Algorithm, part 1.

Dictionary creation

Init split CA-DS into 2 disjoint sets: dictionary, training&testing (for model creation)

input 40 calcium sub-images.

output dictionary of textons (∈ ℝK×n)

for each sub-image { Create filter bank, F Convolve with filter bank, F (∈ □nf) and record convolution value. Combine with DGAS feature set - ℝn for each pixel

}

Concatenate all calcium pixel responses

Create K clusters for each group via K-means (Identify K via “elbow” method).

Output dictionary