ビーベットログイン

About Overview Organization & Management Strategy Open Positions Movie Library Access Research Structure Researchers & Laboratories Research Areas Three Advanced Target Projects Collaboration Achievements Press Releases Media & Award AIMResearch Publications Topics News Seminars & Symposium International Satellites International Partner Institutions Inter-Faculty Exchange Agreements Researcher Exchange Programs Support Support Systems For International Researchers (IAC) For Visitors Researchers for Visitors for Researchers for Enterprise Access AIMR Fund Japanese Press Releases Artificially Cultured Brains Improve Processing of Time Series Data 06/29/2023 The Brain comprises billions of interconnected neurons that transmit and process information and allow it to act as a highly sophisticated information processing system. To make it as efficient as possible, the brain develops multiple modules tasked with different functions, like perception and body control. Within a single area, neurons form multiple clusters and function as modules - an important trait that has remained essentially unchanged throughout evolution. Still, many unanswered questions remain regarding how the specific structure of the brain's network, such as the modular structure, works together with the physical and chemical properties of neurons to process information. Reservoir computing is a computational model inspired by the brain’s powers, where the reservoir comprises a large number of interconnected nodes that transform input signals into a more complex representation. Now, a research team has harnessed machine learning based on reservoir computing to analyze the computational capabilities of an “artificially cultured brain” composed of neurons derived from the cerebral cortex of rats, i.e., rat cortical neurons. The team’s findings were published in the Proceedings of the National Academy of Sciences on June 12, 2023, and was led by Takuma Sumi, Hideaki Yamamoto, and Ayumi Hirano-Iwata, researchers based at Tohoku University. They worked in collaboration with Yuichi Katori from the Future University Hakodate. “Using optogenetics and fluorescent calcium imaging, we first recorded the multicellular responses of the cultured neuronal network,” said Yamamoto. “Then we decoded it using reservoir computing, finding that the artificial cultured brain possessed a short-term memory of several hundred milliseconds, which could be used to classify time-series data, such as spoken digits.” When the artificial cultured brain receives a human speech sound (the number 0 pronounced as “zero” in English) as input, it converts the input into a multicellular response. The response signal is then read out by a linear classifier to achieve classification of the time-series signal. The artificial cultured brain in the figure is designed to grow within four squares connected by thin lines, resembling a modular architecture. In this experiment, we found that such modularity in the artificial cultured brain improves the classification performance. ©Yamamoto et al. Samples with a higher degree of modularity was found to exhibit better classification performance. Moreover, a model trained on one dataset was able to classify another dataset in the same category, revealing that the artificial cultured brain could filter informtion to improve the reservoir computing performance. “The findings advance our mechanistic understanding of information processing within neuronal networks composed of biological neurons and move us toward the potential realization of physical reservoir computers based on biological neurons,” adds Yamamoto. The reservoir computer based on biological neurons could be used to classify spoken digits even when the speakers were switched during training and testing. Classification accuracy after the switch decreased compared to when there was no speaker switching, but classification was achieved above a chance level. Such classification was not possible when the input signal was directly decoded by a linear classifier, suggesting that biological neurons act as a generalization filter to improve the performance of reservoir computing. ©Yamamoto et al. Publication Details Title: Biological neurons act as generalization filters in reservoir computing Authors: Takuma Sumi, Hideaki Yamamoto*, Yuichi Katori, Koki Ito, Satoshi Moriya, Tomohiro Konno, Shigeo Sato, Ayumi Hirano-Iwata Journal: Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.2217008120 Contact Hideaki Yamamoto (Profile)Research Institute of Electrical Communication, Tohoku University E-mail: hideaki.yamamoto.e3&＃64;tohoku.ac.jp Webstie: Hirano laboratorySato-Sakuraba-Yamamoto labolatory Tweet Achievements Press Releases 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 Media & Award 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 AIMResearch About AIMResearch Research Highlights 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 In the Spotlight 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 Email Alert Sign up Publications Headlines 05/22/2024 Machine Learning Accelerates Discovery o... 05/16/2024 New Data-Driven Model Rapidly Predicts D... 05/15/2024 Researchers Unlock Vital Insights into M... Home Achievements Press Releases 2023 Artificially Cultured Brains Improve Processing of Time Series Data TOHOKU UNIVERSITY World Premier International Research Center Initiative For AIMR Members Link Site map Copyright © 2020 Tohoku University. All Rights Reserved.