| 研究実績の概要 |
The overall study and implementation of the machine learning framework which will serve as the basis for the analysis of neural network dynamics has almost being finalized. The implementation of a general framework based on recurrent neural network models, which constitutes the key part of the project, was completed in the previous FY. Although our initial experiments were carried out on data of a slightly different nature from that outlined in the original proposal, the machine learning methodologies we have developed are very general and flexible, and can be readily applicable to neural recordings from different contexts that researchers at the lab are working on,including behavioral tasks involving rewards. From the point of view of scientific results, the project has progressed smoothly. The results obtained during last FY have been presented at a flagship neuroscience conference (SfN 2017, Washington DC, USA), at the Dec-2017 Tokyo Brain-AI area meeting, and the results of our most recent analyses are being collected into a journal paper already submitted for publication.
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