How cortico-basal ganglia-thalamic subnetworks can shift decision policies to increase reward rate
Fig 3
Canonical correlation analysis (CCA) identifies control ensembles (cf. [29]).
Given matrices of average firing rates, F (both summed rates across channels, Σ, and between-channel differences, Δ), and fit DDM parameters, D, derived from a set of networks at baseline (left panels), CCA finds the low-dimensional projections, for firing rates and
for DDM parameters (right panels), which maximize the correlation, ρ, between the projections
and
of F and D. Blue lines in the F plot show left channel activity, orange show right channel activity, and green shows populations that go across both channels.