Publications by topic

[Natural Statistics] [Perception] [Other Neural Theory] [Neural Dynamics] [Neural Encoding] [Neural Decoding] [Machine Learning] [Signal Processing]

Natural Statistics

[1]    M. Pachitariu and M. Sahani.
Learning visual motion in recurrent neural networks.
In P. Bartlett, F. C. N. Pereira, L. Bottou, C. J. C. Burges, and K. Q. Weinberger, eds., Advances in Neural Information Processing Systems, vol. 25, 2012.

[2]    P. Berkes, R. E. Turner, and M. Sahani.
A structured model of video reproduces primary visual cortical organisation.
PLoS Computational Biology, 5(9):e1000495, 2009.
doi pmc pdf.

[3]    P. Berkes, R. E. Turner, and M. Sahani.
On sparsity and overcompleteness in image models.
In J. C. Platt, D. Koller, Y. Singer, and S. Roweis, eds., Advances in Neural Information Processing Systems, vol. 20. Curran Associates, Inc., Red Hook, New York, 2008.
pdf.

[4]    J. Lücke and M. Sahani.
Maximal causes for non-linear component extraction.
Journal of Machine Learning Research, 9:1227–1267, 2008.
journal pdf.

[5]    R. E. Turner and M. Sahani.
Modeling natural sounds with modulation cascade processes.
In J. C. Platt, D. Koller, Y. Singer, and S. Roweis, eds., Advances in Neural Information Processing Systems, vol. 20. Curran Associates, Inc., Red Hook, New York, 2008.
pdf.

[6]    J. Lücke and M. Sahani.
Generalized softmax networks for non-linear component extraction.
In J. Marques de Sá, L. A. Alexandre, W. Duch, and D. Mandic., eds., Artificial Neural Networks – ICANN 2007 Proceedings, Part I, Lecture Notes in Computer Science, pp. 657–667. Springer, Berlin, 2007.
pdf.

[7]    R. E. Turner and M. Sahani.
A maximum-likelihood interpretation for slow feature analysis.
Neural Computation, 19(4):1022–1038, 2007.
doi.

[8]    R. E. Turner and M. Sahani.
Probabilistic amplitude demodulation.
In Independent Component Analysis and Signal Separation, Lecture Notes in Computer Science, pp. 544–551. Springer, 2007.
Best student paper award.
pdf.

Perception

[1]    E. R. Ferré, M. Sahani, and P. Haggard.
Subliminal stimulation and somatosensory signal detection.
Acta Psychologica, 170:103–111, 2016.
doi.

[2]    M. I. Garrido, M. Sahani*, and R. J. Dolan*.
Outlier responses reflect sensitivity to statistical structure in the human brain.
PLoS Computational Biology, 9(3):e1002999, 2013.
* equal contributions.
doi pmc pdf.

[3]    L. Whiteley and M. Sahani.
Attention in a Bayesian framework.
Frontiers in Human Neuroscience, 6:100, 2012.
doi pmc pdf.

[4]    M. I. Garrido, G. R. Barnes, M. Sahani, and R. J. Dolan.
Functional evidence for a dual route to amygdala.
Current Biology, 22(2):129–134, 2012.
doi pmc.

[5]    M. I. Garrido, R. J. Dolan, and M. Sahani.
Surprise leads to noisier perceptual decisions.
i-Perception, 2(2):112–120, 2011.
doi pmc pdf.

[6]    M. B. Ahrens and M. Sahani.
Observers exploit stochastic models of sensory change to help judge the passage of time.
Current Biology, 21(3):200–206, 2011.
doi pmc pdf.

[7]    M. Sahani and L. Whiteley.
Modeling cue integration in cluttered environments.
In M. Landy, K. Körding, and J. Trommershäuser, eds., Sensory Cue Integration. Oxford University Press, 2011.
pdf.

[8]    S. Fleming, L. Whiteley, O. J. Hulme, M. Sahani, and R. J. Dolan.
Effects of category-specific costs on neural systems for perceptual decision-making.
Journal of Neurophysiology, 103:3238–3247, 2010.
doi pmc pdf.

