2022-06-28-brenner22a.md

title	booktitle	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems	Proceedings of the 39th International Conference on Machine Learning	In many scientific disciplines, we are interested in inferring the nonlinear dynamical system underlying a set of observed time series, a challenging task in the face of chaotic behavior and noise. Previous deep learning approaches toward this goal often suffered from a lack of interpretability and tractability. In particular, the high-dimensional latent spaces often required for a faithful embedding, even when the underlying dynamics lives on a lower-dimensional manifold, can hamper theoretical analysis. Motivated by the emerging principles of dendritic computation, we augment a dynamically interpretable and mathematically tractable piecewise-linear (PL) recurrent neural network (RNN) by a linear spline basis expansion. We show that this approach retains all the theoretically appealing properties of the simple PLRNN, yet boosts its capacity for approximating arbitrary nonlinear dynamical systems in comparatively low dimensions. We employ two frameworks for training the system, one combining BPTT with teacher forcing, and another based on fast and scalable variational inference. We show that the dendritically expanded PLRNN achieves better reconstructions with fewer parameters and dimensions on various dynamical systems benchmarks and compares favorably to other methods, while retaining a tractable and interpretable structure.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	brenner22a	0	Tractable Dendritic {RNN}s for Reconstructing Nonlinear Dynamical Systems	2292	2320	2292-2320	2292	false	Brenner, Manuel and Hess, Florian and Mikhaeil, Jonas M and Bereska, Leonard F and Monfared, Zahra and Kuo, Po-Chen and Durstewitz, Daniel	given family Manuel Brenner given family Florian Hess given family Jonas M Mikhaeil given family Leonard F Bereska given family Zahra Monfared given family Po-Chen Kuo given family Daniel Durstewitz	2022-06-28		Proceedings of the 39th International Conference on Machine Learning	162	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v162/brenner22a/brenner22a.pdf