@article {69228, title = {Distinct streams for supervised and unsupervised learning in the visual cortex}, journal = {bioRxiv}, year = {2024}, month = {2024 Feb 27}, abstract = {

Representation learning in neural networks may be implemented with supervised or unsupervised algorithms, distinguished by the availability of feedback. In sensory cortex, perceptual learning drives neural plasticity, but it is not known if this is due to supervised or unsupervised learning. Here we recorded populations of up to 90,000 neurons simultaneously from the primary visual cortex (V1) and higher visual areas (HVA), while mice learned multiple tasks as well as during unrewarded exposure to the same stimuli. Similar to previous studies, we found that neural changes in task mice were correlated with their behavioral learning. However, the neural changes were mostly replicated in mice with unrewarded exposure, suggesting that the changes were in fact due to unsupervised learning. The neural plasticity was concentrated in the medial HVAs and obeyed visual, rather than spatial, learning rules. In task mice only, we found a ramping reward prediction signal in anterior HVAs, potentially involved in supervised learning. Our neural results predict that unsupervised learning may accelerate subsequent task learning, a prediction which we validated with behavioral experiments.

}, doi = {10.1101/2024.02.25.581990}, url = {https://www.biorxiv.org/content/early/2024/02/27/2024.02.25.581990}, author = {Lin Zhong and Scott Baptista and Rachel Gattoni and Jon Arnold and Daniel Flickinger and Carsen Stringer and Marius Pachitariu} } @article {68465, title = {Facemap: a framework for modeling neural activity based on orofacial tracking}, journal = {Nature Neuroscience}, year = {2023}, month = {2023 Nov 20}, abstract = {

Recent studies in mice have shown that orofacial behaviors drive a large fraction of neural activity across the brain. To understand the nature and function of these signals, we need better computational models to characterize the behaviors and relate them to neural activity. Here we developed Facemap, a framework consisting of a keypoint tracking algorithm and a deep neural network encoder for predicting neural activity. We used the Facemap keypoints as input for the deep neural network to predict the activity of \~{}50,000 simultaneously-recorded neurons and in visual cortex we doubled the amount of explained variance compared to previous methods. Our keypoint tracking algorithm was more accurate than existing pose estimation tools, while the inference speed was several times faster, making it a powerful tool for closed-loop behavioral experiments. The Facemap tracker was easy to adapt to data from new labs, requiring as few as 10 annotated frames for near-optimal performance. We used Facemap to find that the neuronal activity clusters which were highly driven by behaviors were more spatially spread-out across cortex. We also found that the deep keypoint features inferred by the model had time-asymmetrical state dynamics that were not apparent in the raw keypoint data. In summary, Facemap provides a stepping stone towards understanding the function of the brainwide neural signals and their relation to behavior.

}, doi = {10.1038/s41593-023-01490-6}, url = {https://doi.org/10.1038/s41593-023-01490-6}, author = {Atika Syeda and Lin Zhong and Renee Tung and Will Long and Marius Pachitariu and Carsen Stringer} } @article {68920, title = {Rastermap: a discovery method for neural population recordings}, journal = {Nat. Neurosci.}, year = {2023}, month = {2024 Oct 16}, abstract = {

Neurophysiology has long progressed through exploratory experiments and chance discoveries. Anecdotes abound of researchers listening to spikes in real time and noticing patterns of activity related to ongoing stimuli or behaviors. With the advent of large-scale recordings, such close observation of data has become difficult. To find patterns in large-scale neural data, we developed {\textquoteright}Rastermap{\textquoteright}, a visualization method that displays neurons as a raster plot after sorting them along a one-dimensional axis based on their activity patterns. We benchmarked Rastermap on realistic simulations and then used it to explore recordings of tens of thousands of neurons from mouse cortex during spontaneous, stimulus-evoked and task-evoked epochs. We also applied Rastermap to whole-brain zebrafish recordings; to wide-field imaging data; to electrophysiological recordings in rat hippocampus, monkey frontal cortex and various cortical and subcortical regions in mice; and to artificial neural networks. Finally, we illustrate high-dimensional scenarios where Rastermap and similar algorithms cannot be used effectively.

}, doi = {10.1038/s41593-024-01783-4}, url = {https://doi.org/10.1038/s41593-024-01783-4}, author = {Carsen Stringer and Lin Zhong and Atika Syeda and Fengtong Du and Marius Pachitariu} }