Energetic and Regulatory Constraints as a Missing Mechanistic Layer in Computational Psychiatry

Current neurocomputational models of psychopathology, particularly those based on Predictive Coding, have successfully described the informational dynamics of disorders such as schizophrenia. However, as noted in recent reviews [1, 2], a persistent mechanistic gap remains between algorithmic-level dysfunction and its underlying biophysical substrate. This paper proposes that this gap can be partially bridged by introducing explicit thermodynamic constraints: Entropic Overhead (Φ) and Dissipative Capacity (α). It is argued that human-specific evolutionary expansions of cortical circuitry impose a high-dimensional neural state space that requires substantial metabolic energy to regulate [3, 4]. Recent empirical findings—including the reported efficacy of fMRI-guided repetitive transcranial magnetic stimulation (rTMS) [5] and convergent evidence of aberrant resting-state gamma activity [6]—are discussed as being structurally consistent with thermodynamic regulatory models [3, 7]. These observations suggest that purely algorithmic accounts of psychopathology may remain incomplete without incorporating energetic and dissipative constraints.Python implementation of the Neurodynamic Rigidity (NR) Index:https://github.com/Dasein193/Neurodynamic-Rigidity-Index