Author:
Paninski Liam,Ahmadian Yashar,Ferreira Daniel Gil,Koyama Shinsuke,Rahnama Rad Kamiar,Vidne Michael,Vogelstein Joshua,Wu Wei
Publisher
Springer Science and Business Media LLC
Subject
Cellular and Molecular Neuroscience,Cognitive Neuroscience,Sensory Systems
Reference120 articles.
1. Ahmadian, Y., Pillow, J., & Paninski, L. (2009a). Efficient Markov Chain Monte Carlo methods for decoding population spike trains. Neural Computation (under review).
2. Ahmadian, Y., Pillow, J., Shlens, J., Chichilnisky, E., Simoncelli, E., & Paninski, L. (2009b). A decoder-based spike train metric for analyzing the neural code in the retina. COSYNE09.
3. Araya, R., Jiang, J., Eisenthal, K. B., & Yuste, R. (2006). The spine neck filters membrane potentials. PNAS, 103(47), 17961–17966.
4. Asif, A., & Moura, J. (2005). Block matrices with l-block banded inverse: Inversion algorithms. IEEE Transactions on Signal Processing, 53, 630–642.
5. Bell, B. M. (1994). The iterated Kalman smoother as a Gauss–Newton method. SIAM Journal on Optimization, 4, 626–636.
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