\tau is a common symbol for time constants which, briefly put, determines how fast something decays over time. Syn for synaptic current and mem for membrane vol

\tau is a common symbol for time constants which, briefly put, determines how fast something decays over time. Syn for synaptic current and mem for membrane voltage. We've written some documentation around our neuron equations in Python that explains this: https://norse.github.io/norse/auto_api/norse.torch.functional.leaky_integrator.html https://norse.github.io/norse/auto_api/norse.torch.functiona... See also our tutorial on neuron parameter optimization to understand how it's useful for machine learning: https://github.com/norse/notebooks#level-intermediate https://github.com/norse/notebooks#level-intermediate There's also a great book on the topic by Gerstner available online: https://neuronaldynamics.epfl.ch/ https://neuronaldynamics.epfl.ch/ Disclaimer: I'm a co-author of the library Norse Regarding the target audience, it's actually not entirely clear to me. This lies in the intersection between computational neuroscience and deep learning, which isn't a huge set of people. So, I think you're question is highly relevant and we (as researchers) have a lot of work in front of us to explain why this is interesting and important.