收货于方法学的要紧逾越和从分子到通盘这个词大脑多档次的数字数据集成及建模
连年来，收货于方法学的要紧逾越和从分子到通盘这个词大脑多档次的数字数据集成及建模，脑科学研究无疑已迈入一个新期间。在这一布景下，神经科学与时间、谋略的交叉规模已取得重要进展。新兴的大脑科学整合了高质料的研究、多档次数据的集成、跨学科的大限制合作文化，同期促进了科研后果的应用转机。就如欧洲东谈主脑谋略（HBP）所提倡的那样，采纳系统化的方法对于顶住畴昔十年内的医学与时间挑战至关重要。
本文旨在为畴昔十年的数字大脑研究发展一套新主见，并与泛泛的研究社区张开研究，寻找共鸣点，以此成就科学的共同磋商。同期，提供一个科学框架，因循面前及畴昔的EBRAINS研究基础设施发展（EBRAINS是HBP责任产生的研究基础设施）。此外，本文还旨在向利益相关者、资助组织和研究机构传达畴昔数字大脑研究的信息，招引他们的参与；探讨概括性大脑模子在东谈主工智能，包括机器学习和深度学习方面的变革后劲；并概述一个包含反念念、对话及社会参与的配合研究方法，以顶住伦理与社会的契机与挑战，行动畴昔神经科学研究的一部分。（本文为著作下篇。）
要害词：东谈主类大脑，数字研究器用，研究道路图，大脑模子，数据分享，研究平台
剪辑
▷Amunts, Katrin, et al. "The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing."Imaging Neuroscience2 (2024): 1-35.
脑科学的全球化
自21世纪初以来，脑科学研究规模数字时间的应用飞速推广，面前咱们不错分析来自数以千计大脑的多模态数据。这些数据通过公开的环球存储库（如英国生物银行）或全球麇集（如 ENIGMA, HCP）提供。天然，要是不行将这些海量数据转机为常识，进而深远清楚大脑的复杂机制过火在正常行动、成长、朽迈及脑疾病中的作用，光稀有据亦然不够的。
因此，咱们见证了复杂生成模子的兴起，这些模子结合了遗传信息和表型信息，跨越不同期间点来追踪大脑情景的时空变化（Iturria-Medina et al., 2018; Vogel et al., 2021; Young et al., 2018）。东谈主工智能策略在将雄伟数据集分类为合理界说的子组中起着越来越重要的作用，这些子组可能适用于定制解读，举例行动倾向的多基因风险评分或药物临床试验的分层。这些方法最终为个性化经管或医疗遏制提供了可能性。
然则，寻找更隐微、更早期的大脑情景变化的生物标记物，频频需要会聚大量数据来揭示那些与这些变化相关或可能导致这些变化的身分。这种搜索伴跟着同质性与代表性之间的常见冲突。诚然无须置疑，大数据技巧应用于泛泛的环球数据存储库，如ADNI，PPMI，UK Biobank等，还是为咱们提供了对于东谈主类大脑机制和回路的通用性质的前所未有的知悉，但这些数据集大多源自西方国度，并不代表全球。
数据存储库的效用需要有余丰富和多元的数据，以确保研究后果过火股东的革命不错在全球范围内的千般化东谈主群和环境中得到引申。性别各异、年事、社会经济地位、种族等身分在神经结构、功能和融会流露上形成了个体各异（Dotson & Duarte, 2020），也影响了不同东谈主群间疾病的发生率、康复和生活率的各异（Sterling et al.，2022; Zahodne et al.，2015）。此外，全球范围内对于研究中回报种族东谈主口信息的作念法存在各异（Goldfarb & Brown, 2022）。同期，低收入及中等收入国度（LMICs）在脑疾病和心境健康问题的会诊和发病率等方面的举措束缚增多，如东南亚国度定约（ASEAN）地区。
全球合作的需求包括麇集、传播和分析来自LMICs的经过用心经管、细心表型和基因分析的数据集，以辨识全球不同亚东谈主群间的相似性和各异性。在莫得获取不同国度具有代表性的数据的情况下，无法对这些相比进行统计上的可靠猜度，这超出了个别实验室的才能。由于对现存数据集的重复使用导致它们的不可幸免的衰减（Thompson et al., 2020），代表性问题不行仅行动过后琢磨，而需成为伏击的优先事项。
在接下来的十年里，跟着洞开数据分享倡议（如英国生物银行，OpenNeuro，CONP, EBRAINS等）在全球的推广，科学家对数据经管和分享的不雅念将执续演变（Donaldson& Koepke, 2022），资助者和学术期刊的守望也将发生变化（可参见2023年Nature Neuroscience社论“咱们怎样促进数据分享”），这将极地面增多全球社区可用的千般化数据量。这将带来对相关和因果身分的新的相识，这些身分导致全球东谈主群中大脑和行动各异的出现。这些数据分享平台，好多还是运行十多年，还是达到了时间上的熟悉，大致因循多国之间的洞开数据分享。
