Nengo: A Python tool for building large-scale functional brain models

Frontiers in Neuroinformatics

Trevor eBekolay¹  Aaron eVoelker¹  Eric eHunsberger¹  Travis eDeWolf¹  Daniel eRasmussen¹  Terrence C Stewart¹  Xuan eChoo¹  Chris eEliasmith¹  James eBergstra¹ 

[1] University of Waterloo;

关键词: Neuroscience;    simulation;    theoretical neuroscience;    Control theory;    python;    neural engineering framework;   

DOI  :  10.3389/fninf.2013.00048

来源: DOAJ

【 摘 要 】

Neuroscience currently lacks a comprehensive theory of how cognitive processes can be implemented in a biological substrate. The Neural Engineering Framework (NEF) proposes one such theory, but has not yet gathered significant empirical support, partly due to the technical challenge of building and simulating large-scale models with the NEF. Nengo is a software tool that can be used to build and simulate large-scale models based on the NEF; currently, it is the primary resource for both teaching how the NEF is used, and for doing research that generates specific NEF models to explain experimental data. Nengo 1.4, which was implemented in Java, was used to create Spaun, the world’s largest functional brain model (Eliasmith et al., 2012). Simulating Spaun highlighted limitations in Nengo 1.4’s ability to support model construction with simple syntax, to simulate large models quickly, and to collect large amounts of data for subsequent analysis. This paper describes Nengo 2.0, which is implemented in Python and overcomes these limitations. It uses simple and extendable syntax, simulates a benchmark model on the scale of Spaun 50 times faster than Nengo 1.4, and has a flexible mechanism for collecting simulation results.

【 授权许可】

Unknown