Neurocomputational mechanisms underlying perception and sentience in the neocortex.

Area of Science:

• Neuroscience
• Quantum Physics
• Computational Biology

Background:

• Neural computation is often modeled using classical electrophysiology.
• The role of quantum mechanics in neural processing remains largely unexplored.
• Understanding information processing in sensory systems like the retina and visual cortex is crucial.

Purpose of the Study:

• To propose a quantum-based model for neural computation, specifically focusing on action potentials and neuronal networks.
• To explain the coding and computation mechanisms in the retina and visual cortex.
• To elucidate the role of quantum-phase processing in visual information abstraction and object recognition.

Main Methods:

• Analysis of action potentials, ion changes, and refractory periods.
• Development of a neuronal model based on quantum-phase ternary computation (PTC).
• Examination of neural patterning and network abstracts in the lateral geniculate nucleus (LGN) and visual cortex.

Main Results:

• Phase ternary computation (PTC) provides a mathematical solution for collective mean sampled frequency at neuron convergences within brain neural networks (BNN).
• Action potentials diffract across neural networks via PTC, annulling parallel collisions.
• The visual cortex generates abstract object representations through quantum-phase processing and frequency pattern analysis.

Conclusions:

• Quantum-phase processing and PTC are fundamental to neural computation, explaining information coding and abstraction in sensory pathways.
• This quantum model offers a universal explanation for information processing at neuronal convergences across various sensory modalities.
• The brain neural network (BNN) utilizes quantum principles for efficient information integration and memory encoding.