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--- |
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language: |
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- en |
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pipeline_tag: tabular-classification |
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tags: |
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- Computational Neuroscience |
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--- |
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To reduce the effort in manual curation, we developed a machine learning approach using Neuropixels probes, |
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incorporating quality metrics to identify noise clusters and isolate single-cell activity automatically. |
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Compatible with the Spikeinterface API, our method generalizes across various probes and species. |
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We generated a machine learning model trained on 11 mice in V1, SC, and ALM using Neuropixels on mice. |
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Each recording was labeled by at least two people and in different combinations. |
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The agreement amongst labelers is 80%. |
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There are two tutorial notebooks: |
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1. **Model_based_curation_tutorial.ipynb** |
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This notebook helps you apply pre-trained models to new recordings. Simply load the models and use them to label your spike-sorted data. |
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We provide "noise_neuron_model.skops" which is used to identify noise, and "sua_mua_model.skops" which is used to isolate SUA. These models |
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can be used if you want to predict labels (SUA,MUA and noise) on mice data generated using Neuropixels. |
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Steps: |
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1. load your recording depending on the acquisition software you used to create the 'recording' object. |
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2. load your sorting depending on the spike sorter you used to create the 'sorting' object. |
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3. Then you can create a Sorting_Analyzer object and you compute quality metrics. |
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These steps are explained in more detail in the Jupyter notebook in the files folder. |
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**auto_label_units** is the main function in this notebook. |
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API link to know the parameters: |
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(https://spikeinterface--2918.org.readthedocs.build/en/2918/api.html#spikeinterface.curation.auto_label_units) |
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# example use of auto-label function |
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from spikeinterface.curation import auto_label_units |
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labels = auto_label_units( |
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sorting_analyzer = sorting_analyzer, |
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model_folder = “SpikeInterface/a_folder_for_a_model”, |
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trusted = [‘numpy.dtype’]) |
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2. **Train_new_model.ipynb** |
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If you have your own manually curated data (e.g., from other species), this notebook allows you to train a new model using your specific data. |
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Here you need to follow the three steps mentioned before but you need to provide your manually curated labels. |
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**train_model** is the main function in this notebook. |
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API link to know the parameters: |
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https://spikeinterface--2918.org.readthedocs.build/en/2918/api.html#spikeinterface.curation.train_model |
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# example use of train_model function |
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from spikeinterface.curation.train_manual_curation import train_model |
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trainer = train_model(mode = "analyzers", |
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labels = labels, |
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analyzers = [labelled_analyzer, labelled_analyzer], |
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output_folder = str(output_folder), |
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imputation_strategies = None, |
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scaling_techniques = None, |
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classifiers = None) # Default to Random Forest only. Other classifiers you can try [ "AdaBoostClassifier", "GradientBoostingClassifier", |
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# "LogisticRegression", "MLPClassifier", "XGBoost", "LightGBM", "CatBoost"] |
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Acknowledgments: |
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I would like to thank people who have helped a lot in this project: |
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* For code refactoring and helping integration in Spikeinterface: Chris Halcrow, Jake Swann, Robyn Greene, Sangeetha Nandakumar(ibots) |
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* Curators: Nilufar Lahiji, Sacha Abou Rachid, Severin Graff, Luca Koenig, Natalia Babushkina, Simon Musall |
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* Advisors: Alessio Buccino, Matthias Hennig and Simon Musall |
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Also all my amazing lab members : https://brainstatelab.wordpress.com/ |
