@article {70374, title = {Linking neural representations to behavior using generalization}, journal = {bioRxiv}, year = {2026}, month = {2026 Apr 24}, abstract = {

Sensory-guided decisions are the result of sensorimotor transformations across many brain areas. Recent studies have localized the motor- and decision-related components of these transformations using brain-wide neural recordings. It has been more difficult to localize sensory computations in the same way. Here we developed a new approach for linking sensory computations to behavior by training mice to discriminate between two stimuli and testing their responses with new stimuli. In separate animals, we calculated the similarity of neural representations between train and test stimuli, using recordings of up to 73,000 simultaneously-recorded neurons from 9 primary and higher-order visual areas (HVAs) across layers 2 and 3. We found that neural discrimination on test but not train images correlated with behavioral discrimination, and this relation required prior visual experience as it was not present in dark-reared mice. The link between neural and behavioral performance was highest in the medial HVAs, suggesting this region is a critical component of sensory transformations and generalization.

}, doi = {10.64898/2026.04.21.719991}, url = {https://www.biorxiv.org/content/early/2026/04/24/2026.04.21.719991}, author = {N{\'u}{\~n}ez-Ochoa, Miguel Angel and Du, Fengtong and Zhong, Lin and Baptista, Scott and Michaelos, Michalis and Sohn, Alex and Baruchin, Liad and Schr{\"o}der, Sylvia and Stringer, Carsen and Pachitariu, Marius} } @article {70287, title = {Orofacial behaviors, not eye movements, govern neural activity in mouse visual cortex}, journal = {bioRxiv}, year = {2026}, month = {2026 Feb 05}, abstract = {

Neural activity in mouse primary visual cortex (V1) correlates strongly with orofacial movements. Such strong modulation has not been found in the primate visual cortex during eye fixation [1], which led to the suggestion that the modulation may primarily depend on eye movements in both species [2]. Here we examined the influence of eye movements on neural activity in mouse visual cortex both in complete darkness and in the presence of different types of visual input. In all cases, we found that eye movements explain a smaller fraction of neural activity variance compared to orofacial behaviors. Additionally, we found that eye movements were correlated to orofacial movements, such as whisking and sniffing, and thus may be indirectly correlated to neural activity. These results further emphasize the impact of movement signals on mouse visual cortex during free viewing behavior.

}, doi = {10.64898/2026.02.04.703800}, url = {https://www.biorxiv.org/content/early/2026/02/05/2026.02.04.703800}, author = {Syeda, Atika and N{\'u}{\~n}ez-Ochoa, Miguel Angel and Zhong, Lin and Pachitariu, Marius and Stringer, Carsen} } @article {69732, title = {A critical initialization for biological neural networks}, journal = {bioRxiv}, year = {2025}, month = {01/2025}, abstract = {

Artificial neural networks learn faster if they are initialized well. Good initializations can generate high-dimensional macroscopic dynamics with long timescales. It is not known if biological neural networks have similar properties. Here we show that the eigenvalue spectrum and dynamical properties of large-scale neural recordings in mice (two-photon and electrophysiology) are similar to those produced by linear dynamics governed by a random symmetric matrix that is critically normalized. An exception was hippocampal area CA1: population activity in this area resembled an efficient, uncorrelated neural code, which may be optimized for information storage capacity. Global emergent activity modes persisted in simulations with sparse, clustered or spatial connectivity. We hypothesize that the spontaneous neural activity reflects a critical initialization of whole-brain neural circuits that is optimized for learning time-dependent tasks.

}, doi = {10.1101/2025.01.10.632397}, url = {https://www.biorxiv.org/content/early/2025/01/10/2025.01.10.632397}, author = {Pachitariu, Marius and Zhong, Lin and Gracias, Alexa and Minisi, Amanda and Lopez, Crystall and Stringer, Carsen} } @article {69557, title = {Bridging tuning and invariance with equivariant neuronal representations}, journal = {bioRxiv}, year = {2024}, month = {2024 Aug 02}, abstract = {

As we move through the world, we see the same visual scenes from different perspectives. Although we experience perspective deformations, our perception of a scene remains stable. This raises the question of which neuronal representations in visual brain areas are perspective-tuned and which are invariant. Focusing on planar rotations, we introduce a mathematical framework based on the principle of equivariance, which asserts that an image rotation results in a corresponding rotation of neuronal representations, to explain how the same representation can range from being fully tuned to fully invariant. We applied this framework to large-scale simultaneous neuronal recordings from four visual cortical areas in mice, where we found that representations are both tuned and invariant but become more invariant across higher-order areas. While common deep convolutional neural networks show similar trends in orientation-invariance across layers, they are not rotation-equivariant. We propose that equivariance is a prevalent computation of populations of biological neurons to gradually achieve invariance through structured tuning.

}, doi = {10.1101/2024.08.02.606398}, url = {https://www.doi.org/10.1101/2024.08.02.606398}, author = {Hoeller, Judith and Zhong, Lin and Pachitariu, Marius and Romani, Sandro} }