Details of the Researcher

PHOTO

Takuma Sumi
Section
Advanced Institute for Materials Research
Job title
Assistant Professor
Degree

  • 博士（医工学）（東北大学）

  • 修士（医工学）（東北大学）

e-Rad No.
31002545

Research History 2

  • 2023/10 - Present
    Tohoku University　Advanced Institute for Materials Research　Assistant Professor

  • 2021/04 - 2023/09
    JSPS Research Fellow (DC1)

Education 3

  • Tohoku University　Graduate School of Biomedical Engineering

    2020/10 - 2023/09

  • Tohoku University　Graduate School of Biomedical Engineering

    2019/04 - 2020/09

  • Tohoku University　Shool of Engineering　Department of Electrical,Information and Physics Engineering

    2015/04 - 2019/03

Papers 8

  1. Online supervised learning of temporal patterns in biological neural networks under feedback control

    Yuki Sono, Hideaki Yamamoto, Yusei Nishi, Takuma Sumi, Yuya Sato, Ayumi Hirano-Iwata, Yuichi Katori, Shigeo Sato

    2025/08/12

    DOI： 10.1101/2025.08.10.669034 　

  2. Modular architecture confers robustness to damage and facilitates recovery in spiking neural networks modeling in vitro neurons Peer-reviewed

    Takuma Sumi, Akke Mats Houben, Hideaki Yamamoto, Hideyuki Kato, Yuichi Katori, Jordi Soriano, Ayumi Hirano-Iwata

    Frontiers in Neuroscience　19　1570783　2025/06/19

    Publisher: Frontiers Media SA

    DOI： 10.3389/fnins.2025.1570783 　

    ISSN： 1662-453X

    eISSN： 1662-453X

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    Impaired brain function is restored following injury through dynamic processes that involve synaptic plasticity. This restoration is supported by the brain’s inherent modular organization, which promotes functional separation and redundancy. However, it remains unclear how modular structure interacts with synaptic plasticity to define damage response and recovery efficiency. In this work, we numerically modeled the response and recovery to damage of a neuronal network in vitro bearing a modular structure. The simulations aimed at capturing experimental observations in cultured neurons with modular traits which were physically disconnected through a focal lesion. The damage reduced the frequency of spontaneous collective activity events in the cultures, which recovered to pre-damage levels within 24 h. We rationalized this recovery in the numerical simulations by considering a plasticity mechanism based on spike-timing-dependent plasticity, a form of synaptic plasticity that modifies synaptic strength based on the relative timing of pre- and postsynaptic spikes. We observed that the in silico numerical model effectively captured the decline and subsequent recovery of spontaneous activity following the injury. The model supports that the combination of modularity and plasticity confers robustness to the damaged neuronal network by preventing the total loss of spontaneous network-wide activity and facilitating recovery. Additionally, by using our model within the reservoir computing framework, we show that information representation in the neuronal network improves with the recovery of network-wide activity.

  3. In silico modeling of reservoir-based predictive coding in biological neuronal networks on microelectrode arrays Peer-reviewed

    Yuya Sato, Hideaki Yamamoto, Yoshitaka Ishikawa, Takuma Sumi, Yuki Sono, Shigeo Sato, Yuichi Katori, Ayumi Hirano-Iwata

    Japanese Journal of Applied Physics　63　(10)　108001　2024/10/01

    Publisher: IOP Publishing

    DOI： 10.35848/1347-4065/ad7ec1 　

    ISSN： 0021-4922

    eISSN： 1347-4065

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    Abstract Reservoir computing and predictive coding together yield a computational model for exploring how neuronal dynamics in the mammalian cortex underpin temporal signal processing. Here, we construct an in-silico model of biological neuronal networks grown on microelectrode arrays and explore their computing capabilities through a sine wave prediction task in a reservoir-based predictive coding framework. Our results show that the time interval between stimulation pulses is a critical determinant of task performance. Additionally, under a fixed feedback latency, pulse amplitude modulation is a favorable encoding scheme for input signals. These findings provide practical guidelines for future implementation of the model in biological experiments.

  4. Integrating predictive coding with reservoir computing in spiking neural network model of cultured neurons Peer-reviewed

    Yoshitaka Ishikawa, Takumi Shinkawa, Takuma Sumi, Hideyuki Kato, Hideaki Yamamoto, Yuichi Katori

    Nonlinear Theory and Its Applications, IEICE　15　(2)　432-442　2024

    Publisher: Institute of Electronics, Information and Communications Engineers (IEICE)

    DOI： 10.1587/nolta.15.432 　

    eISSN： 2185-4106

  5. Biological neurons act as generalization filters in reservoir computing. International-journal Peer-reviewed

    Takuma Sumi, Hideaki Yamamoto, Yuichi Katori, Koki Ito, Satoshi Moriya, Tomohiro Konno, Shigeo Sato, Ayumi Hirano-Iwata

    Proceedings of the National Academy of Sciences of the United States of America　120　(25)　e2217008120　2023/06/20

    DOI： 10.1073/pnas.2217008120 　

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    Reservoir computing is a machine learning paradigm that transforms the transient dynamics of high-dimensional nonlinear systems for processing time-series data. Although the paradigm was initially proposed to model information processing in the mammalian cortex, it remains unclear how the nonrandom network architecture, such as the modular architecture, in the cortex integrates with the biophysics of living neurons to characterize the function of biological neuronal networks (BNNs). Here, we used optogenetics and calcium imaging to record the multicellular responses of cultured BNNs and employed the reservoir computing framework to decode their computational capabilities. Micropatterned substrates were used to embed the modular architecture in the BNNs. We first show that the dynamics of modular BNNs in response to static inputs can be classified with a linear decoder and that the modularity of the BNNs positively correlates with the classification accuracy. We then used a timer task to verify that BNNs possess a short-term memory of several 100 ms and finally show that this property can be exploited for spoken digit classification. Interestingly, BNN-based reservoirs allow categorical learning, wherein a network trained on one dataset can be used to classify separate datasets of the same category. Such classification was not possible when the inputs were directly decoded by a linear decoder, suggesting that BNNs act as a generalization filter to improve reservoir computing performance. Our findings pave the way toward a mechanistic understanding of information representation within BNNs and build future expectations toward the realization of physical reservoir computing systems based on BNNs.

  6. Biomimetic Culture of Primary Neurons on an Ultrasoft Gel Surface Peer-reviewed

    SUMI Takuma, YAMAMOTO Hideaki, HIRANO-IWATA Ayumi

    Vacuum and Surface Science　63　(6)　298-303　2020/06/10

    Publisher: The Japan Society of Vacuum and Surface Science

    DOI： 10.1380/vss.63.298 　

    ISSN： 2433-5835

    eISSN： 2433-5843

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    Mechanical properties of scaffolds have recently been found to impose strong impact on the behavior of cultured cells. Here, we focused on the effect of scaffold's elastic modulus on the development of synaptic connections in cultured neurons, and developed a novel system for culturing rat cortical neurons on an ultrasolft gel surface with an elastic modulus resembling the brain.

  7. Suppression of hypersynchronous network activity in cultured cortical neurons using an ultrasoft silicone scaffold. International-journal Peer-reviewed

    Takuma Sumi, Hideaki Yamamoto, Ayumi Hirano-Iwata

    Soft matter　16　(13)　3195-3202　2020/04/01

    DOI： 10.1039/c9sm02432h 　

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    The spontaneous activity pattern of cortical neurons in dissociated culture is characterized by burst firing that is highly synchronized among a wide population of cells. The degree of synchrony, however, is excessively higher than that in cortical tissues. Here, we employed polydimethylsiloxane (PDMS) elastomers to establish a novel system for culturing neurons on a scaffold with an elastic modulus resembling brain tissue, and investigated the effect of the scaffold's elasticity on network activity patterns in cultured rat cortical neurons. Using whole-cell patch clamp to assess the scaffold effect on the development of synaptic connections, we found that the amplitude of excitatory postsynaptic current, as well as the frequency of spontaneous transmissions, was reduced in neuronal networks grown on an ultrasoft PDMS with an elastic modulus of 0.5 kPa. Furthermore, the ultrasoft scaffold was found to suppress neural correlations in the spontaneous activity of the cultured neuronal network. The dose of GsMTx-4, an antagonist of stretch-activated cation channels (SACs), required to reduce the generation of the events below 1.0 event per min on PDMS substrates was lower than that for neurons on a glass substrate. This suggests that the difference in the baseline level of SAC activation is a molecular mechanism underlying the alteration in neuronal network activity depending on scaffold stiffness. Our results demonstrate the potential application of PDMS with biomimetic elasticity as cell-culture scaffold for bridging the in vivo-in vitro gap in neuronal systems.

  8. Ultrasoft Silicone Gel as a Biomimetic Passivation Layer in Inkjet-Printed 3D MEA Devices. International-journal Peer-reviewed

    Hideaki Yamamoto, Leroy Grob, Takuma Sumi, Kazuhiro Oiwa, Ayumi Hirano-Iwata, Bernhard Wolfrum

    Advanced biosystems　3　(9)　e1900130　2019/09

    DOI： 10.1002/adbi.201900130 　

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    Multielectrode arrays (MEAs) are versatile tools that are used for chronic recording and stimulation of neural cells and tissues. Driven by the recent progress in understanding of how neuronal growth and function respond to scaffold stiffness, development of MEAs with a soft cell-to-device interface has gained importance not only for in vivo but also for in vitro applications. However, the passivation layer, which constitutes the majority of the cell-device interface, is typically prepared with stiff materials. Herein, a fabrication of an MEA device with an ultrasoft passivation layer is described, which takes advantage of inkjet printing and a polydimethylsiloxane (PDMS) gel with a stiffness comparable to that of the brain. The major challenge in using the PDMS gel is that it cannot be patterned to expose the sensing area of the electrode. This issue is resolved by printing 3D micropillars at the electrode tip. Primary cortical neurons are grown on the fabricated device, and effective stimulation of the culture confirms functional cell-device coupling. The 3D MEA device with an ultrasoft interface provides a novel platform for investigating evoked activity and drug responses of living neuronal networks cultured in a biomimetic environment for both fundamental research and pharmaceutical applications.

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Misc. 25

  1. Reservoir computing with the Kuramoto model

    Hayato Chiba, Koichi Taniguchi, Takuma Sumi

    2024/07/23

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    Reservoir computing aims to achieve high-performance and low-cost machine learning with a dynamical system as a reservoir. However, in general, there are almost no theoretical guidelines for its high-performance or optimality. This paper focuses on the reservoir computing with the Kuramoto model and theoretically reveals its approximation ability. The main result provides an explicit expression of the dynamics of the Kuramoto reservoir by using the order parameters. Thus, the output of the reservoir computing is expressed as a linear combination of the order parameters. As a corollary, sufficient conditions on hyperparameters are obtained so that the set of the order parameters gives the complete basis of the Lebesgue space. This implies that the Kuramoto reservoir has a universal approximation property. Furthermore, the conjecture on {\it the edge of bifurcation}, which is a generalization of the famous criterion {\it the edge of chaos} for designing a high-performance reservoir, is also discussed from the viewpoint of its approximation ability. It is numerically demonstrated by a prediction task and a transformation task.

  2. Optimization of synaptic scaling rule, its implementation on modular spiking neural networks and analysis of its effects

    新川拓海, 加藤秀行, 石川慶孝, 住拓磨, 山本英明, 香取勇一

    電子情報通信学会技術研究報告(Web)　123　(354(NLP2023 82-121))　2024

    ISSN： 2432-6380

  3. Localized lesion of micropatterned neuronal networks via laser microdissection

    渡邉啓太, 渡邉啓太, 山本英明, 山本英明, 山本英明, 室田白馬, 室田白馬, 住拓磨, 佐藤茂雄, 佐藤茂雄, 平野愛弓, 平野愛弓, 平野愛弓

    応用物理学会秋季学術講演会講演予稿集(CD-ROM)　85th　2024

    ISSN： 2758-4704

  4. Network Structure and Reservoir Computing in Cultured Neuronal Networks Invited

    住拓磨, 山本英明, 山本英明, 千葉逸人, 香取勇一, 平野愛弓, 平野愛弓

    日本神経回路学会誌　31　(3)　2024

    ISSN： 1340-766X

  5. Modular Topology Enhances Reservoir Computing Performance in Biological Neuronal Networks Peer-reviewed

    Sumi Takuma, Yamamoto Hideaki, Katori Yuichi, Ito Koki, Sato Shigeo, Hirano-Iwata Ayumi

    IEICE Proceeding Series　76　687-688　2023/09/21

    Publisher: The Institute of Electronics, Information and Communication Engineers

    DOI： 10.34385/proc.76.d2l-11 　

    eISSN： 2188-5079

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    Reservoir computing is a machine learning paradigm that employs high-dimensional dynamical systems for information processing. Although biological neuronal networks (BNNs) have been utilized to implement reservoir computing to provide insight into their computation properties, the neurons in conventional cultured neuronal networks are randomly connected, generating atypical dynamics characterized by highly correlated bursting activity not observed in healthy brains. In this study, we used micropatterning technology to fabricate BNNs with modular topology, a structural feature conserved in brain networks, and to understand how the dynamics within non-random networks of neuronal cells are linked to computing. Our study demonstrated that the modular BNN reservoir is capable of classifying both image and time-series data above chance levels. The modular structure in BNN contributes to the increased reservoir computing performance, in line with previous computational models with neuromorphic networks. Combining experiments with biological neuronal network and computational modeling can advance our understanding of computing principles in multicellular neuronal networks.

  6. Evaluation of Reservoir-Based Predictive Coding in Cultured Neurons with Spiking Neural Network Model Peer-reviewed

    Ishikawa Yoshitaka, Shinkawa Takumi, Sumi Takuma, Kato Hideyuki, Yamamoto Hideaki, Katori Yuichi

    IEICE Proceeding Series　76　693-696　2023/09/21

    Publisher: The Institute of Electronics, Information and Communication Engineers

    DOI： 10.34385/proc.76.d2l-14 　

    eISSN： 2188-5079

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    The field of physical reservoir computing, an approach that leverages the dynamics of physical systems for information processing, continues to evolve. Utilizing cultured neurons as a physical system in physical reservoir computing has helped to elucidate the relationship between the structure of neuronal networks and information processing. In this study, we present a model that integrates predictive coding and reservoir computing in cultured neurons, serving as a sensory information processing model. We demonstrate that the relationship between the structure of neuronal networks and the number of neurons influences sensory information processing. This study may provide a foundation for further investigations into information processing in biological neuronal networks.

  7. Spiking and Bursting Properties of Spontaneous Activities in Modular Spiking Neuronal Networks Peer-reviewed

    Kato Hideyuki, Shinkawa Takumi, Ishikawa Yoshitaka, Sumi Takuma, Yamamoto Hideaki, Katori Yuichi

    IEICE Proceeding Series　76　601-601　2023/09/21

    Publisher: The Institute of Electronics, Information and Communication Engineers

    DOI： 10.34385/proc.76.c4l-15 　

    eISSN： 2188-5079

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    The application of cultured neuronal networks as a physical reservoir is one of the attractive topics in reservoir computing. One can easily imagine that the performance of reservoir computing depends on their network topology because the topology influences neuronal activities in the network. In contrast, neurons try to sustain their activities as possible. This property is called the homeostasis of neurons. In this study, we mathematically model the networks with synaptically connected spiking neurons by considering the above two aspects and then evaluate spontaneous neuronal activities from the viewpoints of spiking and bursting behaviors.

  8. Bottom-Up Investigation of Multicellular Computing Within Biological Neuronal Networks Peer-reviewed

    Yamamoto Hideaki, Sumi Takuma, Sato Yuya, Sato Shigeo, Hirano-Iwata Ayumi

    IEICE Proceeding Series　76　594-595　2023/09/21

    Publisher: The Institute of Electronics, Information and Communication Engineers

    DOI： 10.34385/proc.76.c4l-11 　

    eISSN： 2188-5079

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    In this presentation, we will first introduce our studies aimed at reproducing an evolutionarily-conserved net-work structure in cultured neuronal networks on engineered glass coverslips and CMOS-based high-density microelectrode arrays. We then describe our recent attempts to couple the engineered neuronal networks with external stimulation to reveal their response to noise and spatiotemporally-patterned inputs to show that bioengineering technologies offer novel tools in investigating computational aspects of multicellular networks of biological neurons.