然则，在不同平台之间开发了了而无缝的互操作性仍有待完成，以确保末端用户不错在不需要深远清楚复杂时间细节的情况下进行操作。挑战不单是在于提供数据，更重要的是提供既有价值又易于解说的数据，这些数据的起原必须着力FAIR数据分享原则（可查找的、可走访的、可互操作的、可重用的，Wilkinson et al., 2016）。从时间上实现数据互操作性、提供数据刻画符和合同、治服元数据程序，这些措施不仅升迁了数据的价值和实用性，还有助于构建一个更刚烈、更配合、更高效的研究生态系统。
然则，获取特殊念念风趣和可操作数据的必要性，也带来了一系列与数据治理和伦理相关的挑战。这些实践在不同群体间仍在演变，领有千般且随机不兼容的全球框架（Eke et al., 2022）。对于研究中种族东谈主口信息的回报也存在各异（Goldfarb & Brown, 2022），以及生成和处理数据的时间才能、数据麇集的资金和其他社会文化身分亦然考量身分。到面前为止，来自非洲和拉丁好意思洲地区的数据集日常不被包括在全球脑科学研究和革命的研究中。
下一个十年将见证在欧洲（如GDPR）、北好意思、亚洲、澳大利亚和非洲等地区不同的数据治理和伦理框架的和洽，以促进大脑数据在洞开神经科学全球社区内的更泛泛传播。咱们应愈加讲理才能建造、增多东谈主口信息的回报、资助谋略，并最终提高下收入和中等收入国度对数据生成、处理和分享的相识。
毫无疑问，脑科学全球化的最重要的内容将是其“民主化”。不再是只是由高收入国度的科学家分析和发布的数据起原，咱们预测LMIC的科学家将在脑科学职业中饰演越来越重要的脚色。此种民主化天然演化自面前数据分析家数（如CBRAIN、EBRAINS、BrainLife*）所提供的高等分析责任经由的普及。这些家数允许来自天下各地的研究者在其他场地进行复杂的数据分析，摒除了后勤、行政和时间吃力，这些吃力也曾不容LMIC的科学家充分参与到脑科学社区。此外，通过结合数据分享和分析平台，还不错实现派生数据的再行分拨。分享驱散至关重要，大致最大章程地减少科学冗余、增强可重复性，并促进LMIC场景中科学分析的可走访性。
跟着东谈主们对分析决策在学习大脑模子中的作用相识的增强（Botvinik-Nezer et al., 2020），派生数据的传播将使科学探索的迭代和配合方法成为可能，并摒除了参预的主要吃力。这种愿景也带来了需要科罚的一系列行政问题，举例学术认同、晋升、迷惑等，但这些问题还是是面前洞开神经科学辩白的主题。全球化的拓展带来了限制与后勤的挑战，举例讲话和场地治理执法的问题，但这并不改变数据隐秘与洞开科学之间基本的矛盾。咱们预期跟着时间挑战的科罚，全球神经科学整合的愿景将在畴昔十年景为实践。
大脑模子行动畴昔脑研究的推能源
在夙昔二十年里，信息和通讯时间的迅猛发展不仅股东了模拟和机器学习时间的逾越，也使得数据与模子在兼并世态系统中实现互联互通，从而股东了新式脑模子的发展。大脑模拟径直期骗了大脑基础研究的后果，预测将在阐释脑过程的基本方面（通过展示其在体外模拟的才能）如决策制定、嗅觉畅通整合、雅致形成等方面判辨要害作用。尽管咱们需警惕这些研究所带来的伦理与形而上学问题，但也不错遐想期骗这些模子和模拟来探索脑研究中的具体问题。由此，咱们不难遐想怎样定制通用脑模子，以拿获某一特定患者大脑的稀罕特征。举例，个体的结构和功能性脑成像数据不错敛迹一个通用的数字脑模子，使其针对特定个体，从而用作个性化分析模板或体外模拟平台。
这种方法的一个具体例子是捏造癫痫患者，在此方法中，神经影像数据迷惑对癫痫患者大脑的体外模拟，因循会诊和解救遏制、临床决策和后果预测（El Houssaini et al., 2020; Jirsa et al., 2017; Wendling, 2008）。在谋略神经科学的总体趋势下，基于相关神经回路常识，千般癫痫行径模子被构建。这些模子日常将神经元或神经群体麇集的癫痫发作解说为一种高同步性/高振幅节拍情景。在无法径直从受试者获取数据的情况下，多级图谱数据成为另一种不错进一步丰富个性化脑模子的数据起原（Amunts et al., 2022）。
这些个性化的“捏造大脑”不错被看作是向表面和时间上更具挑战性的新阶段迈进的一种跳板，这些挑战在伦理方面可能更为复杂，同期也更适合于大脑行径在通盘时辰方法上的束缚变化。个性化脑模拟的终极磋商不错体面前一个一语气通过着实天下数据得到信息和更新的模子，这种模子被称为“数字孪生”。在这一布景下，“数字孪生”的主见需要被仔细界定，以幸免袒护这种方法的局限性，并幸免制造对精准度的不切本色期待或产生遮人耳目的过度宣传（Evers & Salles, 2021）。
历史上，“数字孪生”的主见发祥于工业和制造规模（Grieves & Vickers, 2017; Grieves, 2019），包括三个组成部分：物理对象、其捏造对应物和两者之间的数据流动。物理对象的实测数据传递给模子，而模子的信息和过程反馈给物理对象。今天，“数字孪生”一词还是泛泛应用于其发祥以外的多个研究规模，包括生物医学规模，尽管该术语背后的主见可能存在各异。
