Neurobiological Computations of Predictive Processing in the Age of Artificial Intelligence: An Analysis of Synaptic Alterations, Cognitive Atrophy, and Active Inference Dynamics

The unprecedented expansion of artificial intelligence systems and exogenous generative models within the human cognitive ecosystem has led to the emergence of novel patterns of neuro-structural adaptation. This study, drawing upon the theoretical frameworks of predictive coding, the Bayesian brain hypothesis, and the free energy principle, dissects the mechanisms of synaptic circuit reorganization and neurochemical shifts resulting from continuous interaction with artificial predictive agents. The central argument of this paper is that the delegation of predictive processing load to algorithmic networks leads to the systematic attenuation of prediction error signal generation in the neocortex. This chronic deficit disrupts experience-dependent neuroplasticity processes, including long-term potentiation and transcriptional pathways of neurotrophic factors such as BDNF. Consequently, this epistemic transition exposes the architecture of the brain's Bayesian inference circuits to computational atrophy syndrome and pathological default mode network dominance. Neuroimaging studies indicate that reduced central executive network activity and increased default mode network hyperconnectivity correlate with phenomena such as rumination and diminished cognitive flexibility. Finally, practical strategies based on controlled uncertainty, digital diet, and evidence-based interventions are proposed as modulatory approaches for preserving cerebral computational homeostasis.

کلیدواژه ها:

Predictive Coding ، Active Inference ، Cognitive Atrophy ، Default Mode Network ، Neuroplasticity ، AI Ethics ، Free Energy Principle ، Salience Network