@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 {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} }