UKRI/BBSRC-NSF/BIO: Interpretable and Noise-Robust Machine Learning for Neurophysiology

机器学习的最新进展已经彻底改变了包括生物医学科学在内的各个领域。然而，现代机器学习技术在神经科学中的应用仍然有限。鉴于神经科学涉及通过利用实验观察来表征非线性系统来识别它们，机器学习在这一领域具有变革性的潜力。在过去的十年中，在开发强大的实验技术方面取得了重大进展，这些技术能够以比以往任何时候都更大的尺度和更高的分辨率观察神经信号。不幸的是，由于数据分析方法缺乏相应的进展，该领域的概念进展缓慢。许多研究仍然依赖于经典的工具，忽视了神经信号的丰富性和复杂性。该项目的目标是创建一个工具包，使系统神经科学家能够构建连接感觉，感知和认知的模型。所提出的框架使神经科学家能够适应灵活的模型，能够直接从神经记录执行基本的感知和认知任务。这项研究的重要性在于它有可能提高我们对大脑复杂功能的理解，这最终可能导致神经系统疾病的先进治疗方法和诊断工具的发展。此外，它将为人类认知提供有价值的见解，可能会增强人工智能和机器学习应用。本研究项目还旨在建立一个综合的教育和推广计划，包括跨学科课程和计划，面向计算机科学，认知科学和医学学院的本科生和研究生。本研究项目的重点是开发一个生物学上合理的和可解释的建模框架，采用神经符号表示来提供感知和认知的层次解释。拟议的框架弥合了机器学习和神经科学之间的差距，为理解和解释大脑功能开辟了新的途径。它包括两个主要阶段：（1）编码阶段，通过尖峰神经元模拟数据转换，模拟早期感觉通路的解剖学和生理学，以及（2）认知阶段，使用模拟更高级别大脑动力学的神经表示和算法建立神经符号模型。认知阶段将坚持生物学上合理的计算，促进在机械水平上解释模型现象。为了验证该框架，将在听觉环境中记录各种尺度和分辨率（从单个单位到EEG）的神经信号。此外，将进行行为实验，以评估该模型在常见的感知和认知任务中复制人类行为的能力。所有开发的工具都将作为开源库发布，作为神经科学社区的宝贵资源，包括建模或编程方面的非专家。这种广泛的可及性不仅促进了知识的扩散，而且鼓励了该领域创新解决方案的开发，进一步增强了其社会影响力。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Recent advancements in machine learning have revolutionized various fields, including the biomedical sciences. However, the adoption of modern machine learning techniques in neuroscience remains limited. Given that neuroscience involves identifying nonlinear systems by utilizing experimental observations to characterize them, machine learning has the potential to be transformative in this domain. Over the past decade, significant progress has been made in developing powerful experimental techniques that enable the observation of neural signals at larger scales and higher resolutions than ever before. Unfortunately, the conceptual progress in the field has been slow due to the lack of corresponding advancements in data analysis approaches. Many studies still rely on classical tools that overlook the richness and complexity of neural signals. The objective of this project is to create a toolkit that empowers systems neuroscientists to construct models connecting sensation, perception, and cognition. The proposed framework enables neuroscientists to fit flexible models capable of performing essential perceptual and cognitive tasks directly from neural recordings. The significance of this research lies in its potential to enhance our understanding of the brain's complex functions, which can ultimately lead to the development of advanced therapeutic methods and diagnostic tools for neurological disorders. Moreover, it will provide valuable insights into human cognition, potentially enhancing artificial intelligence and machine learning applications. This project also aims to establish an integrated educational and outreach plan, including interdisciplinary courses and programs accessible to undergraduate and graduate students from computer science, cognitive science, and the school of medicine.This research project focuses on the development of a biologically plausible and interpretable modeling framework that employs neuro-symbolic representations to offer a hierarchical explanation of perception and cognition. The proposed framework bridges the gap between machine learning and neuroscience, opening up new avenues for understanding and interpreting brain function. It comprises two main stages: (1) an encoding stage that models data transformation through spiking neurons, emulating the anatomy and physiology of early sensory pathways, and (2) a cognitive stage that establishes neuro-symbolic models using neural representations and algorithms that simulate higher-level brain dynamics. The cognitive stage will adhere to biologically plausible computations, facilitating the interpretation of model phenomena at a mechanistic level. To validate the framework, neural signals will be recorded across various scales and resolutions (from single units to EEG) within the context of hearing. Furthermore, behavioral experiments will be conducted to evaluate the model's ability to replicate human behavior in common perceptual and cognitive tasks. All developed tools will be released as open-source libraries, serving as valuable resources for the neuroscience community, including non-experts in modeling or programming. This broad accessibility not only facilitates the proliferation of knowledge but also encourages the development of innovative solutions in the field, further enhancing its societal impact.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.