Towards a next-generation computational neuroscience

迈向下一代计算神经科学

• 批准号: EP/G007543/1
• 负责人: Anil Seth
• 金额: $ 141.31万
• 依托单位: University of Sussex
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2009
• 资助国家: 英国
• 起止时间: 2009 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FG007543%2F1
• 关键词: Towards; next; generation; computational; neuroscience

What don't we know? Recently, the journal Science selected 25 of the biggest unanswered questions facing scientists over the next 25 years. Number two on the list, right after figuring out the composition of the universe, is: What is the biological basis of consciousness? This indeed is a big question. Scientific descriptions of conscious experience, volition, and subjectivity will follow in the footsteps of Copernicus and Darwin by restructuring our relationship with each other and with nature, and many clinical and technological applications will follow.A scientific account of consciousness will not arrive fully formed in a 'Eureka' moment. What is needed is a multidisciplinary, integrative approach combining theory and experiment and exploiting the interchange between the information/computation sciences and the neural, psychological, and medical sciences. At the front-line of this interchange, computational neuroscience (CN) uses computational approaches to model intricate brain processes in much the same way that meteorology uses computers to forecast the weather. In this view and in contrast to early approaches to 'artificial intelligence' (AI), brains are not computers, and intelligent behavior and conscious experience arise from complex brain-body-environment interactions unfolding in temporally precise ways. Much current CN focuses on single levels of description of neural systems (e.g., how neural activity affects connections among neurons) and neglects the multi-scale relations that connect brains, bodies, and behavior. Moreover, current CN is also surprisingly silent with regard to consciousness itself. By targeting and overcoming these limitations, our research will deliver new insights into the neural mechanisms underlying adaptive behavior and conscious experience. We will follow three interacting themes: (i) design and analysis of large-scale CN models to explore how multi-scale neural interactions shape and are shaped by brain-body-environment interactions; (ii) development of new theory to identify causal interactions in complex networks (what we call 'causal network analysis'), and (iii) creation of CN models that account for functionally significant aspects of consciousness, for example that each conscious experience integrates diverse information sources into unified scenes. Theoretical work in the above themes will interact with experimental data from multiple sources. At a fine-grained level we will characterize causal interactions in the intact brain of a pond snail, shedding light on the integrated neural function of a simple (non-conscious) organism as it interacts with its environment. Zooming out, we will apply causal network analysis to brain-imaging data acquired from humans in various states of consciousness, to test predictions based on CN models, and to guide the design of new models. Insights at the fine-grained level will scaffold our understanding of the more complex mechanisms underlying consciousness, with causal networks cross-cutting brains, bodies and environments providing a common theoretical framework. Taken together, these research strands will catalyze an important shift from correlation to explanation in consciousness science.As well as advances in basic science, our research will have important practical benefits at the interface of the biological and information sciences. These will include new design principles for AI/robotic devices, new insights for the design and control of complex technological networks, and new tools for the management of large-scale datasets. A next-generation CN will also underpin new clinical approaches. Many brain-related health problems, from coma to depression to insomnia, can be understood as expressions of disordered consciousness, and many existing clinical approaches are palliative and lacking in theoretical foundation. Our research will provide a theoretical basis for a new generation of effective clinical interventions.

我们还有什么不知道的？最近，《科学》杂志选出了未来25年科学家面临的25个最大的未解问题。在弄清楚宇宙的组成之后，第二个问题是：意识的生物学基础是什么？这确实是一个大问题。对意识经验、意志和主体性的科学描述将追随哥白尼和达尔文的脚步，重构我们与他人以及与自然的关系，许多临床和技术应用也将紧随其后。一个关于意识的科学解释不会在一个“发现”的时刻完全形成。我们需要的是一种多学科、综合的方法，将理论与实验结合起来，利用信息/计算科学与神经科学、心理学和医学科学之间的交流。在这种交流的前沿，计算神经科学（CN）使用计算方法来模拟复杂的大脑过程，就像气象学使用计算机来预测天气一样。在这一观点中，与早期的“人工智能”（AI）方法相反，大脑不是计算机，智能行为和有意识的经验源于复杂的大脑-身体-环境相互作用，以时间精确的方式展开。当前的神经网络主要关注神经系统的单一层次描述（例如，神经活动如何影响神经元之间的连接），而忽略了连接大脑、身体和行为的多尺度关系。此外，目前的网络对于意识本身也出奇地沉默。通过瞄准和克服这些限制，我们的研究将为适应性行为和意识体验背后的神经机制提供新的见解。我们将遵循三个相互作用的主题：(i)设计和分析大规模CN模型，以探索多尺度神经相互作用如何形成和被脑-体-环境相互作用所塑造；（ii）发展新理论以识别复杂网络中的因果相互作用（我们称之为“因果网络分析”），以及（iii）创建CN模型，以解释意识的功能重要方面，例如，每个意识体验将不同的信息源集成到统一的场景中。上述主题的理论工作将与来自多个来源的实验数据相互作用。在一个精细的层面上，我们将描述池塘蜗牛完整大脑中的因果相互作用，揭示一个简单的（无意识的）有机体在与环境相互作用时的综合神经功能。我们将把因果网络分析应用于从不同意识状态的人类获得的脑成像数据，以测试基于CN模型的预测，并指导新模型的设计。细粒度层面的洞见将支撑我们对意识背后更复杂机制的理解，而贯穿大脑、身体和环境的因果网络将提供一个共同的理论框架。综合起来，这些研究将催化意识科学从关联到解释的重要转变。随着基础科学的进步，我们的研究将在生物科学和信息科学的界面上产生重要的实际效益。这些将包括人工智能/机器人设备的新设计原则，复杂技术网络设计和控制的新见解，以及大规模数据集管理的新工具。下一代CN也将支持新的临床方法。许多与大脑相关的健康问题，从昏迷到抑郁到失眠，都可以被理解为意识紊乱的表现，而许多现有的临床方法都是姑息疗法，缺乏理论基础。我们的研究将为新一代有效的临床干预提供理论基础。

项目成果

Characterizing brain states with Granger causality

• DOI: 10.1186/1471-2202-14-s1-p17
• 发表时间: 2013-07-08
• 期刊: BMC Neuroscience
• 影响因子: 2.4
• 作者: Barrett AB;Barnett L;Chorley P;Pigorini A;Nobili L;Boly M;Bruno MA;Noirhomme Q;Laureys S;Massimini M;Seth AK
• 通讯作者: Seth AK

Multivariate Granger Causality and Generalized Variance

多元格兰杰因果关系和广义方差

• DOI: 10.48550/arxiv.1002.0299
• 发表时间: 2010
• 作者: Barrett A

Can grapheme-color synesthesia be induced by hypnosis?

• DOI: 10.3389/fnhum.2014.00220
• 发表时间: 2014
• 期刊: Frontiers in human neuroscience
• 影响因子: 2.9
• 作者: Anderson HP;Seth AK;Dienes Z;Ward J
• 通讯作者: Ward J

Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables

• DOI: 10.1103/physrevlett.103.238701
• 发表时间: 2009-12-04
• 期刊: PHYSICAL REVIEW LETTERS
• 影响因子: 8.6
• 作者: Barnett, Lionel;Barrett, Adam B.;Seth, Anil K.
• 通讯作者: Seth, Anil K.