在制造业中，数字孪生不单是是一个普通的模拟模子。它是为特定对象制定的通用模子的具体实例，由该对象的本色数据因循，举例在工业规模中的飞机引擎（Tao et al., 2019）。最近，在疏通的布景下，研究者还提倡了“数字影子”这一主见行动一种矫正方法。这种方法提供任务和情境依赖的、磋商导向的、团聚的、执久的数据集，能以更纯确凿姿色涵盖多个视角下的复杂实践，何况性能超越实足集成的数字孪生（Becker et al., 2021; Brauner et al., 2022）。
数字孪生的一种解读波及到机器学习和东谈主工智能中生成模子的辩证关系。生成模子保证了模子的可解说性。此外，它们促使咱们从“大数据”向“智能数据”的转变（更确切地说是选拔和整合数据特征，以最大化预期的信息增益）。生成模子是从潜在原因到可测量驱散的映射的概率刻画。在这个风趣风趣上，数字孪生不错看作是一个得当生成某个特定细胞、个体或群体反应的模子的阐扬界说。正确构建生成模子要害在于，它大致提供对实验数据的可解说的机械性解说。此外，它分别在模子拟合（即反演）和模子选拔（即假定）方面诀别了从下到上与从上至下的建模方法。
在构建一个活体器官的“数字孪生”时，靠近的挑战超出了构建一个无人命对象的数字孪生时的挑战。大脑无疑是面前已知的最复杂和多面的器官。那么，在神经科学和大脑研究中，数字孪生的主见大致被多大程度地应用呢？要是马虎地将数字孪生主见1:1地应用于大脑，可能会引起严重的诬蔑。在这里，咱们但愿通过在脑科学的特定布景下明确界说这一术语，为相关研究作念出孝顺。咱们诀别了磋商驱动的数字孪生和大脑的实够数字复成品（或副本/复制），后者代表了大脑通盘层面通盘方面的完整呈现（参见Box 3）。
大脑的实足复制既不可实现，也似乎莫得明确的实用价值。咱们研究中的数字孪生应被清楚为一个捏造模子，旨在充分代表一个对象或过程，受其物理对应物的数据敛迹，并提供模拟数据以迷惑选拔并意料后来果。数字孪生因此是实用风趣风趣上的复制，日常与一个功能或过程的模子相关，其力量在于它在处理其物理对应物所靠近的相关问题时的有用性，保执妥当的抽象水平。因此，其磋商不是尽可能地细心和多档次地模拟生物大脑，而是选拔性地减少那些对特定研究问题具有预测价值的数据信息量，保执模子尽可能马虎，同期确保其复杂度足以顶住需要。
即就是专门用于清楚特定大脑结构和能源学，或是预测特定患者的病情进展的模子，也需要依赖于全面而复杂的数据源，以构建信息丰富的捏造大脑模子。举例，东谈主类大脑谋略已在EBRAINS上建立了一个高分辨率的多档次东谈主类大脑图谱，行动结构与功能数据的集成平台。对于每个模子，咱们齐需要阐述增多的数据是否确凿增强了模子的强度，即这些数据是否使预测更准确、可考据？咱们需要执续监控在更好的预测与麇集数据的可行性及相关资本之间的衡量，并评估这些数据选拔是否得当面前的问题，即是否响应了要害的决定身分（Box 3）。
Box 3：数字大脑模子分类大脑模子：大脑模子是大脑的数字暗示，这一术语在不同的情境中有不同的用途；常见的包括数字图谱、东谈主工神经麇集、剖解模子、生物物理模子、麇集模子、融会和行动模子，以及数学和数据驱动的模子。个性化大脑模子：个性化大脑模子是一种特殊类型的模子，通过将一个个体的特定数据整合到更泛泛的模子中来进行个性化（举例，通过捏造癫痫患者实现）。数字孪生：下一代个性化大脑模子，它们通过束缚地融入实时数据而束缚发展。这些模子是为了科罚特定研究问题而有目标地设计的，整合了相关的数据。实足复制：这是一个假定的主见，指的是在通盘层面上完整地数字化暗示一个大脑的想法，最终包括对数字孪生体的解说。数字孪生与其他个性化捏造大脑模子的一个显赫区别在于，数字孪生能执续汲取来自实践天下的新信息，以实时适合其环境。在神经科学规模，大脑的“数字孪生”极具前程，可用于执续调整功能性神经康复的遏制措施或定制神经时间遏制决策。应用高保确凿准实时更新的东谈主脑数字孪生模子，需要在时间上进行开发，如将孪生大脑生态地千里浸于模拟环境、高带宽牢固的脑机接口和极高的谋略才能等，这些规模的冲破仍是远处的耐久磋商。尽管如斯，数字孪生已在神经科学和医学规模找到应用，前提是充分琢磨到面前大脑模子的局限、个性化过程实时间更新频率的挑战。数字孪生界说了面前数字神经科学发展旅途的视线，并应被视为畴昔发展的驱能源。
尽管大脑的数字孪生在具体应用上还有一段距离，但数字大脑研究的期间还是无疑开动了，岂论是在实践天下照旧在研究规模齐是如斯。数字大脑研究是一个概括主见，涵盖了数据、模子、表面、方法和谋略时间，集成于 HBP 框架下的通盘研究和开发责任。它的价值体面前到手演示里面和外部有用性、生态和构建有用性等方面。这使研究东谈主员大致顶住神经科学数十年来靠近的主要挑战，如个体表里变异性、机制不解确性和多方法复杂性等问题。EBRAINS 提供了一个平台和用户界面，因循数据、模子和方法组件的互操作性，为数字大脑主见在神经科学研究中占据中心舞台提供了操作基础。