[9]    M. B. Ahrens and M. Sahani.
Inferring elapsed time from stochastic neural processes.
In J. C. Platt, D. Koller, Y. Singer, and S. Roweis, eds., Advances in Neural Information Processing Systems, vol. 20. Curran Associates, Inc., Red Hook, New York, 2008.
Best student paper, honourable mention.
pdf.

[10]    L. Whiteley and M. Sahani.
Implicit knowledge of visual uncertainty guides decisions with asymmetric outcomes.
Journal of Vision, 8(3):2, 1–15, 2008.
doi pmc pdf.

Other Neural Theory

[1]    M. Sahani.
A biologically plausible algorithm for reinforcement-shaped representational learning.
In S. Thrun, L. Saul, and B. Schoelkopf, eds., Advances in Neural Information Processing Systems, vol. 16. MIT Press, Cambridge, Massachusetts, 2004.
pdf.

[2]    M. Sahani and P. Dayan.
Doubly distributional population codes: Simultaneous representation of uncertainty and multiplicity.
Neural Computation, 15(10):2255–2279, 2003.
doi pdf ps.gz.

[3]    P. Dayan, M. Sahani, and G. Deback.
Adaptation and unsupervised learning.
In S. Becker, S. Thrun, and K. Obermayer, eds., Advances in Neural Information Processing Systems, vol. 15, pp. 221–228. MIT Press, Cambridge, Massachusetts, 2003.

[4]    M. Sahani and P. Dayan.
Multiplicative modulation of bump attractors.
Technical Report GCNU TR 2000-05, Gatsby Computational Neuroscience Unit, University College, London, 2000.
pdf ps.gz.

Neural Dynamics

[1]    J. H. Macke, L. Buesing, and M. Sahani.
Estimating state and model parameters in state-space models of spike trains.
In Z. Chen, ed., Advanced State Space Methods for Neural and Clinical Data. Cambridge University Press, 2015.

[2]    M. Pachitariu, D. R. Lyamzin, M. Sahani, and N. A. Lesica.
State-dependent population coding in primary auditory cortex.
Journal of Neuroscience, 35(5):2058–2073, 2015.
doi pmc.

[3]    M. Pachitariu, B. Petreska, and M. Sahani.
Recurrent linear models of simultaneously-recorded neural populations.
In L. Bottou, C. J. C. Burges, M. Welling, Z. Ghahramani, and K. Q. Weinberger, eds., Advances in Neural Information Processing Systems, vol. 26, 2013.

[4]    K. V. Shenoy, M. Sahani, and M. M. Churchland.
Cortical control of arm movements: A dynamical systems perspective.
Annual Review of Neuroscience, 36:337–359, 2013.
doi full text.

[5]    L. Buesing, J. H. Macke, and M. Sahani.
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data.
In P. Bartlett, F. C. N. Pereira, L. Bottou, C. J. C. Burges, and K. Q. Weinberger, eds., Advances in Neural Information Processing Systems, vol. 25, 2012.

[6]    L. Buesing, J. H. Macke, and M. Sahani.
Learning stable, regularised latent models of neural population dynamics.
Network: Computation in Neural Systems, 23(1–2):24–47, 2012.
doi pdf.

[7]    J. H. Macke, L. Büsing, J. P. Cunningham, B. M. Yu, K. V. Shenoy, and M. Sahani.
Empirical models of spiking in neural populations.
In J. Shawe-Taylor, R. S. Zemel, P. Bartlett, F. C. N. Pereira, and K. Q. Weinberger, eds., Advances in Neural Information Processing Systems, vol. 24, pp. 1350–1358. Curran Associates, Inc., Red Hook, New York, 2011.
pdf.

[8]    B. Petreska, B. M. Yu, J. P. Cunningham, G. Santhanam, S. I. Ryu, K. V. Shenoy, and M. Sahani.
Dynamical segmentation of single trials from population neural data.
In J. Shawe-Taylor, R. S. Zemel, P. Bartlett, F. C. N. Pereira, and K. Q. Weinberger, eds., Advances in Neural Information Processing Systems, vol. 24, pp. 756–764. Curran Associates, Inc., Red Hook, New York, 2011.
pdf.

[9]    A. Afshar, G. Santhanam, B. M. Yu, S. I. Ryu, M. Sahani*, and K. V. Shenoy*.
Single-trial neural correlates of arm movement preparation.
Neuron, 71(3):555–564, 2011.
* equal contributions.
doi pmc comment.

