A central principle in neuroscience is that neurons within the brain act in concert to produce
perception, cognition, and adaptive behavior. Neurons are organized into specialized brain …

Causal representation learning promises to extend causal models to hidden causal
variables from raw entangled measurements. However, most progress has focused on …

Equation discovery aims to directly extract physical laws from data and has emerged as a
pivotal research domain in nonlinear systems. Previous methods based on symbolic …

In science we are interested in finding the governing equations, the dynamical rules,
underlying empirical phenomena. While traditionally scientific models are derived through …

Symbolic regression is a machine learning method with the goal to produce interpretable
results. Unlike other machine learning methods such as, eg random forests or neural …

Dynamical systems governed by ordinary differential equations (ODEs) serve as models for
a vast number of natural and social phenomena. In this work, we offer a fresh perspective on …

Self-supervised learning (SSL) approaches have brought tremendous success across many
tasks and domains. It has been argued that these successes can be attributed to a link …

Proprioception is essential for planning and executing precise movements. Muscle spindles,
the key mechanoreceptors for proprioception, are the principle sensory neurons enabling …

The promising outcomes of dynamical system identification techniques, such as SINDy
(Brunton et al. in Proc Natl Acad Sci 113 (15): 3932–3937, 2016), highlight their advantages …

We consider learning underlying laws of dynamical systems governed by ordinary
differential equations (ODE). A key challenge is how to discover intrinsic dynamics across …