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ORCIDL. F. Abbott
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- affiliation: Columbia University, Zuckerman Mind Brain Behavior Institute, New York City, NY, USA
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Journal Articles
- 2022
[j33]Ramin Khajeh
, Francesco Fumarola, L. F. Abbott:
Sparse balance: Excitatory-inhibitory networks with small bias currents and broadly distributed synaptic weights. PLoS Comput. Biol. 18(2) (2022)- [j32]Rainer Engelken, Alessandro Ingrosso, Ramin Khajeh, Sven Goedeke, L. F. Abbott:
Input correlations impede suppression of chaos and learning in balanced firing-rate networks. PLoS Comput. Biol. 18(12): 1010590 (2022) - [j31]Denis Turcu, L. F. Abbott:
Sparse RNNs can support high-capacity classification. PLoS Comput. Biol. 18(12): 1010759 (2022) - 2016
- [j30]Baktash Babadi, L. F. Abbott:
Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity. PLoS Comput. Biol. 12(3) (2016) - [j29]Hagai Lalazar, L. F. Abbott, Eilon Vaadia:
Tuning Curves for Arm Posture Control in Motor Cortex Are Consistent with Random Connectivity. PLoS Comput. Biol. 12(5) (2016) - 2014
- [j28]Greg Wayne, L. F. Abbott:
Hierarchical Control Using Networks Trained with Higher-Level Forward Models. Neural Comput. 26(10): 2163-2193 (2014) - 2013
- [j27]Baktash Babadi, L. F. Abbott:
Pairwise Analysis Can Account for Network Structures Arising from Spike-Timing Dependent Plasticity. PLoS Comput. Biol. 9(2) (2013) - [j26]Evan S. Schaffer, Srdjan Ostojic, L. F. Abbott:
A Complex-Valued Firing-Rate Model That Approximates the Dynamics of Spiking Networks. PLoS Comput. Biol. 9(10) (2013) - 2010
- [j25]Baktash Babadi, L. F. Abbott:
Intrinsic Stability of Temporally Shifted Spike-Timing Dependent Plasticity. PLoS Comput. Biol. 6(11) (2010) - 2008
- [j24]M. Brozovic, L. F. Abbott, Richard A. Andersen:
Mechanism of gain modulation at single neuron and network levels. J. Comput. Neurosci. 25(1): 158-168 (2008) - 2005
- [j23]Christian D. Swinehart, L. F. Abbott:
Supervised Learning Through Neuronal Response Modulation. Neural Comput. 17(3): 609-631 (2005) - 2004
- [j22]Christian D. Swinehart, Kris Bouchard, Peretz Partensky, L. F. Abbott:
Control of network activity through neuronal response modulation. Neurocomputing 58-60: 327-335 (2004) - [j21]Clifton C. Rumsey, L. F. Abbott:
Synaptic equalization by anti-STDP. Neurocomputing 58-60: 359-364 (2004) - 2002
- [j20]Patrick J. Drew, L. F. Abbott:
Modeling temporal combination selective neurons of the songbird. Neurocomputing 44-46: 789-794 (2002) - 2001
- [j19]Frances S. Chance, L. F. Abbott:
Input-specific adaptation in complex cells through synaptic depression. Neurocomputing 38-40: 141-146 (2001) - 2000
- [j18]Mark S. Goldman, Jorge Golowasch, Eve Marder, L. F. Abbott:
Dependence of firing pattern on intrinsic ionic conductances: Sensitive and insensitive combinations. Neurocomputing 32-33: 141-146 (2000) - [j17]Frances S. Chance, Sacha B. Nelson, L. F. Abbott:
A recurrent network model for the phase invariance of complex cell responses. Neurocomputing 32-33: 339-344 (2000) - [j16]Sen Song, L. F. Abbott:
Temporally asymmetric Hebbian learning and neuronal response variability. Neurocomputing 32-33: 523-528 (2000) - [j15]Jian Zhang, L. F. Abbott:
Gain modulation of recurrent networks. Neurocomputing 32-33: 623-628 (2000) - [j14]Emilio Salinas, L. F. Abbott:
Do Simple Cells in Primary Visual Cortex Form a Tight Frame? Neural Comput. 12(2): 313-335 (2000) - 1999
- [j13]Mark S. Goldman, Sacha B. Nelson, L. F. Abbott:
Decorrelation of spike trains by synaptic depression. Neurocomputing 26-27: 147-153 (1999) - [j12]L. F. Abbott, Peter Dayan:
The Effect of Correlated Variability on the Accuracy of a Population Code. Neural Comput. 11(1): 91-101 (1999) - [j11]Jorge Golowasch, Michael Casey, L. F. Abbott, Eve Marder:
Network Stability from Activity-Dependent Regulation of Neuronal Conductances. Neural Comput. 11(5): 1079-1096 (1999) - 1997
- [j10]Wulfram Gerstner, L. F. Abbott:
Learning Navigational Maps Through Potentiation and Modulation of Hippocampal Place Cells. J. Comput. Neurosci. 4(1): 79-94 (1997) - 1996
- [j9]K. I. Blum, L. F. Abbott:
A model of spatial map formation in the hippocampus of the rat. Neural Comput. 8(1): 85-93 (1996) - 1995
