Identifying properties of pattern completion neurons in a computational model of the visual cortex
Fig 3
K-means clustering identified ensembles of densely connected neurons.
(A) Steps to find ensembles of strongly connected neurons. Top Left: Example of network before preprocessing; Top Middle: We removed weak excitatory connections (EPSP < 0.9 mV) from the network. Top Right: The network was converted into an undirected graph. Bottom: After preprocessing, we computed a similarity matrix where each cell represented the number of shared excitatory connections between each pair of excitatory neurons. We then performed K-means clustering on this matrix to identify ensembles. (B) K-means clustering identified groups of densely connected neurons from the similarity matrix. The number of clusters was tuned such that the median size of each cluster (ensemble) was about 40 neurons (dashed lines). (C) Three examples of ensembles found in the network. We returned directionality (arrows) to the network after the ensembles were found. (D) Neurons within each ensemble had a higher probability of being bidirectionally connected than randomly selected neurons in the network. n = 10 sets of 100 neuron pairs. (E) Neurons within each ensemble were more correlated than randomly selected neurons in the network. n = 10 sets of 10 neurons. (F) Neurons within each ensemble had more similar POs than randomly selected neurons in the network. n = 10 sets of 10 neurons. *** indicates p < 0.001.