From Synapses to Dynamics: Obtaining Function from Structure in a Connectome Constrained Model of the Head Direction Circuit

How precisely does circuit wiring specify function? This fundamental question is particularly relevant for modern neuroscience, as large-scale electron microscopy now enables the reconstruction of neural circuits at single-synapse resolution across many organisms. To interpret circuit function from such datasets, we must understand the extent to which the measured structure constrains dynamics. We investigate this question in the Drosophila head direction (HD) circuit, which maintains an internal heading estimate through attractor dynamics that integrate self-motion velocity cues. This circuit serves as a sensitive assay for functional specification: continuous attractor networks are theoretically known to require finely tuned wiring symmetries, whereas connectomes omit key cellular parameters such as synaptic gains, neuronal thresholds, and time constants, and reveal that biological wiring can be heterogeneous. We introduce a method that combines selfsupervised and unsupervised learning objectives to estimate unknown parameters at the level of cell types, rather than individual neurons and synapses. Starting from the raw connectivity matrix, our approach recovers a network that exhibits continuous attractor dynamics and accurately integrates a range of velocity inputs, despite minimal parameter tuning on a connectome that notably departs from the symmetric regularity of an idealized ring attractor. We characterize how deviations from the original connectome shape the space of viable solutions. We also perform in-silico ablation experiments to probe the distinct functional roles of specific cell types in the circuit, demonstrating how connectome-derived structure, when augmented with minimal, biologically grounded tuning, can replicate known physiology and elucidate circuit function.

can circuit function be determined from its wiring? Can we do so in the absence of key membrane and synaptic parameters such as cellular thresholds, gains, and time constants? Additionally, connectomes provide detailed synaptic connectivity maps but may contain potential errors from the data pipeline, including misalignment of electron microscopy sections and false positives or negatives in synapse detection Scheffer et al. (2020a). Thus, individual connectomes represent partial and sometimes noisy snapshots of the underlying biological networks that may fail to capture the necessary factors that play a role in shaping circuit behavior.

We explore these questions in the Drosophila head direction (HD) circuit, a canonical example of a continuous ring attractor network in biology (