43 citations · 49 across the 7 of their papers we have counts for
7 papers
Intrinsic Numerical Robustness and Fault Tolerance in a Neuromorphic Algorithm for Scientific Computing
Bradley H. Theilman, James B. Aimone
The potential for neuromorphic computing to provide intrinsic fault tolerance has long been speculated, but the brain's robustness in neuromorphic applications has yet to be demons…
Neuromorphic Simulation of Drosophila Melanogaster Brain Connectome on Loihi 2
Felix Wang, Bradley H. Theilman, Fred Rothganger +3
We demonstrate the first-ever nontrivial, biologically realistic connectome simulated on neuromorphic computing hardware. Specifically, we implement the whole-brain connectome of t…
Solving Sparse Finite Element Problems on Neuromorphic Hardware
Bradley H. Theilman, James B. Aimone
We demonstrate that scalable neuromorphic hardware can implement the finite element method, which is a critical numerical method for engineering and scientific discovery. Our appro…
Decomposing spiking neural networks with Graphical Neural Activity Threads
Bradley H. Theilman, Felix Wang, Fred Rothganger +1
A satisfactory understanding of information processing in spiking neural networks requires appropriate computational abstractions of neural activity. Traditionally, the neural popu…
Stochastic Neuromorphic Circuits for Solving MAXCUT
Bradley H. Theilman, Yipu Wang, Ojas D. Parekh +3
Finding the maximum cut of a graph (MAXCUT) is a classic optimization problem that has motivated parallel algorithm development. While approximate algorithms to MAXCUT offer attrac…
Translating and Evolving: Towards a Model of Language Change in DisCoCat
Tai-Danae Bradley, Martha Lewis, Jade Master +1
The categorical compositional distributional (DisCoCat) model of meaning developed by Coecke et al. (2010) has been successful in modeling various aspects of meaning. However, it f…
Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourage convex latent distributions
Tim Sainburg, Marvin Thielk, Brad Theilman +2
We present a neural network architecture based upon the Autoencoder (AE) and Generative Adversarial Network (GAN) that promotes a convex latent distribution by training adversarial…