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Connection‐Strength Estimation of Neuronal Networks by Fitting for Izhikevich Model
Author(s) -
Isomura Takuya,
Takeuchi Akimasa,
Shimba Kenta,
Kotani Kiyoshi,
Jimbo Yasuhiko
Publication year - 2014
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.22517
Subject(s) - connection (principal bundle) , spike (software development) , computer science , artificial neural network , series (stratigraphy) , maximum likelihood , biological system , estimation , artificial intelligence , mathematics , statistics , engineering , biology , software engineering , systems engineering , paleontology , geometry
SUMMARY Recently, there has been abundant research using multineuron recording, but there are many problems with extracting the features from the obtained spike time series, which are huge in volume and complex. Here we introduce a new method of estimating synaptic connection strengths between neurons by fitting to the Izhikevich model by maximum likelihood estimation. We demonstrate that our method can estimate connection strengths from spike time series given by a simulated neural ensemble and can estimate nonconnectivity between two independent cultured neuronal networks. These results suggest that our method is applicable to network and plasticity analysis of neuronal networks.