Discovering Effective Connectivity in Neural Circuits: Analysis Based on Machine Learning Methodology - Pozo-Jimenez, Pedro; Lucas-Romero, Javier; Lopez-Garcia, Jose A. (Frontiers in Neuroinformatics, 2021)
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 analyse 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 capability 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 C5.0 were built to examine possibilities of increasing the performance of a direct application of the algorithm. Furthermore, we tested the applicability of these processes to a smaller dataset obtained from original biological recordings whose structure is unknown. These were obtained in house from a mouse in vitro preparation of the spinal cord. Results show that this algorithm can retrieve neurons monosynaptic connected to the target in simulated datasets within a single run. Iterative and recursive processes can identify monosynaptic neurons and disynaptic neurons under favourable conditions. Application of these processes to the biological dataset gives clues to identify neurons monosynaptically connected to the target. We conclude that the work presented provides substantial proof of concept for the potential use of AI algorithms to the study of effective connectivity.