07-27, 12:30–14:00 (UTC), BoF
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.
Ph.D. in Cognitive Computational Neuroscience at the Max Planck Institute for Psycholinguistics.
Interested in dendrites, spikes, and sequences.
Postdoc in the Julia Lab at MIT
Tushar Chauhan is a neuroscientist with a keen interest in bio-inspired AI. His work focusses on understanding computations in sensory cortices - both for placing sensory computation in an evolutionary context, as well as drawing inspiration from biological solutions to create highly efficient, robust artificial systems. His methodology is versatile, and an important part of his toolkit includes simulations of neural circuits at various levels of abstraction.
He is a Postdoctoral Research Associate at the Bear Lab, Picower Institute of Learning and Memory, MIT.