Matching tutor to student: rules and mechanisms for efficient two-stage learning in neural circuits
Authors: Tiberiu Tesileanu, Bence Olveczky, Vijay Balasubramanian
This repository contains the code and data for our paper on efficient two-stage learning in songbirds (and beyond).
Most of the detailed simulation code is contained in the files
basic_defs.py. There are tests checking that the code works properly, contained in
tests.py. It's a good idea to run this script first after downloading the code to make sure that everything is set up correctly.
The code uses Python 2.7, and it requires a relatively recent installation of
iPython (including the
seaborn. The optimization code uses CMA-ES optimization routines that can be downloaded from https://www.lri.fr/~hansen/cmaes_inmatlab.html.
The code responsible for generating the results and making the plots from the paper is contained in iPython notebooks
rate_based_simulations.ipynb for the rate-based model and
spiking_simulations.ipynb for the spiking model. The spiking model makes use of the parameters obtained from the optimization procedure described in the Methods section of our paper; these parameters are available in
default_params.pkl. The code makes use of
helpers.py, which contains various functions that are useful for visualizing the results of the simulations.
To perform the parameter optimization, use the
experiment_matcher.ipynb notebook. The data used for the optimization is contained in the
data folder -- we thank Timothy Otchy for parts of this data. Note that due to the stochastic nature of the learning simulations and of the CMA-ES optimization algorithm, the result will change every time this code is run. This means that you will not get exactly the same parameters as in
default_params.pkl upon running this code.
plasticity_plot.ipynb is a short notebook that was used to make the plot of the plasticity curve for our rule when
alpha = beta = 1.
Results and figures are saved in the
figs folders, respectively, by the iPython notebooks.
run_tscale_batch.py are scripts that can be used to generate the job scripts necessary to run the time-consuming spiking simulations on a cluster. These assume a system based on
summarize.py can be used to 'summarize' the results from multiple batch runs by keeping only information about the learning curve and deleting the (very space-consuming) information about intermediate states of the learning process.
If you have any issues or questions regarding the code, please use the issue tracker, or write to Tiberiu Tesileanu at email@example.com.