<p>Major depressive disorder (MDD) is accompanied by abnormal reward processing, altered dopamine transmission in the ventral tegmental area–nucleus accumbens–medial prefrontal cortex (VTA–NAc–mPFC) dopaminergic pathway, and disruptions in both neural dynamics and brain energy metabolism. Yet, how these abnormalities converge within a unified framework of neural dynamics and neural energy coding remains unclear. The purpose of this review is to integrate and critically assess computational models of neural dynamics and neural energy coding in MDD, with a particular emphasis on the multiscale modeling approaches developed in our recent work, and to organize these advances into a coherent conceptual framework linking dopamine-related circuit dysfunction to alterations in neural energy consumption. First, we constructed Hodgkin–Huxley (H-H) models for the NAc medium spiny neuron (MSN) to simulate its neurodynamics. Then, using the neural energy model, we explored the energy consumption characteristics of MSNs and found that, in the MDD condition, MSN energy consumption during spiking was lower than in controls, demonstrating the feasibility and sensitivity of this energy-based methodology. To further examine how these mechanisms scale to functional circuits, we constructed a neural network dynamical model for the VTA–NAc–mPFC dopaminergic pathway and applied an augmented neural-energy computation framework to characterize its energy consumption features. Simulations demonstrated that neural energy consumption was substantially lower in the MDD condition, primarily due to decreased mPFC energy expenditure. Distinct energy-coding patterns emerged across neuronal types, and the energy required to encode a single action potential in both MSNs and pyramidal neurons increased under MDD low dopamine situation, indicating reduced energy efficiency. Moreover, the correlation between membrane potential and instantaneous power was moderate (0.6–0.9) rather than tight, and it changed substantially with dopamine levels. This shows that neural energy consumption carries additional neural information that is not reflected directly in membrane potential signals. Together, these findings establish a unified computational framework that links dopamine deficiency, ion-channel-level dysfunction, microcircuit dynamics impairment, and large-scale reductions in neural energy consumption. Our work highlights neural energy coding as a promising mechanistic indicator and potential biomarker for MDD, and provides a generalizable methodology for investigating other neuropsychiatric disorders.</p>