[10]    K. V. Shenoy, M. T. Kaufman, M. Sahani, and M. M. Churchland.
A dynamical systems view of motor preparation: Implications for neural prosthetic system design.
In A. Green, E. Chapman, J. F. Kalaska, and F. Lepore, eds., Progress in Brain Research: Enhancing Performance for Action and Perception, vol. 192, pp. 33–59. Elsevier, 2011.

[11]    M. M. Churchland, B. M. Yu, J. P. Cunningham, L. P. Sugrue, M. R. Cohen, G. S. Corrado, W. T. Newsome, A. M. Clark, P. Hosseini, B. B. Scott, D. C. Bradley, M. A. Smith, A. Kohn, J. A. Movshon, K. M. Armstrong, T. Moore, S. W. Chang, L. H. Snyder, S. G. Lisberger, N. J. Priebe, I. M. Finn, D. Ferster, S. I. Ryu, G. Santhanam, M. Sahani, and K. V. Shenoy.
Stimulus onset quenches neural variability: a widespread cortical phenomenon.
Nature Neuroscience, 13(3):369–378, 2010.
doi pdf.

[12]    B. M. Yu, J. P. Cunningham, G. Santhanam, S. I. Ryu, K. V. Shenoy*, and M. Sahani*.
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity.
Journal of Neurophysiology, 102:614–635, 2009.
* equal contributions.
doi pmc pdf.

[13]    B. M. Yu, J. P. Cunningham, G. Santhanam, S. I. Ryu, K. V. Shenoy, and M. Sahani.
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity.
In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, eds., Advances in Neural Information Processing Systems, vol. 21, pp. 1881–1888. Curran Associates, Inc., Red Hook, New York, 2009.
pdf.

[14]    J. P. Cunningham, B. M. Yu, K. V. Shenoy, and M. Sahani.
Inferring neural firing rates from spike trains using Gaussian processes.
In J. C. Platt, D. Koller, Y. Singer, and S. Roweis, eds., Advances in Neural Information Processing Systems, vol. 20. Curran Associates, Inc., Red Hook, New York, 2008.
pdf.

[15]    J. P. Cunningham, K. V. Shenoy, and M. Sahani.
Fast Gaussian process methods for point process intensity estimation.
In ICML 2008: Proceedings, Twenty-Fifth International Conference on Machine Learning, pp. 192–199. Omnipress, Madison, Wisconsin, 2008.
pdf.

[16]    M. M. Churchland, B. M. Yu, M. Sahani, and K. V. Shenoy.
Techniques for extracting single-trial activity patterns from large-scale neural recordings.
Current Opinion in Neurobiology, 17(5):609–618, 2007.
doi pmc pdf.

[17]    B. M. Yu, A. Afshar, G. Santhanam, S. I. Ryu, K. V. Shenoy, and M. Sahani.
Extracting dynamical structure embedded in neural activity.
In Y. Weiss, B. Schölkopf, and J. Platt, eds., Advances in Neural Information Processing Systems, vol. 18, pp. 1545–1552. MIT Press, Cambridge, Massachusetts, 2006.
pdf.

[18]    B. Pesaran, J. S. Pezaris, M. Sahani, P. P. Mitra, and R. A. Andersen.
Temporal structure in neuronal activity during working memory in macaque parietal cortex.
Nature Neuroscience, 5(8):705–816, 2002.
doi.

Neural Encoding

[1]    M. Sahani, G. Bohner, and A. Meyer.
Score-matching estimators for continuous-time point-process regression models.
In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016.
pdf.

[2]    R. S. Williamson, M. B. Ahrens, J. F. Linden, and M. Sahani.
Input-specific gain modulation by local sensory context shapes cortical and thalamic responses to complex sounds.
Neuron, 91(1):467–480, 2016.
doi pmc pdf.

[3]    M. Pachitariu, D. R. Lyamzin, M. Sahani, and N. A. Lesica.
State-dependent population coding in primary auditory cortex.
Journal of Neuroscience, 35(5):2058–2073, 2015.
doi pmc.

