- On-line Learning of Dichotomies Racah Institute of Physics
- Name /bam_arbib_104740/Arbib_A005/Arbib_A005.sgm 06/20/2002 10:08AM Plate # 0 # 1 Amplification, Attenuation, and Integration3
- A Neural Network Based Head Tracking System
- Perspective Reading the Book of Memory: Sparse Sampling
- Rigorous Learning Curve Bounds from Statistical Mechanics David Haussler
- Cerebral Cortex V 13 N 11 Oxford University Press 2003; all rights reserved. Cerebral Cortex November 2003;13:11731184; DOI: 10.1093/cercor/bhg099 History Dependence of Rate Covariation
- Machine Learning KL36204(Haus) October 10, 1996 14:3 Machine Learning 25, 195236 (1996)
- 1998 Special Issue Continuous attractors and oculomotor control
- Neuron, Vol. 40, 10631073, December 18, 2003, Copyright 2003 by Cell Press ViewpointLearning in Spiking Neural
- 184 nature neuroscience volume 4 no 2 february 2001 Persistent neural activity refers to a sustained change of sodium
- Plasticity and tuning by visual feedback of the stability of a neural integrator
- Intrinsic bursting enhances the robustness of a neural network model of sequence generation by avian
- Algorithms for Non-negative Matrix Factorization
- 93:1090-1098, 2005. First published Sep 22, 2004; doi:10.1152/jn.00836.2004JN Artem Starovoytov, Jung Choi and H. Sebastian Seung
- Cerebral Cortex V 13 N 11 Oxford University Press 2003; all rights reserved. Cerebral Cortex November 2003;13:11851195; DOI: 10.1093/cercor/bhg095 Robust Persistent Neural Activity in a
- Boundary Learning by Optimization with Topological Constraints Viren Jain4,
- Boundary Learning by Optimization with Topological Constraints Supplementary Material
- Supervised Learning of Image Restoration with Convolutional Networks Viren Jain1
- Actuating a Simple 3D Passive Dynamic Walker Russ Tedrake
- Anatomy and Discharge Properties of Pre-Motor Neurons in the Goldfish Medulla That Have Eye-Position Signals During Fixations
- Stability of the memory of eye position in a recurrent network of conductance-based model neurons
- Stochastic Policy Gradient Reinforcement Learning on a Simple 3D Biped
- Representing Part-Whole Relationships in Recurrent Neural Networks
- UNCORRECTED 2 Spontaneous eye movements in goldfish: Oculomotor
- Perspective Reading the Book of Memory: Sparse Sampling
- Learning curves for stochastic gradient descent in linear feedforward networks
- nimal brains form the centrepiece of nature's development of information-
- ANNEALED THEORIES OF LEARNING H. S. Seung
- Temporal Sparseness of the Premotor Drive Is Important for Rapid Learning in a Neural Network Model of Birdsong
- The Recti ed Gaussian Distribution N. D. Socci, D. D. Lee and H. S. Seung
- Spontaneous eye movements in goldfish: oculomotor integrator performance, plasticity, and dependence on visual feedback
- Machine Learning, 18, 255276 (1995) c 1995 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
- Pattern analysis and synthesis in attractor neural H. Sebastian Seung
- 94:4038-4050, 2005. First published Aug 31, 2005; doi:10.1152/jn.00571.2004JN Dezhe Z. Jin, Valentin Dragoi, Mriganka Sur and H. Sebastian Seung
- Learning to Walk in 20 Minutes Russ Tedrake
- LETTER Communicated by Peter Latham Permitted and Forbidden Sets in Symmetric Threshold-Linear
- Journal of Neuroscience Methods 183 (2009) 165175 Contents lists available at ScienceDirect
- Machine Learning, 28, 133168 (1997) c 1997 Kluwer Academic Publishers. Manufactured in The Netherlands.
- CONEUR-816; NO. OF PAGES 14 Please cite this article in press as: Jain V, et al. MachinesQ1 that learn to segment images: a crucial technology for connectomics, Curr Opin Neurobiol (2010), doi:10.1016/j.conb.2010.07.004
- nature protocols | VOL.5 NO.3 | 2010 | 595 puorGgnihsilbuPerutaN0102natureprotocols/moc.erutan.www//:ptth
- ARTICLE Communicated by Ron Meir Operant Matching as a Nash Equilibrium
- between reward and neural activity Operant matching is a generic outcome of synaptic plasticity based on the covariance
- Unsupervised Learning by Convex and Conic Coding
- Learning Generative Models with the Up-Propagation Algorithm
- LETTER Communicated by Peter Latham Permitted and Forbidden Sets in Symmetric Threshold-Linear
- Equivalence of backpropagation and contrastive Hebbian learning in a layered
- Learning winner-take-all competition between groups of neurons in lateral inhibitory networks
- letters to nature NATURE |VOL 405 |22 JUNE 2000 |www.nature.com 947
- Learning Continuous Attractors in Recurrent Networks
- Proc. Natl. Acad. Sci. USA Vol. 93, pp. 1333913344, November 1996
- Plasticity and tuning of the time course of analog persistent firing in a neural integrator
- Behavioral/Systems/Cognitive Correlated Discharge among Cell Pairs within the
- Linear Regression of Eye Velocity on Eye Position and Head Velocity Suggests a Common Oculomotor Neural Integrator
- Spike-based learning rules and stabilization of persistent neural activity
- Journal of Computational Neuroscience 9, 171185, 2000 c 2000 Kluwer Academic Publishers. Manufactured in The Netherlands.
- Nonlinear network models of the oculomotor integrator
- Learning in Intelligent Embedded Systems Daniel D. Lee
- Natural Image Denoising with Convolutional Networks
- 98:2038-2057, 2007. First published Jul 25, 2007; doi:10.1152/jn.01311.2006J Neurophysiol Ila R. Fiete, Michale S. Fee and H. Sebastian Seung
- Improved Dynamic Stability Using Reinforcement Brain & Cognitive Sciences Department, MIT, Cambridge MA, USA
- Learning in neural networks by reinforcement of irregular spiking
- Minimax and Hamiltonian Dynamics of Excitatory-Inhibitory Networks
- Selectively Grouping Neurons in Recurrent Networks of Lateral Inhibition
- acid, dendritic cells produce more IL12. They also found that IL15 and retinoic acid pro
- Gradient Learning in Spiking Neural Networks by Dynamic Perturbation of Conductances Ila R. Fiete1
- Double-ring network model of the head-direction system Xiaohui Xie,1,
- Query by Committee H. S. Seung
- Vapnik-Chervonenkis entropy of the spherical P. Riegler and H. S. Seung
- Algorithms for Non-negative Matrix Factorization
- LETTER Communicated by Tom Heskes Learning Curves for Stochastic Gradient Descent in Linear
