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Spatial subdomains in the Optic Tectum for the encoding of visual information

This repository contains the code used in the analysis of Shallcross et al 2023

Data Analysis

The analysis pipeline takes the output of 2photon imaging data which has been processed using suite2p. There is also the option to input a binary mask to select subsets of the neurons. The mask should be the same dimensions as was used during 2photon imaging and saved as a tiff, with one mask per imaging plane.

Data structure

/path/to/data/experiment_1/suite2p/combined/suite2p_data /path/to/data/experiment_1/experiment_1_slice_i_mask.tiff

/path/to/data/experiment_2/suite2p/combined/suite2p_data /path/to/data/experiment_2/experiment_2_slice_i_mask.tiff

prefix_list <- c("experiment_1", "experiment_2")

prefix <- prefix_list[1]

Pipeline

0_run_analysis.R - runs the analysis pipeline

1_preprocess.R - takes the output from suite2p and extracts tectal neuron calcium transients and spatial coordinates

2_binarise_traces.R - takes the timeseries and binarises the activity, then finds those neurons whose firing correlates with the presentation of the stimuli

3_calculate_MI.R - calculates the mutual information between neuronal response and the presentation of dot stimuli and grating stimuli

4_ant_post_bias.R - estimates the distribution of visually responsive neurons across the anterior-posterior axis of the tectum

5_gaussian_process.R - estimates how the average amount of information each neuron encodes changes across the anterior-posterior and medial-lateral axis of the tectum

6_neuronal_subtypes.R - splits up visually responsive neurons into feature selective subtypes

7_ant_post_bias_subtypes.R - estimates the distribution of neuronal subtypes across the anterior-posterior axis of the tectum

8_linear_regression.R - calculates how the amount of information per subtype changes as a function of its location across the anterior-posterior axis of the tectum

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This repository contains the code used in the analysis of Shallcross et al 2023

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