Drakopoulos et al. present a model that captures the transformation from sound waves to neural activity patterns underlying early auditory processing. The model reproduces neural responses to a range of complex sounds and key neurophysiological phenomena.

Self-supervised learning models for single-cell RNA sequencing data exhibit poor transferability to spatial transcriptomics for cell-type prediction, although their learned features may enhance spatial analysis.

TCR-TRANSLATE, a deep learning framework adapting machine translation to immune design, demonstrates the successful generation of a functional T cell receptor sequence for a cancer epitope from the target sequence alone.

SemanticLens is a tool that embeds artificial intelligence model components (such as neurons) into a searchable, human-understandable space. This enables automated auditing, validation of decisions and detection of problematic behaviours with minimal human oversight.

GraphPep presents an interaction-derived and protein language model-powered graph learning framework for robust scoring of protein–peptide complexes, substantially enhancing the binding mode prediction of protein–peptide docking.

Powerful generative AI models for designing biological macromolecules are being developed, with applications in medicine, biotechnology and materials science, but these models are expensive to train and modify. Leyva et al. introduce the Key-Cutting Machine, an optimization-based platform for proteins and peptides that iteratively leverages structure prediction to match desired backbone geometries.

Graber et al. characterize biases and data leakage in protein–ligand datasets and show that a cleanly filtered training–test split leads to improved generalization in binding affinity prediction tasks.

Choi et al. introduce a machine learning model that integrates diverse multi-view data to predict disease phenotypes. The model includes an interpretable explainer that identifies interacting biological features, such as synergistic genes in astrocytes and microglia associated with Alzheimer’s disease.

Recurrent neural networks are widely used to model brain dynamics. Tolmachev and Engel show that single-unit activation functions influence task solutions that emerge in trained networks, raising the question of which design choices best align with biology.

ALL-conformations, a dataset capturing the full range of experimentally observed conformations of CDR loops, T cell and antibody regions interacting with antigen targets, is introduced. ITsFlexible—a deep learning tool trained on this new dataset—advances predictions of immune receptor structural dynamics.

Questions over whether neural networks learn universal or model-specific representations framed a community event at the Cognitive Computational Neuroscience conference in August 2025, highlighting future directions on a fundamental topic in NeuroAI.

Most policy proposals aimed at managing the risks of artificial intelligence (AI)-enabled weapons rely heavily on meaningful human control or appropriate human judgment for risk mitigation. This Comment argues that there are various ways humans can exert such control over AI, and that developing a careful taxonomy of these is necessary for building actionable risk-mitigation policies for warfighting AI.

The organizers reflect on how a multi-year, multi-country benchmark aligned AI research in road damage detection with practical and regional constraints, steering it towards deployment relevance.