咱们以为，在短至中期内，数字大脑模子不错在以下三个规模判辨重要作用：（1）基础大脑研究，（2）医学应用，（3）基于大脑的时间开发。
基础大脑研究
数字大脑模子过火模拟并不会替代基础研究和常识积聚，而应视为一种成心的“工程”器用。它面前充任一个在进展中的预测模子，旨在（1）锻真金不怕火现存常识，（2）预测遏制效果。后者尤为引东谈主讲理，因为遏制技巧正束缚增多，诸如深部脑刺激（DBS）、经颅磁刺激（TMS）、经颅直流电刺激（tDCS）、经颅聚焦超声刺激（tFUS）、药物、光遗传学和光药理学等。诚然已有多项研究期骗谋略大脑模子来进行预测、迷惑遏制研究的设计并解说不雅测到的效果（Frank et al.，2004，2007），但这些方法面前经常是基于“半讲授”的应用，波及电极位置、电路勾通、功能及电气模子、神经元类型的遗传启动子、神接管体的抒发模式过火信号通路模子等信息。数字孪生时间可能促进这些参数的合理决策，测试驱散，并随后对模子进行评估和修正等。
为了取得到手，底层模子必须具有生物实践性，即在剖解上精准且在功能上全面。它们最终应能关联大脑结构与功能和行动，并可能用于研究融会、讲话、相识或心境。这需要整合不同档次的高度异质数据，包括体内和离体数据，并将它们置于疏通的空间参考框架中。在一种替代而互补的方法中，细胞图谱麇集（BICAN）将采纳好意思国细胞普查麇集（BICCN）的方法，推广至通盘这个词东谈主脑，对哺乳动物大脑的组成部分进行深远的特征刻画，举例，对低级畅通皮层的最详备、最全面的多模态模子进行研究，这包括单细胞转录组和卵白质组、染色质可及性、DNA甲基化组、空间分辨单细胞转录组、形状和电生理脾性及细胞分辨率输入输出映射（Callaway et al.，2021）。
基于这一主见，大脑模拟在阐释大脑的复杂性中饰演了要害脚色，它通过允许测试对于大脑多级组织过火限度周围躯壳功能的假定来实现（参见下文）。显著，沿此研究标的，不同空间层面上践诺的模拟的互相勾通将变得日益重要。举例，分子层面的 EBRAINS 模拟引擎 Gromacs、细胞层面的 Arbor 和 NEURON、系统层面的 NEST、全脑层面的 Virtual Brain 以及体现生物体过火环境的神经机器东谈主平台（见 Brain-derived technologies）；概述见 Einevoll et al., 2019。
与着实活体大脑不同，镶嵌式模拟大脑不错在职何空间和时辰点进行抽样。因此，咱们大致监测到模拟大脑中通盘基于实践天下数据或物理化学模拟的过程，并使用模拟测量诱导如多阵列电极、fMRI扫描仪来不雅察。表面上，它不错在全身闭环环境中测试千般功能假定；此外，还可能构建能源学剖解图谱，举例在特定刺激下不雅察大脑区域的变化和过程的图谱，通盘这些齐能在着实模拟的实时中实现。
活体大脑的复杂多方法结构、有限的测量可接近性和对大脑过程清楚的不完整，使得数字孪生时间的实施极具挑战。BigBrain 行动一个剖解模子可能成为严格风趣风趣上整合孪生数据的支架（Amunts et al., 2013），这些数据包括其他起原的能源学细胞数据、实验东谈主群研究的数据以及由模子和不同大脑模拟的合成数据。这种方法也界说了数字孪生策略的甘休和有用范围，对于负包袱地使用此时间过火后续的信任至关重要。然则，这些数据驱动的模子可能代表了在职何特定时辰点可实现的活东谈主大脑的最接近的数字暗示。畴昔，数学的新视力将必要塞加快模拟和模子分析（Lehtimäki et al.，2017，2019，2020）。
据此，咱们不错设定以下磋商：（1）发展多层大脑图谱和高分辨率的大脑模子。（2）启用多层大脑模子和模拟。（3）揭示融会和行动的机制。
大脑医学
从这些数字孪生时间中，咱们不错养殖出个性化孪生时间，目标所以全新且高效的姿色改善患者的会诊和解救，因循大脑健康的计谋，正如欧洲神经学院最近发布的相关策略所示 (Bassetti, 2022)。与腹黑数字孪生相似 (Gillette et al., 2021)，即基于临床数据生成的与通盘可用临床不雅察数据相匹配的患者腹黑数字副本，东谈主类的电生理副本在迷惑临床决策方面炫耀出巨大后劲，何况有助于以资本效益高、安全且适合伦理的姿色测试新的诱导解救决策。医学中的数字孪生专注于特定的空间限制，具有明确的粒度，涵盖特定的时辰远隔，干事于特定的目标。近期提倡了针对阿尔茨海默病的数字孪生方法 (Stefanovski et al., 2021)，尽管需要严慎琢磨数据隐秘、安全性和安全方面的问题，但个性化孪生也可能成为解救此类疾病的一个格外有劲的策略。
