Development of a Scalable High Performance Reconfigurable Real-Time Signal Processing Platform for Dynamic Data-Driven Neural Simulations and Modeling

开发用于动态数据驱动神经仿真和建模的可扩展高性能可重构实时信号处理平台

• 批准号: 0096737
• 负责人: Ross Snider
• 金额: $ 76.08万
• 依托单位: Montana State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-04-01 至 2005-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0096737&HistoricalAwards=false
• 关键词: Development; Scalable; Performance; Reconfigurable; Real

This proposal provides support for development of a high performance, reconfigurable signal-processing platform that will permit real-time analysis of large-scale multi-channel neurophysiologic data and subsequent use in simulation and modeling. The computational architecture will be a distributed, real-time system of modular design consisting of computational nodes connected in a three-dimensional mesh. A computational node will include a floating-point digital signal processor (DSP), a field programmable gate array (FPGA), and local memory. This system to be used is reconfigurable, so that algorithms can be directly implemented in the hardware. The FPGAs can act as communication processors, allowing significant bandwidth for communication between computational nodes. Configuring the system as a three-dimensional mesh will allow the system to scale to any number of computational nodes required to process an arbitrary number of real-time I/O data streams. The platform will be developed using the analysis of neural signal processing in a simple nervous system, that of the cricket. Specifically, the platform will be developed to allow investigation of the cooperative neural encoding schemes used to transmit information about air currents within the cricket's nervous system. The platform will enable real-time decoding of neural information, and will thus enable experimental perturbation of the encoded information while the neural signals are in transit between multiple peripheral sensors and the central processing ganglia. If the platform is successfully developed, an unprecedented degree of interactive control in the analysis of neural function will result. This could lead to major insights into the biological basis of neural computation and a new paradigm in experimental and computational neuroscience, one where experimental and theoretical neuroscientists can work together to test hypotheses of neural function in vivo.This proposal provides support for development of a high performance, reconfigurable signal-processing platform that will permit real-time analysis of large-scale multi-channel neurophysiologic data and subsequent use in simulation and modeling. The computational architecture will be a distributed, real-time system of modular design consisting of computational nodes connected in a three-dimensional mesh. A computational node will include a floating-point digital signal processor (DSP), a field programmable gate array (FPGA), and local memory. This system to be used is reconfigurable, so that algorithms can be directly implemented in the hardware. The FPGAs can act as communication processors, allowing significant bandwidth for communication between computational nodes. Configuring the system as a three-dimensional mesh will allow the system to scale to any number of computational nodes required to process an arbitrary number of real-time I/O data streams. The platform will be developed using the analysis of neural signal processing in a simple nervous system, that of the cricket. Specifically, the platform will be developed to allow investigation of the cooperative neural encoding schemes used to transmit information about air currents within the cricket's nervous system. The platform will enable real-time decoding of neural information, and will thus enable experimental perturbation of the encoded information while the neural signals are in transit between multiple peripheral sensors and the central processing ganglia. If the platform is successfully developed, an unprecedented degree of interactive control in the analysis of neural function will result. This could lead to major insights into the biological basis of neural computation and a new paradigm in experimental and computational neuroscience, one where experimental and theoretical neuroscientists can work together to test hypotheses of neural function in vivo.

该建议提供了一个高性能，可重构的信号处理平台，将允许大规模的多通道神经生理数据的实时分析，并随后用于模拟和建模的发展支持。计算架构将是一个分布式的、实时的模块化设计系统，由连接在三维网格中的计算节点组成。计算节点将包括浮点数字信号处理器（DSP）、现场可编程门阵列（FPGA）和本地存储器。所使用的系统是可重构的，使得算法可以直接在硬件中实现。FPGA可以充当通信处理器，允许计算节点之间的通信的显著带宽。将系统配置为三维网格将允许系统扩展到处理任意数量的实时I/O数据流所需的任意数量的计算节点。 该平台将使用一个简单的神经系统，即板球的神经信号处理的分析。具体来说，该平台将被开发用于研究用于传输有关蟋蟀神经系统内气流信息的合作神经编码方案。该平台将实现神经信息的实时解码，从而在神经信号在多个外围传感器和中央处理神经节之间传输时实现编码信息的实验扰动。如果该平台开发成功，将导致神经功能分析中前所未有的交互控制程度。 这可能会导致对神经计算的生物学基础的重大见解，以及实验和计算神经科学的新范式，其中实验和理论神经科学家可以共同努力来测试体内神经功能的假设。可重新配置的信号处理平台，将允许大规模的多通道神经生理数据的实时分析，并随后用于仿真和建模。计算架构将是一个分布式的、实时的模块化设计系统，由连接在三维网格中的计算节点组成。计算节点将包括浮点数字信号处理器（DSP）、现场可编程门阵列（FPGA）和本地存储器。所使用的系统是可重构的，使得算法可以直接在硬件中实现。FPGA可以充当通信处理器，允许计算节点之间的通信的显著带宽。将系统配置为三维网格将允许系统扩展到处理任意数量的实时I/O数据流所需的任意数量的计算节点。 该平台将使用一个简单的神经系统，即板球的神经信号处理的分析。具体来说，该平台将被开发用于研究用于传输有关蟋蟀神经系统内气流信息的合作神经编码方案。该平台将实现神经信息的实时解码，从而在神经信号在多个外围传感器和中央处理神经节之间传输时实现编码信息的实验扰动。如果该平台开发成功，将导致神经功能分析中前所未有的交互控制程度。 这可能导致对神经计算的生物学基础的重大见解，以及实验和计算神经科学的新范式，其中实验和理论神经科学家可以共同努力来测试体内神经功能的假设。