We present new applications of parity inversion and time reversal to the emergence of
complex behavior from simple dynamical rules in stochastic discrete models. Our parity …

Boolean network models provide a computationally efficient way of studying dynamical
processes on networks and are most frequently used to study the dynamical properties of …

AEON. py is a Python library for the analysis of the long-term behaviour in very large
asynchronous Boolean networks. It provides significant computational improvements over …

Working memories are thought to be held in attractor networks in the brain. These attractors
should keep track of the uncertainty associated with each memory, so as to weigh it properly …

Regulatory networks play a central role in cellular behavior and decision making. Learning
these regulatory networks is a major task in biology, and devising computational methods …

Boolean networks form a class of disordered dynamical systems that have been studied in
physics owing to their relationships with disordered systems in statistical mechanics and in …

Motivation: A key goal of studying biological systems is to design therapeutic intervention
strategies. Probabilistic Boolean networks (PBNs) constitute a mathematical model which …

The hypothesis that many living systems should exhibit near-critical behavior is well
motivated theoretically, and an increasing number of cases have been demonstrated …

Random Boolean networks, originally invented as models of genetic regulatory networks,
are simple models for a broad class of complex systems that show rich dynamical structures …

Abstract Summary pystablemotifs is a Python 3 library for analyzing Boolean networks. Its
non-heuristic and exhaustive attractor identification algorithm was previously presented in …