Dual-axis attribution of zebrafish tectal microcircuits for energy-efficient and robust neurocomputing

• URL: http://arxiv.org/abs/2605.13924v1
• Date: Wed, 13 May 2026 15:05:43 GMT
• Title: Dual-axis attribution of zebrafish tectal microcircuits for energy-efficient and robust neurocomputing
• Abstract summary: We investigate whether zebrafish tectal microcircuits can be attributed along two computational axes.<n>The textitns_TIN subcircuit shows a low spike footprint but a measurable influence on prediction error.<n>The textitsuperficial_TIN subcircuit produces the highest sensitivity, suggesting a feedback-like role in maintaining system-level stability.
• Score: 7.684655684329235
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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