-
1
-
-
0030686449
-
Decoding Visual Information From a Population of Retinal Ganglion Cells
-
D. K. Warland, P. Reinagel, and M. Meister, "Decoding Visual Information From a Population of Retinal Ganglion Cells," Journal of Neurophysiology, vol. 78, pp. 2336-2350, 1997.
-
(1997)
Journal of Neurophysiology
, vol.78
, pp. 2336-2350
-
-
Warland, D.K.1
Reinagel, P.2
Meister, M.3
-
2
-
-
84898938636
-
-
W. Wu, M. J. Black, Y. Gao, E. Bienenstock, M. Serruya, A. Shaikhouni, and J. P. Donoghue, "Neural Decoding of Cursor Motion using a Kalman Filter," 2003.
-
(2003)
Neural Decoding of Cursor Motion using a Kalman Filter
-
-
Wu, W.1
Black, M.J.2
Gao, Y.3
Bienenstock, E.4
Serruya, M.5
Shaikhouni, A.6
Donoghue, J.P.7
-
3
-
-
84898984007
-
-
Y. Gao, M. J. Black, E. Bienenstock, S. Shoham, and J. P. Donoghue, "Probabilistic Inference of Hand Motion from Neural Activity in Motor Cortex," 2002.
-
(2002)
Probabilistic Inference of Hand Motion from Neural Activity in Motor Cortex
-
-
Gao, Y.1
Black, M.J.2
Bienenstock, E.3
Shoham, S.4
Donoghue, J.P.5
-
4
-
-
0032530363
-
A Statistical Paradigm for Neural Spike Train Decoding Applied to Position Prediction from Ensemble Firing Patterns of Rat Hippocampal Place Cells
-
E. N. Brown, L. M. Frank, D. Tang, M. C. Quirk, and M. A. Wilson, "A Statistical Paradigm for Neural Spike Train Decoding Applied to Position Prediction from Ensemble Firing Patterns of Rat Hippocampal Place Cells," Journal of Neuroscience, vol. 18, pp. 7411-7425, 1998.
-
(1998)
Journal of Neuroscience
, vol.18
, pp. 7411-7425
-
-
Brown, E.N.1
Frank, L.M.2
Tang, D.3
Quirk, M.C.4
Wilson, M.A.5
-
5
-
-
0013288412
-
Dynamic Bayesian Networks: Representation, Inference and Learning,
-
PhD thesis, UC Berkeley, Computer Science Division
-
K. Murphy, "Dynamic Bayesian Networks: Representation, Inference and Learning," PhD thesis, UC Berkeley, Computer Science Division, 2002.
-
(2002)
-
-
Murphy, K.1
-
7
-
-
70350236064
-
-
N. Friedman, I. Nachman, and D. Pe'er, Learning Bayesian network structure from massive datasets: The sparse candidate algorithm, in In Fifteenth Conference on Uncertainty in Artificial Intelligence (UAl- 99), 1999.
-
N. Friedman, I. Nachman, and D. Pe'er, " Learning Bayesian network structure from massive datasets: The "sparse candidate" algorithm," in In Fifteenth Conference on Uncertainty in Artificial Intelligence (UAl- 99), 1999.
-
-
-
-
8
-
-
0035221560
-
Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks
-
A. J. Hartemink, D. K. Gifford, T. Jaakkola, and R. Young, "Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks," in Pacific Symposium on Biocomputing (PSB01), 2001, pp. 422-433.
-
(2001)
Pacific Symposium on Biocomputing (PSB01)
, pp. 422-433
-
-
Hartemink, A.J.1
Gifford, D.K.2
Jaakkola, T.3
Young, R.4
-
9
-
-
70350226866
-
Identifying Functional Connectivity in Large-Scale Neural Ensemble Recordings: A Multiscale Data Mining Approach, Neural Computation, p
-
in press
-
S. Eldawlatly, R. Jin, and K. G. Oweiss, "Identifying Functional Connectivity in Large-Scale Neural Ensemble Recordings: A Multiscale Data Mining Approach," Neural Computation, p. in press, 2008.
-
(2008)
-
-
Eldawlatly, S.1
Jin, R.2
Oweiss, K.G.3
-
10
-
-
70350212236
-
On The Use of Dynamic Bayesian Networks in Reconstructing Functional Neuronal Networks from Spike Train Ensembles
-
S. Eldawlatly, Y. Zhou, R. Jin, and K. Oweiss, "On The Use of Dynamic Bayesian Networks in Reconstructing Functional Neuronal Networks from Spike Train Ensembles," Submitted to Neural Computation, 2008.
-
(2008)
Submitted to Neural Computation
-
-
Eldawlatly, S.1
Zhou, Y.2
Jin, R.3
Oweiss, K.4
-
11
-
-
0000040173
-
The Identification of Point Process Systems
-
D. Brillinger, "The Identification of Point Process Systems," Annals of Probability, vol. 3, pp. 909-924, 1975.
-
(1975)
Annals of Probability
, vol.3
, pp. 909-924
-
-
Brillinger, D.1
-
13
-
-
55449121121
-
A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback
-
R. Legenstein, D. Pecevski, and W. Maass, "A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback," PLoS Computational Biology, vol. 4, p. e1000180, 2008.
-
(2008)
PLoS Computational Biology
, vol.4
-
-
Legenstein, R.1
Pecevski, D.2
Maass, W.3
-
14
-
-
0037187567
-
Spike-timing-dependent synaptic modification induced by natural spike trains
-
R. C. Froemke and Y. Dan, "Spike-timing-dependent synaptic modification induced by natural spike trains," Nature Neuroscience, vol. 416, pp. 433-438, 2002.
-
(2002)
Nature Neuroscience
, vol.416
, pp. 433-438
-
-
Froemke, R.C.1
Dan, Y.2
|