脳の中の機械学習：回帰性ニューラルネットワークによる報酬の計算機構の解明

作为神经网络动力学分析基础的机器学习框架的总体研究和实现已基本完成。基于循环神经网络模型的总体框架的实施是该项目的关键部分，已于上个财年完成。尽管我们最初的实验是在与原始提案中概述的数据性质略有不同的数据上进行的，但我们开发的机器学习方法非常通用和灵活，并且可以轻松适用于实验室研究人员正在研究的不同背景下的神经记录，包括涉及奖励的行为任务。从科研成果来看，该项目进展顺利。上一财年获得的结果已在旗舰神经科学会议（SfN 2017，美国华盛顿特区）和 2017 年 12 月东京脑人工智能区域会议上公布，我们最新的分析结果正在收集到已提交发表的期刊论文中。

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.