Thermodynamic State Machine Network.

ABSTRACT: We describe a model system-a thermodynamic state machine network-comprising a network of probabilistic, stateful automata that equilibrate according to Boltzmann statistics, exchange codes over unweighted bi-directional edges, update a state transition memory to learn transitions between network ground states, and minimize an action associated with fluctuation trajectories. The model is grounded in four postulates concerning self-organizing, open thermodynamic systems-transport-driven self-organization, scale-integration, input-functionalization, and active equilibration. After sufficient exposure to periodically changing inputs, a diffusive-to-mechanistic phase transition emerges in the network dynamics. The evolved networks show spatial and temporal structures that look much like spiking

SUBMITTER: Hylton T 

PROVIDER: S-EPMC9221775 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature