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Information-PC-Paper

Code associated with the paper, "Higher-Order Spatial Information for Self-Supervised Place Field Learning"

https://github.com/DeightonJared/Information-PC-Paper

Science Score: 54.0%

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    CITATION.cff file
    Found CITATION.cff file
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    Links to: arxiv.org
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    Scientific vocabulary similarity
    Low similarity (10.1%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Code associated with the paper, "Higher-Order Spatial Information for Self-Supervised Place Field Learning"

Basic Info
  • Host: GitHub
  • Owner: DeightonJared
  • Language: Python
  • Default Branch: main
  • Size: 466 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 2 years ago · Last pushed 8 months ago
Metadata Files
Readme Citation

README.md

Information-PC-Paper

Code associated with the paper, "Higher-Order Spatial Information for Self-Supervised Place Field Learning." Some important files are:

-- pc_info.py contains functions for spatial information rates, including Skaggs' spatial information rates Isec(A), Ispike(A) and higher-order spatial information rates Isecjoint(A, B), and Ispikejoint(A, B). These are described in detail in Section 2 of the paper.

-- main.py contains a simple instance of training a model for self-supervised learning via spatial information.

-- model.py contains the construction of the RNN class used to build models.

-- inspect.py contains an example of model inspection, i.e obtaining rate maps, place cell scores, etc.

-- decoding_functions.py contains functions to perform neural decoding using place cell activations. Options include leave-one-out classification and support-vector-machine quadrant classification.

-- placescores.py contains a function to calculate place cell score. This is detailed Section 3.2 of the paper.

-- place_cells.py contains the place cell class, used to embed the initial position into the RNN.

-- grid_cells.py contains the grid cell class, (optionally) used to force grid cell activations in the hidden layer. None of the models/results in the paper rely upon this, but it may be explored by using -- hardcoded_gcs True in a model.

-- sensitivity.py contains a script for running the sensitivity analysis and recording saving the results.

Please cite this code via:

@article{deighton2024higher, title={Higher-Order Spatial Information for Self-Supervised Place Cell Learning}, author={Deighton, Jared and Mackey, Wyatt and Schizas, Ioannis and Boothe Jr, David L and Maroulas, Vasileios}, journal={arXiv preprint arXiv:2407.06195}, year={2024} }

Owner

  • Name: Jared Deighton
  • Login: DeightonJared
  • Kind: user
  • Company: Simmons University

Assistant Professor at Simmons Unviersity.

Citation (CITATION.cff) 

@article{deighton2024higher,
  title={Higher-Order Spatial Information for Self-Supervised Place Cell Learning},
  author={Deighton, Jared and Mackey, Wyatt and Schizas, Ioannis and Boothe Jr, David L and Maroulas, Vasileios},
  journal={arXiv preprint arXiv:2407.06195},
  year={2024}
}

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