Biological neural circuits contain specialized substructures that support distinct computational functions, yet many bio-inspired neural networks borrow biological motifs without identifying their circuit-level origins. In this study, we investigate whether zebrafish tectal microcircuits can be attributed along two computational axes: energy-efficient information processing and robustness-preserving stabilization. We reconstruct a directed zebrafish-inspired retinotectal microcircuit graph and verify retinotectal signal propagation through dynamic simulation. A leaky integrate-and-fire spiking neural network is then used as a nonlinear perturbation testbed, where predefined subcircuits are selectively ablated and evaluated using the Energy Sensitivity Index and the Robustness Sensitivity Index.The results reveal a functional dissociation between two tectal subcircuits.The \textit{ns\_TIN} subcircuit shows a low spike footprint but a measurable influence on prediction error, suggesting a role as a spike-efficient internal information gate.In contrast, the \textit{superficial\_TIN} subcircuit produces the highest robustness sensitivity, suggesting a feedback-like role in maintaining system-level stability.We further transfer these attributed functions into ResNet18-based artificial neural networks and evaluate them on CIFAR-10 under inference-budget reduction and Gaussian noise corruption. The \textit{ns\_TIN}-inspired module improves performance preservation under reduced computation, whereas the \textit{superficial\_TIN}-inspired module improves robustness under input noise. These findings provide a subcircuit-level route for linking biological circuit organization with bio-inspired neural architecture design.

翻译：生物神经回路包含支持不同计算功能的特化子结构，然而许多类脑神经网络在借用生物结构时并未识别其回路层面的来源。本研究探讨了斑马鱼顶盖微电路是否可沿两个计算轴进行归因：节能信息处理与稳健性保持稳定化。我们重构了斑马鱼启发式视网膜-顶盖有向微电路图，并通过动态仿真验证了视网膜-顶盖信号传播。随后将泄漏积分点火脉冲神经网络作为非线性扰动测试平台，对预定义子电路进行选择性消融，并利用能量敏感指数与鲁棒性敏感指数进行评估。结果揭示了两个顶盖子电路之间的功能分离：\textit{ns\_TIN}子电路具有低脉冲足迹但对预测误差有可测量影响，表明其作为脉冲高效内部信息门控的功能；相比之下，\textit{superficial\_TIN}子电路产生最高的鲁棒性敏感度，提示其在维持系统级稳定性中具有类似反馈的作用。我们进一步将这些归因功能迁移至基于ResNet18的人工神经网络，并在推理预算缩减和高斯噪声扰动条件下使用CIFAR-10数据集进行评估。受\textit{ns\_TIN}启发的模块在计算缩减条件下提升了性能保持能力，而受\textit{superficial\_TIN}启发的模块则增强了输入噪声下的鲁棒性。这些发现为连接生物回路组织与类脑神经架构设计提供了子电路层面的途径。