Neuroscientists using DifferentialEquations.jl ecosystem?

#1

I am curious who might be working with the DiffEq packages to do single neuron cell modeling up to small network simulations using Hodgkin Huxley or similar models, either single or multi-compartment, etc.

If you’re out there or know someone heavy into Julia + comp neuroscience, I am very curious whether anyone has played with neural differential equations (i.e. DiffEqFlux.jl) and directly fitting their models to real electrophysiological whole cell recordings of neurons, using it to infer coefficients like conductance/current density, etc

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#2

Most of those cellular level electrophysiological equations are pretty straightforward IIRC. Is there a pacjage that implements standard cellular modeling equations? That would be a good place to add these.

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#3

I’ve been contributing to SpikingNeuralNetworks.jl, which has a few different spiking neuron types available.

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#4

That’s really impressive! A lot of these formulas are just outside the reach of macro scale analysis with EEG (which is more along the lines of what I do). However, it would be interesting to see how well certain frequency domains could be characterized be these. Thoeretically some of these domains are suppose to represent specific cell types.

#5

That looks really solid! I hope I find an excuse to play with it : )

#6

Indeed the implementation of the equations isn’t such a big deal (although, it can get more elaborate when you start adding cell geometry, and networks).

I’m curious whether anyone has attempted using Hodgkin-Huxley or related models as an input layer to DiffEqFlux, and subsequently training on real data to learn the coefficients. If someone has tried it or has some experience, it would be great to hear how it went for them. If not, I may just give it a go by my lonesome :nerd_face:

Also, thanks for the link @jpsamaroo interesting indeed!