Modeling Neural Population Spiking Activity with Gibbs Distributions
暂无分享,去创建一个
[1] Nicholas G. Hatsopoulos,et al. Brain-machine interface: Instant neural control of a movement signal , 2002, Nature.
[2] E. S. Chornoboy,et al. Maximum likelihood identification of neural point process systems , 1988, Biological Cybernetics.
[3] Lucas C. Parra,et al. Maximising Sensitivity in a Spiking Network , 2004, NIPS.
[4] Geoffrey E. Hinton,et al. Learning Sparse Topographic Representations with Products of Student-t Distributions , 2002, NIPS.
[5] George S. Young,et al. Turbulence Structure of the Convective Boundary Layer. Part II. Phonenix 78 Aircraft Observations of Thermals and Their Environment , 1988 .
[6] Rhj Sellin,et al. Engineering Turbulence - Modelling and experiments 3 , 1996 .
[7] J. Garratt. The Atmospheric Boundary Layer , 1992 .
[8] Max Welling,et al. Product of experts , 2007, Scholarpedia.
[9] Peter Dayan,et al. Probabilistic Computation in Spiking Populations , 2004, NIPS.
[10] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[11] Song-Chun Zhu,et al. Minimax Entropy Principle and Its Application to Texture Modeling , 1997, Neural Computation.
[12] Harry Shum,et al. Learning Inhomogeneous Gibbs Model of Faces by Minimax Entropy , 2001, ICCV.
[13] Michael J. Black,et al. Probabilistic Inference of Hand Motion from Neural Activity in Motor Cortex , 2001, NIPS.
[14] J. Deardorff. Numerical Investigation of Neutral and Unstable Planetary Boundary Layers , 1972 .
[15] F. T. M. Nieuwstadt,et al. Atmospheric Turbulence and Air Pollution Modelling , 1982 .
[16] Uri T Eden,et al. A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. , 2005, Journal of neurophysiology.
[17] Fujihiro Hamba,et al. A modified K model for chemically reactive species in the planetary boundary layer , 1993 .
[18] Michael J. Berry,et al. Synergy, Redundancy, and Independence in Population Codes , 2003, The Journal of Neuroscience.
[19] Wei Wu,et al. Neural Decoding of Cursor Motion Using a Kalman Filter , 2002, NIPS.
[20] P. Latham,et al. Synergy, Redundancy, and Independence in Population Codes, Revisited , 2005, The Journal of Neuroscience.
[21] Sheila Nirenberg,et al. Decoding neuronal spike trains: How important are correlations? , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[22] Yee Whye Teh,et al. Energy-Based Models for Sparse Overcomplete Representations , 2003, J. Mach. Learn. Res..
[23] J. Ottino. The Kinematics of Mixing: Stretching, Chaos, and Transport , 1989 .
[24] H. Tennekes,et al. Free Convection in the Turbulent Ekman Layer of the Atmosphere , 1970 .
[25] Stefano Panzeri,et al. Objective assessment of the functional role of spike train correlations using information measures , 2001 .
[26] J. Kaimal,et al. Eddy structure in the convective boundary layer—new measurements and new concepts , 1988 .
[27] Michael J. Black,et al. A quantitative comparison of linear and non-linear models of motor cortical activity for the encoding and decoding of arm motions , 2003, First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings..
[28] Barry Koren,et al. A robust upwind discretization method for advection, diffusion and source terms , 1993 .
[29] Matthew Fellows,et al. Robustness of neuroprosthetic decoding algorithms , 2003, Biological Cybernetics.
[30] D. Brillinger. The Identification of Point Process Systems , 1975 .
[31] Michael J. Black,et al. Fields of Experts: a framework for learning image priors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).