neuronIO
Full name: Single cortical neuron (L5PC) input output simulation at 1ms temporal resolution

## Single cortical neurons as deep artificial neural networks  
This 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.  
The 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.  

The 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.


## All related resources
Github repo: [github.com/SelfishGene/neuron_as_deep_net](https://github.com/SelfishGene/neuron_as_deep_net)  
Neuron version of paper: [cell.com/neuron/fulltext/S0896-6273(21)00501-8](https://www.cell.com/neuron/fulltext/S0896-6273(21)00501-8)  
Open Access (slightly older) bioRxiv version of Paper: [biorxiv.org/content/10.1101/613141v2](https://www.biorxiv.org/content/10.1101/613141v2)  
Dataset 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)  
Dataset 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)  
Notebook 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)  
Notebook exploring the dataset: [kaggle.com/selfishgene/exploring-a-single-cortical-neuron](https://www.kaggle.com/selfishgene/exploring-a-single-cortical-neuron)  
Twitter thread for short visual summery #1: [twitter.com/DavidBeniaguev/status/1131890349578829825](https://twitter.com/DavidBeniaguev/status/1131890349578829825)  
Twitter thread for short visual summery #2: [twitter.com/DavidBeniaguev/status/1426172692479287299](https://twitter.com/DavidBeniaguev/status/1426172692479287299)  
Figure360, author presentation of Figure 2 from the paper: [youtube.com/watch?v=n2xaUjdX03g](https://www.youtube.com/watch?v=n2xaUjdX03g)  
  

If you use this dataset or associated models or code, please cite the following two works:  

1. 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
1. Hay, Etay, Sean Hill, Felix Schürmann, Henry Markram, and Idan Segev. 2011. “Models of Neocortical
Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties.”
Edited by Lyle J. Graham. PLoS Computational Biology 7 (7): e1002107.
doi: https://doi.org/10.1371/journal.pcbi.1002107.

Introducing paper: Single cortical neurons as deep artificial neural networks