David Schnoerr

This study compares continuous and discrete models of Turing patterns, finding discrete models more robust and suggesting deeper principles in Turing mechanisms.

This paper introduces solvable stochastic models of gene expression that account for multiple states and factors affecting transcript abundance, enabling analytical and computational solutions for gene regulation.

The paper develops a method to infer latent neural states from large-scale neuronal recordings using neural field models and moment closure, applied to mouse retinal data to reveal refractory states in retinal waves.

The paper explores Turing pattern design space to determine how common and robust they are, finding that they are common but sensitive to parameter changes.