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

人类和其他动物可以根据有限的经验学习复杂的适应性行为。理解学习的神经基础是系统神经科学和人工智能（AI）的一个关键挑战，它可能导致神经疾病的新治疗方法，并使人工智能系统的发展具有类似人类的学习效率。因此，学术研究机构和b谷歌DeepMind等私人公司目前正在投入大量精力和资金，以了解神经回路在学习过程中如何重组，以提高各种认知、感知和运动任务的表现。神经记录技术的最新进展使数千个神经元的活动能够以毫秒的精度同时被跟踪，并且在数天内稳定地跟踪，因此可以在整个学习过程中对神经活动进行调查。通过对这些录音的仔细分析，科学家们希望确定底层神经回路的变化是如何支持任务表现的改善的。特别是，学习被认为可以改变神经元之间连接的强度，这留下了一个功能特征，可以通过相互连接的神经元群的协调活动来检测。然而，在学习过程中，也会发生许多其他变化，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动。因此，一个关键的挑战是将记录的神经活动中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变化区分开来。拟议的项目将开发新的方法来分析大规模的神经记录，以满足这一需求。

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.