[4]    R. S. Williamson, M. Sahani, and J. W. Pillow.
The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction.
PLoS Computational Biology, 11(4):e1004141, 2015.
doi pmc.

[5]    M. Sahani, R. S. Williamson, M. B. Ahrens, and J. F. Linden.
Probabilistic methods for linear and multilinear models.
In D. Depirieux and M. Elhilahi, eds., Handbook of Modern Techniques in Auditory Cortex. Nova, Hauppage, NY, 2013.

[6]    G. B. Christianson, M. Sahani, and J. F. Linden.
Depth-dependent temporal response properties in core auditory cortex.
Journal of Neuroscience, 31(36):12837–12848, 2011.
doi pmc pdf.

[7]    B. Englitz, M. Ahrens, S. Tolnai, R. Rübsamen, M. Sahani, and J. Jost.
Multilinear models of single cell responses in the medial nucleus of the trapezoid body.
Network: Computation in Neural Systems, 21(1-2):91–124, 2010.
doi pdf.

[8]    M. B. Ahrens, J. F. Linden, and M. Sahani.
Nonlinearities and contextual influences in auditory cortical responses modeled with multilinear spectrotemporal methods.
Journal of Neuroscience, 28(8):1929–1942, 2008.
doi pdf.

[9]    M. B. Ahrens, L. Paninski, and M. Sahani.
Inferring input nonlinearities in neural encoding models.
Network: Computation in Neural Systems, 19(1):35–67, 2008.
doi pdf.

[10]    G. B. Christianson, M. Sahani, and J. F. Linden.
The consequences of response nonlinearities for interpretation of spectrotemporal receptive fields.
Journal of Neuroscience, 28(2):446–455, 2008.
doi.

[11]    J. F. Linden, R. C. Liu, M. Sahani, C. E. Schreiner, and M. M. Merzenich.
Spectrotemporal structure of receptive fields in areas AI and AAF of mouse auditory cortex.
Journal of Neurophysiology, 90(4):2660–2675, 2003.
doi.

[12]    M. Sahani and J. F. Linden.
Evidence optimization techniques for estimating stimulus-response functions.
In S. Becker, S. Thrun, and K. Obermayer, eds., Advances in Neural Information Processing Systems, vol. 15, pp. 301–308. MIT Press, Cambridge, Massachusetts, 2003.
pdf ps.gz.

[13]    M. Sahani and J. F. Linden.
How linear are auditory cortical responses?
In S. Becker, S. Thrun, and K. Obermayer, eds., Advances in Neural Information Processing Systems, vol. 15, pp. 109–116. MIT Press, Cambridge, Massachusetts, 2003.
pdf ps.gz.

Neural Decoding

[1]    B. M. Yu, G. Santhanam, M. Sahani, and K. V. Shenoy.
Neural decoding for motor and communication prostheses.
In K. G. Oweiss, ed., Statistical Signal Processing for Neuroscience, pp. 219–263. Elsevier, 2010.

[2]    G. Santhanam, B. M. Yu, V. Gilja, S. I. Ryu, A. Afshar, M. Sahani, and K. V. Shenoy.
Factor-analysis methods for higher-performance neural prostheses.
Journal of Neurophysiology, 102:1315–1330, 2009.
doi pmc.

[3]    G. Santhanam, B. M. Yu, V. Gilja, S. I. Ryu, A. Afshar, M. Sahani, and K. V. Shenoy.
A factor-analysis decoder for high-performance neural prostheses.
In ICASSP’08: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2008, pp. 5208–11, 2008.
pdf.

[4]    B. M. Yu, J. P. Cunningham, K. V. Shenoy, and M. Sahani.
Neural decoding of movements: From linear to nonlinear trajectory models.
In Neural Information Processing – ICONIP 2007, Proceedings, Part I, Lecture Notes in Computer Science, pp. 586–595. Springer, 2008.
pdf.

[5]    B. M. Yu, C. Kemere, G. Santhanam, A. Afshar, S. I. Ryu, T. H. Meng, M. Sahani*, and K. V. Shenoy*.
Mixture of trajectory models for neural decoding of goal-directed movements.
Journal of Neurophysiology, 97(5):3763–3780, 2007.
* equal contributions.
doi pmc pdf.