捏造大脑（Virtual BigBrain，TVB）允许把柄受试者的神经影像和 EEG 数据以及 BigBrain 模子的剖解数据构建个体化的勾通组 (Jirsa et al., 2017)。正在进行的EPINOV临床试验采纳了 TVB，这在该规模是一大逾越；科学家们开发了患者脑部的个体模子，以迷惑和预测癫痫手术的最好解救效果 (Jirsa et al., 2023; Proix et al., 2017; Wang et al., 2023)。他们所用的策略是将群体数据与个别脑部数据结合，开发出有余着实的捏造脑模子，也就是孪生体，使得不错在手术前进行遏制模拟。对于那些在麻醉期间仍执续发作的难治性癫痫患者，日常需要耐久的重症监护，并靠近极高的弥远神经损害和亏本风险。对这些患者而言，数字孪生不错用来审查大量模子，执续获取来自 EEG 的反馈、药物反应以及血液中离子温情体的浓度等信息，这些齐是重症监护环境中容易获取的数据。
数字大脑建模的实用性由DBS阐述，DBS是几种难治性神经疾病的熟悉外科解救方法。面前，临床上的 DBS 日常采纳“开环”系统，即按照固定参数执续施加刺激。这些参数在植入后可调整，但调整是手动进行的，且操作不频频，主要基于不雅察患者的彰着症状。相对而言，“闭环”、自适合的DBS被开发出来以克服传统DBS的甘休，它把柄实时的临床相关生物反馈信号调整神经回路 (Marceglia et al., 2021)。然则，到手应用这些时间，需要深远清楚神经可塑性和学习机制。
面对局部大脑损害如中风或创伤性脑损害的应用也需雷同的时间。除了侵入性解救遏制，数字孪生亦然一个预测大脑损害后果、病理生理和可塑性的刚烈器用，随机这些可通过谋略神经心境学来刻画，即使用合成损害在谋略模子中模拟损害与劣势之间的关系 (Parr et al., 2018)。这不错显赫升迁咱们个性化神经康复的才能，同期整合由捏造实践和机器东谈主解救产生的复杂信息，以及精准测量患者的反应和逾越。
其他应用不错期骗模拟测试一个限制广泛于着实东谈主群的“临床”模拟东谈主群，从而通过创建“数字患者”群体来放大数据。这种方法对于评估稀薄病、研究性别影响或预测疾病程度尤其有招引力 (Maestú et al., 2021)。此外，使用的数据源越千般和异质，模子在其他数据集上的流露就越好，这也提高了模子的普适性。这是长入系统提供的一大特色，它有助于增多数据起原的千般性（举例，Dayan et al., 2021）。
最近，DeepMind 开发的 AlphaFold 系统 (Jumper et al., 2021)，该系统通过应用深度学习方法，已大致预测卵白质的 3D 结构。这种时间可引申至系统级，用于测试药物与卵白或药物-卵白系统的互相作用。此外，从在捏造环境中测试药物的效果到揭示药物在分子及系统级别的作用机制，这些齐是此时间的进一步发展标的。琢磨到量子力学/分子力学在谋略上的高要求，这种系统级的方法需要在最刚烈的超等谋略机上运行的高度可推广器用。不错使用NEURON和Arbor构建和模拟的考究的局部微电路模子，径直用于映射某些分子（如离子通谈、受体）的局部分散，然后用来模拟药物对这一系统的影响。这些小限制模子不错把柄特定病理条款进行调整，然后转机为针对患者的平均场模子，提高数字孪生的精度。
更泛泛地说，东谈主类大脑研究规模与非东谈主类大脑研究规模的增强交流，可能会协同科罚生物医学科学中耐久存在的问题(Devinsky et al., 2018)。东谈主类和伴侣动物患有一些疏通的疾病（举例癫痫、癌症、痴肥）。像东谈主类一样，患有癫痫的狗在生病时也需接受脑部扫描。这些疾病和解救的重复标明，东谈主类医学和兽医学之间存在未被充分期骗的契机，这些契机不错用于在伴侣动物中测试个性化医学和数字孪生的有用性，进而在东谈主类中实施。
终末，大脑孪生时间预测将有助于发展“东谈主体孪生”时间。这一视角超越了单纯增多一个器官的层面，因为它将允许在系统级别模拟神经系统行径与其他器官的互相作用，举例心脑耦合，以及大脑与胃肠谈的勾通。这些互相作用泛泛且双向。举例，最近的研究发现，东谈主类大脑中有一个固有的调整内环境和内嗅觉系统，包括限度躯壳内环境的皮层限度区域，因循躯壳的恒常性调整 (Kleckner et al., 2017)。此外，如呼吸等躯壳过程是节拍性神经行径的重要推能源 (Tort et al., 2018)。捕捉这些双向互动将有助于咱们清楚大脑怎样因循重要的躯壳功能——可能还包括在功能受损时怎样规复它们。
欧洲委员会面前正在制定的数字东谈主孪生道路图中，多器官或多方法数字孪生的双向和系统性蚁集是一个要害要素 (https://www.edith-csa.eu/)。
因此，研究者不错笃定以下磋商：（1）在人命周期中获取对于大脑可塑性、学习和适合的细心视力。（2）加快数字大脑医学的发展。（3）探索并模拟大脑行动躯壳一部分的模子。
