Simulating neural physiology & networks in Julia

Could Julia be uniquely well-suited for rapidly developing new approaches to simulate the brain ? What if neuroscientists could use a composable set of tools to craft models of ion channels, compartmentalized neuronal morphology, networks of LIF or conductance-based neurons, reinforcement learning, and everything in-between?

Join the discussion on the bof-voice channel in discord.

Julia’s software ecosystem certainly lessens the technical burden for computational neuroscientists—it boasts federated development of high-quality packages for solving differential equations, machine learning, automatic differentiation, and symbolic algebra. Deep language support for multithreaded, distributed, and GPU parallelism also makes the case for models that can span multiple scales, both in biological detail and overall network size.

Come join us for a community discussion about what a fresh Julian take on modeling the brain might look like. Together we will lay out an initial set of goals for building up a domain-specific ecosystem of packages for computational neuroscience.

The discussion will be moderated by Wiktor Phillips, Alessio Quaresima, and Tushar Chauhan.