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Updated: Oct 9, 2026

Patch Clamp Recordings from Embryonic Zebrafish Mauthner Cells
Published on: September 10, 2013
The Mauthner cell and other identified neurons of the brainstem escape network of fish
R C Eaton1, R K Lee, M B Foreman
1Integrative Physiology and Neurobiology Section, Department of EPO Biology, Box 334, University of Colorado at Boulder, Boulder, CO 80309, USA. mauthner@colorado.edu
Abstract:
This paper reviews the development of our research on the motor consequences of Mauthner cell function and related brainstem neurons. These cells activate fast-start responses such as seen in fishes escaping from predatory attacks. Our goal was to devise a neuroethological theory of fish escape that accurately reconciled the underlying neural function with a correct concept of the motor act. The identified neuron concept of invertebrates greatly influenced the initial studies. Horseradish peroxidase technology allowed us and other workers to identify principal neurons in the brainstem escape system. Digital imaging technology permitted adequate kinematic characterization of the behavior. Resulting experiments showed that Mauthner system demonstrates two general principles of motor organization: (1) the Mauthner cell is a command-like higher order neuron that serially outputs to a lower level central pattern generator; and (2) the Mauthner cell participates in a larger parallel, brainstem escape network. In this network, we showed that the spatio-temporal pattern of activity codes the timing and magnitude of agonist and antagonist trunk muscle contractions during the behavior. Because the approach angle of the stimulus determines these parameters, we were able to discover the overall sensorimotor relationship between stimulus angle and motor output. This relationship is given as a set of descriptive equations written in terms of stimulus angle, magnitude and timing variables of trunk muscle contractions, and resulting escape trajectory. The equations unify the apparent variability of C-start movement patterns into a single, quantitative theory. Recent studies by other workers show how this concept can make accurate predictions about the underlying neural processes, even at the level of the single, identified cell.
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