Larry Abbott

Columbia University

Operating Principles of a Learning Network in Electric Fish

12:00 pm, Monday 08 October 2018

Location: Oxford Martin School


The electrosensory lobe (ELL) in mormyrid electric fish is a cerebellar-like structure (also mushroom-body like) that cancels the sensory effects of self-generated electric fields, allowing prey to be detected. Like the cerebellum, the ELL involves two stages of processing, analogous to the Purkinje cells and output cells of the deep cerebellar nuclei.  Through the work of Curtis Bell and others, a model was previously developed to describe the output stage of the ELL, but the role of the Purkinje-cell analogs, the medium ganglion (MG) cells, in the circuit had remained mysterious. I will present a complete circuit description of the ELL, developed in collaboration with Nate Sawtell and Salomon Muller, the reveals a novel role for the MG cells. The resulting model of ELL function relies on a principle of circuit organization based on the learning rather than the response properties of neurons that we have verified in the anatomy of the ELL.


Larry Abbott is the William Bloor Professor of Theoretical Neuroscience at Columbia University. He received his PhD in physics from Brandeis University in 1977 and worked in theoretical particle physics at the Stanford Linear Accelerator Center, CERN (the European center for particle physics), and Brandeis, where he was a member of the physics department from 1979 to 1993. Abbott began his transition to neuroscience research in 1989, joined the Biology Department at Brandeis in 1993, and was the director of the Volen Center at Brandeis from 1997 until 2002. In 2005, he joined the faculty of Columbia University, where he is currently a member of the Departments of Neuroscience and of Physiology and Cellular Biophysics, and co-director of the Center for Theoretical Neuroscience. Abbott is a member of the US National Academy of Sciences, a recipient of an NIH Director’s Pioneer Award, and was awarded the Swartz Prize for Theoretical Neuroscience in 2010 and the Mathematical Neuroscience Prize in 2013.