- [j8]Marco Idiart, Barry Berk, L. F. Abbott:
Reduced Representation by Neural Networks with Restricted Receptive Fields. Neural Comput. 7(3): 507-517 (1995) - 1994
- [j7]Emilio Salinas, L. F. Abbott:
Vector reconstruction from firing rates. J. Comput. Neurosci. 1(1-2): 89-107 (1994) - [j6]Carl Vreeswijk, L. F. Abbott, G. Bard Ermentrout:
When inhibition not excitation synchronizes neural firing. J. Comput. Neurosci. 1(4): 313-321 (1994) - 1993
- [j5]L. F. Abbott, Gwendal Le Masson:
Analysis of Neuron Models with Dynamically Regulated Conductances. Neural Comput. 5(6): 823-842 (1993) - [j4]Carl Vanvreeswijk, L. F. Abbott:
Self-Sustained Firing in Populations of Integrate-and-Fire Neurons. SIAM J. Appl. Math. 53(1): 253-264 (1993) - 1992
- [j3]Thomas B. Kepler, L. F. Abbott, Eve Marder:
Reduction of conductance-based neuron models. Biol. Cybern. 66(5): 381-387 (1992) - 1991
- [j2]L. F. Abbott, Edward Farhi, Sam Gutmann:
The path integral for dendritic trees. Biol. Cybern. 66(1): 49-60 (1991) - [j1]L. F. Abbott, Eve Marder, Scott L. Hooper:
Oscillating Networks: Control of Burst Duration by Electrically Coupled Neurons. Neural Comput. 3(4): 487-497 (1991)
Conference and Workshop Papers
- 2021
- [c8]David G. Clark, L. F. Abbott, SueYeon Chung:
Credit Assignment Through Broadcasting a Global Error Vector. NeurIPS 2021: 10053-10066 - 2010
- [c7]Kanaka Rajan, L. F. Abbott, Haim Sompolinsky:
Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics. NIPS 2010: 1975-1983 - 1999
- [c6]Eve Marder, Jorge Golowasch, Kathryn S. Richards, Cristina Soto-Treviño, William L. Miller, L. F. Abbott:
Self-Assembly of Oscillatory Neurons and Networks. IWANN (1) 1999: 1-11 - 1998
- [c5]L. F. Abbott, Sen Song:
Temporally Asymmetric Hebbian Learning, Spike liming and Neural Response Variability. NIPS 1998: 69-75 - [c4]Frances S. Chance, Sacha B. Nelson, L. F. Abbott:
Recurrent Cortical Amplification Produces Complex Cell Responses. NIPS 1998: 90-96 - 1992
- [c3]Sylvie Renaud-Le Masson, Gwendal Le Masson, Eve Marder, L. F. Abbott:
Hybrid Circuits of Interacting Computer Model and Biological Neurons. NIPS 1992: 813-819 - 1990
- [c2]L. F. Abbott, Scott L. Hooper, Thomas B. Kepler, Eve Marder:
Oscillating networks: modeling the pyloric circuit of the stomatogastric ganglion. IJCNN 1990: 175-180 - [c1]Thomas B. Kepler, L. F. Abbott, Eve Marder:
Order Reduction for Dynamical Systems Describing the Behavior of Complex Neurons. NIPS 1990: 55-61
Informal and Other Publications
- 2023
- [i11]David G. Clark, L. F. Abbott:
Theory of coupled neuronal-synaptic dynamics. CoRR abs/2302.08985 (2023) - 2022
- [i10]Rainer Engelken, Alessandro Ingrosso, Ramin Khajeh, Sven Goedeke, L. F. Abbott:
Input correlations impede suppression of chaos and learning in balanced rate networks. CoRR abs/2201.09916 (2022) - [i9]Samuel Lippl, L. F. Abbott, SueYeon Chung:
The Implicit Bias of Gradient Descent on Generalized Gated Linear Networks. CoRR abs/2202.02649 (2022) - [i8]David G. Clark, L. F. Abbott, Ashok Litwin-Kumar:
Dimension of Activity in Random Neural Networks. CoRR abs/2207.12373 (2022) - 2021
- [i7]SueYeon Chung, L. F. Abbott:
Neural population geometry: An approach for understanding biological and artificial neural networks. CoRR abs/2104.07059 (2021) - [i6]David G. Clark, L. F. Abbott, SueYeon Chung:
Credit Assignment Through Broadcasting a Global Error Vector. CoRR abs/2106.04089 (2021) - 2018
- [i5]Theodore H. Moskovitz, Ashok Litwin-Kumar, L. F. Abbott:
Feedback alignment in deep convolutional networks. CoRR abs/1812.06488 (2018) - [i4]Alessandro Ingrosso, L. F. Abbott:
Training dynamically balanced excitatory-inhibitory networks. CoRR abs/1812.11424 (2018) - 2017
- [i3]Ran Rubin, L. F. Abbott, Haim Sompolinsky:
Balanced Excitation and Inhibition are Required for High-Capacity, Noise-Robust Neuronal Selectivity. CoRR abs/1705.01502 (2017) - [i2]Brian DePasquale, Christopher J. Cueva, Kanaka Rajan, G. Sean Escola, L. F. Abbott:
full-FORCE: A Target-Based Method for Training Recurrent Networks. CoRR abs/1710.03070 (2017) - 2016
- [i1]David Sussillo, Rafal Józefowicz, L. F. Abbott, Chethan Pandarinath:
LFADS - Latent Factor Analysis via Dynamical Systems. CoRR abs/1608.06315 (2016)
Coauthor Index
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