We have built neurolib because like for many computational neuroscientists, working with neural models is daily business. However, no open-source framework was available to help us implement our own models, run large-scale simulations and handle huge amounts of simulated data efficiently. As it happens so often in research, we ended up writing our own software for our own special case. This is why we decided to join our forces and models to create neurolib , a library that solves these common issues and more.

Other software projects that we're familiar with like TheVirtualBrain offer a lot of functionality with a useful UI. In neurolib , our goal is to create a hackable framework for coders and focus on the simulation and optimization machinery. We are not planning to add many “utility functions” like plotting data or more than just basic signal processing. In our experience, every researcher has their own workflow and we don’t want to make others rely on our implementations, when they’re usually more than fine using their own processing pipeline with everything that Python has to offer, including matplotlib , numpy , pandas , and scipy .