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Code for papers:

To run the code

  • Please follow the instruction in "install_a_conda_enviroment" first.

  • Download supporting files from here

  • Make sure the file structure is like: dir/parent_folder/GLIF_network/network

    parent_folder should contain alternate_small_stimuli.pkl, many_small_stimuli.pkl, EA_LGN.h5 (these three are for dataset)

    GLIF_network should contain input_dat.pkl, network_dat.pkl,

    network contains ‘v1_node_types.csv’ and ‘v1_nodes.h5’

    When you run the code, you should give dir/parent_folder/GLIF_network to flag "data_dir".

    For LGN supporting file, you have to give it in /lgn_model/lgn.py line 88-99 where I hard coded there. Or, you can specify the path to lgn_full_col_cells_3.csv as an argument when you call LGN in stim_dataset.py.

  • do python multi_training.py to train the V1 model for 5 tasks together.

Authorship

The code package was written by Franz Scheer and Guozhang Chen (equally contributed).

Citation

@article{chen2022data, title={A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing}, author={Chen, Guozhang and Scherr, Franz and Maass, Wolfgang}, journal={Science Advances}, volume={8}, number={44}, pages={eabq7592}, year={2022}, publisher={American Association for the Advancement of Science} }

@article{chen2023data, title={Data-based large-scale models provide a window into the organization of cortical computations}, author={Chen, Guozhang and Scherr, Franz and Maass, Wolfgang}, journal={bioRxiv}, pages={2023--04}, year={2023}, publisher={Cold Spring Harbor Laboratory} }

The following paper(s) have utilized or derived from the code provided in this repository:

Fraile, J. G., Scherr, F., Sukia, J. R., Arkhipov, A., Maass, W., & Santos, C. R. M. (2023). Prediction error computation in cortical neurons via competition between bottom-up visual input and recurrent inhibition. IBRO Neuroscience Reports, 15, S780.