Spike Sorting
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Transcript of 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
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
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
Features Plot
Remap2pin02 Spikes
Training Features Plot
Training Features Plot
Training Features Plot
Future Direction
• Optimal feature choice
• Training algorithm– Bayesian clustering– Nearest neighbor– Support Vector Machine– Neural Network
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