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How cpplab binds code

This document describes how cpplab binds C++ code to MATLAB objects. xolotl is based on cpplab, and the xolotl class inherits from the cpplab class. Thus, all xolotl objects are also cpplab objects.

The problem with existing approaches

MATLAB already has tools (codegen, mex which link C++ code to MATLAB. However, these approaches have limitations that prevent us from using C++ code to its full potential.

The limitations of codegen

The MATLAB codegen function automatically translates MATLAB code to C/C++. This translated code can be much faster.

However, codegen has several limitations:

  • it can only translate functions in MATLAB, and offers no support for object-oriented programming source
  • codegen respects the primacy of MATLAB code, and therefore means that generated C++ code cannot use features in C++ that do not exist in MATLAB like pointers.
  • codegen is also awfully slow, and generates a lot of junk files that clutter your disk

xolotl is a component-oriented neuron and network simulator. As such, these "components" (i.e., compartments, synapses, conductances and mechanisms) are objects with a distinct type that inherit from abstract classes that define them. If we were to use codegen, we would have to throw away the rich structure of the xolotl model tree and translate everything into a function, which would be very cumbersome.

Why not use mex?


Xolotl solves the problem of ease-of-use vs. computational efficiency by designing the integration environment "under-the-hood" in C++ and adding a robust simulation environment in MATLAB on top. This isn't just a pretty interface -- adding a compartment in xolotl actually changes C++ code. When the code is compiled, and when the integrate function is called in MATLAB, parameters are passed to the C++ function which is then run and the results returned in MATLAB.

In principle, the C++ code can be compiled and run by itself. However, xolotl offers a very rich suite of tools to make working with the model much easier, without ever having to leave MATLAB. A model can be written and inspected in the command window and integrated, with top-tier C++ speed, without the user having to touch any C++ code. All the transpiling and compiling happens silently behind the scene.

The main limitation of this approach is that only model components which have been pre-specified can be used. This means someone has to write out all the C++ code "beforehand", so that models can be constructed from these building blocks. A lot of work has already been done to provide hundreds of network components, and several functions and templates exist to auto-generate, or at least, vastly simplify, the process of creating new components.

We hope that as our user-base grows, users will contribute their custom network components, especially ones from published papers, which will be added to the growing database of components available for any xolotl model.

How cpplab works

How cpplab reads a C++ header file