F A C U L T Y P R O F I L E

ABBOTT, LAURENCE F. PH.D.
Mathematical modeling and analysis of neurons and neural networks.
Email:lfabbott@columbia.edu

Current Research
My research involves the mathematical modeling and analysis of neurons and neural networks. Analytic techniques and computer simulation are used to study how different conductances contribute to the electrical characteristics of a neuron, how neurons interact to produce functioning neural circuits, and how large populations of neurons represent, store, and process information.
Selected Publications
Hattori, D., Aso, A., Rubin, G.M., Abbott, L.F. and Axel, R. (2017) Representations of Novelty and Familiarity in a Mushroom Body Compartment. Cell 169:956969.
Caron, S. and Abbott, L.F. (2017) Intelligence in the Honeybee Mushroom Body (Dispatch). Current Opinion in Biol. 27:R220R222.
LitwinKumar, A., Harris, K.D., Axel, R., Sompolinsky, H. and Abbott, L.F. (2017) Optimal Degrees of Synaptic Connectivity. Neuron 93:1153–1164.
Sussillo, D., Jozefowicz, R., Abbott, L.F. and Pandarinath, C. (2016) LFADS  Latent Factor Analysis via Dynamical Systems. arXiv:1608.06315.
DePasquale, B., Churchland, M.M. and Abbott, L.F. (2016) Using FiringRate Dynamics to Train Recurrent Networks of Spiking Model Neurons. arXive:1601.07620.
Lalazar, H., Abbott, L.F. and Vaadia, E. (2016) Tuning Curves for ArmPosture Control are Consistent with Random Connectivity. PLoS Comput. Biol. 12:e1004910. PMC4880440
Babadi, B. and Abbott, L.F. (2016) Stability and Competition in MultiSpike Models of SpikeTiming Dependent Plasticity. PLoS Comput. Biol. 12:e1004750. PMC4777380
Gabitto, M.I., Pakman, A., Bikoff, J.B., Abbott, L.F., Jessell, T.M. and Paninski, L. (2016) Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons. Cell 165:220233. PMC4831714
Abbott, L.F., DePasquale, B. and Memmesheimer, R.M. (2016) Building Functional Networks of Spiking Model Neurons. Nature Neurosci. 19:350355. PMC4928643
Churchland, A.K. and Abbott, L.F. (2016) Technical and Conceptual Advances Define a Key Moment for Theoretical Neuroscience (commentary). Nat. Neurosci. 19:348349.
Sussillo, D. and Abbott, L.F. (2015) Random Walk Initialization for Training Very Deep Feedforward Networks. arXiv:1412.6558.
Mendelsohn, A., Simon, C.M., Abbott, L.F., Mentis, G.Z. and Jessell, T.M. (2015) Activity Regulates the Incidence of Heteronymous SensoryMotor Connections. Neuron 87:11123. PMC4504246
Sawtell, N. and Abbott, L.F. (2015) Strength in More Than Numbers (News and Views). Nature Neurosci. 18:614616.
Kato, S., Xu, Y., Cho, C., Abbott, L.F. and Bargmann, C. (2014) Temporal Responses of C. Elegans Chemosensory Neurons are Matched to Behavior. Neuron 81:616628.
Kennedy, A., Wayne, G., Kaifosh, P., Alvina, K., Abbott, L.F. and Sawtell, N.B. (2014) A Temporal Basis for Predicting the Sensory Consequences of Motor Commands in an Electric Fish. Nature Neurosci. 17:416424.
Fink, A.J.P., Croce, K.R., Huang, Z.J., Abbott, L.F., Jessell, T.M. and Azim, E. (2014) Presynaptic Inhibition of Spinal Sensory Feedback Ensures Smooth Movement. Nature 509:4348.
Le Masson, G., Przedborski, S. and Abbott, L.F. (2014) A Computational Model of Motor Neuron Degeneration. Neuron 83:975988.
Wayne, G. and Abbott, L.F. (2014) A Design Procedure for Hierarchical Network Control. Neural Comp. 24:131.
Stern, M., Sompolinsky, H. and Abbott, L.F. (2014) Dynamics of Random Neural Networks with Bistable Units. (submitted).
Aso, Y., Hattori, D., Yu, Y., Johnston, R.M., Iyer, N., Ngo, T.B., Dionne, H., Abbott, L.F., Axel, R., Tanimoto, H. and Rubin,(2014) The Neuronal Architecture of the Mushroom Body Provides a Logic for Associative Learning. (submitted).
Schaffer, E.S., Ostojic, S. and Abbott L.F. (2013) A ComplexValued FiringRate Model that Approximates the Dynamics of Spiking Networks. PLoS Comput. Biol. 9:e1003301.
Caron, S.J.C, Ruta, V., Abbott, L.F. and Axel, R. (2013) Random Convergence of Afferent Olfactory Inputs in the Drosophila Mushroom Body. Nature 497:113117.
Barak, O., Sussillo, D., Romo, R., Tsodyks, M. and Abbott, L.F. (2013) From Fixed Points to Chaos: Three Models of Delayed Discrimination. Prog. in Neurobiol. 103:214222.
Babadi, B. and Abbott, L.F. (2013) Pairwise Analysis Can Account for Network Structures Arising from SpikeTiming Dependent Plasticity. PLoS Comput. Biol. 9:e1002906.
White, B., Abbott, L.F. and Fiser, J. (2012) Suppression of cortical neural variability is stimulus and statedependent. J. Neurophysiol. 108:23832392.
Sussillo, D. and Abbott, L.F. (2012) Transferring Learning from External to Internal Weights in EchoState Networks with Sparse Connectivity. PLoS One 7:e37372.
