The visual cortex is the part of the brain that enables visual perception. In this area millions of nerve cells, called neurons, process stimuli from the outside world. They only react when objects with certain characteristics come into our field of vision. According to textbooks, the first stage of the visual cortex has two main types of neurons that specialize in edges – sharp transitions between light and dark. An international team of researchers from Stanford University and the University of Göttingen has now used machine learning techniques to find neurons in mice that use a previously unknown process in the brain to share this cognitive processing. These neurons respond to different "spatial frequencies", meaning the change in patterns of different objects in the visual field. The research was published in Nature Neuroscience.
For their discovery, the researchers used deep neural networks, also used in AI models, to create digital twins of mouse neurons. These models can predict the activity of individual neurons and thus systematically investigate which images activate cells best. Researchers from Göttingen University played a key role in the development of these digital twins. "Neural networks are essential tools for discovering new properties from large data sets – such as these novel neuronal properties," explains Professor Fabian Sinz at Göttingen University's Institute of Computer Science. "The predicted best images are not fantasies of our AI model," emphasizes Professor Alexander Ecker at the same institute. "Targeted experiments in real mouse brains, led by researchers at Stanford University, have confirmed the properties predicted by our model are real."
Each neuron in the visual cortex is responsible for a specific area of the visual field. The neuron only reacts when an appropriate stimulus appears in the relevant part of the visual field – such as an edge in the upper left corner of the field of vision. The relevant area is known as the neuron's "receptive field". Classic textbook models distinguish between two types of neurons in the visual system: "simple cells" which are stimulated when an edge – meaning a sharp transition between light and dark – appears at a specific position in their receptive field; and "complex cells" which also respond to edges, but regardless of their exact position, as long as the edge has a preferred orientation. Both cell types are therefore specialized in detecting differences in brightness.
The newly discovered neurons have a two-part receptive field: one part responds to textures, such as the detailed patterns found in the background of a photo or a bird's plumage; the other part is only stimulated when patterns are precisely arranged, such as the mouth and nose on a face. The key factor is that both parts specialize in different "spatial frequencies", meaning how often patterns such as bars or pixels repeat per unit of distance. A high frequency describes a dense pattern with fine details and sharp lines, while a low frequency describes a coarse pattern with larger, uniform areas. "Classic simple and complex cells are tuned to simple edges defined by differences in brightness," summarizes Professor Andreas Tolias, Stanford University. "In contrast, the two-part neurons we found respond to more complex information about edges – that is, differences in texture or spatial frequency. These are precisely the kinds of signals needed to separate an object from its background."
Original Publication: Ding Z, Tran DT et al. Functional bipartite invariance in mouse primary visual cortex receptive fields. Nature Neuroscience (2026). DOI: 10.1038/s41593-026-02213-3
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