深層学習アルゴリズムを用いた皮質ネットワークの表現方法と計算処理の研究

研究目的：本研究计划的目标是验证大规模非线性动态网络模型（即递归神经网络- RNN）是一个理想的数学框架，可以从大型神经元群体的动态中推导出基本的计算原理。我们想在知觉学习（视觉辨别任务）的背景下检验这一假设。作为模型系统，我们使用了小鼠的视觉皮层。这个动物模型有一套无与伦比的实验工具箱来记录和干扰大量皮质神经元的动态。成就：博士后nerez Imamoglu博士因个人原因不得不离开实验室。鉴于他的机器学习背景，Imamoglu博士花了大量时间学习系统神经科学的基本概念，这对他来说是一个新颖的研究领域。此外，通过与其他实验室成员的持续互动，Imamoglu博士熟悉了实验程序和他正在分析的神经元数据的关键特征。最后，他为其他成员项目的各个方面提供了重要的技术支持，帮助进行软件分析和实现。不幸的是，关于这项奖学金提案，在初步或可发表的结果取得任何进展之前，Imamoglu博士不得不离开实验室。

Purpose of the research:The goal of this research proposal was to test the hypothesis that large-scale non-linear dynamical network models (i.e. recurrent neural networks - RNN) are an ideal mathematical framework to derive fundamental computational principles from the dynamics of large neuronal populations. We wanted to test this hypothesis in the context of perceptual learning (visual discrimination task). As a model system, we used the visual cortex of the mouse. This animal model has an unmatched set of experimental toolboxes to record and perturb the dynamics of large populations of cortical neurons.Achievements:The postdoctoral fellow awarded this prestigious fellowship, Dr. Nevrez Imamoglu had to leave the Laboratory for personal-related reasons. Given his Machine Learning background, Dr. Imamoglu spent a significant amount of time learning basic concepts in Systems Neuroscience, a novel field of research for him. Moreover, through continuous interactions with other Lab members, Dr. Imamoglu familiarized with the experimental procedures and the key characteristics of the neuronal data he was analyzing. Finally, he has provided important technical support to various aspects of other members’ projects, helping with software analysis and implementations. Unfortunately, regarding this fellowship proposal, Dr. Imamoglu had to leave the Laboratory before any progress could be made in terms of preliminary or publishable results.