Source code: Spatial synchronization codes from coupled rate-phase neurons
softwareposted on 26.01.2019 by Joseph Monaco, Rose M. De Guzman, Hugh T. Blair, Kechen Zhang
Code as a research output can either be uploaded directly from your computer or through the code management system GitHub. Versioning of code repositories is supported.
This repository contains the source code for the 'spatial phase codes' (spc) project that characterized recordings of neurons termed 'phaser cells' from the lateral septum and hippocampus. This software produced the results (data, figures, statistics) for the two manuscripts regarding phaser cells:
First, in 2017, we published a preprint with early iterations of the analysis and simulations:
• Monaco, J. D., Blair, H. T., & Zhang, K. (2017). Spatial theta cells in competitive burst synchronization networks: Reference frames from phase codes. BioRxiv, 211458. doi:10.1101/211458
Second, we have published a more comprehensive narrative of the phaser cell phenomena and simulation studies:
• Monaco, J. D., De Guzman, R. M., Blair, H. T., & Zhang, K. (2019). Spatial synchronization codes from coupled rate-phase neurons. PLOS Computational Biology 15(1): e1006741. doi:10.1371/journal.pcbi.1006741
The code was developed on a macOS installation of the Anaconda (https://www.anaconda.com/download/) scientific python distribution using python version 3.4.5. The development environment is specified in the 'environment.yml' file. Thus, you should be able to recreate the original python environment on a Mac with the 'conda' command:
$ cd /path/to/spc-code
$ conda create -n spc --file environment.yml
For additional details about installing the code, downloading the datasets, and running the code, please see the README file.
This is research code, so there is no guarantee that it is fit for any particular purpose. It is a software artifact that was used to produce the results presented in the manuscripts. This code should allow reproduction of all results, in combination with the datasets described above, but unforeseen problems with technical compatibility in the future may prevent reproducibility. If there are obvious problems or crashes that should be resolved, the code may be forked and patched, or you can email "jmonaco [at] jhu [dot] edu".