jdmonaco / skaggs

Neural data analysis framework for single-unit spatial experiments [WIP]

Note: skaggs has not yet been refactored as an independent package. You should have no expectation that it will run or import correctly in its current state.

I hope to ameliorate this as soon as I have time; of course, PRs welcome.

 

skaggs [Work In Progress]

The skaggs package supports the import, storage, data structures, preprocessing, and analysis of neurobehavioral datasets of the type produced by rodent spatial navigation (e.g., place cell) experiments, for cluster-sorted single-unit data and 2D head-position trajectories. Data analysis functionality includes both signal processing and information theoretic calculations.

Origin

This code was used to conduct the analysis of hippocampal and subcortical recordings and position-tracking data presented in this paper:

• Monaco JD, De Guzman RM, Blair HT, and Zhang K. (2019). Spatial synchronization codes from coupled rate-phase neurons. PLOS Computational Biology, 15(1), e1006741. doi: 10.1371/journal.pcbi.1006741

The complete code archive for the paper is available on figshare (doi: 10.6084/m9.figshare.6072317.v1) and the dataset is archived on OSF (doi: 10.17605/osf.io/psbcw). The skaggs package is based on the spc.lib subpackage in that code archive; the name is an homage, of course, to Bill Skaggs.

Dependencies

Note: This section will be updated as the packaging and dependencies are fixed.

The skaggs package requires a typical scientific python computing environment, which can be set up using Anaconda or similar distributions (see the requirements.txt).

Todo

• Fix dependencies for other packages of mine (e.g., remove or add as submodules)
• Update the setup.py to ensure correct installation, etc.
• Improve function and class APIs to enhance usabililty and convenience
• Code style and formatting consistency (e.g., flake8 validation)