Larry F. Abbott

Physiology and Cellular Biophysics Columbia University, New York, NY 
Computation & Theory
"Larry Abbott"
Professor Abbott is a physicist-turned-biologist who uses mathematical modeling to study the neural networks that are responsible for our actions and behaviors. His thesis work at Brandeis University was in the area of theoretical elementary particle physics, culminating in a PhD in 1977. He then worked in theoretical particle physics at the Stanford Linear Accelerator Center and, later, at CERN, the European center for particle physics research. He became an assistant professor in the physics department at Brandeis in 1979, received tenure in 1982, and became a full professor of physics in 1988. Abbott began his transition to neuroscience research in 1989 and moved to the Biology Department at Brandeis in 1993. From 1994-2005, he was the co-director of the Sloan-Swartz Center for Theoretical Neuroscience at Brandeis. In 2005, Abbott joined the faculty of Columbia University where his research involves using analytic techniques and computer simulation to study the electrical characteristics of single neurons, to determine how neurons interact to produce functioning neural circuits, and to investigate how large populations of neurons represent, store, and process information.
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Mean distance: 12.02 (cluster 17)
Cross-listing: Physics Tree


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Howard J. Schnitzer grad student 1977 Brandeis (Physics Tree)
 (The Hartree approximation in quantum field theory)


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Daniel J. Needleman research assistant 1997 Brandeis (Chemistry Tree)
Andrew JP Fink research assistant 2005-2006 Columbia
Frances Chance grad student Brandeis
Sean Luo grad student Columbia
Kanaka Rajan grad student Columbia
Clifton Rumsey grad student Brandeis
Kamal Sen grad student Brandeis
David Sussillo grad student Columbia
Carl van Vreeswijk grad student Brandeis
Greg Wayne grad student 2007- Columbia
Patrick Kaifosh grad student 2010- Columbia
Emilio Salinas grad student 1992-1996 Wake Forest
Mark S. Goldman grad student 2000 Harvard
Sen Song grad student 1996-2002 Brandeis
Hamid Reza Maei grad student 2002-2003 Brandeis
Patrick J. Drew grad student 1999-2004 Brandeis
Christian D. Swinehart grad student 1998-2005 Brandeis
Tim Vogels grad student 2001-2007 Brandeis
Joseph D. Monaco grad student 2004-2009 Columbia
Evan S. Schaffer grad student 2005-2011 Columbia
Qing Yuan grad student 2007-2012 Columbia University Medical School
Saul Kato grad student 2008-2013 Columbia
Ann Kennedy grad student 2015 Columbia
Brian D. DePasquale grad student 2009-2016 Columbia
Claudia Clopath post-doc Columbia
Vladimir Itskov post-doc University of Nebraska
Taro Toyoizumi post-doc RIKEN BSI
Pablo Esteban Jercog post-doc 2008- Columbia University Medical School
Wulfram Gerstner post-doc 1995 Brandeis
Jonathan E. Peelle post-doc 2005-2006 Columbia
Xaq Pitkow post-doc 2006-2010 Columbia
Omri Barak post-doc 2009-2012 Columbia
Sean Escola post-doc 2010-2014 Columbia
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Stern M, Bolding KA, Abbott LF, et al. (2018) A transformation from temporal to ensemble coding in a model of piriform cortex. Elife. 7
Lalazar H, Abbott LF, Vaadia E. (2016) Tuning Curves for Arm Posture Control in Motor Cortex Are Consistent with Random Connectivity. Plos Computational Biology. 12: e1004910
Gabitto MI, Pakman A, Bikoff JB, et al. (2016) Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons. Cell
Churchland AK, Abbott LF. (2016) Conceptual and technical advances define a key moment for theoretical neuroscience. Nature Neuroscience. 19: 348-9
Babadi B, Abbott LF. (2016) Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity Plos Computational Biology. 12
Abbott LF, DePasquale B, Memmesheimer RM. (2016) Building functional networks of spiking model neurons Nature Neuroscience. 19: 350-355
Mendelsohn AI, Simon CM, Abbott LF, et al. (2015) Activity Regulates the Incidence of Heteronymous Sensory-Motor Connections. Neuron. 87: 111-23
Sawtell NB, Abbott LF. (2015) Strength in more than numbers. Nature Neuroscience. 18: 614-6
Stern M, Sompolinsky H, Abbott LF. (2014) Dynamics of random neural networks with bistable units. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 90: 062710
Aso Y, Hattori D, Yu Y, et al. (2014) The neuronal architecture of the mushroom body provides a logic for associative learning. Elife. 3: e04577
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