Mechanistic understanding of neural systems is daunting to achieve in large part due to the heterogeneity of the neuronal elements in both form and function and the complexity of the circuits formed by these elements. In this talk, we provide an update on our efforts to understand a neural system with a multi-faceted approach combining large-scale imaging of neuronal activity, reconstruction and analysis of network connectivity, and computational models of network function. We focus our work on the oculomotor neural integrator of the zebrafish, a circuit involved in the control of eye position that has been demonstrated to perform a mathematical integral of its inputs.  We identified through calcium imaging and targeted ablations neurons involved in planning, initiating, and maintaining changes in eye position. We used serial-section electron microscopy and crowd-sourced imaged analysis to reconstruct the circuit formed by these neurons and many of their synaptic partners. Computational analysis revealed a strongly-recurrent module in the circuit, consistent with theoretical predictions for the circuit mechanism of neural integration.  A whole-circuit, neural network model built from the underlying connectome reproduced the encoding of eye position seen experimentally across multiple species. We conclude with thoughts on how this approach can be extended to other domains.

Learning Objectives:

1. Understand how functional imaging, connectomic, and computational modeling approaches can be combined to investigate circuit mechanisms

2. Understand the role of recurrent excitation and mutual inhibition in generating and coordinating persistent neural activity