Gatsby Computational Neuroscience Unit, University College, London
|Professor of Theoretical Neuroscience and Machine Learning||10/13||–|
|Reader (Associate Professor)||10/09||–||9/13|
|Lecturer (Assistant Professor)||5/04||–||9/09|
Dept. of Electrical Engineering, Stanford University, Stanford, California
|Visiting Associate Professor||8/10||–||8/13|
|Visiting Assistant Professor||8/04||–||7/10|
University of California, San Francisco, California
Gatsby Computational Neuroscience Unit, University College, London
|Senior Research Fellow||6/99||–||8/02|
California Institute of Technology, Pasadena, California
|Ph.D. Computation and Neural Systems||5/99|
Dissertation: Latent Variable Models for Neural Data Analysis.
Advisors: R. A. Andersen and J. J. Hopfield.
|Co-founder and organiser, Neural Coding, Computation and Dynamics meeting||07, 15, 17|
|Selection Committee, Swartz Prize for Theoretical and Computational Neuroscience||14||–||16|
|General Chair, Computational and Systems Neuroscience Conference (COSYNE).||10|
|Programme Chair, COSYNE.||09|
|Workshops Chair, Neural Information Processing Systems (NIPS).||08|
|Programme Committee, COSYNE.||07|
|Programme Committee, NIPS.||04, 06|
|Member, Board of Directors of the Computational Neuroscience Organization.||03||–||06|
|Workshops Chair, Computational Neuroscience Meeting.||99||–||03|
|Fellow, European Laboratory for Learning and Intelligent Systems (ELLIS)||21|
|Member, Society for Neuroscience.||95|
Editorial roles: Current Opinions in Neurobiology, special issue on Big Data and Neuroscience; Faculty of 1000, “Machine learning: life sciences” collection; Neural Computation; Neural Systems and Circuits; Network: Computation in Neural Systems
Advisory, Review or Award panels: Donders Institute for Brain, Cognition and Behaviour; Barcelona Summer School on Advanced Modelling of Behaviour; The Edmond and Lily Safra Center for Brain Sciences, Hebrew University; Swartz Prize for Theoretical and Computational Neuroscience (SFN); Bernstein Prize (BMBF, Germany); COSYNE; Workshops on Computational Audition
E Trautmann, DJ O’Shea, X Sun, J Marshel, A Crow, B Hsueh, S Vesuna, L Cofer, G Bohner, W Allen, I Kauvar, S Quirin, M MacDougall, Y Chen, M Whitmire, C Ramakrishnan, M Sahani, E Seidemann, SI Ryu, K Deisseroth, and KV Shenoy. Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface. Nature Communications, 12(3689), 2021.
T Gothner, PJ Gonçalves, M Sahani, JF Linden, and KJ Hildebrandt. Sustained activation of PV+ interneurons in core auditory cortex enables robust divisive gain control for complex and naturalistic stimuli. Cerebral Cortex, 2020.
L Duncker, L Driscoll, KV Shenoy, M Sahani, and D Sussillo. Organizing recurrent network dynamics by task-computation to enable continual learning. In H Larochelle, M Ranzato, R Hadsell, MF Balcan, and HT Lin, eds., Advances in Neural Information Processing Systems 33. Curran Associates, Inc., 2020.
V Rutten, A Bernacchia, M Sahani, and G Hennequin. Non-reversible gaussian processes for identifying latent dynamical structure in neural data. In H Larochelle, M Ranzato, R Hadsell, MF Balcan, and HT Lin, eds., Advances in Neural Information Processing Systems 33. Curran Associates, Inc., 2020.
LK Wenliang, T Moskovitz, H Kanagawa, and M Sahani. Amortised learning by wake-sleep. In Proceedings of the 37th International Conference on Machine Learning, vol. 98 of Proceedings of Machine Learning Research. PMLR, 2020.
E Vértes and M Sahani. A neurally plausible model learns successor representations in partially observable environments. In H Wallach, H Larochelle, A Beygelzimer, F d’Alché Buc, E Fox, and R Garnett, eds., Advances in Neural Information Processing Systems 32, pp. 13692–13702. Curran Associates, Inc., 2019.
