Robust timing and motor patterns by taming chaos in recurrent neural networks
Author: Julien Vitay firstname.lastname@example.org
Professorship for Artificial Intelligence, Department of Computer Science, Chemnitz University of Technology, D-09107 Chemnitz, Germany.
A reference implementation of:
Laje, R. and Buonomano, D.V. (2013). Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat Neurosci. 16(7) pp 925-33 doi://10.1038/nn.3405
The original article and the associated data/code can be found online on Pubmed: http://www.ncbi.nlm.nih.gov/pubmed/23708144
The standard scientific Python stack is required:
- Python 2.7 or >= 3.4
- Numpy 1.10 (lower versions may work but not tested)
- Scipy 0.17
- Matplotlib 1.3
The handwriting patterns for Fig. 2 are available on PMC (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3753043), but the copyright holder is the Nature Publishing Group and no free license is provided, so it cannot be included in this repository. In order to reproduce Fig. 2, one has to download the provided data to obtain a
The data is located at http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3753043/bin/NIHMS472497-supplement-3.zip. This zip file should then be decompressed in the
data/ older, so that the file
DAC_handwriting_output_targets.mat lies there.
data/ is provided a
get_handwriting.sh script for Linux/Mac OS users that automatically performs these steps:
wget http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3753043/bin/NIHMS472497-supplement-3.zip -O data.zip unzip data.zip DAC_handwriting_output_targets.mat
The model is implemented by a class
RecurrentNetwork in the file
code/RecurrentNetwork.py. The scripts
code/Fig3.py allow to reproduce the corresponding figures of the manuscript.
As the script for Figure 3 takes 3 days of computation on a standard computer, we provide the script
code/Fig3-Load.py that only produces the figure, based on recorded data stored in