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 1.Max peak height 2.Voltage difference between max and second max 3.Sum of max positive and max negative peaks 4.Time between max positive and max negative peaks 5.Max width of a polarization

5. Features 1.Max peak height -- Color 2.Voltage difference between max and second max -- Z-axis 3.Sum of max positive and max negative peaks -- Y-axis 4.Time between max positive and max negative peaks -- X-axis 5.Max width of a polarization -- Size

6. Features Plot

7. Remap2pin02 Spikes

8. 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