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，关键计算来自于大型神经元群体的相互作用。描述这些相互作用从实验和建模的角度都提出了挑战：实验必须能够以单个神经元分辨率提供大规模神经活动的测量，并且模型必须能够准确捕获单个神经元及其神经元的行为。幸运的是，实验和计算方法的最新进展终于使得应对这一挑战成为可能。在这项提案中，我们概述了一个开发一套工具的工作计划，这些工具将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构建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，这些模型可以直接根据神经记录（颅内和非侵入性）执行关键的感知和认知任务。这种能力将通过提供系统假设生成和测试的平台来极大地推进底层系统的研究，并且还将允许在广泛的其他应用中进行类脑模拟感知和认知。我们将使用超维计算（HDC）开发一个生物学上合理且可解释的建模框架，称为 HDNeuro，它利用神经符号表示来提供感知和认知的分层解释。 HDNeuro 模型由两个主要阶段组成：在编码阶段，HDNeuro 模型通过模仿早期感觉通路的解剖学和生理学的尖峰神经元来转换数据；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来转换数据。在认知阶段，HDNeuro 通过模拟高级大脑动力学的 HDC 表示和算法建立神经符号模型。重要的是，HDNeuro 模型的认知阶段将仅限于生物学上合理的计算，从而可以在机械层面解释模型现象。HDNeuro 模型使用动态神经元来维持实验数据的内在结构，以在单个神经元水平。然后，HDNeuro 模式采用超维抽象操作来自然记忆、关联和组合神经表征，同时保留认知任务所需的信息。大规模空间和长期时间信息在分层网络中表示，该网络使用符号推理和分布式表示，并根据需要组合起来以建立与感知和认知功能的联系。