Evolutionary Algorithm Development for Applications in Brain Connectomics and Other Complex Systems

脑连接组学和其他复杂系统应用的进化算法开发

• 批准号: RGPIN-2020-04500
• 负责人: Hughes, James
• 金额: $ 1.75万
• 依托单位: St. Francis Xavier University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717040
• 关键词: Evolutionary; Algorithm; Development; Applications; Brain

The brain is a computational system that performs an astronomically large number of calculations per second; however, it works in a different way than a desktop computer. The brain is made up of billions of distributed neural units that work together to perform complex functions. Unfortunately, exactly how this is done remains unknown. Brain function is what enables us to understand the world around us and solve problems; yet ironically the system one that has enabled humans to develop complex societies and fly to the moon remains so poorly understood by itself. The proposed research program will address this in several ways. New evolutionary algorithms (a classification of algorithms inspired by evolution) will be developed to enable the modelling of this complex computational system the human brain. This increasingly important type of algorithm is particularly well suited for such poorly understood systems as it is less constrained and can provide novel perspectives since, by using a machine to make decisions, we eliminate many human assumptions about the problems being studied. These algorithms will be used to decipher which areas of the brain are working together, how the relationships between these areas change over time, and how the physical connections between them develop. Current modelling strategies for finding these relationships are very effective at describing most of these relationships; however, there are limitations and the popular strategies are mathematically incapable of truly modelling the underlying system. Removing these constraints is a nontrivial task that requires an unconventional solution. The careful development of these algorithms is important as it will enable us to remove many limitations on the modelling techniques to create more accurate models of the brain. With more accurate models of the brain, we can better understand how it works as a computing system, which can lead to better human-engineered systems of computation and algorithms. These models also have important clinical applications. With current modelling techniques, variations between individuals' models can be used to predict certain medical conditions, and more accurate models may allow us to diagnose and find differences more effectively. Learning how the brain develops and grows into a complex collection of wired connections can teach us about constraints on the brain, and even provide insights or explanations for why it grows the way it does. Although the focus is the human brain, the algorithms to be developed are widely applicable and will be made publicly accessible to be used by others to enable their research. Complex natural systems of information processing can be found anywhere from complex human brain networks to an ant colony working together. Understanding these systems of computation is important as it can give us new insights into different types of computation and lead to novel algorithms for solving complex problems.

大脑是一个计算系统，每秒执行海量的计算；但是，它的工作方式与台式计算机不同。大脑由数十亿个分布式神经单元组成，这些神经单元协同工作以执行复杂的功能。不幸的是，具体是如何完成的仍然未知。大脑功能使我们能够理解周围的世界并解决问题；然而具有讽刺意味的是，这个使人类能够发展出复杂社会并飞向月球的系统，其本身却仍然知之甚少。 拟议的研究计划将以多种方式解决这个问题。 新的进化算法（受进化启发的算法分类）将被开发出来，以实现对人脑这一复杂计算系统的建模。这种日益重要的算法类型特别适合这种人们知之甚少的系统，因为它受到的约束较少，并且可以提供新颖的视角，因为通过使用机器做出决策，我们消除了许多人类对所研究问题的假设。 这些算法将用于破译大脑的哪些区域正在协同工作，这些区域之间的关系如何随时间变化，以及它们之间的物理连接如何发展。当前用于查找这些关系的建模策略对于描述大多数这些关系非常有效。然而，它存在局限性，并且流行的策略在数学上无法真正对底层系统进行建模。消除这些限制是一项艰巨的任务，需要非常规的解决方案。这些算法的精心开发非常重要，因为它将使我们能够消除建模技术的许多限制，从而创建更准确的大脑模型。 有了更准确的大脑模型，我们可以更好地理解它作为一个计算系统是如何工作的，这可以带来更好的人类工程计算和算法系统。这些模型也具有重要的临床应用。利用当前的建模技术，个体模型之间的差异可用于预测某些医疗状况，更准确的模型可以让我们更有效地诊断和发现差异。了解大脑如何发育并成长为复杂的有线连接集合可以让我们了解大脑的限制，甚至可以为大脑为何以这种方式生长提供见解或解释。 尽管重点是人脑，但要开发的算法具有广泛的适用性，并将公开供其他人使用以进行研究。从复杂的人脑网络到协同工作的蚁群，复杂的自然信息处理系统随处可见。理解这些计算系统很重要，因为它可以让我们对不同类型的计算有新的见解，并产生解决复杂问题的新算法。