List of Publications

[Also available sorted by topic.]

[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]    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.

[4]    D. J. O’Shea, E. Trautmann, C. Chandrasekaran, S. Stavisky, J. Kao, M. Sahani, S. Ryu, K. Deisseroth, and K. V. Shenoy.
The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces.
Experimental Neurology, 2016.
doi.

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

[6]    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.
* equal contributions.
doi pmc pdf.

[7]    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.

[8]    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.

[9]    M. Park, W. Jitkrittum, A. Qamar, Z. Szabó, L. Buesing, and M. Sahani.
Bayesian manifold learning: The locally linear latent variable model.
In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, eds., Advances in Neural Information Processing Systems, vol. 28, pp. 154–162. Curran Associates, Inc., 2015.
proceedings, pdf.

[10]    J. Poort, A. G. Khan, M. Pachitariu, A. Nemri, I. Orsolic, J. Krupic, M. Bauza, M. Sahani, G. B. Keller, T. D. Mrsic-Flogel, and S. B. Hofer.
Learning enhances sensory and multiple non-sensory representations in primary visual cortex.
Neuron, 86(6):1478–1490, 2015.
doi pmc.

[11]    D. Schulz, M. Sahani, and M. Carandini.
Five key factors determining pairwise correlations in visual cortex.
Journal of Neurophysiology, 114(2):1022–1033, 2015.
doi.

[12]    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.

[13]    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.

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

[15]    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.

[16]    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.

[17]    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.

[18]    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.

[19]    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.

[20]    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.

[21]    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.

[22]    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.

[23]    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.

[24]    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.

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

[26]    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.

[27]    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.

[28]    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.

[29]    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.

[30]    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.

[31]    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.

[32]    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.

[33]    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.

[34]    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.

[35]    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.

[36]    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.

[37]    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.

[38]    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.

[39]    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.

[40]    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.

[41]    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.

[42]    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.

[43]    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.

[44]    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.

[45]    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.

[46]    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.

[47]    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.

[48]    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.

[49]    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.

[50]    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.

[51]    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.

[52]    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.

[53]    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.

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

[55]    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.

[56]    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.

[57]    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.

[58]    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.

[59]    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.

[60]    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.

[61]    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.

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

[63]    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.

[64]    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.

[65]    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.

[66]    K. Sekihara, M. Sahani, and S. S. Nagarajan.
A simple nonparametric statistical thresholding for MEG spatial-filter source reconstruction images.
Neuroimage, 27(2):368–76, 2005.
doi pmc.

[67]    K. Sekihara, M. Sahani, and S. S. Nagarajan.
Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction.
Neuroimage, 25(4):1056–67, 2005.
doi pmc.

[68]    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.

[69]    M. Sahani and S. S. Nagarajan.
Reconstructing MEG sources with unknown correlations.
In S. Thrun, L. Saul, and B. Schoelkopf, eds., Advances in Neural Information Processing Systems, vol. 16. MIT Press, Cambridge, Massachusetts, 2004.
pdf.

[70]    K. Sekihara, M. Sahani, and S. S. Nagarajan.
Bootstrap-based statistical thresholding for MEG source reconstruction images.
In Proceedings of the 26th Annual International Conference of the IEEE EMBS, vol. 2, pp. 1018–1021, 2004.
pdf.

[71]    G. Santhanam, M. Sahani, S. Ryu, and K. V. Shenoy.
An extensible infrastructure for fully automated spike sorting during online experiments.
In Proceedings of the 26th Annual International Conference of the IEEE EMBS, vol. 6, pp. 4380–4384, 2004.
pdf.

[72]    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.

[73]    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.

[74]    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.

[75]    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.

[76]    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.

[77]    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.

[78]    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.

[79]    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.

[80]    M. Sahani.
Latent variable models for neural data analysis.
PhD thesis, California Institute of Technology, Pasadena, California, 1999.
download.

[81]    J. S. Pezaris, M. Sahani, and R. A. Andersen.
Response correlations in parietal cortex.
Neurocomputing, 26–27:471–476, 1999.

[82]    M. Wehr, J. S. Pezaris, and M. Sahani.
Simultaneous paired intracellular and tetrode recordings for evaluating the performance of spike sorting algorithms.
Neurocomputing, 26–27:1061–1068, 1999.

[83]    J. S. Pezaris, M. Sahani, and R. A. Andersen.
Extracellular recording from multiple neighboring cells: Correlation analysis of spike trains in parietal cortex.
In J. M. Bower, ed., Computational Neuroscience: Trends in Research, 1998. Plenum, 1998.

[84]    M. Sahani, J. S. Pezaris, and R. A. Andersen.
On the separation of signals from neighboring cells in tetrode recordings.
In M. I. Jordan, M. J. Kearns, and S. A. Solla, eds., Advances in Neural Information Processing Systems, vol. 10. MIT Press, Cambridge, Massachusetts, 1998.
ps.gz.

[85]    M. Sahani, J. S. Pezaris, and R. A. Andersen.
Extracellular recording from multiple neighboring cells: A maximum-likelihood solution to the spike-separation problem.
In J. M. Bower, ed., Computational Neuroscience: Trends in Research, 1998. Plenum, 1998.

[86]    J. S. Pezaris, M. Sahani, and R. A. Andersen.
Tetrodes for monkeys.
In J. M. Bower, ed., Computational Neuroscience: Trends in Research, 1997. Plenum, 1997.
































































version of August 10, 2016