F A C U L T Y   P R O F I L E 

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Professor of Physiology & Cellular Biophysics

Mathematical modeling and analysis of neurons and neural networks.


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

Pandarinath, C., O’Shea, D.J., Collins, J., Jozefowicz, R., Stavisky, S., Kao, J.C., Trautmann, E.M., Churchland, M., Kaufman, M.T., Blabe, C.H., Nuyujukian, P., Sorice, B., Sarma, A., Eskandar, E.N., Ryu, S.I., Hochberg, L.R., Henderson, J.M., Shenoy, K.V., Abbott, L.F. and Sussillo, D. (2018) Inferring Single-Trial Neural Population Dynamics Using Sequential Auto-Encoders. Nature Methods 15:805–815.

Enikolopov, A., Abbott, L.F. and Sawtell, N.B. (2018) Internally-generated predictions enhance neural and behavioral detection of sensory stimuli in an electric fish. Neuron 99:135-146.

Schaffer, E.S., Stettler, D.D., Kato, D., Choi, G.B., Axel, R. and Abbott, L.F. (2018) Odor Perception on the Two Sides of the Brain: Consistency Despite Randomness. Neuron 98:736-742.

Stern, M., Bolding, K.A., Abbott, L.F and Franks, K.M. (2018) A Transformation from Latency to Ensemble Coding in a Model of Piriform Cortex. eLife 7:e34831.

Russo, A., Bittner, S., Perkins, S., Seely, J., London, B., Lara, A., Miri, A., Marshall, N., Kohn, A., Jessell, T., Abbott, L.F., Cunningham, J., Churchland, M. (2018) Motor Cortex Activity Embeds Muscle-like Commands in an Untangled Population Response. Nature Neurosci. 97:953-966.

DePasquale, B., Cueva, C.J., Rajan, K., Escola, G.S. and Abbott, L.F. (2018) full-FORCE: A Target-Based Method for Training Recurrent Networks. PLoS One 13:e0191527.

Abbott, L.F. Angelaki, D., Carandini, M., Churchland, A., Dan, Y., Deneve, S., Dayan, P., Fiete, I., Ganguli, S., Harris, K., Hausser, M., Hofer, S., Latham, P., Mainen, Z., Mrsic-Flogel, T., Paninski, L., Pillow, J., Pouget, A., Svoboda, K., Witten, I. and Zador, A. (The International Brain Laboratory) (2017) An International Laboratory for Systems and Computational Neuroscience. Neuron 96:1213:1218.

Rubin, R., Abbott, L.F. and Sompolinsky, H. (2017) Balanced Excitation and Inhibition is Required for High-Capacity, Noise-Robust Neuronal Selectivity. Proc. Natl. Acad. Sci. (USA) 114:E9366-E9375.

Eichler, K., Li, F., Litwin Kumar, A., Park, Y., Andrade, I, Schneider-Mizell, C., Saumweber, T., Huser, A., Eschbach, C., Gerber, B., Fetter, R.D., Truman, J.W., Priebe, C.E., Abbott, L.F., Thum, A., Zlatic, M. and Cardona, A. (2017) The Complete Connectome of a Learning and Memory Centre in an Insect Brain. Nature 548:175-182. PMC5806122.

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 169:956-969.

Caron, S. and Abbott, L.F. (2017) Intelligence in the Honeybee Mushroom Body (Dispatch). Current Opinion in Biol. 27:R220-R222.

Litwin-Kumar, 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 Firing-Rate Dynamics to Train Recurrent Networks of Spiking Model Neurons. arXive:1601.07620.

Lalazar, H., Abbott, L.F. and Vaadia, E. (2016) Tuning Curves for Arm-Posture Control are Consistent with Random Connectivity. PLoS Comput. Biol. 12:e1004910. PMC4880440

Babadi, B. and Abbott, L.F. (2016) Stability and Competition in Multi-Spike Models of Spike-Timing 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 165:220-233. PMC4831714

Abbott, L.F., DePasquale, B. and Memmesheimer, R.-M. (2016) Building Functional Networks of Spiking Model Neurons. Nature Neurosci. 19:350-355. PMC4928643

Churchland, A.K. and Abbott, L.F. (2016) Technical and Conceptual Advances Define a Key Moment for Theoretical Neuroscience (commentary). Nat. Neurosci. 19:348-349.

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 Sensory-Motor Connections. Neuron 87:111-23. PMC4504246

Sawtell, N. and Abbott, L.F. (2015) Strength in More Than Numbers (News and Views). Nature Neurosci. 18:614-616.

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 81:616-628.

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. 17:416-424.

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 509:43-48.

Le Masson, G., Przedborski, S. and Abbott, L.F. (2014) A Computational Model of Motor Neuron Degeneration. Neuron 83:975-988.

Wayne, G. and Abbott, L.F. (2014) A Design Procedure for Hierarchical Network Control. Neural Comp. 24:1-31.

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 Complex-Valued Firing-Rate 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 497:113-117.

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. 103:214-222.

