This repository is forked from flexible_switch_2ISP. The original neuronal simulation model was used to produce results and figures for the publication: Complementary Inhibitory Weight Profiles Emerge from Plasticity and Allow Flexible Switching of Receptive Fields.
Everton J. Agnes (1), Andrea I. Luppi (1), and Tim P. Vogels (1,2).
1 - Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
2 - Institute for Science and Technology Austria
This forked repository is used to generate data for information-theoretic analysis of neuronal activity. It is part of an investigation of how synaptic plasticity rules affect synergistic information processing.
For our investigation, we only required simulations of neuronal learning and of step input stimulation, so excluded all other parts of the original repo. The main modificaton to the learning simulation is outputting 'snapshots' of the system as its inhibitory weights take shape. We also modified the step input process to output neuronal spiking and firing rate data.
For reproducibility the code posted here was rewritten to run with GNU Fortran (gfortran; free). Plots are generated with gnuplot. To run the code from this repository you need both GNU Fortran (gfortran) and Gnuplot installed. Figures were generated with gnuplot and edited with Inkscape.
GNU Fortran
brew install gcc
https://www.gnu.org/software/gcc/fortran/
Gnuplot
brew install gnuplot
Each folder contains the code to run simulations and generate figures.
To run learning simulations, navigate to the learning directory, where the README.md file will provide instructions/explanation. Similarly, to run step_input simulations, navigate to the README.md IN THE step_input directory.
Feel free to contact me if you have any questions about the code.