Computational neuroscience articles from across Nature Portfolio

A classic question in cognitive science is whether learning requires innate, domain-specific inductive biases to solve visual tasks. A recent study trained machine-learning systems on the first-person visual experiences of children to show that visual knowledge can be learned in the absence of innate inductive biases about objects or space.

RoboEM, an artificial intelligence (AI)-based flight agent, automatically steers through three-dimensional electron microscopy (3D-EM) images of brain tissue to follow neurites. RoboEM substantially improves state-of-the-art automated reconstructions, eliminating manual proofreading needs in complex connectomic analysis problems and paving the way for high-throughput, cost-effective, large-scale mapping of neuronal networks — connectomes.

How interhemispheric connections are organized and how interhemispheric communication are regulated are not fully understood. Here authors delineate the diverse single-neuron projection patterns of interhemispheric connections in mice and uncover their influence on functional dynamics, highlighting the importance of heterotopic projections in interhemispheric communication.

This study, utilizing deep learning to model semantic saturation, shows its tie to repetitive visual stimuli processing by the primary visual cortex, suggesting a bottom-up neurocognitive process.

Perception and neural processing of sensory information are influenced by prior expectations. Here the authors show investigate how prior expectations contribute to face processing in the brain.

A classic question in cognitive science is whether learning requires innate, domain-specific inductive biases to solve visual tasks. A recent study trained machine-learning systems on the first-person visual experiences of children to show that visual knowledge can be learned in the absence of innate inductive biases about objects or space.

A new study captures nearly the full repertoire of primate natural behaviour and reveals that highly distributed cortical activity maintains multifaceted dynamic social relationships.

RoboEM, an artificial intelligence (AI)-based flight agent, automatically steers through three-dimensional electron microscopy (3D-EM) images of brain tissue to follow neurites. RoboEM substantially improves state-of-the-art automated reconstructions, eliminating manual proofreading needs in complex connectomic analysis problems and paving the way for high-throughput, cost-effective, large-scale mapping of neuronal networks — connectomes.