Emulating I - biomed 2010.

Area of Science:

• Neuroscience
• Computer Science
• Artificial Intelligence

Background:

• Describes personal experiences with biofeedback and meditative techniques for autonomic nervous system control.
• Introduces the concept of a resting state of consciousness (little i) characterized by minimized neural activity and specific EEG patterns.
• Details the development of a Human Nervous System Function Emulator (HNSFE) using Forth language.

Purpose of the Study:

• To create a computational model simulating the resting state of consciousness.
• To develop a system capable of generating a synthetic electroencephalogram (EEG) signal.
• To enable voluntary reactivation of brain functions from a simulated resting state.

Main Methods:

• Extended the HNSFE program to imitate neural-cognitive operations, AI, synthetic emotions, and somatic control.
• Implemented the HNSFE on a PC/104 multiprocessor network with analog circuit boards for neural network simulation.
• Integrated executive functions onto a separate ARM-based processor and added a waveform generator for synthetic EEG production.

Main Results:

• Successfully created a facsimile of the conscious brain's resting state on a dedicated CPU board.
• Generated a synthetic EEG signal with frequency modulated by an ARM processor.
• Demonstrated the potential for voluntary or sensory-driven reactivation of brain functions from the simulated state.

Conclusions:

• The developed system accurately models the brain's resting state, characterized by minimal neural activity and specific EEG signatures.
• The synthetic EEG generation and executive function integration offer a novel platform for studying consciousness and brain function.
• This computational approach paves the way for further research into brain-computer interfaces and artificial intelligence.