Here you will find code and data for the analysis of neural populations in response to an ensemble of stimuli. For more information, please refer to the papers:
Dyballa, L., Field, G. D., Stryker, M. P., & Zucker, S. W. (2024). Functional organization and natural scene responses across mouse visual cortical areas revealed with encoding manifolds. bioRxiv, 2024-10. https://doi.org/10.1101/2024.10.24.620089
Note: Code for the preprint above (using data from the Allen Institute) is currently being added to the folder ``allen-data-analysis` in this repository. We expect to be done very soon.
Dyballa, L., Rudzite, A. M., Hoseini, M. S., Thapa, M., Stryker, M. P., Field, G. D., & Zucker, S. W. (2024), "Population encoding of stimulus features along the visual hierarchy", Proceedings of the National Academy of Sciences, 121(4), e2317773121. https://doi.org/10.1073/pnas.2317773121
Additional code for analyzing spike waveforms and CSD can be found here.
Code files are organized into folders:
creating-the-tensor - Creating the tensor of response maps from spike files (includes kernel smoothing, displaying response maps for multiple stimuli, building the tensor).
permuted-decomposition - Running the permuted factorization (MATLAB files), selection of the number of tensor components to use, and plotting resulting factors.
encoding-manifold - Inferring the encoding manifold (building the neural factor matrix, handling of non-significant responses, inferring the data graph and underlying manifold, dimensionality).
CNNs - Convolutional neural networks (activity across layer, stimulus classification, and sampling procedure).
Simply copy the functions used througout the examples in each folder. You will still need to install the following dependencies to run all the necessary examples:
- Python >= 3.9
- NumPy >= 1.21.2
- SciPy >= 1.7.3
- scikit-learn >= 1.0.2
- Matplotlib >= 3.4.2
- tensor_toolbox >= v3.1 (MATLAB, for permuted decomposition)
- IAN >= 1.1.2 (https://github.com/dyballa/IAN)
- tensorflow >= 2.10 (for running the CNN example)
You will find detailed usage examples in the python notebooks present in each folder. Feel free to contact me by email if you have any questions.