MIIND is a software platform for easily and efficiently simulating the behaviour of interacting populations of neurons governed by any 1D or 2D dynamical system. The simulator is entirely agnostic to the underlying neuron model of each population and provides an intuitive method for controlling the amount of noise which can significantly affect the overall behaviour. A network of populations can be set up quickly and easily using MIIND’s XML-style simulation file format describing simulation parameters such as how populations interact, transmission delays, post-synaptic potentials, and what output to record. During simulation, a visual display of each population’s state is provided for immediate feedback of the behaviour and population activity can be output to a file or passed to a Python script for further processing. The Python support also means that MIIND can be integrated into other software such as The Virtual Brain. MIIND’s population density technique is a geometric and visual method for describing the activity of each neuron population which encourages a deep consideration of the dynamics of the neuron model and provides insight into how the behaviour of each population is affected by its neighbourhood. By utilising GPU architecture, MIIND performs comparably or better than current direct simulation solutions for large population networks. It can be used to build neural systems that bridge the scales between an individual neuron model and a population network. This allows researchers to maintain a plausible path back from mesoscopic to microscopic scales while minimising the complexity of managing large numbers of interconnected neurons. In this paper, we introduce the MIIND system, its usage, and provide implementation details where appropriate.

中文翻译：

---

MIIND：神经种群的不可知模型

MIIND是一个软件平台，可轻松高效地模拟由任何一维或二维动力学系统控制的相互作用神经元群体的行为。该模拟器完全不了解每个人群的基础神经元模型，并提供了一种直观的方法来控制可能显着影响总体行为的噪声量。使用MIIND的XML样式的仿真文件格式可以快速，轻松地建立人口网络，该文件描述了模拟参数，例如人口如何相互作用，传输延迟，突触后电位以及要记录的输出。在仿真过程中，将提供每个种群状态的可视化显示，以立即反馈行为，并且种群活动可以输出到文件中或传递给Python脚本进行进一步处理。Python支持还意味着MIIND可以集成到其他软件中，例如The Virtual Brain。MIIND的种群密度技术是一种几何和视觉方法，用于描述每个神经元种群的活动，这鼓励深入考虑神经元模型的动力学，并深入了解每个种群的行为如何受到其周围环境的影响。通过利用GPU架构，MIIND的性能与当前针对大型人口网络的直接仿真解决方案相当或更好。它可用于构建在单个神经元模型和种群网络之间建立比例关系的神经系统。这使研究人员可以维持从介观尺度到微观尺度的合理路径，同时最大程度地减少管理大量互连神经元的复杂性。在本文中，