Computational Neuroscience and Algorithmic Dynamics

The Department of Computational Neuroscience and Algorithmic Dynamics investigates the dynamic interactions between biological regulatory systems, cognitive processes, and adaptive algorithmic environments.
Our research focuses on developing mathematical and computational models that describe how endocrine, dopaminergic, inflammatory, and autonomic systems respond to structured information inputs — and how these interactions shape perception, behavior, and long-term state transitions.

Our work integrates:

• Neuroendocrinology

• Computational Neuroscience

• Algorithmic Feedback Systems

• Machine Learning & Reinforcement Learning

• Dynamical Systems Theory

Current projects include the analysis of algorithmically induced biological state shifts, modeling attractor dynamics in physiological-cognitive systems, identifying individual susceptibility profiles, and establishing the theoretical foundation of a new research direction: algorithmic biopsychodynamics.