AI-Driven Physics-Informed Bio-Silicon Intelligence System: Integrating Hybrid Systems, Biocomputing, Neural Networks, and Machine Learning, for Advanced Neurotechnology

TL;DR

This paper introduces the Bio-Silicon Intelligence System (BSIS), a hybrid platform integrating biological neural networks with silicon computing, employing advanced AI, physics, and neurotechnology for high-fidelity neural interfacing and bidirectional communication.

Contribution

The paper presents a novel hybrid system combining biological neural networks with silicon-based computing using physics-informed reinforcement learning and advanced interfacing techniques.

Findings

Successful neural interfacing with rat brains using carbon nanotube electrodes

Effective bidirectional communication between biological and silicon systems

Implementation of a hybrid reinforcement learning state machine for neurotechnology

Abstract

We present the Bio-Silicon Intelligence System (BSIS), an innovative hybrid platform that integrates biological neural networks with silicon-based computing. The BSIS, a Physics-Informed Hybrid Hierarchical Reinforcement Learning State Machine, employs carbon nanotube-coated electrodes to interface rat brains with computational systems, enabling high-fidelity neural interfacing and bidirectional communication through self-organizing systems in both biological and silicon forms. Our system leverages both analogue and digital AI theory, incorporating concepts from computational theory, chaos theory, dynamical systems theory, physics, and quantum mechanics. Additionally, the BSIS replicates the neuronal dynamics typical of intelligent brain tissue, employing nonlinear operations underlying learning and information storage. Neural signals are read through the FreeEEG32 board and BrainFlow software, then features are extracted and mapped to game actions by tracking feature changes in continuous data. Metadata is encoded into both analogue and digital brain stimulation signals at the microvolt level using our proprietary software and hardware. The system employs a dual signaling approach for training the rat brain, incorporating a reward solution and sound as well as human-inaudible distress sounds. This paper details the design, theory, functionality, and technical specifications of the BSIS, highlighting its interdisciplinary approach and advanced technological integration.