大脑养殖时间
一项基本挑战在于笃定大脑建模所需的考究度级别、过渡性谋略以及模拟开发的类型，以便因循千般融会和嗅觉畅通功能的败露。模拟东谈主类大脑的模子被设立在具体环境中，即这些模子能限度捏造或实体的躯壳与实践的捏造或本色的物理环境互动，并汲取依时辰变化的输入流来产生行动输出，这为研究大脑结构、大脑行径与融会及功能流露之间的推敲提供了一个极具招引力的平台。
怎样评价这种从下到上的组合及数字孪生系统的败露行动与生物数据的一致性，仍是一个执续的挑战，因为典型的合成发展环境与天然环境不一致。Yong (2019) 在《大泰西》[12]杂志的特稿《东谈主类大脑神态未能实现其应许》（The Human Brain Project Hasn’t Lived Up to Its Promise）中指出，“大限制模拟有助于清楚适意和星系，但行星系统只讲理它们自身。而大脑则是为了处理其他事务而构建的……模拟组织是可行的，但没特殊念念风趣。”
前文段落列举了几例模拟在基础神经科学和大脑医学中取得进展的例子，针对的是明确的研究问题。此外，从一开动，HBP便旨在发展时间，以便研究大脑与环境的互动（Booklet，2016）。换言之，某些大脑过程的模拟被镶嵌到一个着实或模拟的躯壳中，其通盘传感器和践诺器齐与模拟贯串。原则上，这些传感器和践诺器不错是着实的、模拟的，或两者的结合。相同，这个躯壳被置于一个着实或捏造的天下中。一朝领有了这些元素，岂论是模拟的照旧着实的，咱们就能以任何合理的姿色组合它们。
显著，这种方法高度依赖于模拟着实天下物理时势的模子，何况还需要复杂的软件来高保真地模拟空间环境，并提供有余的环境、传感器和践诺器物理模拟，勾通大脑模拟器，提供存储模拟驱散的设施、图形渲染和这些复杂软件模块的和洽。通盘这些（共同）模拟不错在不同的时辰方法上运行（联想情况下天然是实时的），在闭环或开环的情景中，何况以不同的粒度对实体进行建模。
HBP 的神经机器东谈主平台[13]是一个专为践诺通盘这些身手而设计的软件环境，它基于来自生物实验的千般化数据集和着实天下机器东谈主的输入运行模拟，并在这些模拟的基础上整合了机器学习。诚然这个平台领先是为设计那些由生物学启发的大脑模子限度的神经机器东谈主而构念念的，但它跟着时辰的推移已演变成一个大致勾通和整合从模拟小鼠躯壳到复杂传感器模子，以及千般神经元和大脑模拟器的千般实体的软件环境。如今，神经机器东谈主平台不仅是一个机器东谈主设计的环境，同期亦然践诺神经科学实验的平台。因此，它是一个刚烈的捏造神经科学器用，致使不错用实足在该平台内运行的谋略机实验取代系统级体内实验。
此外，神经机器东谈主平台还允许在机器东谈主建造之前，用着实的神经科学数据来教师具体化机器东谈主的“大脑”（基于 AI 的限度器）。不错假想，一个模拟的着实环境副本可行动教师的基础，从而让机器东谈主在被录用给末端用户之前进行预教师，用户只需对（败露的）行动作念出小的调整，以确保机器东谈主大致完好践诺其任务。咱们将这种模式下的方法称为大脑养殖时间，因为它们径直基于并建立在大脑研究的发现之上。重要的是，这些发现不错在不同的组织层面得以实施。
在神经形状工程中，主要组件即生物神经元，通过功能等效的电路被模拟，构建高能效的模拟处理器和传感器。运行在这些系统上的神经模子不错源于已在生物大脑中识别的特定类型的神经元、微电路或大脑区域。当这些系统与机器东谈主实体（岂论是模拟的照旧物理的）或生物体相勾通时，它们不错复制感知、融会和行动的完整闭环的某些方面。因此，建模不错推广到通盘这个词有机体，并覆盖复杂融会过程在行动层面的通盘方面。大脑养殖时间因此不仅限于师法大脑的结构特征，还不错包括融会模子和架构以过火基础的神经能源学。这些时间将成为大脑研究的新器用，并股东谋略、机器东谈主学和东谈主工智能规模的革命。
神经康复规模预测将极地面受益于这种方法，其中实践的大脑-躯壳互动模子将有助于揭示判辨作用的神经机制（Rowald & Amft, 2022）。通过将详备的大脑模子与脊髓和肌肉骨骼系统的模子结合，为咱们提供了稀罕的契机，来详备地研究神经行径与畅通行动之间的关系。因此，个性化模子因此不错整合到决策因循系统中，匡助医师或解救师选拔和组合康复策略。它们还可能因循中央神经系统（包括脊髓）刺激时间和功能性电刺激的冲破性发展，提高这些时间的效果并扩大它们的适用范围。最近一项到手的硬脊膜外电刺激解救脊髓损害的应用报谈炫耀了这种方法的后劲（Rowald et al., 2022）。
相同，高保确凿东谈主体肌肉骨骼系统和中央神经系统模子的结合，有望因循所谓的电子药物（electroceuticals）的谋略机时间的出现，这些诱导用于解救目标的医疗诱导（举例，在帕金森病、癫痫等疾病中提供神经刺激）。医疗诱导行业无疑对迷惑其家具设计、生成疗效预测以及合座裁汰家具开发过程的风险具有根人道的酷爱。因此，期骗 HBP 创建的大脑图谱和多方法大脑模拟器，似乎应该实时琢磨麇集和整合新数据（举例介电脾性），行动开发用于评估电子药物的模拟器用和干事的前奏。琢磨到DBS已被泛泛使用，模拟这些电子药物的效果显著一衣带水。
