EAGER: Implementation of Computational Circuits Based on Neural Dynamical Systems

EAGER：基于神经动力学系统的计算电路的实现

• 批准号: 0964983
• 负责人: David Parent
• 金额: $ 5.73万
• 依托单位: San Jose State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2010-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0964983&HistoricalAwards=false
• 关键词: EAGER; Implementation; Computational; Circuits; Based

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The objective of this project is to realize silicon neurons as physical circuits. A key requirement is to match the internal dynamics of the circuit, including timing, with the biological system, as needed for hybrid hardware-biological systems. The approach builds on existing work by the investigator that uses dynamical system theory. This project extends this model to include physiologically relevant parameters that are extracted from functioning biological cells. The project will also develop extraction routines and tools for reverse engineering of small biological neural networks. This offers the potential of enabling the realization of a wider range of neural parameters and a wider range of neural computational circuits.With respect to intellectual merit, the research is motivated by the ability of silicon neurons to implement hybrid electronic-biological systems and to provide a fundamental understanding of the neurobiological systems that are emulated. The specific goal of a physical circuit implementation, versus a software implementation, is motivated by the ability to enable real-time operation, ease interfacing to partial biological systems, and enable integration into robotics and control systems. Expected outcomes include physiologically relevant data to aid simulation, a parameter extraction scheme, and reproducible circuits.With respect to broader impact, the research has the potential to transform the understanding of and the application of hybrid electronic-biological systems by coupling circuit design, dynamical systems, and neuro-physiology. The research engages undergraduates, including those from underrepresented groups, through capstone design projects. Prototype circuits, documentation of the extraction method, and parametric values extracted from identified cells will be made available to other researchers.

该奖项是根据2009年美国复苏和再投资法案(公法111-5)资助的。该项目的目标是将硅神经元实现为物理电路。一个关键的要求是将电路的内部动态(包括时序)与生物系统相匹配，这是混合硬件-生物系统所需的。该方法建立在研究人员使用动力系统理论的现有工作基础上。该项目将这一模型扩展到包括从正常运作的生物细胞中提取的生理相关参数。该项目还将为小型生物神经网络的逆向工程开发提取例程和工具。这提供了实现更广泛的神经参数和更广泛的神经计算电路的可能性。就智力方面的优点而言，这项研究的动机是硅神经元实现混合电子-生物系统的能力，并提供对被仿真的神经生物系统的基本理解。与软件实现相比，物理电路实现的具体目标是实现实时操作、简化与部分生物系统的接口以及实现与机器人和控制系统的集成的能力。预期结果包括用于辅助模拟的生理相关数据、参数提取方案和可重现的电路。就更广泛的影响而言，这项研究有可能通过耦合电路设计、动力学系统和神经生理学来改变对混合电子-生物系统的理解和应用。这项研究通过顶石设计项目吸引了本科生，包括那些来自代表不足的群体的本科生。原型电路、提取方法的文档和从已识别的单元中提取的参数值将提供给其他研究人员。