Reservoir Computing Model For Multi-Electrode Electrophysiological Data Analysis

被引：0

作者：

Auslender, Ilya ^{[1
]}

Pavesi, Lorenzo ^{[1
]}

机构：

[1] Univ Trento, Dept Phys, Trento, Italy

来源：

2023 IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, CIBCB | 2023年

基金：

欧洲研究理事会;

关键词：

modeling of neuronal cultures; simulation; reservoir computing; electrophysiology; CORTICAL NETWORKS; NEURONS;

D O I：

10.1109/CIBCB56990.2023.10264895

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper we present a computational model which decodes the spatio-temporal data from electrophysiological measurements of neuronal networks and reconstructs the network structure on a macroscopic domain, representing the connectivity between neuronal units. The model is based on reservoir computing network (RCN) approach, where experimental data is used as training and validation data. Consequently, the model can be used to study the functionality of different neuronal cultures and simulate the network response to external stimuli.

引用

页码：151 / 156

页数：6

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