HBP已因循 SpiNNaker 多核和 BrainScaleS 物理模拟神经形状谋略平台建立首个洞开的神经形状谋略干事，并为这些时间的进一步发展作念出了孝顺（Furber & Bogdan, 2020）。神经形状时间，其中数据传输和处理齐是基于事件的，即基于脉冲的，为边际谋略、移动机器东谈主和神经义肢时间提供了多种契机。
琢磨到移动系统自动化和“永恒在线”传感器阵列确面前趋势，至极是神经形状诱导有望提供增强的低延长容量，用于感知、融会和行动，同期减少系统上操作对系统能源奢华的影响（Cramer et al., 2022; Göltz et al., 2021）。举例，将产生脉冲的处理单位与产生脉冲的传感器（举例，动态视觉传感器、动态音频传感器）结合成完整的神经形状系统，将使数据交融愈加容易，并克服与数据起原异质性相关的甘休。通过突触可塑性，尤其是神经回路学习的神经谋略清楚的进展，也将为赋予神经形状电路更复杂功能提供新的方法，并裁汰教师资本（举例，一次性和一语气在线学习）。至极是，对局部可塑性的甘休组成了相对于传统冯诺依曼架构的彰着上风。
如 BrainScaleS 所示，模拟生物神经元的离子流动的模拟神经形状处理系统的电路是通过电流实现的。与基于经典冯·诺依曼架构的传统微处理器不同，每个硅神经元齐被物理地镶嵌到芯片中，配备专用组件。就像大脑中的神经元一样，这些硅神经元通过脉冲交换信息，这种姿色极为高效，亦然神经形状系统成为新一代实时且节能谋略机的前程光明的原因之一。他们径直从大脑的结构派生的重要后果是，神经形状处理器日常不得当加载外部数据，而是因循实时在线学习。这种稀罕的功能使新类型的学习章程成为可能，这些章程不需要雄伟的数据集，而是不错把柄需要动态适合。
基于脉冲时序依赖性可塑性的学习章程是大脑养殖系统的一个显赫到手案例（Diamond et al., 2019; Kreutzer et al., 2022）。它们径直植根于实验驱散，并已成为表面神经科学和神经形状工程研究学习算法的基石。值得把稳的是，传统机器学习也极地面受益于大脑研究。其中最驰名的例子可能是卷积神经麇集，其理念领先就是从视觉皮层的结构中索求而来的。
神经形状传感器是基础大脑研究促进新时间出现的另一个重要规模，尤其是动态视觉传感器和动态音频传感器。前者师法视网膜的功能，何况像神经形状处理器一样，用尖峰编码信息。它们的特质与传统的同类家具实足不同。由于它们只发出变化信号而不是拿获完整图像帧，因此它们能以极高的效率运行，催生了新式图像处理算法，并联想地与神经形状处理器相结合。
从时间角度来看，东谈主类大脑也被视为在东谈主工系统中实现高等融会才能的最有前程的“罗塞塔石碑”。当代东谈主工智能体的特质是才能水平有限，难以在提供的教师集以外进行泛化，其对环境的领融会常也较为简便。短缺泛化才能意味着需要大数据集（资源密集型的大数据范式）、执续的东谈主工监督（汉典限度系统）或泛泛且严格的任务辩论以顶住千般情况（如用于行星或海洋探索）。感知的简便和短缺可解说性导致东谈主工感知系统的鲁棒性和可靠性不及，这是实现存效的自动驾驶等时间的已知吃力之一。为了克服这些甘休，必须开发与新的具身和增量学习算法相结合的大脑启发的多区域模子架构，以寻找最能模拟东谈主类感知融会功能机制的那些算法。期骗这些机制并清楚融会功能的败露将是创建可解说、可靠并最终更通用的东谈主工智能的要害。
大脑的功能架构过火不同区域是为时间系统界说许多类型融会架构的基础。这对于机器东谈主学尤其如斯，其中大脑养殖方法被泛泛研究。包括研究与具身相关的时势或开发新式感知和传感系统的例子，如受本色啮齿动物的体感系统启发的东谈主造触须。
东谈主工智能应用的神经麇集畴昔的发展将看到主流东谈主工智能与神经形状时间之间的交融。多方法大脑模子不错为构建高等机器东谈主限度器作念出要害孝顺。这些限度器不错镶嵌塑性章程并通过与环境的互动自主适合。因此，基础大脑科学将是这些时间发展的要害信息起原。此外，神经形状谋略可能有助于减少大型深度学习模子的大量碳萍踪（Strubell et al., 2019）。
由此，不错推导出以下磋商：（1）桥接东谈主类与机器智能之间的差距。（2）构建神经形状大脑模子和仿生东谈主工智能。
论断
要深远清楚大脑功能，必须愈加深远地了解大脑的组织结构以及基本的生物过程、它们之间的互相相关过火章程。这是提高防范、解救及基于机制的会诊效率的基础。在畴昔十年的数字大脑研究中，一个有但愿的标的是开发大致进行个性化模拟的大脑数字孪生体。诚然面前已可行，但大脑的数字孪生体仍处于初期阶段，开发完成后必须经过严格的测试和考据，才能有用顶住大脑疾病，并成为颠覆性新式健康时间的基础。因此，咱们需要清楚系统过火子系统的谋略磋商和算法，以明确在个案实施中的甘休和可能性。此外，大脑孪生体所引发的伦理问题需要咱们与社会公开对话并加以科罚。孪生体可视为大脑模子和分析执续发展的一个绝顶。
