| 研究実績の概要 |
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.
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