@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 {69514, title = {A simplified minimodel of visual cortical neurons}, journal = {Nat Commun}, year = {2025}, month = {2025 Jul 01}, abstract = {

Artificial neural networks (ANNs) have been shown to predict neural responses in primary visual cortex (V1) better than classical models. However, this performance often comes at the expense of simplicity and interpretability. Here we introduce a new class of simplified ANN models that can predict over 70\% of the response variance of V1 neurons. To achieve this high performance, we first recorded a new dataset of over 29,000 neurons responding to up to 65,000 natural image presentations in mouse V1. We found that ANN models required only two convolutional layers for good performance, with a relatively small first layer. We further found that we could make the second layer small without loss of performance, by fitting individual "minimodels" to each neuron. Similar simplifications applied for models of monkey V1 neurons. We show that the minimodels can be used to gain insight into how stimulus invariance arises in biological neurons.

Preprint:\ https://www.biorxiv.org/content/early/2024/07/02/2024.06.30.601394

}, doi = {10.1038/s41467-025-61171-9}, url = {https://doi.org/10.1038/s41467-025-61171-9}, author = {Du, Fengtong and N{\'u}{\~n}ez-Ochoa, Miguel Angel and Pachitariu, Marius and Stringer, Carsen} }