Parameters

Force Membership Parameters

par.template_sdnum: max radius of cluster in std devs.

par.template_type: type of clustering algorithm used to distribute unsorted spikes into the predefined clusters when hitting the 'Force' button or automatically by Do_clustering.m.

1. 'nn': refers to the k-nearest neighbours algorithm. We take an unsorted spike and its k nearest neighbours in the feature-space among the already clustered spikes but only within a limiting distance. We then assign the unsorted spike to the cluster which contained the most nearest neighbours. (The limiting distance is computed as 'par.template_sdnum' times the square root of the sum of variance in each feature dimension for all of the clustered spikes.)
2. 'center': We create a template, one for each cluster. This is done by taking the means of each feature-dimension and the overall SD in all feature-dimensions ("std dev of the euclidean distance from mean"). We then look up for each unsorted spike the the closest template but only if it is within its SD times 'par.template_sdnum' distance. The spike is sorted in the cluster of this template.
3. 'ml': We fit normal distributions (gaussians) to each cluster. We then calculate for each unsorted spike the probability of belonging to each cluster. The cluster with the highest probability is chosen and the spike is assigned to it. 'par.template_sdnum' is not used, thus all spikes are assigned to one of the clusters.
4. 'mahal': We take the Mahalonobis distance of an unsorted spike from each cluster and look the closest cluster. We then take the square root of the average Mahalonobis distance of the clustered spikes from their own cluster (averaged over each cluster). If this latter times 'par.tempalte_sdnum' is larger than the spikes distance from the cluster then the spike is accepted as a member of the cluster.

par.template_k: "# of nearest neighbours, used only if 'par.template_type' = 'nn'.

par.template_k_min: min # of nn for vote, used only if 'par.template_type' = 'nn'.

par.force_feature: We can both use the wavelet coefficients('wav') or all datapoints of a given spike ('spk'). Usually the spike shapes are used because it's possible to have a noise or overlapping in the wavelets, but the distance in the spike shapes space is small.