Maximum likelihood estimation of cascade point-process neural encoding models
作者： Liam Paninski
DOI: 10.1088/0954-898X/15/4/002
关键词: Artificial neural network 、 Estimator 、 Artificial intelligence 、 Nonlinear system 、 Algorithm 、 Point process 、 Machine learning 、 Mathematics 、 Estimation theory 、 Maxima and minima 、 Models of neural computation 、 Likelihood function
摘要: Recent work has examined the estimation of models of stimulus-driven neural activity in which some linear filtering process is followed by a nonlinear, probabilistic spiking stage. We …
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David L. Donoho, Iain M. Johnstone, Gérard Kerkyacharian, Dominique Picard, Wavelet Shrinkage: Asymptopia? Journal of the Royal Statistical Society: Series B (Methodological). ,vol. 57, pp. 301- 337 ,(1995) , 10.1111/J.2517-6161.1995.TB02032.X
Wulfram Gerstner, Werner M. Kistler, Spiking Neuron Models: Single Neurons, Populations, Plasticity ,(2002)
A. W. van der Vaart, Asymptotic Statistics Cambridge University Press. ,(1998) , 10.1017/CBO9780511802256
Julian J. Bussgang, Crosscorrelation functions of amplitude-distorted gaussian signals Research Laboratory of Electronics, Massachusetts Institute of Technology. ,(1952)
Peter McCullagh, John Ashworth Nelder, Generalized Linear Models ,(1983)
Renaud Jolivet, Timothy J., Wulfram Gerstner, The spike response model: a framework to predict neuronal spike trains international conference on artificial neural networks. pp. 846- 853 ,(2003) , 10.1007/3-540-44989-2_101
Daniel S. Reich, Jonathan D. Victor, Bruce W. Knight, The Power Ratio and the Interval Map: Spiking Models and Extracellular Recordings The Journal of Neuroscience. ,vol. 18, pp. 10090- 10104 ,(1998) , 10.1523/JNEUROSCI.18-23-10090.1998
W. James, Charles Stein, Estimation with Quadratic Loss Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics. pp. 443- 460 ,(1992) , 10.1007/978-1-4612-0919-5_30
Michael I. Miller, Donald L. Snyder, Random Point Processes in Time and Space ,(1991)
Nello Cristianini, J Shawe-Taylor, An introduction to Support Vector Machines Cambridge University Press (2000). ,(2000)