The Relationship Between Dynamic Causal Modeling and Brain Architectures

dynamic causal modeling is just a procedure it's a procedure used to assimilate data usually in the context of unknown latent states generating observable time series where you also have unknown parameters. In one sense that model inversion that inverting the forward model to recover the unseeable the hidden causes of all our sensory data is exactly the problem the brain faces. The brain may well use very very similar mathematical procedures such as these variational techniques to to assimilate not experimental or engineering or plant but the sensory signals from our eyes and our ears in the form of interceptive data.