  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.
