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

TL;DR

This study identifies and attributes two distinct computational functions to zebrafish tectal microcircuits, linking biological circuit organization to bio-inspired neural network design for energy efficiency and robustness.

Contribution

It introduces a dual-axis attribution method for zebrafish tectal microcircuits and demonstrates how these functions can enhance artificial neural networks.

Findings

The ns_TIN subcircuit acts as an energy-efficient information gate.

The superficial_TIN subcircuit enhances robustness to input noise.

Transferring these functions improves neural network performance under constraints.

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.