Skip to content

lindermanlab/PPSeq.jl

master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Code

Latest commit

* Add mask cleanup function, demo notebook, etc.

* Implemented suggested edits to clean_masks

* Fixed a couple of types

* Changed function name, made code clearer, and fixed bug in visualisation
cdda7b7

Git stats

Files

Permalink
Failed to load latest commit information.

PP-Seq

This repo implements the point process model of neural sequences (PP-Seq) described in:

Alex H. Williams , Anthony Degleris 🌄, Yixin Wang, Scott W. Linderman 📢.
Point process models for sequence detection in high-dimensional neural spike trains.
Neural Information Processing Systems 2020, Vancouver, CA.

This model aims to identify sequential firing patterns in neural spike trains in an unsupervised manner. For example, consider the spike train below(1):

image

By eye, we see no obvious structure in these data. However, by re-ordering the neurons according to PP-Seq's inferred sequences, we obtain:

image

Further, the model provides (probabilistic) assignment labels to each spike. In this case, we fit a model with two types of sequences. Below we use the model to color each spike as red (sequence 1), blue (sequence 2), or black (non-sequence background spike):

image

The model is fully probabilistic and Bayesian, so there are many other nice summary statistics and plots that we can make. See our paper for full details.

Footnote. (1) These data are deconvolved spikes from a calcium imaging recording from zebra finch HVC. These data were published in Mackevicius*, Bahle*, et al. (2019) and are freely available online at https://github.com/FeeLab/seqNMF.

About

Neyman-Scott point process model to identify sequential firing patterns in high-dimensional spike trains

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages