An open-source realtime computational platform (short WIP paper)

Pavan Mehrotra, Sabar Dasgupta, S. Robertson, P. Nuyujukian
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引用次数: 3

Abstract

Systems neuroscience studies involving in-vivo models often require realtime data processing. In these studies, many events must be monitored and processed quickly, including behavior of the subject (e.g., movement of a limb) or features of neural data (e.g., a neuron transmitting an action potential). Unfortunately, most realtime platforms are proprietary, require specific architectures, or are limited to low-level programming languages. Here we present a hardware-independent, open-source realtime computation platform that supports high-level programming. The resulting platform, LiCoRICE, can process on order 10e10 bits/sec of network data at 1 ms ticks with 18.2 µs jitter. It connects to various inputs and outputs (e.g., DIO, Ethernet, database logging, and analog line in/out) and minimizes reliance on custom device drivers by leveraging peripheral support via the Linux kernel. Its modular architecture supports model-based design for rapid prototyping with C and Python/Cython and can perform numerical operations via BLAS/LAPACK-optimized NumPy that is statically compiled via Numba’s pycc. LiCoRICE is not only suitable for systems neuroscience research, but also for applications requiring closed-loop realtime data processing from robotics and control systems to interactive applications and quantitative financial trading.
一个开源的实时计算平台(短WIP论文)
涉及体内模型的系统神经科学研究通常需要实时数据处理。在这些研究中,必须对许多事件进行监测和快速处理,包括受试者的行为(例如,肢体的运动)或神经数据的特征(例如,传递动作电位的神经元)。不幸的是,大多数实时平台都是专有的,需要特定的体系结构,或者仅限于低级编程语言。在这里,我们提出了一个硬件独立的、开源的、支持高级编程的实时计算平台。由此产生的平台LiCoRICE可以在1毫秒的时间内以18.2µs的抖动处理10e10位/秒的网络数据。它连接到各种输入和输出(例如,DIO、以太网、数据库日志和模拟线输入/输出),并通过利用Linux内核的外设支持来最大限度地减少对自定义设备驱动程序的依赖。它的模块化架构支持基于模型的设计,使用C和Python/Cython进行快速原型设计,并可以通过BLAS/ lapack优化的NumPy执行数值操作,该NumPy通过Numba的pycc静态编译。LiCoRICE不仅适用于系统神经科学研究,也适用于需要闭环实时数据处理的应用,从机器人和控制系统到交互式应用和定量金融交易。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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