Learning via Mechanosensitivity and Activity in Cytoskeletal Networks

Abstract

In this work we show how a network inspired by a coarse-grained description of the cytoskeleton can learn—in a contrastive learning framework—from environmental perturbations if it is endowed with mechanosensitive proteins and motors. Our work is a proof of principle for how force-sensitive proteins and molecular motors can form the basis of a general strategy to learn in biological systems without neurons. Our work identifies a minimal biologically plausible learning mechanism and also explores its implications for commonly occurring phenomenology, such as adaptation and homeostasis.