Biologically Plausible Memristive Decision-Making Circuit for Adaptive Control in Industrial Autonomous Navigation

In biological decision-making, adaptive behavior arises from the interaction between structured task context, expectation, action selection, and feedback-based learning. While most existing studies reproduce reward-driven responses under clear sensory stimuli, decision formation under weak or absent sensory evidence, such as low or 0% contrast conditions, remains insufficiently explored. To address this issue, this work proposes a biologically plausible memristive decision-making framework based on a block-structured task paradigm. The proposed system adopts a closed-loop architecture composed of four functional modules: stimulus, expectation, action, and reward/punishment. Sensory information is encoded when available, while the expectation pathway provides prior-guided modulation when sensory evidence becomes weak or unreliable. Action selection is generated through competitive integration, and reward–punishment feedback dynamically corrects decision bias and reinforces appropriate responses. Through this hierarchical interaction, stable decision behavior can be achieved even in the absence of explicit sensory inputs. PSPICE simulations are conducted to analyze system dynamics and validate the corrective role of the reward–punishment mechanism under weak and ambiguous conditions. In addition, the proposed framework is demonstrated in an industrial autonomous navigation scenario, illustrating its scalability and applicability for adaptive decision-making under uncertainty.