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有关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。但是，在控制感知和认知的较高阶段，关键计算来自大型神经元种群的相互作用。描述这些相互作用从实验和建模的角度提出了一个挑战：实验必须能够通过单个神经元分辨率提供大规模的神经活动的度量，并且模型必须能够准确地捕获单个神经元的行为及其在生物学上的框架中的大规模相互作用，这在实验和计算方面都无法实现。在此提案中，我们概述了一个开发一组工具的工作计划，该工具将使系统神经科学家能够在层次范围内跨越多个空间尺度的层次结构框架中构建模型，以将感觉与感知和认知联系起来。我们的工具将使神经科学家能够拟合灵活的模型，这些模型可以直接从神经记录（颅内和非侵入性）中执行关键的感知和认知任务。 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感知和认知的解释。 HDNeuro模型由两个主要阶段组成：在编码阶段，HDNeuro模型通过尖峰神经元刺激了数据，这些神经元模仿了早期感觉途径的解剖学和生理学；在认知阶段，HDNeuro通过模仿高级脑动力学的HDC表示和算法建立了神经符号模型。重要的是，Hdneuro模型的认知阶段将被限制在生物学上可见的计算上，从而可以在机械级别上解释模型现象。Hdneuro模型使用动态神经元维持实验数据的内在结构来复制单个神经元级别的空间神经活动。然后，HDNeuro模式采用高维抽象操作来自然记忆，关联和结合神经表示，同时保留认知任务所需的信息。大规模的空间和长期时间信息在层次网络中表示，该网络使用具有分布式表示形式的符号推理，并根据需要组合使用，以建立具有感知和认知功能的连接。