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Spike waveform classifier aimed at: 1- Removing noise during preprocessing for improved clustering 1.5- Output from classifier provides an additional high quality feature for clustering on 2- Recommending electrodes with neurons for user ease

== Note:

• Classifier threshold is internationally set conservatively so false negatives are minimized (i.e. number of actual spikes discarded is minimized).

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Dataset

############################################################ Aiming for a 2GB dataset, half and half for spikes and noise.

== Suggestion for updating dataset Add maximum diversity of both noise and spike clusters (i.e. if we have a total of 2000 neurons, and space for 100,000 waveforms, then every neuron should contribute 100,000/2000 waveforms if possible)

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Integration

############################################################ Aimed at being integrated into blech_clust/blech_process.py, prior to actually performing clustering

== Method of operation:

• Classifier will be trained and stored in central location
• Each time a user initiates sorting, classifier will be checked for updates and downloaded as needed, then run on the local computer

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Future Challenges

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• Dealing with waveforms of different lengths 1- Either have multiple models, 2- Or standardize waveform length ** Best waveform snapshot can be empirically determined

• Getting more labelled data for "not spikes"

  • Approches:
    • Adding during spike sorting
    • Using semi-supervised methods to iteratively label more data

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Experimentation, Model Registry, Data Availability

############################################################ Experimentation and model + data tracking is done on Neptune.ai. These details are not currently available publicly. Dataset used here can be accessed at https://drive.google.com/drive/folders/1i1WPL7gt0ckvpuGVoZKfnu27bRRVUQEX?usp=sharing