Feasibility of a Personal Neuromorphic Emulation

Area of Science:

• Neuroscience
• Computational Neuroscience
• Artificial Intelligence

Background:

• Intelligence is represented by neural connection patterns in brains and machines.
• Brain development is activity-dependent and influenced by individual experience.
• The theory of active inference offers a framework for understanding sentient systems.

Purpose of the Study:

• To interpret active inference theory in terms of information processing.
• To explore the emergence of consciousness from self-organizing information systems.
• To propose the possibility of reconstructing brain/mind computational dynamics.

Main Methods:

• Interpreting active inference theory through the lens of information and self-evidencing.
• Analyzing the principles of hierarchical and reentrant self-evidencing in complex systems.
• Applying these principles to the concept of neuromorphic computational emulation.

Main Results:

• Mind can be described in information terms, independent of physical substrate.
• Consciousness emerges as a consequence of a 'good regulator' in complex self-organizing systems.
• Neuromorphic computational emulation may allow reconstruction of brain/mind dynamics.

Conclusions:

• The mind's informational basis transcends specific physical implementations.
• Consciousness is an emergent property of complex, self-organizing information processing systems.
• Computational reconstruction of the human brain/mind is theoretically achievable with advanced neuromorphic technology.