Centre for Theoretical Neuroscience, University of Waterloo Waterloo, ON, Canada.
Front Neuroinform. 2009 Mar 24;3:7. doi: 10.3389/neuro.11.007.2009. eCollection 2009.
Nengo (http://nengo.ca) is an open-source neural simulator that has been greatly enhanced by the recent addition of a Python script interface. Nengo provides a wide range of features that are useful for physiological simulations, including unique features that facilitate development of population-coding models using the neural engineering framework (NEF). This framework uses information theory, signal processing, and control theory to formalize the development of large-scale neural circuit models. Notably, it can also be used to determine the synaptic weights that underlie observed network dynamics and transformations of represented variables. Nengo provides rich NEF support, and includes customizable models of spike generation, muscle dynamics, synaptic plasticity, and synaptic integration, as well as an intuitive graphical user interface. All aspects of Nengo models are accessible via the Python interface, allowing for programmatic creation of models, inspection and modification of neural parameters, and automation of model evaluation. Since Nengo combines Python and Java, it can also be integrated with any existing Java or 100% Python code libraries. Current work includes connecting neural models in Nengo with existing symbolic cognitive models, creating hybrid systems that combine detailed neural models of specific brain regions with higher-level models of remaining brain areas. Such hybrid models can provide (1) more realistic boundary conditions for the neural components, and (2) more realistic sub-components for the larger cognitive models.
Nengo(http://nengo.ca)是一个开源的神经模拟器,最近增加了 Python 脚本接口,功能得到了极大的增强。Nengo 提供了广泛的功能,非常适合生理模拟,包括独特的功能,可使用神经工程框架(NEF)方便地开发群体编码模型。该框架使用信息论、信号处理和控制理论来形式化大规模神经电路模型的开发。值得注意的是,它还可用于确定导致观察到的网络动态和表示变量转换的突触权重。Nengo 提供了丰富的 NEF 支持,包括可定制的尖峰生成模型、肌肉动力学模型、突触可塑性模型和突触整合模型,以及直观的图形用户界面。通过 Python 接口可以访问 Nengo 模型的所有方面,允许通过编程创建模型、检查和修改神经参数,以及自动化模型评估。由于 Nengo 结合了 Python 和 Java,因此它也可以与任何现有的 Java 或 100% Python 代码库集成。目前的工作包括将 Nengo 中的神经模型与现有的符号认知模型连接起来,创建混合系统,将特定脑区的详细神经模型与剩余脑区的更高层次模型结合起来。这种混合模型可以提供(1)神经组件的更现实边界条件,(2)更大认知模型的更现实子组件。