Information storage is a fundamental requirement for adaptive behavior. At the cellular level, this storage is implemented by neurons through synaptic plasticity—the ability of synapses to strengthen or weaken in response to activity. This paper develops an interpretation within Energy-Efficiency Theory (EET). Starting from Yang's Axioms, we propose that the neuron is an informational buffer: a constrained-state energy structure that stores information as the texture of synaptic connectivity. We define the neuron's boundary as a dual-layer structure (cell membrane and synaptic membrane) and establish the constraint barrier EbEb as a measurable quantity related to AMPA receptor number and spine volume. Long-term potentiation (LTP) and long-term depression (LTD) are the mechanisms by which the neuron establishes and releases constraints on information flow. We map the neuron's operation to Yang's Ben-Shi Sliding: conservation mode corresponds to synaptic homeostasis and E/I balance (bias toward Ben), while expansion mode corresponds to plasticity activation and new synapse formation (bias toward Shi). The neuron's Energy-Efficiency Cycle is dissected into five detailed stages: disturbance (presynaptic activity), response (calcium influx), stabilization (kinase activation), constraint (LTP/LTD maintenance via inertia), and transition (synaptic pruning). We extend the framework to neural networks, defining network-level constraint barriers and Ben-Shi Sliding (default mode network vs. executive control network), bridging the gap from single neurons to cognition. The framework unifies the membrane (cellular), body (physiological), and cognitive buffers, and yields testable predictions with priority ranking and experimental protocols.
Yang (Thu,) studied this question.