Discovering Effective Connectivity in Neural Circuits: Analysis Based on Machine Learning Methodology.

ABSTRACT: As multielectrode array technology increases in popularity, accessible analytical tools become necessary. Simultaneous recordings from multiple neurons may produce huge amounts of information. Traditional tools based on classical statistics are either insufficient to analyze multiple spike trains or sophisticated and expensive in computing terms. In this communication, we put to the test the idea that AI algorithms may be useful to gather information about the effective connectivity of neurons in local nuclei at a relatively low computing cost. To this end, we decided to explore the capacity of the algorithm C5.0 to retrieve information from a large series of spike trains obtained from a simulated neuronal circuit with a known structure. Combinatory, iterative and recursive processes using

SUBMITTER: Pozo-Jimenez P 

PROVIDER: S-EPMC8007904 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature