1
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Jeon I, Kim T. Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network. Front Comput Neurosci 2023; 17:1092185. [PMID: 37449083 PMCID: PMC10336230 DOI: 10.3389/fncom.2023.1092185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Although it may appear infeasible and impractical, building artificial intelligence (AI) using a bottom-up approach based on the understanding of neuroscience is straightforward. The lack of a generalized governing principle for biological neural networks (BNNs) forces us to address this problem by converting piecemeal information on the diverse features of neurons, synapses, and neural circuits into AI. In this review, we described recent attempts to build a biologically plausible neural network by following neuroscientifically similar strategies of neural network optimization or by implanting the outcome of the optimization, such as the properties of single computational units and the characteristics of the network architecture. In addition, we proposed a formalism of the relationship between the set of objectives that neural networks attempt to achieve, and neural network classes categorized by how closely their architectural features resemble those of BNN. This formalism is expected to define the potential roles of top-down and bottom-up approaches for building a biologically plausible neural network and offer a map helping the navigation of the gap between neuroscience and AI engineering.
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Affiliation(s)
| | - Taegon Kim
- Brain Science Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea
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2
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Godement P. A Stay in Friedrich Bonhoeffer's Lab in Tubingen in the Mid-eighties. Neuroscience 2023; 508:52-61. [PMID: 36464176 DOI: 10.1016/j.neuroscience.2022.11.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 11/15/2022] [Accepted: 11/23/2022] [Indexed: 12/03/2022]
Abstract
The main focus of research for which Friedrich Bonhoeffer's work is known in the Neuroscience community was pioneer experiments on how axonal projections could organize into "maps", what mechanisms are involved in axon guidance and involve gradients of guiding molecules, and isolation of the first such molecules, e.g. RAGS (ephrin A5) and RGM (repulsive guidance molecule). Other papers have described in detail these contributions as well as Friedrich Bonhoeffer's personality. In the mid-eighties, I made a 2-year stay in his lab and initiated a line of research on development of binocular connections in Mammals, particularly the guidance of retinal fibers to one or the other side of the brain. In this paper I recall these circumstances as they pertain to Neuroscience as it stood at the time, and explain as best as I can how his lab was a conducive setting for the discoveries made there and how Friedrich Bonhoeffer acted for me as a scientist and a tutor.
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Affiliation(s)
- Pierre Godement
- Centre National de la Recherche Scientifique, Paris, France.
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3
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Abstract
The establishment of a functioning neuronal network is a crucial step in neural development. During this process, neurons extend neurites—axons and dendrites—to meet other neurons and interconnect. Therefore, these neurites need to migrate, grow, branch and find the correct path to their target by processing sensory cues from their environment. These processes rely on many coupled biophysical effects including elasticity, viscosity, growth, active forces, chemical signaling, adhesion and cellular transport. Mathematical models offer a direct way to test hypotheses and understand the underlying mechanisms responsible for neuron development. Here, we critically review the main models of neurite growth and morphogenesis from a mathematical viewpoint. We present different models for growth, guidance and morphogenesis, with a particular emphasis on mechanics and mechanisms, and on simple mathematical models that can be partially treated analytically.
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4
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Behpour S, Field DJ, Albert MV. On the Role of LGN/V1 Spontaneous Activity as an Innate Learning Pattern for Visual Development. Front Physiol 2021; 12:695431. [PMID: 34776991 PMCID: PMC8589027 DOI: 10.3389/fphys.2021.695431] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 10/05/2021] [Indexed: 11/16/2022] Open
Abstract
Correlated, spontaneous neural activity is known to play a necessary role in visual development, but the higher-order statistical structure of these coherent, amorphous patterns has only begun to emerge in the past decade. Several computational studies have demonstrated how this endogenous activity can be used to train a developing visual system. Models that generate spontaneous activity analogous to retinal waves have shown that these waves can serve as stimuli for efficient coding models of V1. This general strategy in development has one clear advantage: The same learning algorithm can be used both before and after eye-opening. This same insight can be applied to understanding LGN/V1 spontaneous activity. Although lateral geniculate nucleus (LGN) activity has been less discussed in the literature than retinal waves, here we argue that the waves found in the LGN have a number of properties that fill the role of a training pattern. We make the case that the role of “innate learning” with spontaneous activity is not only possible, but likely in later stages of visual development, and worth pursuing further using an efficient coding paradigm.
