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

UKRI/BBSRC-NSF/BIO:用于神经生理学的可解释且抗噪声的机器学习

基本信息

  • 批准号:
    2321840
  • 负责人:
  • 金额:
    $ 79.68万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-10-01 至 2026-09-30
  • 项目状态:
    未结题

项目摘要

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.
机器学习的最新进展已经彻底改变了各个领域,包括生物医学科学。然而,现代机器学习技术在神经科学领域的应用仍然有限。鉴于神经科学涉及通过利用实验观察来表征非线性系统,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,这些技术使神经信号的观测能够在比以往更大的尺度和更高的分辨率上进行。不幸的是,由于数据分析方法缺乏相应的进步,该领域的概念进展缓慢。许多研究仍然依赖于经典工具,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。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中执行基本的感知和认知任务。这项研究的意义在于它有可能增强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,有可能增强人工智能和机器学习的应用。该项目还旨在建立一个综合的教育和推广计划,包括跨学科课程和项目,供计算机科学、认知科学和医学院的本科生和研究生使用。本研究项目侧重于开发一种生物学上合理且可解释的建模框架,该框架采用神经符号表征来提供感知和认知的分层解释。提出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;(2)认知阶段,使用神经表征和算法建立神经符号模型,模拟更高层次的大脑动力学。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,将在听力背景下以不同的尺度和分辨率(从单个单元到脑电图)记录神经信号。此外,将进行行为实验来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发展,进一步增强了其社会影响。该奖项反映了美国国家科学基金会的法定使命,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。

项目成果

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Mohsen Imani其他文献

Design of Ultra-Compact Content Addressable Memory Exploiting 1T-1MTJ Cell
利用 1T-1MTJ 单元的超紧凑内容可寻址存储器设计
Sparsity Controllable Hyperdimensional Computing for Genome Sequence Matching Acceleration
用于基因组序列匹配加速的稀疏可控超维计算
Lightning Talk: Bridging Neuro-Dynamics and Cognition
闪电演讲:连接神经动力学和认知
Self-Attention Based Semantic Decomposition in Vector Symbolic Architectures
向量符号架构中基于自注意力的语义分解
  • DOI:
    10.48550/arxiv.2403.13218
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Calvin Yeung;Prathyush P. Poduval;Mohsen Imani
  • 通讯作者:
    Mohsen Imani
Efficient Exploration in Edge-Friendly Hyperdimensional Reinforcement Learning
边缘友好的超维强化学习的高效探索

Mohsen Imani的其他文献

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{{ truncateString('Mohsen Imani', 18)}}的其他基金

CPS: Small: Brain-Inspired Memorization and Attention for Intelligent Sensing
CPS:小:智能传感的受大脑启发的记忆和注意力
  • 批准号:
    2312517
  • 财政年份:
    2023
  • 资助金额:
    $ 79.68万
  • 项目类别:
    Standard Grant
Neurally-Inspired Integration of Communication and Cognitive Computation in Hyperspace
超空间中通信和认知计算的神经启发集成
  • 批准号:
    2319198
  • 财政年份:
    2023
  • 资助金额:
    $ 79.68万
  • 项目类别:
    Standard Grant
Hyperdimensional Neural Computation for Real-Time Cognitive Learning
用于实时认知学习的超维神经计算
  • 批准号:
    2127780
  • 财政年份:
    2021
  • 资助金额:
    $ 79.68万
  • 项目类别:
    Standard Grant

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