Toyoizumi, T. and Abbott, L.F. (2011) Beyond the Edge of Chaos: Amplification and Temporal Integration by Recurrent Networks in the Chaotic Regime. Phys. Rev E 84:051908.
Monaco, J.D. and Abbott, L.F. (2011) Modular Realignment of Entorhinal Grid Cell Activity as a Basis for Hippocampal Remapping. J. Neurosci. 31:94149425.
Babadi, B. and Abbott, L.F. (2010) Intrinsic Stability of Temporally Shifted SpikeTiming Dependent Plasticity. PLoS Comput. Biol. 6:e1000961.
Chalasani, S., Kato, S., Albrecht, D., Nakagawa, T., Abbott, L.F. and Bargmann, C. (2010) Neuropeptide Feedback Modifies OdorEvoked Dynamics in C. Elegans Olfactory Neurons. Nature Neurosci. 13:615621.
Luo, S., Axel, R. and Abbott, L.F. (2010) Generating Sparse and Selective ThirdOrder Responses in the Olfactory System of the Fly. Proc. Natl. Acad. Sci. USA 107:1071310718.
Rajan, K., Abbott, L.F. and Sompolinsky, H. (2010) StimulusDependent Suppression of Chaos in Recurrent Neural Networks. Phys. Rev. E 82:011903.
Sussillo, D. and Abbott, L.F. (2009) Generating Coherent Patterns of Activity from Chaotic Neural Networks. Neuron 63:544557.
Muzzio, I.A., Levita, L., Kulkarni, J., Monaco, J., Kentros, C., Stead, M., Abbott, L.F. and Kandel, E.R. (2009) Stability of Hippocampal Representations and Neuronal Synchrony are Differentially Modulated by Attention to Spatial and NonSpatial Contigencies. PLoS Biol. 7:e1000140.
George, M.S., Abbott, L.F. and Siegelbaum, S.A. (2009) HyperpolarizationActivated HCN Cation Channels Exert Inhibit Subthreshold EPSPs Through Interactions with MType K+ Channels. Nature Neurosci. 12:577584.
Vogels, T.P. and Abbott, L.F. (2009) Gating Multiple Signals through Detailed Balance of Excitation and Inhibition in Spiking Networks. Nature Neurosci. 12:483491
Abbott, L.F. (2008) Theoretical Neuroscience Rising. Neuron 60:489495.
Abbott, L.F., and Luo, S.X. 2007. A Step Toward Optimal Coding in Olfaction (news and views). Nature Neurosci. 10:13421343.
Abbott, L.F, and Rohrkempter, R. 2007. A Simple Growth Model Constructs Critical Avalanche Networks. Prog. Brain Res. 165:1319.
Rumsey, C., and Abbott, L.F. 2006. Synaptic Democracy in Active Dendrites. J. Neurophys. 96:23072318.
Rajan, K., and Abbott, L.F. 2006. Eigenvalue Spectra of Random Matrices for Neural Networks. Phys. Rev. Lett. 97:188104.
Swinehard, C., and Abbott, L.F. 2006. Dimensional Reduction in Reward Based Learning. Network: Comp. Neural Sys. 17:235252.
Billimoria, C.P., DiCaprio, R.A., Birmingham, J.T., Abbott, L.F. and Marder, E. 2006. Neuromodulation of spike timing precision in sensory neurons. J. Neurosci. 26:59105919.
Drew, P.J. and Abbott, L.F. 2006. Extending the Effects of STDP to Behavioral Timescales. Proc. Natl. Acad. Sci. USA 103:88768881.
Drew, P.J. and Abbott, L.F. 2006. Models and Properties of PowerLaw Adaptation in Neural Systems. J. Neurophysiol. 96:826833.
Rumsey, C. and Abbott, L.F. 2006. Synaptic Democracy in Active Dendrites. J. Neurophys. doi:10.1152/jn.00149.
Swinehard, C. and Abbott, L.F. 2006. Dimensional Reduction in RewardBased Learning. Network: Comp. Neural Sys. (3):23552.
Swinehart, C., and Abbott, L.F. 2005. Supervised Learning Through Neuronal Response Modulation. Neural Computation 17:609631.
Fusi, S., Drew, P., and Abbott, L.F. 2005. Cascade Models of Synaptically Stored Memories. Neuron 45:599611.
Vogels, T.P., Rajan, K., and Abbott, L.F. 2005. Neural Network Dynamics. Annu. Rev. Neurosci. 28:357376.
Vogels, T.P. and Abbott, L.F. 2005. Signal Propagation in Networks of IntegrateandFire Neurons. J. Neurosci.
Rumsey, C., and Abbott, L.F. 2004. Equalization of Synaptic Efficacy by Activity  and TimingDependent Synaptic Plasticity. J. Neurophysiol. 91:22732280.
Prinz, A.A., Abbott, L.F., and Marder, E. 2004. The Dynamic Clamp Comes of Age. Trends in Neurosci 27:218224.
Abbott, L.F., and Regehr, W. 2004. Synaptic Computation. Nature 431:796803.
Awards and Honors
2010 Swartz Prize for Theoretical and Computational Neuroscience
2013 Mathematical Neuroscience Prize
2014 National Academy of Sciences
Major Grants
20142019 Advanced Graduate Training Program in Theoretical Neuroscience, PI, National Institute of Neurological Disorders and Stroke
20142016 Random Circuits and Representations within Structured Brain and Spinal Cord Regions Support Flexible Behaviors, CoPI, Mathers Foundation
20142019 Gatsby Initiative in Brain Circuitry, CoPI,Gatsby Foundation
20142017 Modeling HigherLevel Olfactory Circuits, PI , Simons Foundation
20142017 From sensation to perception: cellular and circuit mechanisms underlying prey detection in an electric fish, CoPI, $720,000, NSF CRCNS
