22-BBSRC/NSF-BIO - Interpretable & Noise-robust Machine Learning for Neurophysiology

22-BBSRC/NSF-BIO - 可解释

• 批准号: BB/Y008758/1
• 负责人: Nicholas Lesica
• 金额: $ 64.79万
• 依托单位: University College London
• 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%2FY008758%2F1
• 关键词: 22; BBSRC; NSF; BIO; Interpretable

One of the primary goals of systems neuroscience is to provide mechanistic descriptions of the network-level computations that link sensation to perception and cognition. The early stages of sensory processing can be well understood through analysis of the encoding properties of individual neurons. But in the higher stages that govern perception and cognition, key computations emerge from interactions across large neuronal populations. Describing these interactions presents a challenge from both an experimental and a modelling perspective: experiments must be able to provide measures of neural activity on a large scale with single neuron resolution, and models must be able to accurately capture the behavior of both single neurons and their large-scale interactions within a biologically-plausible framework.Fortunately, recent advances in both experimental and computational methods have finally made it possible to meet this challenge. In this proposal, we outline a program of work to develop a set of tools that will enable systems neuroscientists to build models that link sensation to perception and cognition within a hierarchical framework that spans multiple spatial scales. Our tools will allow neuroscientists to fit flexible models that can perform key perceptual and cognitive tasks directly from neural recordings (both intracranial and non-invasive). This capability will greatly advance the study of the underlying systems by providing a platform for systematic hypothesis generation and testing, and will also allow for brain-like simulation of perception and cognition in wide range of other applications.We will use Hyperdimensional Computing (HDC) to develop a biologically-plausible and interpretable modelling framework, called HDNeuro, that leverages neuro-symbolic representation to provide a hierarchical explanation of perception and cognition. HDNeuro models are composed of two main stages: in the encoding stage, HDNeuro models transform data through spiking neurons that emulate the anatomy and physiology of early sensory pathways; in the cognitive stage, HDNeuro establishes neuro-symbolic models through HDC representations and algorithms that emulate higher-level brain dynamics. Importantly, the cognitive stage of HDNeuro models will be constrained to biologically-plausible computations, allowing for interpretation of the model phenomena at a mechanistic level.HDNeuro models use dynamic neurons that maintain the intrinsic structure of experimental data to replicate spatial-temporal neural activity at the single neuron level. HDNeuro modes then employ hyperdimensional abstract operations to naturally memorize, associate, and combine neural representations while preserving the information required for cognitive tasks. Large-scale spatial and long-term temporal information are represented within a hierarchical network that uses symbolic reasoning with distributed representations that are combined as needed to establish connections with perceptual and cognitive functions.

系统神经科学的主要目标之一是提供将感觉与感知和认知联系起来的网络级计算的机械描述。通过分析单个神经元的编码特性，可以很好地理解感觉处理的早期阶段。但在控制感知和认知的更高阶段，关键计算来自大型神经元群体之间的相互作用。从实验和建模的角度来描述这些相互作用是一个挑战：实验必须能够提供具有单个神经元分辨率的大规模神经活动的测量，并且模型必须能够在生物学上合理的框架内准确地捕获单个神经元及其大规模相互作用的行为。幸运的是，最近在实验和计算方法方面的进展终于使我们有可能迎接这一挑战。在这项提案中，我们概述了一项工作计划，以开发一套工具，使系统神经科学家能够在跨越多个空间尺度的分层框架内建立将感觉与感知和认知联系起来的模型。我们的工具将使神经科学家能够适应灵活的模型，这些模型可以直接从神经记录（颅内和非侵入性）执行关键的感知和认知任务。这一能力将通过提供一个系统假设生成和测试的平台，极大地推进对底层系统的研究，并将允许在广泛的其他应用中对感知和认知进行类似大脑的模拟。我们将使用超维计算（HDC）开发一个生物学上合理的和可解释的建模框架，称为HDNeuro，它利用神经符号表征来提供感知和认知的层次解释。HDNeuro模型由两个主要阶段组成：在编码阶段，HDNeuro模型通过模拟早期感觉通路的解剖学和生理学的尖峰神经元转换数据;在认知阶段，HDNeuro通过模拟更高级别大脑动力学的HDC表示和算法建立神经符号模型。重要的是，HDNeuro模型的认知阶段将被限制在生物学上合理的计算，允许在机械水平上解释模型现象。HDNeuro模型使用动态神经元，这些动态神经元保持实验数据的内在结构，以在单个神经元水平上复制时空神经活动。然后，HDNeuro模式采用超维抽象操作来自然地记忆、关联和联合收割机组合神经表征，同时保留认知任务所需的信息。大规模的空间和长期的时间信息表示在一个分层网络，使用符号推理与分布式表示，根据需要结合起来，建立与感知和认知功能的连接。