Probabilistic Neural Signal Decoding and Distributed Interface Architectures for High-Channel Brain–Machine Systems

This preprint explores probabilistic neural signal decoding architectures for large-scale brain–machine interfaces capable of recording from thousands of neural channels. Building on recent advances in neural interface hardware and distributed neural processing systems, we describe a framework for interpreting neural recordings using entropy-reduction models derived from classical information theory. Drawing conceptual parallels to the thought experiments of Erwin Schrödinger and James Clerk Maxwell, we discuss how neural decoding pipelines operate as information filters that separate statistically meaningful neural events from stochastic background activity. Within high-channel neural recording systems, decoding algorithms function as hierarchical signal partitioning mechanisms that progressively reduce informational entropy across neural datasets. The paper outlines architectural principles for distributed neural interface platforms, including on-device signal filtering, probabilistic spike classification, and hierarchical decoding pipelines capable of operating under strict power and bandwidth constraints. These concepts are discussed in the context of emerging brain–machine interface systems developed for high-density neural recording and real-time neural interpretation. Although primarily conceptual, the framework presented here provides a useful perspective for understanding the relationship between information theory, neural signal processing, and next-generation neural interface design.