EAGER: Collaborative Research: Non-Local Cortical Computation and Enhanced Learning with Astrocytes

EAGER:协作研究:非局部皮质计算和星形胶质细胞增强学习

基本信息

项目摘要

The brain is composed of two major cell types: Neurons and glial cells. Glial cells are traditionally regarded as the brain's supportive cells. However, many lines of work over the past decade have documented that glial cells may also participate in complex neural processes and thereby comprise an integral element of higher cognitive function, such as working memory, learning, and sleep. Other lines of work have shown that human astrocytes are larger and structurally more complex than astrocytes in the rodent brain. In support of this concept, transplantation of human glial cells into mice resulted in generation of mice that were faster learners and performed better on memory tests. However, existing computational modeling techniques employed for understanding the processes involved in learning and memory do not include glial cells. The aim of the proposed research is to: 1) Develop computational modeling techniques that incorporate glial cells. 2) Use these novel computational modeling techniques to make predictions regarding the role of glial cells in learning and memory. 3) Test the predictions using a combination of patch clamping and Ca2+ imaging. 4) Use the data collected to continuously refine the computational modeling techniques. The broader impact of this proposal will be to further the scientific understanding of underappreciated, yet essential substrates of learning and memory. Including glial cells in addition to neurons in modeling approaches additionally carries the hope of increasing computational power and processing capabilities of adaptive learning technology, in addition to improving the performance of bio-integrated prostheses for individuals with impaired learning or other debilitating neurological disorders.
大脑由两种主要的细胞类型组成:神经元和神经胶质细胞。神经胶质细胞传统上被认为是大脑的支持细胞。然而,过去十年的许多工作证明,神经胶质细胞也可能参与复杂的神经过程,从而构成更高认知功能的组成部分,如工作记忆、学习和睡眠。其他领域的研究表明,人类星形胶质细胞比啮齿动物大脑中的星形胶质细胞更大,结构更复杂。为了支持这一概念,将人神经胶质细胞移植到小鼠体内,产生了学习速度更快、记忆测试表现更好的小鼠。然而,现有的用于理解学习和记忆过程的计算建模技术并不包括神经胶质细胞。这项研究的目的是:1)开发包含神经胶质细胞的计算建模技术。2)使用这些新的计算建模技术来预测神经胶质细胞在学习和记忆中的作用。3)结合膜片钳技术和钙离子成像技术对预测结果进行检验。4)利用收集到的数据不断完善计算建模技术。这一提议的更广泛的影响将是促进对学习和记忆的未被重视的、但必不可少的底物的科学理解。在建模方法中除了神经元之外,还包括神经胶质细胞,除了提高学习受损或其他衰弱神经疾病患者的生物集成假体的性能外,还有望增加自适应学习技术的计算能力和处理能力。

项目成果

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Terrence Sejnowski其他文献

Activity dependent modulation of synaptic transmission by presynaptic calcium stores: A dichotomy of short-term depression and facilitation
  • DOI:
    10.1186/1471-2202-14-s1-p351
  • 发表时间:
    2013-07-08
  • 期刊:
  • 影响因子:
    2.300
  • 作者:
    Suhita Nadkarni;Thomas Bartol;Herbert Levine;Terrence Sejnowski
  • 通讯作者:
    Terrence Sejnowski
MCell Model of Presynaptic Calcium Dynamics Predicts the Structural Correlates of Short-term Synaptic Plasticity
  • DOI:
    10.1016/j.bpj.2008.12.3482
  • 发表时间:
    2009-02-01
  • 期刊:
  • 影响因子:
  • 作者:
    Suhita Nadkarni;Thomas Bartol;Terrence Sejnowski;Hebert Levine
  • 通讯作者:
    Hebert Levine
1. How Are Memories Consolidated During Sleep and Why Do We Dream?
  • DOI:
    10.1016/j.biopsych.2017.02.012
  • 发表时间:
    2017-05-15
  • 期刊:
  • 影响因子:
  • 作者:
    Terrence Sejnowski
  • 通讯作者:
    Terrence Sejnowski
The effects of audiovisual inputs on solving the cocktail party problem in the human brain: An fMRI study
  • DOI:
    DOI: 10.1093/cercor/bhx235
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
  • 作者:
    Yuanqing Li;Fangyi Wang;Yongbin Chen;Andrzej Cichocki;Terrence Sejnowski
  • 通讯作者:
    Terrence Sejnowski

Terrence Sejnowski的其他文献

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{{ truncateString('Terrence Sejnowski', 18)}}的其他基金

NCS-FO: Collaborative Research: Computational Analysis of Synaptic Nanodomains
NCS-FO:协作研究:突触纳米域的计算分析
  • 批准号:
    2219979
  • 财政年份:
    2022
  • 资助金额:
    $ 10万
  • 项目类别:
    Standard Grant
NeuroNex Research Program Workshop, San Diego, California, November 7-8, 2018
NeuroNex 研究计划研讨会,加利福尼亚州圣地亚哥,2018 年 11 月 7-8 日
  • 批准号:
    1901715
  • 财政年份:
    2019
  • 资助金额:
    $ 10万
  • 项目类别:
    Standard Grant
NCS-FO: Collaborative Research: Integrative Foundations for Interactions of Complex Neural and Neuro-Inspired Systems with Realistic Environments
NCS-FO:协作研究:复杂神经和神经启发系统与现实环境相互作用的综合基础
  • 批准号:
    1735004
  • 财政年份:
    2017
  • 资助金额:
    $ 10万
  • 项目类别:
    Standard Grant
Machine learning algorithms for analyzing auditory scenes with multiple sound sources
用于分析具有多个声源的听觉场景的机器学习算法
  • 批准号:
    0535251
  • 财政年份:
    2006
  • 资助金额:
    $ 10万
  • 项目类别:
    Continuing Grant
Active Visual Depth Perception by Looming
Looming 的主动视觉深度感知
  • 批准号:
    0096790
  • 财政年份:
    2001
  • 资助金额:
    $ 10万
  • 项目类别:
    Standard Grant
IGERT Full Proposal: Computational Neurobiology Graduate Program
IGERT 完整提案:计算神经生物学研究生项目
  • 批准号:
    9987614
  • 财政年份:
    2000
  • 资助金额:
    $ 10万
  • 项目类别:
    Continuing Grant
General Monte Carlo Computer Simulation of Subcellular Biochemical Signaling: Phase II
亚细胞生化信号转导的通用蒙特卡罗计算机模拟:第二阶段
  • 批准号:
    9985964
  • 财政年份:
    2000
  • 资助金额:
    $ 10万
  • 项目类别:
    Continuing Grant
Active Visual Depth Perception by Looming
Looming 的主动视觉深度感知
  • 批准号:
    9975048
  • 财政年份:
    1999
  • 资助金额:
    $ 10万
  • 项目类别:
    Standard Grant
General Monte Carlo Computer Simulation of Subcellular Biochemical Signaling
亚细胞生化信号转导的通用蒙特卡罗计算机模拟
  • 批准号:
    9603611
  • 财政年份:
    1997
  • 资助金额:
    $ 10万
  • 项目类别:
    Continuing Grant
Workshop on Neuromorphic Engineering; June 25, 1995 - July 8, 1995; Telluride, Colorado
神经形态工程研讨会;
  • 批准号:
    9511637
  • 财政年份:
    1995
  • 资助金额:
    $ 10万
  • 项目类别:
    Continuing Grant

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