Nanostructured Au films fabricated by the assembling of nanoparticles produced in the gas phase have shown properties suitable for neuromorphic computing applications: they are characterized by a non-linear and non-local electrical behavior, featuring switches of the electric resistance whose activation is typically triggered by an applied voltage over a certain threshold. These systems can be considered as complex networks of metallic nanojunctions where thermal effects at the nanoscale cause the continuous rearrangement of regions with low and high electrical resistance. In order to gain a deeper understanding of the electrical properties of this nano granular system, we developed a model based on a large three dimensional regular resistor network with non-linear conduction mechanisms and stochastic updates of conductances. Remarkably, by increasing enough the number of nodes in the network, the features experimentally observed in the electrical conduction properties of nanostructured gold films are qualitatively reproduced in the dynamical behavior of the system. In the activated non-linear conduction regime, our model reproduces also the growing trend, as a function of the subsystem size, of quantities like Mutual and Integrated Information, which have been extracted from the experimental resistance series data via an information theoretic analysis. This indicates that nanostructured Au films (and our model) possess a certain degree of activated interconnection among different areas which, in principle, could be exploited for neuromorphic computing applications.

Electroencephalography (EEG) is an important noninvasive diagnostic tool for detecting neurodegenerative disorders. In this context, EEG-based deep learning models show promise due to their ability to capture nonlinear brain dynamics but often suffer from poor generalizability caused by high inter-subject variability. Self-supervised learning (SSL) offers a promising solution by enabling models to learn robust representations from large unlabeled datasets. This study introduces a double-masking representation learning framework for EEG analysis. Using data aggregated from eight multi-center datasets (3156 subjects; 439 h of EEG recordings), a hybrid convolutional-transformer model (TransformEEG) is pretrained to enhance generalization in neurodegenerative disease classification, focusing on Parkinson’s and Alzheimer’s diseases. This approach combines phase-swap data augmentation, designed to facilitate the learning of EEG phase-amplitude coupling, with a double-masking function that operates at both the signal and the transformer’s token levels. The pretrained model was evaluated against a fully supervised baseline using Monte Carlo cross-validation with 100 splits across three public pathology detection datasets. Pretraining led to consistent improvements in both median balanced accuracy and Inter-Quartile Range (IQR) across all fine-tuning datasets. Compared to the fully supervised baseline, the proposed approach increases median balanced accuracy by 2.8% for Parkinson’s disease detection and by 4.2% for Alzheimer’s disease detection, while also reducing performance variability across 100 Monte Carlo splits. These results demonstrate that SSL can enhance EEG deep learning performance, though achieving robust generalization in clinical applications remains an open research challenge.

Microstimulation is a powerful tool for causal interrogation of neural circuits and for therapeutic neuromodulation. However, predicting network-level responses to focal perturbations remains a major challenge. To address this problem in a tractable manner, we combine experiments on networks of hippocampal neurons cultured on high-density multielectrode arrays with spiking network model simulations. To characterize spontaneous and stimulation-evoked network dynamics, we employ a combination of direct electrophysiological readouts and information-theoretic measures. We find that single-channel stimulation reliably evokes a small set of site-specific, stereotyped network activity patterns. Remarkably, effective connectivity inferred from spontaneous activity captures the spatial organization of perturbation responses, enabling reliable ranking of stimulation-evoked effects across the network. Our spiking network model reproduces these observations and reveals the interplay between short-term synaptic depression and distance-dependent excitatory and inhibitory connectivity in shaping both spontaneous and evoked interactions at different effective scales. Spontaneous activity involves local structural routes, while perturbation-evoked responses engage comparatively longer, polysynaptic pathways. By unifying in silico modeling with experimental measurements, this work links spontaneous network structure to stimulation-evoked dynamics, suggesting that spontaneous effective connectivity may serve as a tractable proxy for stimulation targeting in recurrent circuits, with potential implications for the rational design of neuromodulatory interventions.