153 citations · 188 across the 19 of their papers we have counts for
5 papers · 1 filter
Sparsifying networks by traversing Geodesics
Guruprasad Raghavan, Matt Thomson
The geometry of weight spaces and functional manifolds of neural networks play an important role towards 'understanding' the intricacies of ML. In this paper, we attempt to solve c…
Persistent fluid flows defined by active matter boundaries
Zijie Qu, Dominik Schildknecht, Shahriar Shadkhoo +5
Biological systems achieve precise control over ambient fluids through the self-organization of active protein structures including flagella, cilia, and cytoskeletal networks. In a…
Phenomenological model of motility by spatiotemporal modulation of active interactions
Dominik Schildknecht, Matt Thomson
Transport at microscopic length scales is essential in biological systems and various technologies, including microfluidics. Recent experiments achieved self-organized transport ph…
Self-organization of multi-layer spiking neural networks
Guruprasad Raghavan, Cong Lin, Matt Thomson
Living neural networks in our brains autonomously self-organize into large, complex architectures during early development to result in an organized and functional organic computat…
Geometric algorithms for predicting resilience and recovering damage in neural networks
Guruprasad Raghavan, Jiayi Li, Matt Thomson
Biological neural networks have evolved to maintain performance despite significant circuit damage. To survive damage, biological network architectures have both intrinsic resilien…