RE: https://mathstodon.xyz/@DurstewitzLab/116549716016889895
🧠 New preprint by Brändle et al./ @DurstewitzLab: Continuous-Time Piecewise-Linear #RecurrentNeuralNetworks introduces continuous-time #PLRNNs for #DynamicalSystems reconstruction.
The model combines interpretability and analytical tractability of pw-linear #RNN with cont.-time dynamics, allowing semi-analytic analysis of equilibria and limit cycles while handling irregularly sampled data better than standard Neural #ODEs.
Reconstructing computational system dynamics from neural data with #RecurrentNeuralNetworks by @DurstewitzLab :
🌍 https://www.nature.com/articles/s41583-023-00740-7
#RNN #CompNeuro https://mathstodon.xyz/@DurstewitzLab/111198955816667183
The prospects for applying dynamical systems theory in neuroscience are changing dramatically. In this Perspective, Durstewitz et al. discuss dynamical system reconstruction using recurrent neural networks to directly infer a formal surrogate from an experimentally probed system and consider its potential for revolutionizing neuroscience.
Fabrizio Musacchio
Fabrizio Musacchio