1 citations · 3 across the 4 of their papers we have counts for
4 papers
Neuronal Temporal Filters as Normal Mode Extractors
Siavash Golkar, Jules Berman, David Lipshutz +3
To generate actions in the face of physiological delays, the brain must predict the future. Here we explore how prediction may lie at the core of brain function by considering a ne…
Nonlinear embeddings for conserving Hamiltonians and other quantities with Neural Galerkin schemes
Paul Schwerdtner, Philipp Schulze, Jules Berman +1
This work focuses on the conservation of quantities such as Hamiltonians, mass, and momentum when solution fields of partial differential equations are approximated with nonlinear…
Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks
Jules Berman, Benjamin Peherstorfer
Training neural networks sequentially in time to approximate solution fields of time-dependent partial differential equations can be beneficial for preserving causality and other p…
Bridging the Gap: Point Clouds for Merging Neurons in Connectomics
Jules Berman, Dmitri B. Chklovskii, Jingpeng Wu
In the field of Connectomics, a primary problem is that of 3D neuron segmentation. Although deep learning-based methods have achieved remarkable accuracy, errors still exist, espec…