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README.md
README.md
 
 
TFR_PrevSuccess_PrevFail_AM.ipynb
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TFR_PrevSuccess_PrevFail_LM.ipynb
TFR_PrevSuccess_PrevFail_LM.ipynb
 
 
TFR_PrevSuccess_PrevFail_V1.ipynb
TFR_PrevSuccess_PrevFail_V1.ipynb
 
 
TFR_bias_blocks_AM.ipynb
TFR_bias_blocks_AM.ipynb
 
 
TFR_bias_blocks_LM.ipynb
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TFR_bias_blocks_V1.ipynb
TFR_bias_blocks_V1.ipynb
 
 
TFR_expected_unexpected_AM.ipynb
TFR_expected_unexpected_AM.ipynb
 
 
TFR_expected_unexpected_LM.ipynb
TFR_expected_unexpected_LM.ipynb
 
 
TFR_expected_unexpected_V1.ipynb
TFR_expected_unexpected_V1.ipynb
 
 
TFR_expected_unexpected_nostim_V1.ipynb
TFR_expected_unexpected_nostim_V1.ipynb
 
 
TFR_stim_noStim.ipynb
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__init__.py
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all_together.ipynb
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test_time2sample.ipynb
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README.md

AcknowledgmentsTime-Frequency Representations (TFR)

The goal of this project is to compute and visualize TFR for different task conditions across Visual processing related regions.

Regions:

  • primary visual area (VISp) (V1)
  • Anteromedial visual area (VISam) (AM)
  • lateral visual area (VISl) (LM)

Conditions:

  • Bias Blocks: Right-bias trials (probability_left = 0.2) and left-bias trials (probability_left = 0.8). The first ten trials of each bias block are excluded to ensure the mouse is likely aware of the block it is in.

  • Expected/Unexpected: Trials where the stimulus appears on the side matching the bias block (e.g., on the right when the probability for the right side is 80%) are classified as expected, while those appearing on the opposite side are unexpected. The first ten trials of each bias block and trials with contrast levels below 20% are excluded.

  • Previous Success/Fail: Trials where the previous trial was successful or unsuccessful.

  • Stim/NoStim: Trials with high contrast (100%) stimulus and low contrast (<10%) stimulus.

Time-Frequency Parameters:

  • freqs = np.concatenate([np.arange(1, 10, 0.5), np.arange(10, 45, 1)])
  • n_cycles = freqs / 2.
  • time_bandwidth = 3.5

Usage:

Begin by downloading the LFP data for your sessions of interest using the Extraction project. (A future update will enable direct analysis by loading data directly from the IBL server.)

Current scripts rely on parallel computation using Submitit on a server (local computation will be added soon).

Authors and Acknowledgments:

This repository is developed during my work at the Lyon Neuroscience Research Center (CRNL) under the supervision of Dr. Romain Ligneul. Please note that this repository is actively evolving, with certain analyses and computations still awaiting review by my supervisor.