A Hebbian/Anti-Hebbian network derived from online non-negative matrix factorization can cluster and discover sparse features

Conference Record - Asilomar Conference on Signals, Systems and Computers

Volumn 2015-April, Issue , 2015, Pages 769-775

  Pehlevan, Cengiz ^a,b   Chklovskii, Dmitri B ^b  

^a HOWARD HUGHES MEDICAL INSTITUTE   (United States)

^b Simons Center for Data Analysis   (United States)

Author keywords

Feature learning; Learning rules; Neuron; Non negative matrix factorization; Online clustering

Indexed keywords

BIOMIMETICS; CIRCUIT SIMULATION; COST FUNCTIONS; FACTORIZATION; LEARNING ALGORITHMS; NETWORK LAYERS; NEURAL NETWORKS; NEURONS;

BIOLOGICALLY INSPIRED; BIOPHYSICAL PROPERTIES; FEATURE LEARNING; LEARNING RULES; NEURONAL ACTIVITIES; NONNEGATIVE MATRIX FACTORIZATION; ONLINE-CLUSTERING; SYNAPTIC PLASTICITY RULES;

MATRIX ALGEBRA;

EID: 84940476893     PISSN: 10586393     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACSSC.2014.7094553     Document Type: Conference Paper

References (18)

1. L. Paninski, Maximum likelihood estimation of cascade point-process neural encoding models, Network: Computation in Neural Systems, vol. 15, no. 4, pp. 243-262, 2004
2. W. Truccolo, U. T. Eden, M. R. Fellows, J. P. Donoghue, and E. N. Brown, A point-process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects, J. Neurophysiol., vol. 93, pp. 1074-1089, 2005
3. E. Brown, L. Frank, D. Tang, M. Quirk, and M. 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, no. 18, pp. 7411-7425, 1998
4. E. N. Brown, D. Nguyen, L. Frank, M. Wilson, and V. Solo, An analysis of neural receptive field plasticity by point process adaptive filtering, PNAS, vol. 98, no. 21, pp. 12 261-12 266, 2001
5. D. Ba, B. Babadi, P. Purdon, and E. Brown, Robust spectrotemporal decomposition by iteratively re-weighted least squares, Proceedings of the National Academy of Sciences, in press
6. L. Lewis, V. Weiner, E. Mukamel, J. Donoghue, E. Eskandar, J. Madsen, W. Anderson, L. Hochberg, S. Cash, E. Brown et al., Rapid fragmentation of neuronal networks at the onset of propofol-induced unconsciousness, Proceedings of the National Academy of Sciences, vol. 109, no. 49, pp. E3377-E3386, 2012
7. L. M. Frank, G. B. Stanley, and E. N. Brown, New places and new representations: Place field plasticity in the hippocampus and cortex, in Annual Meeting of Society for Neuroscience (SfN), Washington D.C., (Program No. 91.18) 2003
8. P. Huber, Robust statistics. 1981
9. L. I. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, vol. 60, no. 1, pp. 259-268, 1992
10. R. M. Willett, Z. T. Harmany, and R. F. Marcia, Poisson image reconstruction with total variation regularization, in Image Processing (ICIP), 2010 17th IEEE International Conference on. IEEE, 2010, pp. 4177-4180
11. A. Beck and M. Teboulle, Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems, Image Processing, IEEE Transactions on, vol. 18, no. 11, pp. 2419-2434, 2009
12. A. Smith and E. Brown, Estimating a state-space model from point process observations, Neural Computation, vol. 15, no. 5, pp. 965-991, 2003
13. K. Lange and J. Sinsheimer, Normal/independent distributions and their applications in robust regression, Journal of Computational and Graphical Statistics, pp. 175-198, 1993
14. L. Fahrmeir and G. Tutz, Multivariate Statistical Modeling Based on Generalized Linear Models, 2nd ed. Springer, 2001
15. D. Ba, B. Babadi, P. Purdon, and E. Brown, Convergence and stability of iteratively re-weighted least squares algorithms, IEEE Transactions on Signal Processing, vol. 62, no. 1, 2014
16. I. Daubechies, R. DeVore, M. Fornasier, and C. Güntürk, Iteratively reweighted least squares minimization for sparse recovery, Communications on Pure and Applied Mathematics, vol. 63, no. 1, pp. 1-38, 2009
17. D. Bertsekas, Nonlinear programming. Athena Scientific, 1999
18. P. De Jong and M. J. Mackinnon, Covariances for smoothed estimates in state space models, Biometrika, vol. 75, no. 3, pp. 601-602, 1988