[6]    B. M. Yu, K. V. Shenoy, and M. Sahani.
Expectation propagation for inference in non-linear dynamical models with Poisson observations.
In Proceedings of the Nonlinear Statistical Signal Processing Workshop. IEEE, 2006.
pdf.

[7]    C. Kemere, M. Sahani, and T. Meng.
Robust neural decoding of reaching movements for prosthetic systems.
In Proceedings of the 25th Annual International Conference of the IEEE EMBS, vol. 3, pp. 2079–2082, 2003.
pdf.

Machine Learning

[1]    M. Sahani, G. Bohner, and A. Meyer.
Score-matching estimators for continuous-time point-process regression models.
In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016.
pdf.

[2]    V. Adam, J. Hensman, and M. Sahani.
Scalable transformed additive signal decomposition by non-conjugate gaussian process inference.
In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016.
pdf.

[3]    G. Bohner and M. Sahani.
Convolutional higher order matching pursuit.
In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016.
pdf.

[4]    J. H. Macke, L. Buesing, and M. Sahani.
Estimating state and model parameters in state-space models of spike trains.
In Z. Chen, ed., Advanced State Space Methods for Neural and Clinical Data. Cambridge University Press, 2015.

[5]    M. Henniges, R. Turner, M. Sahani, J. Eggert, and J. Lücke.
Efficient occlusive components analysis.
Journal of Machine Learning Research, 15:2689–2722, 2014.
journal pdf.

[6]    M. Pachitariu, B. Petreska, and M. Sahani.
Recurrent linear models of simultaneously-recorded neural populations.
In L. Bottou, C. J. C. Burges, M. Welling, Z. Ghahramani, and K. Q. Weinberger, eds., Advances in Neural Information Processing Systems, vol. 26, 2013.

[7]    M. Pachitariu, N. Petitt, H. Dagleish, A. Packer, M. Haüsser, and M. Sahani.
Extracting regions of interest from biological images with convolutional sparse block coding.
In L. Bottou, C. J. C. Burges, M. Welling, Z. Ghahramani, and K. Q. Weinberger, eds., Advances in Neural Information Processing Systems, vol. 26, 2013.

[8]    L. Buesing, J. H. Macke, and M. Sahani.
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data.
In P. Bartlett, F. C. N. Pereira, L. Bottou, C. J. C. Burges, and K. Q. Weinberger, eds., Advances in Neural Information Processing Systems, vol. 25, 2012.

[9]    L. Buesing, J. H. Macke, and M. Sahani.
Learning stable, regularised latent models of neural population dynamics.
Network: Computation in Neural Systems, 23(1–2):24–47, 2012.
doi pdf.

[10]    G. Mysore and M. Sahani.
Variational inference in non-negative factorial hidden Markov models for efficient audio source separation.
In ICML 2012: Proceeding, Twenty-Ninth International Conference on Machine Learning. Omnipress, Madison, WI, 2012.
pdf.

[11]    R. E. Turner and M. Sahani.
Decomposing signals into a sum of amplitude and frequency modulated sinusoids using probabilistic inference.
In ICASSP’12: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2012.
pdf.

[12]    R. E. Turner and M. Sahani.
Probabilistic amplitude and frequency demodulation.
In J. Shawe-Taylor, R. S. Zemel, P. Bartlett, F. C. N. Pereira, and K. Q. Weinberger, eds., Advances in Neural Information Processing Systems, vol. 24, pp. 981–989. Curran Associates, Inc., Red Hook, New York, 2011.
pdf.

[13]    R. E. Turner and M. Sahani.
Two problems with variational expectation maximisation for time-series models.
In D. Barber, A. T. Cemgil, and S. Chiappa, eds., Bayesian Time Series Models. Cambridge University Press, 2011.
pdf.

[14]    J. Lücke, R. E. Turner, M. Sahani, and M. Henniges.
Occlusive components analysis.
In Advances in Neural Information Processing Systems, vol. 22. Curran Associates, Inc., Red Hook, New York, 2009.
pdf.

[15]    P. Berkes, R. E. Turner, and M. Sahani.
A structured model of video reproduces primary visual cortical organisation.
PLoS Computational Biology, 5(9):e1000495, 2009.
doi pmc pdf.

[16]    P. Berkes, R. E. Turner, and M. Sahani.
On sparsity and overcompleteness in image models.
In J. C. Platt, D. Koller, Y. Singer, and S. Roweis, eds., Advances in Neural Information Processing Systems, vol. 20. Curran Associates, Inc., Red Hook, New York, 2008.
pdf.

[17]    J. Lücke and M. Sahani.
Maximal causes for non-linear component extraction.
Journal of Machine Learning Research, 9:1227–1267, 2008.
journal pdf.

[18]    J. Lücke and M. Sahani.
Generalized softmax networks for non-linear component extraction.
In J. Marques de Sá, L. A. Alexandre, W. Duch, and D. Mandic., eds., Artificial Neural Networks – ICANN 2007 Proceedings, Part I, Lecture Notes in Computer Science, pp. 657–667. Springer, Berlin, 2007.
pdf.

[19]    R. E. Turner and M. Sahani.
A maximum-likelihood interpretation for slow feature analysis.
Neural Computation, 19(4):1022–1038, 2007.
doi.

[20]    B. M. Yu, K. V. Shenoy, and M. Sahani.
Expectation propagation for inference in non-linear dynamical models with Poisson observations.
In Proceedings of the Nonlinear Statistical Signal Processing Workshop. IEEE, 2006.
pdf.

Signal Processing

[1]    V. Adam, J. Hensman, and M. Sahani.
Scalable transformed additive signal decomposition by non-conjugate gaussian process inference.
In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016.
pdf.

[2]    G. Bohner and M. Sahani.
Convolutional higher order matching pursuit.
In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016.
pdf.

[3]    R. Turner and M. Sahani.
Time-frequency analysis as probabilistic inference.
IEEE Transactions on Signal Processing, 62(23):6171–6183, 2014.
doi pdf.

[4]    G. Mysore and M. Sahani.
Variational inference in non-negative factorial hidden Markov models for efficient audio source separation.
In ICML 2012: Proceeding, Twenty-Ninth International Conference on Machine Learning. Omnipress, Madison, WI, 2012.
pdf.

[5]    R. E. Turner and M. Sahani.
Decomposing signals into a sum of amplitude and frequency modulated sinusoids using probabilistic inference.
In ICASSP’12: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2012.
pdf.

[6]    R. E. Turner and M. Sahani.
Probabilistic amplitude and frequency demodulation.
In J. Shawe-Taylor, R. S. Zemel, P. Bartlett, F. C. N. Pereira, and K. Q. Weinberger, eds., Advances in Neural Information Processing Systems, vol. 24, pp. 981–989. Curran Associates, Inc., Red Hook, New York, 2011.
pdf.

[7]    R. E. Turner and M. Sahani.
Demodulation as probabilistic inference.
IEEE Transactions on Audio, Speech and Language Processing, 19(8):2398–2411, 2011.
doi pdf.

[8]    R. E. Turner and M. Sahani.
Two problems with variational expectation maximisation for time-series models.
In D. Barber, A. T. Cemgil, and S. Chiappa, eds., Bayesian Time Series Models. Cambridge University Press, 2011.
pdf.

[9]    R. E. Turner and M. Sahani.
Statistical inference for single- and multi-band probabilistic amplitude demodulation.
In ICASSP’10: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2010.
pdf.

[10]    R. E. Turner and M. Sahani.
Modeling natural sounds with modulation cascade processes.
In J. C. Platt, D. Koller, Y. Singer, and S. Roweis, eds., Advances in Neural Information Processing Systems, vol. 20. Curran Associates, Inc., Red Hook, New York, 2008.
pdf.

[11]    R. E. Turner and M. Sahani.
Probabilistic amplitude demodulation.
In Independent Component Analysis and Signal Separation, Lecture Notes in Computer Science, pp. 544–551. Springer, 2007.
Best student paper award.
pdf.

[12]    R. E. Turner and M. Sahani.
A maximum-likelihood interpretation for slow feature analysis.
Neural Computation, 19(4):1022–1038, 2007.
doi.

[13]    B. M. Yu, K. V. Shenoy, and M. Sahani.
Expectation propagation for inference in non-linear dynamical models with Poisson observations.
In Proceedings of the Nonlinear Statistical Signal Processing Workshop. IEEE, 2006.
pdf.