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This code is part of a data analysis and modeling pipeline for neuroscience or behavioral data. It includes functions for data preprocessing, visualization, and statistical analysis. The specific usage of these functions and the details of the data being analyzed would depend on the context in which this code is applied.

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BehaviorNeuroLink: Neural Activity and Behavior Analysis Toolkit

NIMH DSST fork of Neural-Analysis Code

BehaviorNeuroLink is a Python package for analyzing the relationship between neural activity and behavior using multiple linear regression. It provides a suite of tools for preprocessing calcium imaging data, constructing regression models, and interpreting the encoding of behavioral information in neural signals.

Key Features

  • Data Preprocessing:

    • Downsampling of calcium imaging time-series data
    • Design matrix construction for behavioral variables
  • Multicollinear Diagnostics:

    • Tools to assess and address multicollinearity in behavioral predictors
  • Neural Encoding Analysis:

    • Linear regression-based encoding models to relate neural activity to behavioral variables
    • Time kernel incorporation to account for temporal dynamics in neural responses

Who Should Use BehavioralNeuralRegressor

  • Neuroscientists studying the neural basis of behavior
  • Researchers working with calcium imaging or fiber photometry data

Getting Started

Check out our documentation for installation instructions, tutorials, and API reference.

Citation

If you use BehavioralNeuralRegressor in your research, please cite our paper:

[Link to paper]

Contributing

We welcome contributions! Please see our contributing guidelines for more information.

License

BehavioralNeuralRegressor is released under the MIT License. See the LICENSE file for details.

About

This code is part of a data analysis and modeling pipeline for neuroscience or behavioral data. It includes functions for data preprocessing, visualization, and statistical analysis. The specific usage of these functions and the details of the data being analyzed would depend on the context in which this code is applied.

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