课题基金 / 基金详情

Design, Analysis and Validation of Biologically Plausible Computational Models.

Design, Analysis and Validation of Biologically Plausible Computational Models.
生物学上合理的计算模型的设计、分析和验证。
批准号:
0422718
负责人:
Jose Principe
金额:
$51.25万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2004
资助国家:
美国
项目状态:
已结题
起止时间:
2004-08-01 至 2008-07-31

项目摘要

项目成果

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中文摘要
翻译
这个项目将尝试第一次真正的“逆向工程”,研究大脑外的大脑皮层细胞的行为,这些细胞生长在一种特殊的芯片上,这种芯片可以从这些细胞的网络中进行密集的读取和读取。大脑皮层是人类大脑(以及许多其他哺乳动物的大脑)中最大的部分。逆向工程,如果成功的话,将使我们第一次了解这些细胞是如何作为一个功能工程系统在电路层面上工作的。由于其独特性和新颖性,这项工作将存在严重的风险。其中一位合作者将把这些细胞网络的行为与他的新神经元模型——液态模型(LSM)进行比较。另一个将比较单元和LSM与使用人工神经网络解决的通用学习挑战,用于现实世界的工程任务,如预测、控制和分类等。跨学科和国际合作在使这一新的研究方向成为可能方面发挥着至关重要的作用。
英文摘要
This project will attempt the first true "reverse engineering" of the behavior of cells from the cerebral cortex studied outside the brain, grown on a special chip which allows dense read-ins and read-outs from a network of these cells. The cerebral cortex is the largest part of the human brain (and of many other mammalian brains). Reverse engineering, if successful, would give us the first understanding of how these cells work as a functional, engineering system at the circuit level. Because of its uniqueness and novelty, there will be serious risks in this work. One of the collaborators will be comparing the behavior of these networks of cells against his new model of neurons, the liquid state model (LSM). Another will be comparing the cells and the LSM against general-purpose learning challenges which have been addressed using artificial neural networks, for real-world engineering tasks such as prediction and control and classification and so on. The crossdisciplinary and international collaborations play a crucial role in making this new direction for research possible.
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RAPID: Inexpensive, rapidly manufacturable respiratory monitor to provide safe emergency ventilation during the COVID-19 pandemic
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    2028709
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  • 财政年份:
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Testing the Feasibility of Batteryless Physiological Monitoring
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Collaborative Research: NCS-FO: A Computational Neuroscience Framework for Olfactory Scene Analysis within Complex Fluid Environments
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    1631759
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    2016
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    Jose Principe
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RI: Medium: Quantifying Causality in Distributed Spatial Temporal Brain Networks
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    Standard Grant
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    2010
  • 负责人:
    Jose Principe
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