Biological Neural Calculator Using Plant-Based Electromagnetic Responses

Abstract

I propose a novel concept: a Biological Neural Calculator based on plant-derived electromagnetic fields. This work explores the possibility of harnessing bioelectromagnetic signals from plants as computational units, forming a new paradigm of information processing. By modeling plant electrophysiological activity as nodes within an information gradient network, I demonstrate how computation can be achieved beyond silicon-based systems. This article provides the theoretical framework, mathematical demonstrations, system architecture, operational design, and practical steps to build and use such a device.

The originality of this approach lies in the integration of living systems into computational theory. Unlike traditional hardware, which is rigid and energy intensive, plants operate as dynamic, adaptive entities responding to multiple external stimuli. Their natural electromagnetic emissions can be captured, quantified, and transformed into logical operations. Thus, the biological neural calculator offers a sustainable and self-adaptive alternative to conventional computing.

This work also situates the concept within broader scientific contexts, linking plant electrophysiology to computational neuroscience, bio-inspired artificial intelligence, and green technology. By providing a practical blueprint and mathematical foundation, the article bridges abstract theory with experimental feasibility. The proposal therefore represents both a conceptual breakthrough and a call for interdisciplinary exploration.