21 citations · 64 across the 20 of their papers we have counts for
5 papers · 1 filter
Model selection for hybrid dynamical systems via sparse regression
Niall M Mangan, Travis Askham, Steven L Brunton +2
Hybrid systems are traditionally difficult to identify and analyze using classical dynamical systems theory. Moreover, recently developed model identification methodologies largely…
Built to Last: Functional and structural mechanisms in the moth olfactory network mitigate effects of neural injury
Charles B Delahunt, Pedro D Maia, J. Nathan Kutz
Most organisms suffer neuronal damage throughout their lives, which can impair performance of core behaviors. Their neural circuits need to maintain function despite injury, which…
Engineering Structural Robustness in Power Grid Networks Susceptible to Coherent Swing Instability
Daniel Dylewsky, Xiu Yang, Alexandre Tartakovsky +1
Networked power grid systems are susceptible to a phenomenon known as Coherent Swing Instability (CSI), in which a subset of machines in the grid lose synchrony with the rest of th…
Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, with Applications to Neural Nets
Charles B. Delahunt, Jeffrey A. Riffell, J. Nathan Kutz
The insect olfactory system, which includes the antennal lobe (AL), mushroom body (MB), and ancillary structures, is a relatively simple neural system capable of learning. Its stru…
Putting a bug in ML: The moth olfactory network learns to read MNIST
Charles B. Delahunt, J. Nathan Kutz
We seek to (i) characterize the learning architectures exploited in biological neural networks for training on very few samples, and (ii) port these algorithmic structures to a mac…