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Affiliation(s)
- Sahar Behpour
- Department of Information Science, University of North Texas, Denton, TX, United States
| | - David J Field
- Department of Psychology, Cornell University, Ithaca, NY, United States
| | - Mark V Albert
- Department of Computer Science and Engineering, University of North Texas, Denton, TX, United States.,Department of Biomedical Engineering, University of North Texas, Denton, TX, United States
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5
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Huang J, Ren H, Jiang Y, Wu X, Ren J. Technique Advances in Enteroatmospheric Fistula Isolation After Open Abdomen: A Review and Outlook. Front Surg 2021; 7:559443. [PMID: 33553237 PMCID: PMC7855170 DOI: 10.3389/fsurg.2020.559443] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 12/03/2020] [Indexed: 12/15/2022] Open
Abstract
Enteroatmospheric fistula (EAF) after open abdomen adds difficulties to the management and increases the morbidity and mortality of patients. As an effective measurement, reconstructing gastrointestinal tract integrity not only reduces digestive juice wasting and wound contamination, but also allows expedient restoration of enteral nutrition and intestinal homeostasis. In this review, we introduce several technologies for the temporary isolation of EAF, including negative pressure wound therapy, fistuloclysis, fistula patch, surgical covered stent, three-dimensional (3D) printing stent, and injection molding stent. The manufacture and implantation procedures of each technique with their pros and cons are described in detail. Moreover, the approach in combination with finger measurement, x-ray imaging, and computerized tomography is used to measure anatomic parameters of fistula and design appropriate 3D printer-recognizable stereolithography files for production of isolation devices. Given the active roles that engineers playing in the technology development, we call on the cooperation between clinicians and engineers and the organization of clinical trials on these techniques.
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Affiliation(s)
| | | | | | - Xiuwen Wu
- Research Institute of General Surgery, Jinling Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Jianan Ren
- Research Institute of General Surgery, Jinling Hospital, School of Medicine, Southeast University, Nanjing, China
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6
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Bozhko DV, Galumov GK, Polovian AI, Kolchanova SM, Myrov VO, Stelmakh VA, Schiöth HB. BCNNM: A Framework for in silico Neural Tissue Development Modeling. Front Comput Neurosci 2021; 14:588224. [PMID: 33551782 PMCID: PMC7855713 DOI: 10.3389/fncom.2020.588224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 12/18/2020] [Indexed: 12/02/2022] Open
Abstract
Cerebral (“brain”) organoids are high-fidelity in vitro cellular models of the developing brain, which makes them one of the go-to methods to study isolated processes of tissue organization and its electrophysiological properties, allowing to collect invaluable data for in silico modeling neurodevelopmental processes. Complex computer models of biological systems supplement in vivo and in vitro experimentation and allow researchers to look at things that no laboratory study has access to, due to either technological or ethical limitations. In this paper, we present the Biological Cellular Neural Network Modeling (BCNNM) framework designed for building dynamic spatial models of neural tissue organization and basic stimulus dynamics. The BCNNM uses a convenient predicate description of sequences of biochemical reactions and can be used to run complex models of multi-layer neural network formation from a single initial stem cell. It involves processes such as proliferation of precursor cells and their differentiation into mature cell types, cell migration, axon and dendritic tree formation, axon pathfinding and synaptogenesis. The experiment described in this article demonstrates a creation of an in silico cerebral organoid-like structure, constituted of up to 1 million cells, which differentiate and self-organize into an interconnected system with four layers, where the spatial arrangement of layers and cells are consistent with the values of analogous parameters obtained from research on living tissues. Our in silico organoid contains axons and millions of synapses within and between the layers, and it comprises neurons with high density of connections (more than 10). In sum, the BCNNM is an easy-to-use and powerful framework for simulations of neural tissue development that provides a convenient way to design a variety of tractable in silico experiments.
