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 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 reduced dataset obtained from original biological recordings with unknown connectivity. This was obtained in house from a mouse in vitro preparation of the spinal cord. Results show that this algorithm can retrieve neurons monosynaptically connected to the target in simulated datasets within a single run. Iterative and recursive processes can identify monosynaptic neurons and disynaptic neurons under favorable 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.
Highlights
The understanding of neuronal circuits within the nervous system has become a major focus of interest in current neurobiology
The group of neurons identified by this procedure was called the “relevant group.” - Iterative processes: We ranked all neurons according to their individual Matthews Correlation Coefficient (MCC) values and trained the model with subsets of neurons so that the less significant neuron in the rank was removed at each epoch. We developed this workflow to check if the best metrics were obtained by groups formed by the neurons with best individual metrics. - Recursive process: The process starts with an analysis of the complete neuronal sample and produces two groups of neurons based on variable importance of each neuron
The present results show that application of the algorithm C5.0 to the analysis of spike trains can lead to the identification of the neurons whose firing is highly relevant to the firing of a target neuron
Summary
The understanding of neuronal circuits within the nervous system has become a major focus of interest in current neurobiology. The advent of novel techniques, such as those enabling the monitoring of neuronal activity across populations of neurons, is opening the door to circuit analysis. Depending on the biological preparation used, electrode matrixes may record action potentials from dozens to thousands of neurons producing huge amounts of data. These recordings are obtained under blind conditions and the structural and functional relation between the recorded neurons is unknown or insufficiently described. AI Analysis of Effective Connectivity the different units recorded is becoming a focus of major interest in neurobiology. At the level of single neurons, it involves the analysis of temporal causality between different activations of neurons in a network (Andalibi et al, 2016)
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