{"url":"/dataset/neuronio","name":"neuronIO","full_name":"Single cortical neuron (L5PC) input output simulation at 1ms temporal resolution","description_markdown":"## Single cortical neurons as deep artificial neural networks  \r\nThis dataset contains training and testing subsets of the input/output relationship of a single cortical layer 5 pyramidal cell (L5PC) neuron at 1ms single spike temporal resolution.  \r\nThe data is obtained via a simulation that contains all of the currently (2021) known and well modeled \"messy biological details\" that relate to the operation of single neurons in the brain.  \r\n\r\nThe goal with this dataset is to allow machine learning modeling experts easier access to high quality biological data and eventually find **as-small-as-possible** models that **as-accurately-as-possible** capture the simulation data of a single cortical neuron at 1ms temporal resolution. Where in this case \"a small model\" can refer to: \"fast\", \"parameter efficient\", \"conceptually simple\", \"elegant\", etc.\r\n\r\n\r\n## All related resources\r\nGithub repo: [github.com/SelfishGene/neuron_as_deep_net](https://github.com/SelfishGene/neuron_as_deep_net)  \r\nNeuron version of paper: [cell.com/neuron/fulltext/S0896-6273(21)00501-8](https://www.cell.com/neuron/fulltext/S0896-6273(21)00501-8)  \r\nOpen Access (slightly older) bioRxiv version of Paper: [biorxiv.org/content/10.1101/613141v2](https://www.biorxiv.org/content/10.1101/613141v2)  \r\nDataset and pretrained networks: [kaggle.com/selfishgene/single-neurons-as-deep-nets-nmda-test-data](https://www.kaggle.com/selfishgene/single-neurons-as-deep-nets-nmda-test-data)  \r\nDataset for training new models: [kaggle.com/selfishgene/single-neurons-as-deep-nets-nmda-train-data](https://www.kaggle.com/selfishgene/single-neurons-as-deep-nets-nmda-train-data)  \r\nNotebook with main result: [kaggle.com/selfishgene/single-neuron-as-deep-net-replicating-key-result](https://www.kaggle.com/selfishgene/single-neuron-as-deep-net-replicating-key-result)  \r\nNotebook exploring the dataset: [kaggle.com/selfishgene/exploring-a-single-cortical-neuron](https://www.kaggle.com/selfishgene/exploring-a-single-cortical-neuron)  \r\nTwitter thread for short visual summery #1: [twitter.com/DavidBeniaguev/status/1131890349578829825](https://twitter.com/DavidBeniaguev/status/1131890349578829825)  \r\nTwitter thread for short visual summery #2: [twitter.com/DavidBeniaguev/status/1426172692479287299](https://twitter.com/DavidBeniaguev/status/1426172692479287299)  \r\nFigure360, author presentation of Figure 2 from the paper: [youtube.com/watch?v=n2xaUjdX03g](https://www.youtube.com/watch?v=n2xaUjdX03g)  \r\n  \r\n\r\nIf you use this dataset or associated models or code, please cite the following two works:  \r\n\r\n1. David Beniaguev, Idan Segev and Michael London. \"Single cortical neurons as deep artificial neural networks.\" Neuron. 2021; 109: 2727-2739.e3 doi: https://doi.org/10.1016/j.neuron.2021.07.002\r\n1. Hay, Etay, Sean Hill, Felix Schürmann, Henry Markram, and Idan Segev. 2011. “Models of Neocortical\r\nLayer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties.”\r\nEdited by Lyle J. Graham. PLoS Computational Biology 7 (7): e1002107.\r\ndoi: https://doi.org/10.1371/journal.pcbi.1002107.","description_withheld":null,"homepage":"https://www.kaggle.com/datasets/selfishgene/single-neurons-as-deep-nets-nmda-test-data","introduced_date":"2021-08-10","introduced_date_note":null,"introduced_by":{"paper":"/paper/single-cortical-neurons-as-deep-artificial","title":"Single cortical neurons as deep artificial neural networks","first_author":"David Beniaguev","url":null},"license":{"name":"MIT","url":"https://opensource.org/license/mit/"},"modalities":[{"name":"Biology","url":"/datasets/modality/biology"},{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Time Series","url":"/task/time-series-1","datasets_with_task":"/datasets/task/time-series-1"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["neuronIO"],"data_loaders":[{"repo":"https://github.com/AaronSpieler/elmneuron","url":"https://github.com/AaronSpieler/elmneuron","frameworks":["pytorch"]}],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}