Code to run the experiment 1 of the paper:
Giorgia Dellaferrera, Toshitake Asabuki, Tomoki Fukai
https://www.frontiersin.org/articles/10.3389/fnins.2022.855753/full
Frontiers in Neuroscience (2022)
We run the experiments with the following:
Numpy framework: Python 3.6.5, Numpy 1.17.3
Libraries:
librosa 0.7.1, scipy 1.1.0, sklearn 0.19.1, matplotlib 2.2.2, pylab, pydub,
The main experiments are run through Recovering_sound_sources_matlab_mult.py.
For example, to run with the standard settings:
python Recovering_sound_sources_matlab_mult.py --exp_name Experiment1 \
--n_sounds 2 --N 8
Substitute --n_sounds 2 with --n_sounds 3 to run the experiment with a larger number of mixtures.
Substitute --N 8 with N 12 to run the experiment with a larger number of output neurons.
Add --all_comb to run the experiment in the "all combination" set up.
Add --sparse_connectivity to modify the network from fully connected to sparse connectivity.
Please cite our work as:
@ARTICLE{10.3389/fnins.2022.855753,
AUTHOR={Dellaferrera, Giorgia and Asabuki, Toshitake and Fukai, Tomoki},
TITLE={Modeling the Repetition-Based Recovering of Acoustic and Visual Sources With Dendritic Neurons},
JOURNAL={Frontiers in Neuroscience},
VOLUME={16},
YEAR={2022},
URL={https://www.frontiersin.org/article/10.3389/fnins.2022.855753},
DOI={10.3389/fnins.2022.855753},
ISSN={1662-453X},
}