CBS CoCoNUT: A new interest group that comes together every two weeks to have an informal exchange about cognitive computational neuroscience.

Abstract. Recent advances in neural networks trained on natural language have revealed that category-selective regions encode complex semantics and contextual information of natural scenes in addition...

Adaptive cognition requires structured internal models of objects and their relations. Predictive neural networks are often proposed to learn such world models, but how these are instantiated and how ...

By combining magnetoencephalography and eye tracking, this study sheds light on why people fixate on some parts of natural scenes longer than others. Rather than visual complexity, fixation durations ...

Cambridge Core - Neurosciences - Scientific Theories of Consciousness

This study gives an example of how complex computations in neural networks can emerge from simple physical principles. Training a model to optimize internal energy efficiency while performing eye move...

Figuring out how the brain uses information from visual neurons may require new tools. I asked nine experts to weigh in.

Fed the right information, large language models can match what the brain sees when it takes in an everyday scene such as children playing or a big city skyline, a new study led by Ian Charest finds.

Lu et al. introduce all-topographic neural networks as a parsimonious model of the human visual cortex.