Natural computing in the brain: geometry, criticality, and fusion.

大自然进行计算，这一点在灵长类动物的大脑中表现得最为明显。 理解大脑是理解人类行为和认知的关键，同样重要的是，也是更好地理解计算本身的本质的关键。事实上，目前的神经网络人工智能复兴是由大脑计算启发的模型推动的。 但大脑是科学研究过的最复杂的系统之一。 我的研究将通过自然计算的形式化框架和采用新的数学工具来解决这种复杂性。 当代神经科学将大脑作为一个网络来研究；这是对由许多不同的、相互通信的子单元组成的复杂计算机器的恰当比喻。我的团队将在网络隐喻的成功基础上，通过基于自组织临界性和数据融合的方法来扩展大脑模型。 处于临界状态的动力系统显示出巨大的计算复杂性。临界状态是无聊的、同质的、停滞的和无法控制的混乱之间的刀刃。 神经学家研究“关键大脑假说”已经有几十年了，现在已经有了令人信服的实验证据支持。从文献中已经丰富描述的大脑网络开始，我的团队将使用自组织临界性的框架和热带几何学的新工具来研究这些网络上的计算动力学。 这些模型将为神经计算的本质提供新的见解，使我们能够回答有关观察到的大脑网络的最佳性的问题，并推动从自然中提取的复杂网络上的自组织临界性的基本数学研究。但大脑网络中的所有节点都是平等的吗？ 进化创造了由不同亚单位组成的大脑；例如，脑干的结构、功能和连通性与前额叶皮质截然不同。然而，大脑似乎是作为一个单一的、集成的单元进行计算的。在传感器无限增长和多尺度计算能力的世界里，计算机科学家和工程师开发了数据融合工具，以准确地应对这种集成挑战。 我的团队还将采用数据融合的方法，对从高分辨率神经成像推断的大脑网络中的通信进行建模。这将为理解神经计算过程中发生的复杂协调和交流提供一个框架。 我在自然计算领域有20年的经验，在神经科学领域也有近10年的经验，我在2011年利用休假获得了神经科学硕士学位。我是韦斯特大脑和心灵研究所的核心成员，该研究所是人工智能向量研究所的教职员工分支机构，也是劳森健康研究所的副科学家。我的团队拥有从事这项工作的经验、专业知识、环境和已建立的合作关系。

Nature computes, and nowhere is this more evident than in the primate brain. Understanding the brain holds the key to understanding human behaviour, cognition, and equally significantly, to better understanding the nature of computation itself. Indeed, the current neural network AI renaissance is powered by models inspired by computation in the brain. But the brain is one of the most complex systems ever studied by science. My research will attack this complexity through the formalized framework of Natural Computing and adoption of new mathematical tools. Contemporary neuroscience studies the brain as a network; this is an apt metaphor for a complex computing machine consisting of many distinct, communicating, subunits. My team will build on the success of the network metaphor by extending models of the brain with approaches based on self-organized criticality and data fusion. Dynamical systems in a critical state display immense computational complexity. Critical states are the knife edge between boring, homogenous, stasis, and unmanageable chaos. Neuroscientists have been studying the “critical brain hypothesis” for decades and there now exists compelling supporting experimental evidence. Starting with the brain networks already richly described in the literature, my team will study computational dynamics on these networks using the framework of self-organized criticality and new tools from tropical geometry. These models will offer new insights into the nature of neural computation, permit us to answer questions about the optimality of observed brain networks, and also drive forward the basic mathematical study of self-organized criticality on complex networks extracted from nature. But are all nodes in brain networks equal? Evolution has created brains composed of distinct subunits; the structure, function, and connectivity of, e.g., the brainstem, is profoundly unlike that of the prefrontal cortex. Nevertheless, the brain appears to compute as a single, integrated, unit. In a world of unbounded sensor growth, and multiscale computational capacity, computer scientists and engineers have developed the tools of data fusion to deal with exactly this challenge of integration. My team will also take a data fusion approach to modelling communication in brain networks inferred from high-resolution neuroimaging. This will provide a framework for understanding the complex co-ordination, and communication, that takes place during neural computation. I have two decades of experience in the field of natural computing, and nearly a decade of experience in neuroscience, having used a sabbatical leave in 2011 to earn an MSc in neuroscience. I am a core member of Western's Brain and Mind Institute, a faculty affiliate of the Vector Institute for Artificial Intelligence and an associate scientist at the Lawson Health Research Institute. My team has the experience, expertise, environment, and established collaborations to pursue this work.