Spike detection competition

The main drawback of functional calcium imaging is its slow dynamics. This is not only due to limited frame rates, but also due to calcium dynamics, which are a slow transient readout of fast spiking activity.

A perfect algorithm would infer the spike times of each neuron from the calcium imaging traces. Despite ongoing effort for more than 10 years, no such algorithm is around – as most inverse problems, this one is a hard one, suffering from noise and variability. Then, it is difficult to generate ground truth (electrophysiological attached-cell recording of an intact cell and simultaneous calcium imaging). Plus, algorithms working for one dataset do not easily generalize to others.spikes

To make comparison between algorithms easier, a competition was set up, based on several ground truth datasets from four different labs. If you are using an algorithm for deconvolution, test it out on their data. The datasets are easy to load in Matlab and Python (the spike train/calcium trace above is taken from one of the datasets) and are interesting by themselves even independent of this competition. Please check out the website of Spikefinder.
If I understand it correctly, it is mostly managed by Philipp Berens (Tuebingen/Germany) and Jeremy Freeman (Janelia/US).

I hope this competition will get a lot of attention and will make different algorithms easier to compare!

P.S. This competition made me also aware of another one going on earlier this year, which was less about spike finding, and more about cell identification and segmentation for calcium imaging data (Neurofinder).

This entry was posted in Calcium Imaging, Data analysis, machine learning and tagged , , . Bookmark the permalink.

2 Responses to Spike detection competition

  1. Pingback: The spikefinder dataset | A blog about neurophysiology

  2. Pingback: A convolutional network to deconvolve calcium traces, living in an embedding space of statistical properties | A blog about neurophysiology

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