Spiking Neural Networks (SNNs) and neuromorphic models are more efficient and have more biological realism than the activation functions typically used in deep neural networks, transformer models and generative AI. SNNs have local learning rules, are able to learn on small data sets, and can adapt through neuromodulation. Although research has shown their advantages, there are still few compelling practical applications, especially at the edge where sensors and actuators need to be processed in a timely fashion. One reason for this might be that SNNs are much more challenging to understand, build, and operate due to their intrinsic properties. For instance, the mathematical foundation involves differential equations rather than basic activation functions. To address these challenges, we have developed CARLsim++. It is an integrated toolbox that enables fast and easy creation of neuromorphic applications. It encapsulates the mathematical intrinsics and low-level C++ programming by providing a graphical user interface for users who do not have a background in software engineering but still want to create neuromorphic models. Developers can easily configure inputs and outputs to devices and robots. These can be accurately simulated before deploying on physical devices. CARLsim++ can lead to rapid development of neuromorphic applications for simulation or edge processing.

翻译：脉冲神经网络（SNN）与神经形态模型相较于深度神经网络、Transformer模型及生成式AI中常用的激活函数，具有更高的能效和更强的生物真实性。SNN具备局部学习规则，能够在小数据集上进行学习，并可通过神经调节实现自适应。尽管研究已证实其优势，但当前仍缺乏具有说服力的实际应用，特别是在需要实时处理传感器与执行器的边缘场景中。究其原因，SNN因其固有特性在理解、构建及操作层面存在更大挑战——例如其数学基础涉及微分方程而非基础激活函数。为应对这些挑战，我们开发了CARLsim++集成工具箱，通过图形化用户界面封装数学底层逻辑与C++底层编程，使不具备软件工程背景但仍希望创建神经形态模型的用户能够快速便捷地开发应用。开发者可轻松配置设备与机器人的输入输出接口，并在部署至物理设备前进行精确仿真。CARLsim++将推动面向仿真或边缘处理的神经形态应用快速开发。