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

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Presentation transcript:

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

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