cluster_spacetime_()

Run spatiotemporal DPCLUS clustering and determine the density (rho) and nearest distance (delta) for each spiking event. For a given site K, the algorithm computes the distances between events that occurs nearby in time and space using a following criteria: for a spiking event having a peak site at K, it computes distances to other events whose peak occurs at K or whose second peak occurs at K. The distance is only computed for temporal neighbors to deal with probe drift. If P.nTime_clu==1, all neighbors are used, and if P.nTime_clu==2, half of the neighbors are used based on the temporal proximity.

After distances are computed, rho is computed for each event by counting the number of neighbors having distance less than dc (cut-off distance). delta is computed for each event by finding the nearest neighbor having a greater density value. rho is normalized by dividing by the number of spatiotemporal neighbors being compared, and the nearest distance (delta) is normalized by dc.

Syntax

• S_clu = cluster_spacetime_(S0, P)
• S_clu = cluster_spacetime_(S0, P, vlRedo_spk)

Input

• S0: Master struct (S0)
• P: Parameters struct (P)
• vlRedo_spk: logical vector indicating spikes to be re-clustered (optinoal, default: [])

Output

• S_clu: Cluster struct (S_clu)

Operations

• For each site k
  • fet12_site_(): Collect features of the spikes whose peak (S0.viSite_spk) occurs at site K (n1 number of spikes), or whose second peak (S0.viSite2_spk) occurs at site K (n2 number of spikes).
  • compute_dc2_(): Calculate the distance cutoff (dc), which is calculated using the distance percentile (P.dc_percent) parameter.
  • cuda_rho_(): Calculate Rho (density) for n1 spikes
• For each site k
  • cuda_delta_(): Calculate delta (nearest distance) and nearest neighbor having a greater rho for n1 spikes

See also

sort_(), fet2clu_()