Learning algorithms articles from across Nature Portfolio

We often encounter mental conflict in our lives. Such mental conflict has long been regarded as subjective. However, a machine learning method can be used to quantify the temporal dynamics of conflict between reward and curiosity from behavioral time-series.

We developed a wearable platform (the Neuro-stack) for recording single-neuron and local field potentials in freely moving humans. The Neuro-stack enabled the recording of single-neuron activity during walking behavior in humans. The platform also enables personalized stimulation during real-time decoding of neural activity, which can potentially improve neurostimulation treatments.

Computational psychiatry holds promise for basic research and clinical practice in safeguarding mental health. In this Comment, we discuss why China needs computational psychiatry, why its development in China will benefit the field globally, and the challenges of promoting computational psychiatry in China and how to tackle them.

For decades, researchers have wondered whether algorithms used by artificial neural networks might be implemented by biological networks. Payeur et al. have strengthened the connection between neuroscience and artificial intelligence by showing that biologically plausible mechanisms can approximate key features of an essential artificial intelligence learning algorithm.

A study in Nature Human Behaviour proposes a biologically plausible algorithm producing near-optimal behaviour in uncertain and volatile environments through computational imprecision. A complementary study in the same issue shows that, depending on context, uncertainty itself guides different decisions and is differentially represented in the brain.