What the modular computation does and does not expose

A declared modular template is

Here $\psi$ is identity or external tanh. The gate is scalar at each trial. For a nonmodular variant set $m_t=u_t$.

This template records the distinction between recurrent state and a pointwise readout branch. The symbols “cortical,” “striatal,” and “thalamic” can name computational motifs without constituting an anatomical identification.

The PyTorch candidate convention is

followed by

Dropout in a multilayer nn.GRU is placed between recurrent layers, not after the final recurrent layer PyTorchGRU.

The GRU can retain information about past inputs. It cannot turn past rule-independent points into evidence about the hidden rule in Theorem thm:observability. Post-RNN readout noise and a pointwise branch also do not provide missing feedback.