Peripheral neural interfaces have been successfully used in the recent past to restore sensory-motor functions in disabled subjects and for the neuromodulation of the autonomic nervous system. The optimization of these neural interfaces is crucial for ethical, clinical and economic reasons. In particular, hybrid models (HMs) constitute an effective framework to simulate direct nerve stimulation and optimize virtually every aspect of implantable electrode design: the type of electrode (for example, intrafascicular versus extrafascicular), their insertion position and the used stimulation routines. They are based on the combined use of finite element methods (to calculate the voltage distribution inside the nerve due to the electrical stimulation) and computational frameworks such as NEURON (https://neuron.yale.edu/neuron/) to determine the effects of the electric field generated on the neural structures. They have already provided useful results for different applications, but the overall usability of this powerful approach is still limited by the intrinsic complexity of the procedure. Here, we illustrate a general, modular and expandable framework for the application of HMs to peripheral neural interfaces, in which the correct degree of approximation required to answer different kinds of research questions can be readily determined and implemented. The HM workflow is divided into the following tasks: identify and characterize the fiber subpopulations inside the fascicles of a given nerve section, determine different degrees of approximation for fascicular geometries, locate the fibers inside these geometries and parametrize electrode geometries and the geometry of the nerve–electrode interface. These tasks are examined in turn, and solutions to the most relevant issues regarding their implementation are described. Finally, some examples related to the simulation of common peripheral neural interfaces are provided.

周围神经接口最近已成功用于恢复残疾受试者的感觉运动功能以及自主神经系统的神经调节。对于神经，临床和经济方面的原因，这些神经接口的优化至关重要。特别是，混合模型（HMs）构成了一个有效的框架，可以模拟直接神经刺激并实际上优化可植入电极设计的各个方面：电极的类型（例如，束内或束外），其插入位置和使用的刺激程序。它们基于有限元方法（以计算由于电刺激而引起的神经内部电压分布）和诸如NEURON（https：//neuron.yale。edu / neuron /）确定电场对神经结构的影响。他们已经为不同的应用程序提供了有用的结果，但是这种强大方法的整体可用性仍然受到过程固有复杂性的限制。在这里，我们说明了将HM用于外围神经接口的通用，模块化和可扩展框架，在其中可以轻松确定和实施回答不同类型研究问题所需的正确近似度。HM工作流程分为以下任务：识别和表征给定神经节的筋膜内的纤维亚群，确定筋膜几何结构的不同近似程度，将纤维定位在这些几何形状内，并参数化电极几何形状和神经-电极界面的几何形状。依次检查这些任务，并描述有关其实现的最相关问题的解决方案。最后，提供了一些与公共外围神经接口的仿真有关的示例。