Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly.

ABSTRACT: Inspired by biology's most sophisticated computer, the brain, neural networks constitute a profound reformulation of computational principles^1-3. Analogous high-dimensional, highly interconnected computational architectures also arise within information-processing molecular systems inside living cells, such as signal transduction cascades and genetic regulatory networks^4-7. Might collective modes analogous to neural computation be found more broadly in other physical and chemical processes, even those that ostensibly play non-information-processing roles? Here we examine nucleation during self-assembly of multicomponent structures, showing that high-dimensional patterns of concentrations can be discriminated and classified in a manner similar to neural network computa

SUBMITTER: Evans CG 

PROVIDER: S-EPMC10794147 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature