arXiv subjects

Marcello Barylli

Publications and source records attributed to Marcello Barylli.

3 recordsLinked to original sources

arXiv2026

Self-Organising Digital Circuits

Fault tolerance in classical computing has traditionally relied on static strategies like hardware redundancy and error-correcting codes. Biological systems, in contrast, exhibit adaptive plasticity, maintaining function through dynamic re-organisation around damage. Inspired by this principle, we introduce Self-Organising Digital Circuits, framing functional logic generation and maintenance as a meta-learning problem on graphs. Our architecture employs a topology-masked Transformer that configures the Lookup Tables (LUT) of a circuit's Boolean gates. Extending the pattern-generation paradigm of Neural Cellular Automata (NCA), it navigates the degenerate Boolean search space to satisfy a computational task, rather than regenerating a fixed target state. We demonstrate that it can self-assemble functional circuits from scratch and rapidly re-route logic around permanent, previously unseen hardware faults. For soft errors, the policy achieves near-perfect recovery (>99.99\% accuracy) from damage sizes far exceeding training conditions. We further observe generalisation across circuit scales: accuracy improves on graphs substantially wider than those seen during training. This work bridges the principles of biological self-organisation with the practical domain of digital hardware.

cs.AI↗

arXiv2025

Hypernetworks That Evolve Themselves

How can neural networks evolve themselves without relying on external optimizers? We propose Self-Referential Graph HyperNetworks, systems where the very machinery of variation and inheritance is embedded within the network. By uniting hypernetworks, stochastic parameter generation, and graph-based representations, Self-Referential GHNs mutate and evaluate themselves while adapting mutation rates as selectable traits. Through new reinforcement learning benchmarks with environmental shifts (CartPoleSwitch, LunarLander-Switch), Self-Referential GHNs show swift, reliable adaptation and emergent population dynamics. In the locomotion benchmark Ant-v5, they evolve coherent gaits, showing promising fine-tuning capabilities by autonomously decreasing variation in the population to concentrate around promising solutions. Our findings support the idea that evolvability itself can emerge from neural self-reference. Self-Referential GHNs reflect a step toward synthetic systems that more closely mirror biological evolution, offering tools for autonomous, open-ended learning agents.

cs.NE↗

arXiv2025

Biological Multi-Layer and Single Cell Network-Based Multiomics Models - a Review

Recent advances in single cell sequencing and multi-omics techniques have significantly improved our understanding of biological phenomena and our capacity to model them. Despite combined capture of data modalities showing similar progress, notably single cell transcriptomics and proteomics, simultaneous multi-omics level probing still remains challenging. As an alternative to combined capture of biological data, in this review, we explore current and upcoming methods for post-hoc network inference and integration with an emphasis on single cell transcriptomics and proteomics. By examining various approaches, from probabilistic models to graph-based algorithms, we outline the challenges and potential strategies for effectively combining biological data types while simultaneously highlighting the importance of model validation. With this review, we aim to inform readers of the breadth of tools currently available for the purpose-specific generation of heterogeneous multi-layer networks.

q-bio.MN↗