Nature Machine Intelligence, Published online: 16 June 2026; doi:10.1038/s42256-026-01255-3

Pengfei Sun et al. develop a spiking neural network with a dual memory pathway, co-designed with a custom neuromorphic chip. The approach delivers over 4× throughput and 5x energy efficiency gains while using 40–60% fewer parameters than state-of-the-art implementations.

Nature Machine Intelligence, Published online: 12 June 2026; doi:10.1038/s42256-026-01256-2

Psychiatric disorders are heterogeneous, and care depends on interpreting unstructured longitudinal narratives, creating variability that hinders standardization. A study now shows that a psychiatry-specific large language model (LLM) may help clinicians to deliver more consistent, high-quality care.

Nature Machine Intelligence, Published online: 11 June 2026; doi:10.1038/s42256-026-01244-6

Synthetic datasets are becoming crucial for the development of biomedical machine learning models. Victoriano et al. discuss the persistent simulation-to-reality gap that limits how well synthetic performance predicts real-world performance.

Nature Machine Intelligence, Published online: 11 June 2026; doi:10.1038/s42256-026-01250-8

Xiong et al. introduce ConfSeq, a molecular conformation description language that enables language models to perform three-dimensional molecular modelling tasks, including conformer prediction, three-dimensional molecular generation and representation, with strong performance.