[1]    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 K. Weinberger, P. Bartlett, F. Pereira, J. Shawe-Taylor, and R. Zemel, eds., Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., 2011.
pdf.

[2]    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 K. Weinberger, P. Bartlett, F. Pereira, J. Shawe-Taylor, and R. Zemel, eds., Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., 2011.
pdf.

[3]    R. E. Turner and M. Sahani.
Probabilistic amplitude and frequency demodulation.
In K. Weinberger, P. Bartlett, F. Pereira, J. Shawe-Taylor, and R. Zemel, eds., Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., 2011.
pdf.

[4]    M. I. Garrido, G. R. Barnes, M. Sahani, and R. J. Dolan.
Functional evidence for a dual route to amygdala.
Current Biology, accepted.

[5]    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.
pdf doi.

[6]    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 comment.

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

[9]    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.
doi.

[10]    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.
pdf doi.

[11]    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.

[12]    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., Inference and Learning in Dynamic Models. Cambridge University Press, 2011.
pdf.

[13]    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.
pdf doi.

[14]    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.

[15]    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.
pdf doi.

[16]    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.
pdf doi.

[17]    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.

[18]    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., 2009.
pdf.

[19]    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.
pdf doi.

[20]    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.

[21]    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.
doi.

[22]    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., 2009.
pdf.

[23]    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.

[24]    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.

[25]    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., 2008.
Best student paper, honourable mention.
pdf.

[26]    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., 2008.
pdf.

[27]    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.

[28]    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., 2008.
pdf.

[29]    J. P. Cunningham, K. V. Shenoy, and M. Sahani.
Fast Gaussian process methods for point process intensity estimation.
In ICML ’08: Proceedings of the 25th international conference on Machine learning, pp. 192–199, Helsinki Finland, 2008. Omni Press.
pdf.

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

[31]    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.

[32]    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., 2008.
pdf.

[33]    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.
doi pdf.

[34]    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 pdf.

[35]    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 pdf.

[36]    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, Berlin, 2007. Springer.
doi pdf.

[37]    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.
doi pdf.

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

[39]    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 pdf.

[40]    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.

[41]    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, Cambridge, MA, 2006. MIT Press.
pdf.

[42]    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.

[43]    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.

[44]    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, Cambridge, MA, 2004. MIT Press.
pdf.

[45]    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, Cambridge, MA, 2004. MIT Press.
pdf.

[46]    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.

[47]    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.

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

[49]    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.

[50]    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, Cambridge, MA, 2003. MIT Press.
pdf ps.gz.

[51]    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, Cambridge, MA, 2003. MIT Press.
pdf ps.gz.

[52]    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, Cambridge, MA, 2003. MIT Press.

[53]    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.

[54]    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.

[55]    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.

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

[57]    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.

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

[59]    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, Cambridge, MA, 1998. MIT Press.
ps.gz.

[60]    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.

[61]    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.

[62]    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.