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Affiliation(s)
- Dmitrii V Bozhko
- JetBrains Research Department, Space Office Center, Saint Petersburg, Russia
| | - Georgii K Galumov
- JetBrains Research Department, Space Office Center, Saint Petersburg, Russia
| | | | - Sofiia M Kolchanova
- JetBrains Research Department, Space Office Center, Saint Petersburg, Russia.,Department of Biology, University of Puerto Rico at Mayaguez, Mayaguez, PR, United States.,Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, Saint Petersburg, Russia
| | - Vladislav O Myrov
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.,Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Viktoriia A Stelmakh
- JetBrains Research Department, Space Office Center, Saint Petersburg, Russia.,Skolkovo Institute of Science and Technology, Center of Life Sciences, Moscow, Russia
| | - Helgi B Schiöth
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden.,Institute for Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University, Moscow, Russia
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7
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Ferreira Castro A, Baltruschat L, Stürner T, Bahrami A, Jedlicka P, Tavosanis G, Cuntz H. Achieving functional neuronal dendrite structure through sequential stochastic growth and retraction. eLife 2020; 9:e60920. [PMID: 33241995 PMCID: PMC7837678 DOI: 10.7554/elife.60920] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 11/15/2020] [Indexed: 02/06/2023] Open
Abstract
Class I ventral posterior dendritic arborisation (c1vpda) proprioceptive sensory neurons respond to contractions in the Drosophila larval body wall during crawling. Their dendritic branches run along the direction of contraction, possibly a functional requirement to maximise membrane curvature during crawling contractions. Although the molecular machinery of dendritic patterning in c1vpda has been extensively studied, the process leading to the precise elaboration of their comb-like shapes remains elusive. Here, to link dendrite shape with its proprioceptive role, we performed long-term, non-invasive, in vivo time-lapse imaging of c1vpda embryonic and larval morphogenesis to reveal a sequence of differentiation stages. We combined computer models and dendritic branch dynamics tracking to propose that distinct sequential phases of stochastic growth and retraction achieve efficient dendritic trees both in terms of wire and function. Our study shows how dendrite growth balances structure-function requirements, shedding new light on general principles of self-organisation in functionally specialised dendrites.
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Affiliation(s)
- André Ferreira Castro
- Frankfurt Institute for Advanced StudiesFrankfurt am MainGermany
- Ernst Strüngmann Institute (ESI) for Neuroscience in cooperation with Max Planck SocietyFrankfurt am MainGermany
- Center for Neurodegenerative Diseases (DZNE)BonnGermany
| | | | - Tomke Stürner
- Center for Neurodegenerative Diseases (DZNE)BonnGermany
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | | | - Peter Jedlicka
- Frankfurt Institute for Advanced StudiesFrankfurt am MainGermany
- Faculty of Medicine, ICAR3R – Interdisciplinary Centre for 3Rs in Animal Research, Justus Liebig University GiessenGiessenGermany
- Neuroscience Center, Institute of Clinical Neuroanatomy, Goethe UniversityFrankfurt am MainGermany
| | - Gaia Tavosanis
- Center for Neurodegenerative Diseases (DZNE)BonnGermany
- LIMES Institute, University of BonnBonnGermany
| | - Hermann Cuntz
- Frankfurt Institute for Advanced StudiesFrankfurt am MainGermany
- Ernst Strüngmann Institute (ESI) for Neuroscience in cooperation with Max Planck SocietyFrankfurt am MainGermany
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8
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Wilkes OR, Moore AW. Distinct Microtubule Organizing Center Mechanisms Combine to Generate Neuron Polarity and Arbor Complexity. Front Cell Neurosci 2020; 14:594199. [PMID: 33328893 PMCID: PMC7711044 DOI: 10.3389/fncel.2020.594199] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 11/02/2020] [Indexed: 01/15/2023] Open
Abstract
Dendrite and axon arbor wiring patterns determine the connectivity and computational characteristics of a neuron. The identities of these dendrite and axon arbors are created by differential polarization of their microtubule arrays, and their complexity and pattern are generated by the extension and organization of these arrays. We describe how several molecularly distinct microtubule organizing center (MTOC) mechanisms function during neuron differentiation to generate and arrange dendrite and axon microtubules. The temporal and spatial organization of these MTOCs generates, patterns, and diversifies arbor wiring.
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Affiliation(s)
- Oliver R Wilkes
- Laboratory for Neurodiversity, RIKEN Center for Brain Science, Wako-shi, Japan.,Department of Cellular and Molecular Biology, Institute for Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Adrian W Moore
- Laboratory for Neurodiversity, RIKEN Center for Brain Science, Wako-shi, Japan
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9
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Nguyen PTM, Hayashi Y, Baptista MDS, Kondo T. Collective almost synchronization-based model to extract and predict features of EEG signals. Sci Rep 2020; 10:16342. [PMID: 33004963 PMCID: PMC7530765 DOI: 10.1038/s41598-020-73346-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 09/15/2020] [Indexed: 01/11/2023] Open
Abstract
Understanding the brain is important in the fields of science, medicine, and engineering. A promising approach to better understand the brain is through computing models. These models were adjusted to reproduce data collected from the brain. One of the most commonly used types of data in neuroscience comes from electroencephalography (EEG), which records the tiny voltages generated when neurons in the brain are activated. In this study, we propose a model based on complex networks of weakly connected dynamical systems (Hindmarsh–Rose neurons or Kuramoto oscillators), set to operate in a dynamic regime recognized as Collective Almost Synchronization (CAS). Our model not only successfully reproduces EEG data from both healthy and epileptic EEG signals, but it also predicts EEG features, the Hurst exponent, and the power spectrum. The proposed model is able to forecast EEG signals 5.76 s in the future. The average forecasting error was 9.22%. The random Kuramoto model produced the outstanding result for forecasting seizure EEG with an error of 11.21%.
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Affiliation(s)
- Phuong Thi Mai Nguyen
- Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology, Tokyo, 184-8588, Japan
| | - Yoshikatsu Hayashi
- Biomedical Science/Engineering, School of Biological Sciences, University of Reading, Reading, RG6 6UR, UK
| | - Murilo Da Silva Baptista
- Institute for Complex System and Mathematical Biology, University of Aberdeen, Aberdeen, AB24 3UE, UK
| | - Toshiyuki Kondo
- Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology, Tokyo, 184-8588, Japan.
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10
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Wang J, Yuan R, Zhu X, Ao P. Adaptive Landscape Shaped by Core Endogenous Network Coordinates Complex Early Progenitor Fate Commitments in Embryonic Pancreas. Sci Rep 2020; 10:1112. [PMID: 31980678 PMCID: PMC6981170 DOI: 10.1038/s41598-020-57903-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 12/07/2019] [Indexed: 02/06/2023] Open
Abstract
The classical development hierarchy of pancreatic cell fate commitments describes that multipotent progenitors (MPs) first bifurcate into tip cells and trunk cells, and then these cells give rise to acinar cells and endocrine/ductal cells separately. However, lineage tracings reveal that pancreatic progenitors are highly heterogeneous in tip and trunk domains in embryonic pancreas. The progenitor fate commitments from multipotency to unipotency during early pancreas development is insufficiently characterized. In pursuing a mechanistic understanding of the complexity in progenitor fate commitments, we construct a core endogenous network for pancreatic lineage decisions based on genetic regulations and quantified its intrinsic dynamic properties using dynamic modeling. The dynamics reveal a developmental landscape with high complexity that has not been clarified. Not only well-characterized pancreatic cells are reproduced, but also previously unrecognized progenitors-tip progenitor (TiP), trunk progenitor (TrP), later endocrine progenitor (LEP), and acinar progenitors (AciP/AciP2) are predicted. Further analyses show that TrP and LEP mediate endocrine lineage maturation, while TiP, AciP, AciP2 and TrP mediate acinar and ductal lineage maturation. The predicted cell fate commitments are validated by analyzing single-cell RNA sequencing (scRNA-seq) data. Significantly, this is the first time that a redefined hierarchy with detailed early pancreatic progenitor fate commitment is obtained.
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Affiliation(s)
- Junqiang Wang
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ruoshi Yuan
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaomei Zhu
- Shanghai Center for Quantitative Life Sciences and Physics Department, Shanghai University, Shanghai, China
| | - Ping Ao
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.
- Shanghai Center for Quantitative Life Sciences and Physics Department, Shanghai University, Shanghai, China.
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
- State Key Laboratory for Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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11
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Isomura T, Toyoizumi T. Multi-context blind source separation by error-gated Hebbian rule. Sci Rep 2019; 9:7127. [PMID: 31073206 PMCID: PMC6509167 DOI: 10.1038/s41598-019-43423-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 04/23/2019] [Indexed: 11/08/2022] Open
Abstract
Animals need to adjust their inferences according to the context they are in. This is required for the multi-context blind source separation (BSS) task, where an agent needs to infer hidden sources from their context-dependent mixtures. The agent is expected to invert this mixing process for all contexts. Here, we show that a neural network that implements the error-gated Hebbian rule (EGHR) with sufficiently redundant sensory inputs can successfully learn this task. After training, the network can perform the multi-context BSS without further updating synapses, by retaining memories of all experienced contexts. This demonstrates an attractive use of the EGHR for dimensionality reduction by extracting low-dimensional sources across contexts. Finally, if there is a common feature shared across contexts, the EGHR can extract it and generalize the task to even inexperienced contexts. The results highlight the utility of the EGHR as a model for perceptual adaptation in animals.
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Affiliation(s)
- Takuya Isomura
- Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Wako, Saitama, 351-0198, Japan.
| | - Taro Toyoizumi
- Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Wako, Saitama, 351-0198, Japan.
- RIKEN CBS-OMRON Collaboration Center, Wako, Saitama, 351-0198, Japan.
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