A simple democratic way to decode classes using bayes rule.

The underlying algorithm is based on ideas from a neuroscience paper,

Insanally, M. N. et al. Spike-timing-dependent ensemble encoding by non-classically responsive cortical neurons. eLife 8, (2019).

We put these same ideas to use, with a more general focus. The big idea is still very simple.

To train the decoder we,

1. build a kernel probability dist,
2. tune its bandwidth by CV,
3. use the result to do a sequential bayesian decode, where each channels dist gets a 'vote'.

To test, this scheme gets repeated, minus tuning, for rolling time windows.

That's it.

TODO

git clone pip install .

• scikit learn
• standard conda install