The algorithmic agent perspective and computational neuropsychiatry: from etiology to advanced therapy in major depressive disorder

Major Depressive Disorder is a complex and heterogeneous condition impacting millions of individuals globally. Computational neuropsychiatry is poised to deliver significant breakthroughs through mechanistic modeling of this condition. To address the multifactorial character of Major Depressive Disorder, we frame our analysis within the Algorithmic or Kolmogorov Information Theory of consciousness, where agents interact with the world driven by an objective function that evaluates {\em valence}. In this context, depression is defined as a state characterized by persistently low valence. This view suggests that depression may originate from inaccurate world models (leading to negative bias), a dysfunctional objective function (anhedonia), deficient planning (cognitive deficits), or unfavorable environmental conditions --- potentially linking the model with proposed depression biotypes. Building upon existing literature, we bridge algorithmic, dynamical systems and neurobiological concepts, emphasizing the role of plasticity --- the ability of the agent to update elements such as their world model or objective function --- in maintaining psychological health. This perspective connects and expands on emerging concepts in the REBUS and CANAL perspectives, such as {\em landscape flattening and canalization}. Finally, we explore how the synergies between brain stimulation, psychotherapy and plasticity-enhancing compounds such as psychedelics can be used to repair neural circuits, and how these therapies can be optimized using personalized computational models - {\em neurotwins}.