Inferring the evolution of functional connectivity over learning in large-scale neural recordings using low-tensor-rank recurrent neural networks

使用低张量秩递归神经网络推断大规模神经记录中功能连接学习的演变

• 批准号: BB/Y513957/1
• 负责人: Angus Chadwick
• 金额: $ 24.8万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FY513957%2F1
• 关键词: Inferring; evolution; functional; connectivity; over

Humans and other animals can learn to perform complex and adaptive behaviours based on limited experience. Understanding the neural basis of learning is a key challenge in systems neuroscience and artificial intelligence (AI) that could lead to novel treatments for neurological disorders and enable the development of AI systems that learn with human-like efficiency. Thus, significant effort and funding is currently being invested to understand how neural circuits reorganise during learning to improve performance in various cognitive, perceptual, and motor tasks, both in academic research organisations and private companies such as Google DeepMind.Recent advances in neural recording technologies enable the activity of thousands of neurons to be tracked simultaneously at millisecond precision, and stably over days, so that neural activity can be surveyed over the entire course of learning. Through careful analysis of these recordings, scientists hope to determine how changes in the underlying neural circuit support improvements in task performance. In particular, learning is thought to modify the strength of connections between neurons, which leaves a functional signature that can be detected via the coordinated activity of interconnected groups of neurons. However, during learning, many other changes also take place, including changes in motor behaviour, attention, and sensory input, all of which may influence the activity of the recorded neurons. Thus, a key challenge is to disentangle the learning-related changes in recorded neural activity from those arising from sensory, motor, and internal state variables which covary with learning. The proposed project will develop novel methodologies for analysis of large-scale neural recordings to address this need.

人类和其他动物可以学会根据有限的经验来执行复杂和适应性的行为。了解学习的神经基础是系统神经科学和人工智能（AI）的关键挑战，可以导致对神经系统疾病的新治疗，并能够开发以人类效率学习的AI系统。因此，目前正在投入大量努力和资金，以了解在学习过程中如何重组神经回路，以改善各种认知，感知和运动任务的绩效，包括学术研究组织和私人公司，以及诸如Google DeepMind之类的私人公司。学习课程。通过仔细分析这些录音，科学家希望确定基础神经回路的变化如何支持任务绩效的改善。特别是，人们认为学习可以改变神经元之间的连接强度，该神经元之间留下了一个功能特征，可以通过互连的神经元组的协调活性来检测到功能特征。但是，在学习过程中，还发生了许多其他变化，包括运动行为，注意力和感觉输入的变化，所有这些都可能影响记录的神经元的活性。因此，一个关键的挑战是将记录的神经活动与学习相关的变化与与学习相关的感觉，运动和内部状态变量引起的那些与学习相关的变化。拟议的项目将开发新的方法，用于分析大规模神经记录以满足这一需求。