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

大脑中的机器学习：利用循环神经网络阐明奖励计算机制

• 批准号: 16F16734
• 负责人: ベヌッチ アンドレア
• 金额: $ 1.41万
• 依托单位: Institute of Physical and Chemical Research
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for JSPS Fellows
• 财政年份: 2016
• 资助国家: 日本
• 起止时间: 2016-07-27 至 2019-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-16F16734/
• 关键词: recurrent neural network; large neuronal networks; machine learning; theoretical neuroscience; visual cortex

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.

机器学习框架将作为神经网络动态分析的基础，其总体研究和实施工作已接近尾声。在上一财政年度完成了一个以循环神经网络模型为基础的总体框架的实施，这是该项目的关键部分。虽然我们最初的实验是在与原始提案中概述的数据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，并且可以很容易地应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，项目进展顺利。在上一财年获得的结果已在旗舰神经科学会议（SfN 2017，华盛顿DC，美国）上发表，在2017年12月东京Brain-AI领域会议上，我们最近的分析结果正在收集到一份已提交出版的期刊论文中。

项目成果

Modeling responses to visual stimuli in the mouse cortex with recurrent neural networks

用循环神经网络模拟小鼠皮层对视觉刺激的反应

• 发表时间: 2017
• 作者: Emanuele Frandi

Sculpting the dynamics of neuronal networks in the mouse cortex using optogenetic tools

使用光遗传学工具塑造小鼠皮层神经元网络的动态

• 发表时间: 2016
• 作者: Tsubota;T. (speaker);Frandi;E.;Benucci;A.
• 通讯作者: A.

Plasticity for stimulus selectivity in the visual cortex of adult mice induced by patterned optogenetic stimulation

图案化光遗传学刺激诱导成年小鼠视觉皮层刺激选择性的可塑性

• 发表时间: 2017
• 作者: Tadashi Tsubota;Emanuele Frandi;Andrea Benucci
• 通讯作者: Andrea Benucci