subject areas

  • Physics and Astronomy(all)


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