Neural Transmission Protocol Based on Relative Binary Spikes: An Approach for Consciousness Synchronization and Mental State Upload.

This paper proposes a new paradigm for Brain-Computer Interfaces (BCI) focused on bandwidth efficiency and sensory fidelity preservation. Unlike current models that rely on decoding complex signals into conventional data formats, the proposed architecture utilizes Spiking Neural Networks (SNN) for the capture and retransmission of discrete action potentials (spikes). Through a neural addressing system and topology mapping between distinct biological systems, the possibility of transmitting perceptive states (such as vision) via relative binary impulses in real-time is suggested. The theoretical model addresses the need for a mediation layer (AI Middleware) for protocol translation between different connectomes, aiming for the integration of minds in distributed networks and the maintenance of brain homeostasis in synthetic life-support environments.