Functional connectivity-based Attractor Neural Networks (fcANNs) offer a simple, interpretable computational alternative to conventional descriptive analyses of brain function. In this theoretically-inspired computational framework, large-scale brain dynamics are understood in relation to attractor states; neurobiologically meaningful activity configurations that minimize the free energy of the system.
