740 citations · 2.4k across the 58 of their papers we have counts for
59 papers
Coherent advantage in the computational expressivity of excitonic networks
Matthew Du, Carlos Floyd, Dipti Jasrasaria +1
The rising energy consumption of AI has generated interest in physical systems as alternative substrates for trainable computation. Recent experimental advances have enabled precis…
In-context learning emerges in chemical reaction networks without attention
Carlos Floyd, Hector Manuel Lopez Rios, Aaron R. Dinner +1
We investigate whether chemical processes can perform in-context learning (ICL), a mode of computation typically associated with transformer architectures. ICL allows a system to i…
Unifying Theories in High-Dimensional Biology: Approaches, Challenges and Opportunities
Marianne Bauer, Akshit Goyal, Sidhartha Goyal +19
Across biological subdisciplines, the last decade has seen an explosion of high-dimensional datasets, including datasets for cells, species, immune systems, neurons and behaviour.…
Neuromodulation-inspired gated associative memory networks: extended memory retrieval and emergent multistability
Daiki Goto, Hector Manuel Lopez Rios, Monika Scholz +1
Classical autoassociative memory models have been central to understanding emergent computations in recurrent neural circuits across diverse biological contexts. However, they typi…
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Andrew Ferguson, Marisa LaFleur, Lars Ruthotto +97
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 w…
Local imperfect feedback control in non-equilibrium biophysical systems enabled by thermodynamic constraints
Carlos Floyd, Aaron R. Dinner, Suriyanarayanan Vaikuntanathan
How biological networks achieve robust control despite relying on imperfect, local information remains an important open question. Here, we identify thermodynamic constraints that…