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Welcome to PLOS Complex Systems

February 18, 2026

Welcome to PLOS Complex Systems

       

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03/05/2026

research article

Cascades and convergence: Dynamic signal flow in a synapse-level brain network

Connectomes constrain how signals flow through the nervous system, shaping the transmission of sensory information to downstream targets involved in perception, decision-making, and action. The authors use a simple, network-based spreading model to simulate sensory signal propagation across the adult Drosophila connectome to trace modality-specific cascades and quantify their zones of overlap.

Image credit: Amal Biju Unni

Cascades and convergence: Dynamic signal flow in a synapse-level brain network

03/04/2026

research article

Fast algorithms to improve fair information access in networks

The authors consider the problem of selecting k seed nodes in a network to maximize the minimum probability of activation under an independent cascade beginning at these seeds. The motivation is to promote fairness by ensuring that even the least advantaged members of the network have good access to information.

Image credit: Logan Voss

Fast algorithms to improve fair information access in networks

02/23/2026

research article

Can adversarial attacks by large language models be attributed?

Attributing outputs from Large Language Models (LLMs) in adversarial settings—such as cyberattacks and disinformation campaigns—presents significant challenges that are likely to grow in importance. The authors approach this attribution problem from both a theoretical and empirical perspective, drawing on formal language theory (identification in the limit) and data-driven analysis of the expanding LLM ecosystem

Image credit: Markus Spiske

Can adversarial attacks by large language models be attributed?

01/14/2026

research article

A generalized Bayesian framework for maximizing information gain and model selection

This work presents a novel Bayesian Optimal Experiment Design Selection principle for generalised parameter distributions. The generalization is achieved by extending the β-information gain to the discrete distributions. The β-information gain is based on what is known as the Bhattacharyya coefficient. 

A generalized Bayesian framework for maximizing information gain and model selection

Image credit: Robert Clark

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PLOS Complex Systems | ISSN: 2837-8830 (online)