为实现这一磋商，构建一个大致承载大脑数字孪生体的数字基础设施，有助于咱们清楚章程并矫正数字大脑孪生体，直至通过考据测试，并可用于临床应用。此外，这种基础设施联想情况下应当提供互操作性、信息安全、多档次数据以及走访基于常识的谋略资源，包括高性能谋略和其他相关时间。EBRAINS 就是一个能承载这些发展的基础设施。要到手实现这一磋商，对年青一代进行培训，使其大致熟练期骗这些基础设施和新的数字器用，显得尤为要害。
构建结构化数据和常识，以便研究社区大致纯粹再行组合并集成，从而构建出稠密的数字大脑孪生体，并提供践诺这些孪生体复杂模拟的刚烈时间，这自身就可能成为一种颠覆性时间，匡助咱们获取科学上的新洞见。
科学磋商：一份道路图
以下的“道路图”概述了畴昔十年内八个相交叉的研究规模的磋商，涵盖了从近期或面前责任，中期，到耐久的不同阶段。这是基于之前提供的输入得出的论断。
开发多档次大脑图谱和高分辨率大脑模子
近期：将从通盘这个词大脑到细胞的数据整合成一个全面、高分辨率的大脑图谱，行动深远清楚大脑组织基本原则的基础，以预测图谱不完整部分的特征，并迷惑对于物种间相似性和各异的相比研究。
中期：制作详备的、数据驱动的、多方法模子，以研究东谈主类大脑组织在不同人命阶段及不同条款下的变异性。
耐久：阐释大脑组织和结构中负责复杂行动、才能和挚友趣干方面。
启用多档次大脑模子和模拟
近期：实现模子的多方法整合，从局部生物物理属性到通盘这个词大脑模子，包括详备的从下到上和从上至下的模子。这些模子将由数据过火预测测试驱动和调整。
中期：期骗多方法、全脑模子模拟生物学着实的复杂大脑功能，逐步实现具体应用场景的数字大脑孪生。
耐久：将模子预测应用于基础科学、医学和东谈主工智能的大限制应用案例中，从而股东模子的测试和进一步完善，形成一个“分娩性轮回”。
阐发融会和行动的机制
近期：从多方法角度动身（从嗅觉和视觉畅通功能到更复杂的融会功能），建立刻画融会功能机制的连贯框架。
中期：构建一个对于讲话的连贯框架，行动东谈主类特有的复杂融会功能，交融讲话学和神经科学的研究洞见，通过研究发展过程窥察大脑专科化，并为讲话模子和东谈主工智能的发展提供基础。
耐久：将千般假定下的主见和自我相识互相推敲，并与细胞、分子及遗传层面的机制相结合。
在人命周期中获取大脑可塑性、学习和适合的深远洞见
近期：识别可塑性、学习和适合的章程并将其整合到现存的多档次大脑模子中。
中期：笃定大脑可塑性的甘休，并开发器用以利于患者。
耐久：揭示雅致巩固的机制，并将其应用于医学和时间规模。
加快数字大脑医学的发展
近期：期骗大脑图谱和个东谈主病例数据，开发并应用个性化模子，会诊和解救千般大脑疾病（如癫痫、肿瘤、畅通吃力、中风、精神疾病等）。
中期：构建数据驱动的发育和朽迈模子并将其应用于不同庚事组（从儿童到老年东谈主）的大脑医学。
耐久：开发并应用数字化躯壳副本，执续适合新的实践生活传感器数据，用于大脑医学的各个方面（如会诊、康复、重症照应和手术）。
将大脑行动躯壳的一部分来探索和建模
近期：将先进的数字大脑模子与基于多级图谱的脊髓模子推敲起来，从中开发新的刺激方法。
中期：对交互、任务流露和导航的嗅觉畅通整合和和洽进行建模。
耐久：将躯体和自主调整整合到组合的多器官模子中，构建大致响应神经系统、器官和躯壳调整功能的孪生患者，并开发和应用大致模拟神经系统、内分泌/激素、免疫调整和稳态机制的细胞层面躯壳副本。
缩庸东谈主类与机器智能之间的差距
近期：使用与丰富环境交互的机器东谈主来模拟复杂的行动；促进神经形状时间促进深度学习东谈主工智能和基于事件（尖峰）神经麇集的交融；以洞开、透明的姿色民主化和开发复杂的（受大脑启发的）东谈主工智能模子，包括大讲话模子。
中期：应用对融会功能（如感知和决策）背后大脑机制的知悉，模拟东谈主工智能和神经形状时间规模的学习和发展过程，并测试器官类群和类器官智能（OI）的潜在作用。
耐久：将全新的主见和算法应用于机器学习和新颖的工程应用（举例，新材料、东谈主造人命、替代和增刚烈脑功能）。
类脑模子和仿生东谈主工智能
近期：使用基于集成与激勉（leaky-integrate-and-fire）的神经元模子，开发基于尖峰的深度神经麇集的教师方法。在模拟环境中整合复杂的硬件神经元模子。
中期：使用复杂的神经元模子，开发大限制且高性能的尖峰麇集模子的硬件和教师方法。
耐久：将可塑性研究的后果整合进来，发展具有内置学习才能的大限制尖峰麇集。
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