Babadi, B. and Abbott, L.F. (2013) Pairwise Analysis Can Account for Network Structures Arising from Spike-Timing Dependent Plasticity. PLoS Comput. Biol. 9:e1002906.

White, B., Abbott, L.F. and Fiser, J. (2012) Suppression of cortical neural variability is stimulus- and state-dependent. J. Neurophysiol. 108:2383-2392.

Sussillo, D. and Abbott, L.F. (2012) Transferring Learning from External to Internal Weights in Echo-State 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. 31:9414-9425.

Babadi, B. and Abbott, L.F. (2010) Intrinsic Stability of Temporally Shifted Spike-Timing 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 Odor-Evoked Dynamics in C. Elegans Olfactory Neurons. Nature Neurosci. 13:615-621.

Luo, S., Axel, R. and Abbott, L.F. (2010) Generating Sparse and Selective Third-Order Responses in the Olfactory System of the Fly. Proc. Natl. Acad. Sci. USA 107:10713-10718.

Rajan, K., Abbott, L.F. and Sompolinsky, H. (2010) Stimulus-Dependent 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 63:544-557.

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 Non-Spatial Contigencies. PLoS Biol. 7:e1000140.

George, M.S., Abbott, L.F. and Siegelbaum, S.A. (2009) Hyperpolarization-Activated HCN Cation Channels Exert Inhibit Subthreshold EPSPs Through Interactions with M-Type K+ Channels. Nature Neurosci. 12:577-584.

Vogels, T.P. and Abbott, L.F. (2009) Gating Multiple Signals through Detailed Balance of Excitation and Inhibition in Spiking Networks. Nature Neurosci. 12:483-491

Abbott, L.F. (2008) Theoretical Neuroscience Rising. Neuron 60:489-495.

Abbott, L.F., and Luo, S.X. 2007. A Step Toward Optimal Coding in Olfaction (news and views). Nature Neurosci. 10:1342-1343.

Abbott, L.F, and Rohrkempter, R. 2007. A Simple Growth Model Constructs Critical Avalanche Networks. Prog. Brain Res. 165:13-19.

Rumsey, C., and Abbott, L.F. 2006. Synaptic Democracy in Active Dendrites. J. Neurophys. 96:2307-2318.

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. 17:235-252.

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. 26:5910-5919.

Drew, P.J. and Abbott, L.F. 2006. Extending the Effects of STDP to Behavioral Timescales. Proc. Natl. Acad. Sci. USA 103:8876-8881.

Drew, P.J. and Abbott, L.F. 2006. Models and Properties of Power-Law Adaptation in Neural Systems. J. Neurophysiol. 96:826-833.

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 Reward-Based Learning. Network: Comp. Neural Sys. (3):235-52.

Swinehart, C., and Abbott, L.F. 2005. Supervised Learning Through Neuronal Response Modulation. Neural Computation 17:609-631.

Fusi, S., Drew, P., and Abbott, L.F. 2005. Cascade Models of Synaptically Stored Memories. Neuron 45:599-611.

Vogels, T.P., Rajan, K., and Abbott, L.F. 2005. Neural Network Dynamics. Annu. Rev. Neurosci. 28:357-376.

Vogels, T.P. and Abbott, L.F. 2005. Signal Propagation in Networks of Integrate-and-Fire Neurons. J. Neurosci.

Rumsey, C., and Abbott, L.F. 2004. Equalization of Synaptic Efficacy by Activity - and Timing-Dependent Synaptic Plasticity. J. Neurophysiol. 91:2273-2280.

Prinz, A.A., Abbott, L.F., and Marder, E. 2004. The Dynamic Clamp Comes of Age. Trends in Neurosci 27:218-224.

Abbott, L.F., and Regehr, W. 2004. Synaptic Computation. Nature 431:796-803.


Awards and Honors

2010 Swartz Prize for Theoretical and Computational Neuroscience

2013 Mathematical Neuroscience Prize

2014 National Academy of Sciences


Major Grants

2014-2019 Advanced Graduate Training Program in Theoretical Neuroscience, PI, National Institute of Neurological Disorders and Stroke

2014-2016 Random Circuits and Representations within Structured Brain and Spinal Cord Regions Support Flexible Behaviors, Co-PI, Mathers Foundation

2014-2019 Gatsby Initiative in Brain Circuitry, Co-PI,Gatsby Foundation

2014-2017 Modeling Higher-Level Olfactory Circuits, PI , Simons Foundation

2014-2017 From sensation to perception: cellular and circuit mechanisms underlying prey detection in an electric fish, Co-PI, $720,000, NSF CRCNS

2017-2018 Swartz Program in Theoretical Neuroscience, PI, Swartz Foundation

2017-2021 Columbia University Theoretical Neuroscience Hub, PI, NSF Neuronex

2017-2022 The representation and control on internal state in the fly brain, PI, Simons Foundation

2017-2022 Leveraging dynamic smoothness to predict motor cortex population activity, Co-PI, Simons Foundation

2017-2022 Computational and circuit mechanisms underlying motor control, Co-PI, NINDS

2018-2023 Understanding V1 circuit dynamics and computations, Co-PI, NINDS