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.

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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.

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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.

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Kevin Paramero-Arias, Alejandro P. Riascos

We study synchronization in network-coupled oscillators subject to stochastic resetting of a subset of nodes. We show that resetting can induce collective coherence in oscillators that are never directly reset, through the interplay of network structure, frequency heterogeneity, and the organization of the reset subsystem. In the infinite-resetting-rate limit, the reset nodes become effectively pinned to a reference phase, and linearization near synchronization maps the dynamics of the remaining subsystem onto a grounded-Laplacian problem. This formulation yields an analytical relation between the synchronization level and the reset parameters, providing an estimate of the coupling strength required to achieve a prescribed degree of coherence. Beyond the linear regime, we develop a mean-field description in which the reset subsystem acts as an effective ordering field for the remaining oscillators. Numerical simulations across different network topologies confirm these predictions and show that synchronization is enhanced by increasing both the reset fraction and network connectivity. In heterogeneous networks, the placement of reset nodes can strongly influence the synchronization response. For frequency distributions with nonzero mean, resetting competes with the intrinsic collective rotation and can drive the system from a rotating synchronized state to a phase-locked state. These results establish subsystem resetting as a mechanism for inducing and controlling collective synchronization in complex networks.

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A.N. Montanari, P. Zanin, A.E. Motter,  
Science 393, 6817 (2026). 

Previous studies of network dynamics have suggested that heterogeneity among nodes inhibits stability, which is at odds with the ubiquity of inherently heterogeneous natural and engineered systems. Here, we show that this conclusion arises from model reductions introduced for mathematical tractability and breaks down when nodal dynamics are higher-dimensional, yielding non-Hermitian Jacobians. In such systems, including neural, power-grid, and material networks, nodal heterogeneity can instead enhance stability, even when parameters are randomly disordered. Non-Hermiticity also underlies the stabilizing effects of network heterogeneity, which can arise even in one-dimensional nodal dynamics through nonreciprocal interactions, as shown for ecological networks. Our framework reveals disorder not as a liability but as a general resource for stabilizing complex systems.

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