Information engines: A physical model of evolutionary dynamics

Antonis Mistriotis

BioSystems
Volume 270, December 2026, 105982

An abstractive model based on the concept of information engines is proposed, which simulates evolution. The model, representing an elementary living system, comprises two interrelated stochastic optimization processes that maximize both its information content and energy-capturing performance. It is assumed that the system exploits stored information and selected mutations to optimize its capability to capture useful energy from the environment. It then consumes work to increase its information content further. Such a cross-amplification mechanism leads to the system’s evolution towards higher levels of information processing capacity and increased energy-gaining efficiency, which is expressed as a persistent increase in its negentropy. Consequently, the simulated evolution process exhibits directionality, which also characterizes biological evolution, as other studies have observed. Moreover, the proposed model is compatible with the recently presented hypothesis of Life’s Universal Architecture (LUA) that summarizes the structural requirements of a living system. The LUA hypothesis states that thermodynamic considerations impose the presence of information-processing and energy-gaining modules in all living systems.

Read the full article at: www.sciencedirect.com

A repeating sequence of simple rules creates a universal computer

Riz Fernando Noronha

We show that the Turing complete rule 110 in elementary cellular automata can be emulated by alternating three simple symmetric rules in space. As the constituent rules themselves are trivial, this shows that diversity alone can generate emergent phenomena. The rules cannot distinguish left from right, and the chirality of rule 110 is created by their arrangement. Random initial conditions lock on to rule 110 over 90% of the time, explained through the motion of domain walls between desynchronized regions. Adding random noise creates errors that seed domain walls, whose fraction scales with the square root of noise strength, explained through a simple rate equation. This points to a route to complexity through repeated, perturbed units, common in biology, and shows that in cellular automaton models of quantum field theory, broken symmetries such as parity need not be present in the local rules.

Read the full article at: arxiv.org

Three reasons why AI is not conscious and perhaps never will be

Anil K. Seth, Ned Block & Giulio Tononi

Large language models can mimic thought and feeling with uncanny skill. But evidence from neuroscience suggests that intelligence alone is not enough for subjective experience.

Read the full article at: www.nature.com

Chaotic Griffiths phase in neuron map networks

Juan Velez-Rojas, M. G. Cosenza

Griffiths phases provide a mechanism for sustaining critical dynamics over extended parameter regions without fine-tuning to an isolated transition point. This behavior has been mainly associated with structural heterogeneity in complex networks. Here we show that quenched heterogeneity in local parameters can generate a chaotic Griffiths phase in a network of map-based neurons. We study globally coupled Chialvo maps with quenched disorder in a local neuronal parameter, thereby isolating intrinsic parameter disorder from topological disorder. As the coupling strength is varied, a chaotic Griffiths phase emerges between desynchronized and coherent chaotic dynamics, characterized by intermittent formation and breakup of neuronal clusters and large fluctuations in the fraction of clustered neurons. Within this regime, cluster sizes exhibit power-law distributions with coupling-dependent exponents, providing evidence of critical behavior over a finite parameter interval. The chaotic Griffiths phase is absent in the homogeneous limit and broadens progressively as the degree of parameter heterogeneity increases. We further introduce topological disorder through small-world connectivity and find that its combined action with local parameter heterogeneity substantially extends the chaotic Griffiths phase. In both globally coupled and small-world networks, the number of positive Lyapunov exponents displays anomalous scaling with system size. These results demonstrate that intrinsic dynamical heterogeneity constitutes an independent mechanism for generating chaotic Griffiths phases and that its interplay with structural disorder enhances extended criticality in neuronal networks.

Read the full article at: arxiv.org