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View Code? Open in Web Editor NEWSpikeShip: A method for fast, unsupervised discovery of high-dimensional neural spiking patterns.
License: MIT License
SpikeShip: A method for fast, unsupervised discovery of high-dimensional neural spiking patterns.
License: MIT License
How can we install on Mac? setup.py is deprecated...
I'm working on implementing SpikeShip on my data but I often have the issue that after running diss_spikeship = spikeship.distances(ripple_spike_times, ii_spike_times)
there are NaNs in diss_spikeship
. I've tried setting various minimum criteria (minimum number of neurons/events/spikes etc) which sometimes works, but not always. Under which conditions does the algorithm output a NaN exactly? Then I can make sure to exclude those.
Cheers!
Hi Boris,
SpikeShip itself is running smooth and fast but getting my spiking data in the right input format for SpikeShip is currently taking very long and I was wondering if you have conversion functions already. The required input format is very unintuitive to me and I'm losing a lot of time in for loops but I can't easily see how to make it faster.
My data is in the format used by the IBL (and I believe Allen as well). It consist of an array 1xN spike_times
which contains all the times of N spikes. Then there is a 1xN spike_clusters
array which contains the neuron identity of each spike. Then I have a Nx2 events
array which contains the start (first column) and stop (second column) times of all my N events.
Do you happen to have a conversion script to go from this data format to the one required by SpikeShip or do you have an idea on how to do this efficiently?
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