  9. Performance Evaluation of Reservoir Computing in Cultured Neural Systems Using a Spiking Neuron Model

    石川慶孝, 新川拓海, 住拓磨, 加藤秀行, 山本英明, 香取勇一

    電子情報通信学会技術研究報告(Web)　122　(373(NLP2022 81-106))　2023

    ISSN： 2432-6380

  10. Reservoir computing properties of large-scale modular neuronal networks

    伊藤亘輝, 伊藤亘輝, 山本英明, 住拓磨, 住拓磨, 香取勇一, 佐藤茂雄, 平野愛弓, 平野愛弓, 平野愛弓

    応用物理学会秋季学術講演会講演予稿集(CD-ROM)　84th　2023

    ISSN： 2758-4704

  11. Reservoir computing properties of cultured neuronal networks with modular structure

    住拓磨, 住拓磨, 山本英明, 伊藤亘輝, 伊藤亘輝, 香取勇一, 佐藤茂雄, 平野愛弓, 平野愛弓, 平野愛弓

    応用物理学会春季学術講演会講演予稿集(CD-ROM)　70th　2023

    ISSN： 2758-4704

  12. Fabrication and spontaneous activity analysis of large-scale modular neuronal networks

    伊藤亘輝, 伊藤亘輝, 山本英明, 山本英明, 山本英明, 住拓磨, 住拓磨, 室田白馬, 室田白馬, 門間信明, 門間信明, 佐藤茂雄, 佐藤茂雄, 佐藤茂雄, 平野愛弓, 平野愛弓, 平野愛弓, 平野愛弓, 平野愛弓

    応用物理学会春季学術講演会講演予稿集(CD-ROM)　70th　2023

    ISSN： 2758-4704

  13. 人工神経細胞回路を用いた物理リザバーコンピューティング

    山本英明, 住拓磨, 平野愛弓, 平野愛弓, 佐藤茂雄

    化学とマイクロ・ナノシステム学会研究会講演要旨集(CD-ROM)　48th　2023

  14. Time-Series Classification in Micropatterned Neuronal Network Reservoirs Peer-reviewed

    Takuma Sumi, Hideaki Yamamoto, Yuichi Katori, Koki Ito, Shigeo Sato, Ayumi Hirano-Iwata

    IEICE Proceeding Series　71　173-175　2022/12/12

    Publisher: The Institute of Electronics, Information and Communication Engineers

    DOI： 10.34385/proc.71.a5l-d-02 　

    eISSN： 2188-5079

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    Reservoir computing provides a novel framework to understand how the dynamics within biological neuronal networks (BNNs) is linked to information processing. Here, we used micropatterned substrates to fabricate BNNs with modular topology, one of the important structural features of brain networks, and realized a reservoir system with the modular BNN. Using image and time-series classification tasks, we evaluated the reservoir computing properties of the BNN reservoirs. The results show that modularity facilitates the separation between the trajectories of the neuronal responses to different spatial patterns, pointing to the functional advantage of the animals to modular topology within the nervous systems.

  15. Fabrication of artificial neuronal network for analyzing neuronal ensemble functions

    室田白馬, 室田白馬, 山本英明, 山本英明, 住拓磨, 住拓磨, 佐藤茂雄, 佐藤茂雄, 平野愛弓, 平野愛弓, 平野愛弓, 平野愛弓

    電子情報通信学会技術研究報告(Web)　122　(195(NC2022 32-42))　2022

    ISSN： 2432-6380

  16. Perturbation responses of artificial neuronal networks with modular structure

    山本英明, 竹室汰貴, 竹室汰貴, 住拓磨, 住拓磨, 金野智浩, SORIANO Jordi, 佐藤茂雄, 平野愛弓, 平野愛弓, 平野愛弓

    応用物理学会秋季学術講演会講演予稿集(CD-ROM)　83rd　2022

    ISSN： 2758-4704

  17. Reservoir computing properties of in-silico/in-vitro modular neuronal networks

    Sumi Takuma, Yamamoto Hideaki, Takemuro Taiki, Moriya Satoshi, Sato Shigeo, Hirano-Iwata Ayumi

    JSAP Annual Meetings Extended Abstracts　2021.1　2185-2185　2021/02/26

    Publisher: The Japan Society of Applied Physics

    DOI： 10.11470/jsapmeeting.2021.1.0_2185 　

    ISSN： 2758-4704

    eISSN： 2436-7613

  18. Reservoir property of micropatterned cultured neuronal network

    住拓磨, 住拓磨, 山本英明, 守谷哲, 竹室汰貴, 竹室汰貴, 金野智浩, 佐藤茂雄, 平野愛弓, 平野愛弓, 平野愛弓

    電子情報通信学会技術研究報告(Web)　121　(223(NC2021 18-27))　2021

    ISSN： 2432-6380

  19. Evaluation of pattern classification properties of artificial neuronal networks using reservoir computing

    住拓磨, 住拓磨, 山本英明, 守谷哲, 竹室汰貴, 竹室汰貴, 金野智浩, 佐藤茂雄, 平野愛弓, 平野愛弓, 平野愛弓

    日本神経回路学会全国大会講演論文集　31st (CD-ROM)　2021

  20. An optical perturbation system for micropatterned neuronal networks

    竹室汰貴, 竹室汰貴, 山本英明, 脇村桂, 脇村桂, 住拓磨, 住拓磨, 金野智浩, 佐藤茂雄, SORIANO Jordi, 平野愛弓, 平野愛弓, 平野愛弓

    応用物理学会秋季学術講演会講演予稿集(CD-ROM)　82nd　2021

    ISSN： 2758-4704

  21. Micropatterned culture of modular neuronal networks on multi-electrode arrays

    佐藤有弥, 佐藤有弥, 山本英明, 山本英明, 竹室汰貴, 竹室汰貴, 住拓磨, 住拓磨, 酒井原一守, 酒井原一守, 谷井孝至, 佐藤茂雄, 佐藤茂雄, 平野愛弓, 平野愛弓, 平野愛弓, 平野愛弓

    応用物理学会秋季学術講演会講演予稿集(CD-ROM)　82nd　2021

    ISSN： 2758-4704

  22. Impact of electrical field noise on micropatterned neuronal networks

    Sumi Takuma, Yamamoto Hideaki, Wakimura Kei, Sato Shigeo, Hirano-Iwata Ayumi

    JSAP Annual Meetings Extended Abstracts　2020.1　3653-3653　2020/02/28

    Publisher: The Japan Society of Applied Physics

    DOI： 10.11470/jsapmeeting.2020.1.0_3653 　

    ISSN： 2758-4704

    eISSN： 2436-7613

  23. 脳組織に近い弾性率を有するシリコーン樹脂の生体界面材料応用—応用物理学会 有機分子・バイオエレクトロニクス分科会研究会 ここまで進んだ有機分子・バイオエレクトロニクス研究

    山本 英明, 住 拓磨, 佐藤 茂雄, 平野 愛弓

    Molecular electronics and bioelectronics = 応用物理学会,有機分子・バイオエレクトロニクス分科会会誌 / 応用物理学会有機分子・バイオエレクトロニクス分科会 編　31　(2)　77-80　2020

    Publisher: 応用物理学会有機分子・バイオエレクトロニクス分科会

    ISSN： 2423-8805

  24. Culturing primary neurons on an ultrasoft silicone gel with biomimetic stiffness

    Sumi Takuma, Yamamoto Hideaki, Hirano-Iwata Ayumi

    JSAP Annual Meetings Extended Abstracts　2019.2　2852-2852　2019/09/04

    Publisher: The Japan Society of Applied Physics

    DOI： 10.11470/jsapmeeting.2019.2.0_2852 　

    ISSN： 2758-4704

    eISSN： 2436-7613

  25. Biomimetic culture of primary neurons using ultrasoft gels

    Sumi Takuma, Yamamoto Hideaki, Hayakawa Takeshi, Ide Katsuya, Kino Hisashi, Tanaka Tetsu, Hirano Ayumi

    Abstract of annual meeting of the Surface Science of Japan　2018　180　2018

    Publisher: The Japan Society of Vacuum and Surface Science

    DOI： 10.14886/sssj2008.2018.0_180 　

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    Mechanical properties of scaffolds have recently been found to impose strong impact on the behavior of cultured cells. Here, we focused on the effect of scaffold’s elastic modulus on the development of synaptic connections in cultured neurons, and established a novel system for culturing rat cortical neurons on an ultrasolft gel surface with an elastic modulus resembling the brain. Impact of the soft scaffold on the amplitude of synaptic currents and spontaneous neuronal activity, analyzed via patch -clamp recording and fluorescence calcium imaging, will also be presented.

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