Computer code

I publish the code I use in publications, as well as some personal projects, on GitHub: Below are links to some of the projects I've used in papers:

twostagelearning: download
This is the code used for the birdsong paper, Rules and mechanisms for efficient two-stage learning in neural circuits. It contains code that can be used to run and monitor simulations involving multiple functional layers working in a feed-forward manner.
multiCOV: download
PyMulticov: download
multiCOV provides a comprehensive set of functions for performing statistical analyses on multiple sequence alignments; this was used for the paper on protein sectors, Protein sectors: statistical coupling analysis versus conservation. The basic functionality is based on statistical coupling analysis (SCA) and direct coupling analysis (DCA), but greatly extended to include, for example, working with non-protein alignments and alignments of biomolecules with different alphabets (such as RNA and protein). multiCOV is in Matlab, while PyMulticov is a Python version with similar functionality.
nnet2: download
nnet2 is a framework for implementing simulations of neural network, whether rate-based or spiking.

Spring simulation with viscosity, gravity, and non-elastic collisions

masses can have different sizes, as indicated by their radius
extension or compression of springs is indicated by their redness
a uniform acceleration akin to gravity acts on all the masses
click around the red arrow in the lower-right corner to change
the dots move at constant velocity, except for reflections at the walls
masses are slowed down by resistance proportional to their speed
collisions between the masses are perfectly elastic
collisions with the walls lose energy