How mice see: newly discovered nerve cells perceive more than just edges

3D reconstruction of neurons from electron microscope data as part of the MICrONS project   
Image Credit: Tyler Sloan, Quorumetrix Studio
(CC BY 4.0)

Scientific Frontline: "At a Glance" Summary: Novel Visual Cortex Neurons in Mice

• Main Discovery: Researchers identified a new class of neurons in the mouse primary visual cortex possessing a two-part receptive field tuned to complex textures and spatial frequencies, challenging the classical model that these early-stage neurons only detect simple transitions in brightness.
• Methodology: Investigators employed deep neural networks to construct digital twins of mouse neurons. These machine learning models systematically predicted which specific images would maximize individual cellular activation, and these AI-generated predictions were subsequently validated through targeted in vivo experiments in actual mouse brains.
• Key Data: The bipartite neurons exhibit a dual response mechanism based on spatial frequency. One distinct part of the receptive field responds to generalized textures, such as background plumage, while the other part activates exclusively in response to precisely arranged spatial patterns, such as facial features.
• Significance: This discovery necessitates a revision of foundational neurobiology textbook models by demonstrating that the primary visual cortex actively processes complex textural and spatial variations. These specific signals are the fundamental biological mechanisms required to separate distinct objects from complex natural backgrounds.
• Future Application: The successful integration of digital twin models with biological mapping can be leveraged to refine artificial neural network architectures, improve machine vision systems, and accelerate diagnostic modeling for neurological sensory research.
• Branch of Science: Computational Neuroscience, Neurobiology, and Artificial Intelligence
• Additional Detail: The research was conducted as a collaborative effort between Stanford University and the University of Göttingen, with the findings published in Nature Neuroscience.

A "digital twin" model of mouse visual cortex enabled researchers to generate diverse images that activate single neurons, revealing a bipartite receptive field structure aligned with natural object boundaries.   
Image Credit: Adapted from Ding Z, Tran DT et al., Nature Neuroscience, DOI: 10.1038/s41593-026-02213-3; lizensiert nach
(CC BY 4.0)

The visual cortex is 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.” 

Published in journal: Nature Neuroscience

Title: Functional bipartite invariance in mouse primary visual cortex receptive fields

Authors: Zhiwei Ding, Dat Tran, Kayla Ponder, Zhuokun Ding, Rachel Froebe, Lydia Ntanavara, Paul G. Fahey, Erick Cobos, Luca Baroni, Maria Diamantaki, Eric Y. Wang, Andersen Chang, Stelios Papadopoulos, Jiakun Fu, Taliah Muhammad, Christos Papadopoulos, Santiago A. Cadena, Alexandros Evangelou, Konstantin Willeke, Fabio Anselmi, Sophia Sanborn, Jan Antolik, Emmanouil Froudarakis, Saumil Patel, Edgar Y. Walker, Jacob Reimer, Fabian H. Sinz, Alexander S. Ecker, Katrin Franke, Xaq Pitkow, and Andreas S. Tolias

Source/Credit: Georg-August-Universität Göttingen

Reference Number: ns031026_01

Privacy Policy | Terms of Service | Contact Us