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Presseinformation: AI looks deeper into visual system
Nr. 63 - 10.04.2025
Artificial intelligence models provide insights to understand the processing of visual stimuli in the brain
How can artificial intelligence enhance our understanding of the visual system in the brain? An international research team (MICrONS), with the participation of the University of Göttingen, has developed new AI models to decode the complex processing of visual stimuli in the brain. The researchers investigated how the shape, connectivity and activity of nerve cells in the mouse brain are related. The results were published in a series of articles in Nature and Nature Communications.
The research paper “Foundation Model of Neural Activity Predicts Response to New Stimulus Types and Anatomy” presents a new AI model that has learned from huge amounts of data. The model can apply this learning flexibly to new tasks. The team analysed over 135,000 nerve cells in the visual system of mice and developed a model that reliably predicts neuronal responses to new stimuli – even to ones it has never encountered before. “For example, our model can predict responses to coherent patterns of movement, random images and static natural images, without ever having been shown or trained with these types of stimuli,” explains Professor Fabian Sinz, Institute of Computer Science and the Campus Institute for Data Science at Göttingen University, who contributed to the development of the model. These types of stimuli are crucial for understanding neural information processing.
In a related study, the team examined the shape and structure of certain nerve cells in the visual cortex of the brain. The research paper “An unsupervised map of excitatory neurons’ dendritic morphology in the mouse visual cortex” shows that “pyramidal neurons”, which have a pyramid shape and transmit important signals to other cells in the visual cortex, are more diverse than previously thought. This study was led by Professor Alexander Ecker, from the same institute, who explains: “We have developed machine learning methods that encode the complex 3D shape of a neuron in a kind of barcode. These barcodes can then be reconstructed and analysed.” Based on 30,000 pyramidal neurons, the researchers found continuous variation of shapes between neurons, rather than clearly defined cell types.
The models that the Göttingen researchers helped develop were used in many ways, including to create a “digital twin” of the nerve cells in the MICrONS data set. This digital twin was able to successfully predict the shape and structure of pyramidal neurons without accessing anatomical information for training. This suggests that the functional and anatomical properties of nerve cells are closely linked.
The results provide important insights into the organisation of the brain and could help to enable more efficient neuroscience experiments in the future. Instead of conducting elaborate and time-consuming experiments “in vivo” – meaning in living animals – researchers could first conduct experiments “in silico” – that is, in a computer model – to identify promising hypotheses and then verify them in experiments.
Information about the project and the published papers: www.nature.com/immersive/d42859-025-00001-w/index.html
Original publications:
Eric Y. Wang et al. “Foundation model of neural activity predicts response to new stimulus types”. Nature (2025). DOI: 10.1038/s41586-025-08829-y
Marissa A. Weis et al. “An unsupervised map of excitatory neurons’ dendritic morphology in the mouse visual cortex”. Nature Communications (2025). DOI: 10.1038/s41467-025-58763-w
Contact:
Professor Fabian Sinz
University of Göttingen
Institute of Computer Science and Campus Institute of Data Science
Goldschmidtstraße 1, 37077 Göttingen, Germany
Tel: +49 (0)551 39-21258
Email: sinz@uni-goettingen.de
Professor Alexander S Ecker
University of Göttingen
Institute for Computer Science and Campus Institute for Data Science
Goldschmidtstraße 1, 37077 Göttingen, Germany
Tel: +49 (0)551 39-21272
Email: ecker@cs.uni-goettingen.de