Vieth Marius, Stöber Tristan M, Triesch Jochen
Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany.
Front Neuroinform. 2021 Nov 1;15:715131. doi: 10.3389/fninf.2021.715131. eCollection 2021.
The Python Modular Neural Network Toolbox (PymoNNto) provides a versatile and adaptable Python-based framework to develop and investigate brain-inspired neural networks. In contrast to other commonly used simulators such as Brian2 and NEST, PymoNNto imposes only minimal restrictions for implementation and execution. The basic structure of PymoNNto consists of one network class with several neuron- and synapse-groups. The behaviour of each group can be flexibly defined by exchangeable modules. The implementation of these modules is up to the user and only limited by Python itself. Behaviours can be implemented in Python, Numpy, Tensorflow, and other libraries to perform computations on CPUs and GPUs. PymoNNto comes with convenient high level behaviour modules, allowing differential equation-based implementations similar to Brian2, and an adaptable modular Graphical User Interface for real-time observation and modification of the simulated network and its parameters.
Python模块化神经网络工具箱（PymoNNto）提供了一个通用且适应性强的基于Python的框架，用于开发和研究受大脑启发的神经网络。与其他常用模拟器（如Brian2和NEST）不同，PymoNNto在实现和执行方面只施加了最小的限制。PymoNNto的基本结构由一个网络类和几个神经元组及突触组组成。每个组的行为可以通过可交换模块灵活定义。这些模块的实现由用户决定，并且仅受Python本身的限制。行为可以在Python、Numpy、Tensorflow和其他库中实现，以便在CPU和GPU上执行计算。PymoNNto附带了方便的高级行为模块，允许进行类似于Brian2的基于微分方程的实现，以及一个适应性强的模块化图形用户界面，用于实时观察和修改模拟网络及其参数。
