Thermodynamic Advantage of Transient Coherent Dynamics in Hierarchical Decision Architectures: Coherent Resonant Netting (CRN) via Open System Formalism

The brain faces a thermodynamic paradox: while local neural computation is metabolically inexpensive, long-range communication (spiking and broadcasting) incurs a high energetic cost. To explore vast hypothesis spaces under these constraints, the brain requires a mechanism to filter competing options without committing to expensive global signaling for every candidate. We propose Coherent Resonant Netting (CRN), a hierarchical architecture where hypothesis selection occurs in a transient wave-dynamical layer governed by an effective Hamiltonian on a graph. By modeling this process using the Lindblad master equation for open systems, we demonstrate that noise—often considered a nuisance—can be exploited as a resource. We identify a robust Environment-Assisted Transport (inspired by environment-assisted quantum transport, ENAQT; Rebentrost et al., 2009) regime where moderate dephasing accelerates convergence to a consensus state, maximizing thermodynamic efficiency. This "netting" mechanism allows the system to pre-select high-value hypotheses in a quasi-reversible coherent mode before triggering the irreversible, metabolically taxing readout. We derive explicit energy checkpoints (E_save > E_tax) and falsifiable predictions regarding the spectral signatures of this phase-sensitive filtering, offering a biophysically grounded solution to the communication cost problem in neural architectures.