Alpha1 Record ID: 0A36F9A7
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Hulse, B. K.; Aneesh, P. B.; Romani, S.; Jayaraman, V.; Hermundstad, A. M.
Theoretical models can explain how network structure shapes neural computation, but they typically assume idealized connectivity that is inconsistent with the heterogeneous wiring of biological circuits. We address this issue in the Drosophila head-direction system, a recurrent network with ring-attractor dynamics that enable angular velocity integration. The networks symmetric wiring motifs are reminiscent of classical models, but with additional heterogeneity that should, in principle, destabilize attractor dynamics. Inspired by novel architectures discovered through machine-learning-based optimization, we develop an algorithm that transforms attractor models with symmetric connectivity into functionally equivalent models with heterogeneous connectivity. By replacing each unit with multiple clones that preserve its output, the algorithm embeds hidden symmetries in heterogeneous connectivity, maintaining ring-attractor dynamics and accurate integration. Analysis of multiple fly connectomes provides evidence for duplicated units whose connectivity reflects hidden symmetries, consistent with our theory. Our framework helps reconcile idealized models of neural computation with heterogeneous biological circuits.
Fly brains pull off elegant navigation with messy, uneven neural wiring instead of perfect symmetry— a quirky real-world hack that keeps their internal compass spinning smoothly like a wonky but reliable fidget spinner.
Shared by Brad Hulse (@BradKHulse) in a detailed story thread, loved by neuroscientists for the clever circuit insights
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