In recent years, the zebrafish has emerged as an important model organism as researchers have become able to obtain detailed information about the connectivity and activity of its brain. The laboratory of the Principal Investigator recently presented work that makes use of this wealth of available information to better understand zebrafish behavior at the level of specific neural circuitry. To that end, a realistic network model, composed of experimental verified functional cell types, was developed that accurately models the fish's tendency to swim in a particular direction in response to a moving visual stimulus. The purpose of this collaboration project is to develop a similar theoretical circuit model for mechanosensory-based rheotaxis, a more sophisticated behavior that also is displayed readily by larval zebrafish. The project combines experimental and computational approaches, and constitutes a significant step towards establishing the larval zebrafish as a vertebrate model where whole-brain imaging can be combined with whole-brain circuit modelling in order to generate a unified theoretical basis for the holistic study of neural circuits. The project also provides undergraduate and graduate students from diverse backgrounds opportunities to receive hands-on laboratory experiences.
As a first step towards the scientific goal of the project, the investigators demonstrated that larval zebrafish can perform efficient rheotaxis in complete darkness and in the absence of any other direct cues from the external reference frame. Furthermore, the investigators showed that this behavior requires the presence of a flow velocity gradient, and presented behavioral data that support a novel algorithm that fish use to efficiently navigate laminar flow: detailed behavioral analysis shows that fish use the hair-cells of their lateral line to measure (1) the curl of the local velocity vector field to detect the presence of flow, and (2) the temporal change in curl magnitude following swim bouts to deduce flow direction. As such, a precise and predictive algorithm is presented, whose quantitative description allows to examine its neural implementation, and eventually to generate a realistic and testable circuit model of all brain areas involved in executing this computation. The complementary expertise of the researchers of the two participating laboratories allows testing, verification, and refinement of this model-circuit based on an iterative combination of whole-brain imaging, genetic perturbations and quantitative modeling. A companion project is being funded by the Federal Ministry of Education and Research, Germany (BMBF).
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
