@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 {69349, title = {Transformers do not outperform Cellpose}, journal = {bioRxiv}, year = {2024}, month = {2024 Apr 7}, abstract = {

In a recent publication, Ma et al [1] claim that a transformer-based cellular segmentation method called Mediar [2] {\textemdash} which won a Neurips challenge {\textemdash} outperforms Cellpose [3] (0.897 vs 0.543 median F1 score). Here we show that this result was obtained by artificially impairing Cellpose in multiple ways. When we removed these impairments, Cellpose outperformed Mediar (0.861 vs 0.826 median F1 score on the updated test set). To further investigate the performance of transformers for cellular segmentation, we replaced the Cellpose backbone with a transformer. The transformer-Cellpose model also did not outperform the standard Cellpose (0.848 median F1 test score). Our results suggest that transformers do not advance the state-of-the-art in cellular segmentation.

}, doi = {10.1101/2024.04.06.587952}, url = {https://www.biorxiv.org/content/early/2024/04/07/2024.04.06.587952}, author = {Carsen Stringer and Marius Pachitariu} } @article {69091, title = {Distributed representations of innate behaviors in the hypothalamus do not predict specialized functional centers.}, journal = {bioRxiv}, year = {2023}, month = {2023 Nov 21}, abstract = {

Survival behaviors are orchestrated by hardwired circuits located in deep subcortical brain regions, most prominently the hypothalamus. Artificial activation of spatially localized, genetically defined hypothalamic cell populations is known to trigger distinct behaviors, suggesting a nucleus-centered organization of behavioral control. However, no study has investigated the hypothalamic representation of innate behaviors using unbiased, large-scale single neuron recordings. Here, using custom silicon probes, we performed recordings across the rostro-caudal extent of the medial hypothalamus in freely moving animals engaged in a diverse array of social and predator defense ({\textquotedblleft}fear{\textquotedblright}) behaviors. Nucleus-averaged activity revealed spatially distributed generic {\textquotedblleft}ignition signals{\textquotedblright} that occurred at the onset of each behavior, and did not identify sparse, nucleus-specific behavioral representations. Single-unit analysis revealed that social and fear behavior classes are encoded by activity in distinct sets of spatially distributed neuronal ensembles spanning the entire hypothalamic rostro-caudal axis. Individual ensemble membership, however, was drawn from neurons in 3-4 adjacent nuclei. Mixed selectivity was identified as the most prevalent mode of behavior representation by individual hypothalamic neurons. Encoding models indicated that a significant fraction of the variance in single neuron activity is explained by behavior. This work reveals that innate behaviors are encoded in the hypothalamus by activity in spatially distributed neural ensembles that each span multiple neighboring nuclei, complementing the prevailing view of hypothalamic behavioral control by single nucleus-restricted cell types derived from perturbational studies.

}, doi = {10.1101/2023.11.21.568163}, url = {https://doi.org/10.1101/2023.11.21.568163}, author = {Stefanos Stagkourakis and Giada Spigolon and Markus Marks and Michael Feyder and Joseph Kim and Pietro Perona and Marius Pachitariu and David J. Anderson} } @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} } @article {68578, title = {Solving the spike sorting problem with Kilosort}, journal = {bioRxiv}, year = {2023}, month = {2023 Jan 07}, abstract = {

Spike sorting is the computational process of extracting the firing times of single neurons from recordings of local electrical fields. This is an important but hard problem in neuroscience, complicated by the non-stationarity of the recordings and the dense overlap in electrical fields between nearby neurons. To solve the spike sorting problem, we have continuously developed over the past eight years a framework known as Kilosort. This paper describes the various algorithmic steps introduced in different versions of Kilosort. We also report the development of Kilosort4, a new version with substantially improved performance due to new clustering algorithms inspired by graph-based approaches. To test the performance of Kilosort, we developed a realistic simulation framework which uses densely sampled electrical fields from real experiments to generate non-stationary spike waveforms and realistic noise. We find that nearly all versions of Kilosort outperform other algorithms on a variety of simulated conditions, and Kilosort4 performs best in all cases, correctly identifying even neurons with low amplitudes and small spatial extents in high drift conditions.

}, doi = {10.1101/2023.01.07.523036}, url = {https://doi.org/10.1101/2023.01.07.523036}, author = {Marius Pachitariu and Shashwat Sridhar and Carsen Stringer} } @article {67908, title = {Structured random receptive fields enable informative sensory encodings}, journal = {bioRxiv}, year = {2022}, month = {2022 Feb 13}, abstract = {

Brains must represent the outside world so that animals survive and thrive. In early sensory systems, neural populations have diverse receptive fields structured to detect important features in inputs, yet significant variability has been ignored in classical models of sensory neurons. We model neuronal receptive fields as random, variable samples from parametrized distributions in two sensory modalities, using data from insect mechanosensors and neurons of mammalian primary visual cortex. We show that these random feature neurons perform a randomized wavelet transform on inputs which removes high frequency noise and boosts the signal. Our result makes a significant theoretical connection between the foundational concepts of receptive fields in neuroscience and random features in artificial neural networks. Further, these random feature neurons enable learning from fewer training samples and with smaller networks in artificial tasks. This structured random model of receptive fields provides a unifying, mathematically tractable framework to understand sensory encodings across both spatial and temporal domains.

}, doi = {10.1101/2021.09.09.459651}, author = {Biraj Pandey and Marius Pachitariu and Bingni W. Brunton and Kameron Decker Harris} }