This repository contains the code and files that are associated with the paper titled "Chance, long tails, and inference in a non-Gaussian, Bayesian theory of vocal learning in songbirds".

Folder "code":

generate_figures.m generates all the panels shown in Fig. 2, Fig. 3, Fig. 4, and Fig. 5 of the paper.

dynamical_Bayesian.m simulates one iteration of the dynamical Bayesian filter model shown in Fig. 1 of the paper.

songbird_single_timescale_stable.m simulates the four constant-shift experiments, and the staircase-shift experiment.

song_acquisition.m simulates the song acquisition period during which the bird begins to sing and gradually refines its song until the beginning of the adulthood.

shift_experiment.m simulates the shift experiment given the shift size for each day.

generate_kernels_likelihoods.m generates one kernel, and two likelihoods that are used in the Bayesian filter given a set of input parameters.

get_shifted_likelihood.m generates shifted likelihood corresponding to the shifted auditory channel.

stable_distri_laguerre_bergstrom.m simulates the 1d symmetric stable distribution.

powerlaw_distribution.m simulates the 1d symmetric power law distribution.

histnorm.m is some helper function adapted from Arturo Serrano.

test_code.m is for authors' own usage and can be ignored. But one can use it to test how those learning curves change when parameters change.

Folder "data":

In this folder, processed experimental data are stored.

Folder "figures":

The Fig. 2, Fig. 3, Fig. 4, and Fig. 5 in the paper, which are generated from generate_figures.m, are stored in this folder.

Folder "variables":

In this folder, theoretical variables simulated from the model and optimization are stored.

Folder "stable_distribution_compare":

Different methods to simulate the stable distribution are compared in this folder.