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Spike Sorting

Spike Sorting. Goal: Extract neural spike trains from MEA electrode data Method 1: Convolution of template spikes Method 2: Sort by spikes features. Cluster Cutting. Advantages: Better separation Requires less information Disadvantages Computationally intensive. Remap2pin02 Spikes.

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Spike Sorting

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  1. Spike Sorting • Goal: Extract neural spike trains from MEA electrode data • Method 1: Convolution of template spikes • Method 2: Sort by spikes features

  2. Cluster Cutting • Advantages: • Better separation • Requires less information • Disadvantages • Computationally intensive

  3. Remap2pin02 Spikes

  4. Selected Features • Max peak height • Voltage difference between max and second max • Sum of max positive and max negative peaks • Time between max positive and max negative peaks • Max width of a polarization

  5. Features • Max peak height-- Color • Voltage difference between max and second max -- Z-axis • Sum of max positive and max negative peaks -- Y-axis • Time between max positive and max negative peaks -- X-axis • Max width of a polarization -- Size

  6. Features Plot

  7. Remap2pin02 Spikes

  8. Training Features Plot

  9. Training Features Plot

  10. Training Features Plot

  11. Future Direction • Optimal feature choice • Training algorithm • Bayesian clustering • Nearest neighbor • Support Vector Machine • Neural Network

  12. Conclusion • Data suggests we should be able to isolate individual neural firing patterns from MEA data • Use MEA data to model and study network of neurons in culture

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