Dual-axis attribution of zebrafish tectal microcircuits for energy-efficient and robust neurocomputing
For neuromorphic computing and bio-inspired AI, this work provides a subcircuit-level mapping from biological microcircuits to computational functions, though the results are preliminary and domain-specific.
The study attributes zebrafish tectal microcircuits to energy-efficient processing and robustness-preserving stabilization, showing that ns_TIN subcircuits reduce spike footprint while maintaining prediction accuracy, and superficial_TIN subcircuits enhance system stability. Transferring these functions to ResNet18 improves performance under reduced computation and noise on CIFAR-10.
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.