Quantum Bio-Systems Modeling for Neural Network Dynamics in Complex Organisms

Abstract

The integration of quantum theory with biological modeling has generated new possibilities for understanding neural network dynamics in complex organisms. Traditional computational neuroscience frameworks rely on classical assumptions of synaptic transmission, electrochemical signaling, and network plasticity. However, these models often struggle to account for nonlinear, emergent, and context-dependent behaviors observed in biological cognition. This study investigates how quantum bio-systems modeling offers an expanded theoretical foundation for explaining neural processes by incorporating principles such as superposition, entanglement, and probability amplitude interference. Through qualitative synthesis of literature in quantum biology, cognitive neuroscience, and computational modeling, the research explores how quantum-level interactions—particularly within microtubules, ion channels, and molecular signaling networks—may influence large-scale neural dynamics. The analysis further examines how quantum-inspired mathematical structures can capture complex patterns such as rapid state transitions, adaptive learning, and multi-scale interactions in neural circuits. The findings suggest that quantum bio-systems modeling not only enhances theoretical understanding of cognition but also holds promise for applications in neuromorphic computing, mental health diagnostics, and bio-inspired artificial intelligence. Ultimately, the study positions quantum-biological frameworks as an emerging frontier for advancing our knowledge of neural complexity, bridging molecular processes with organism-level behavior.