Our Perspective on reconstructing computat. system dynamics from neural data finally out in @NatRevNeurosci!

We survey generative models that can be trained on time series to mimic the behavior of the neural substrate.
#AI #neuroscience #DynamicalSystems nature.com/articles/s4158…
With training algorithms incorporating control-theoretical ideas, RNNs can learn to behave like the underlying dynamical system they have been trained on, and the role of different latent states in dynamics can be dissected.

Interested in reconstructing computational dynamics from neural data using RNNs?

Here we review dynamical systems (DS) concepts, recent #ML/ #AI methods for recovering DS from data, evaluation, interpretation, analysis, & applic. in #Neuroscience:
biorxiv.org/content/10.110…
A 🧵... Some important take homes:
1) To formally constitute a true state space or reconstructed DS, some math. conditions need to be met. PCA and other dim. reduc. tools often won’t give you a state space in the DS sense (may even destroy it). Just training an RNN on data may not either