LK Wenliang and M Sahani. A neurally plausible model for online recognition and postdiction. In H Wallach, H Larochelle, A Beygelzimer, F d’Alché Buc, E Fox, and R Garnett, eds., Advances in Neural Information Processing Systems 32, pp. 9641–9652. Curran Associates, Inc., 2019.
R Singh, M Sahani, and A Gretton. Kernel instrumental variable regression. In H Wallach, H Larochelle, A Beygelzimer, F d’Alché Buc, E Fox, and R Garnett, eds., Advances in Neural Information Processing Systems 32, pp. 4595–4607. Curran Associates, Inc., 2019.
L Duncker, G Bohner, J Boussard, and M Sahani. Learning interpretable continuous-time models of latent stochastic dynamical systems. In K Chaudhuri and R Salakhutdinov, eds., Proceedings of the 36th International Conference on Machine Learning, vol. 97 of Proceedings of Machine Learning Research, pp. 1726–1734, Long Beach, California, USA, 09–15 Jun 2019. PMLR.
I Lieder, V Adam, O Frenkel, S Jaffe-Dax, M Sahani, and M Ahissar. Perceptual bias reveals slow-updating in autism and fast-forgetting in dyslexia. Nature Neuroscience, 22(2):256–264, 2019.
E Vértes and M Sahani. Flexible and accurate inference and learning for deep generative models. In S Bengio, H Wallach, H Larochelle, K Grauman, N Cesa-Bianchi, and R Garnett, eds., Advances in Neural Information Processing Systems 31, pp. 4169–4178. Curran Associates, Inc., 2018.
L Duncker and M Sahani. Temporal alignment and latent Gaussian process factor inference in population spike trains. In S Bengio, H Wallach, H Larochelle, K Grauman, N Cesa-Bianchi, and R Garnett, eds., Advances in Neural Information Processing Systems 31, pp. 10465–10475. Curran Associates, Inc., 2018.
A Khan, J Poort, A Chadwick, A Blot, M Sahani, TD Mrsic-Flogel, and SB Hofer. Distinct learning-induced changes in stimulus selectivity and interactions of GABAergic interneuron classes in visual cortex. Nature Neuroscience, 21:851–859, 2018.
AF Meyer, J Poort, J O’Keefe, M Sahani, and JF Linden. A head-mounted camera system integrates detailed behavioral monitoring with multichannel electrophysiology in freely moving mice. Neuron, 100:46–60, 2018.
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.
RS Williamson, MB Ahrens, JF 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.
JH 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.
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, ND Lawrence, DD Lee, M Sugiyama, and R Garnett, eds., Advances in Neural Information Processing Systems, vol. 28, pp. 154–162. Curran Associates, Inc., 2015.
J Poort, AG Khan, M Pachitariu, A Nemri, I Orsolic, J Krupic, M Bauza, M Sahani, GB Keller, TD Mrsic-Flogel, and SB Hofer. Learning enhances sensory and multiple non-sensory representations in primary visual cortex. Neuron, 86(6):1478–1490, 2015.
RS Williamson, M Sahani, and JW Pillow. The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction. PLoS Computational Biology, 11(4):e1004141, 2015.
R Turner and M Sahani. Time-frequency analysis as probabilistic inference. IEEE Transactions on Signal Processing, 62(23):6171–6183, 2014.
MI Garrido, M Sahani*, and RJ Dolan*. Outlier responses reflect sensitivity to statistical structure in the human brain. PLoS Computational Biology, 9(3):e1002999, 2013. * equal contributions.
KV Shenoy, M Sahani, and MM Churchland. Cortical control of arm movements: A dynamical systems perspective. Annual Review of Neuroscience, 36:337–359, 2013.
L Buesing, JH Macke, and M Sahani. Learning stable, regularised latent models of neural population dynamics. Network: Computation in Neural Systems, 23(1–2):24–47, 2012.
L Buesing, JH Macke, and M Sahani. Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. In P Bartlett, FCN Pereira, L Bottou, CJC Burges, and KQ Weinberger, eds., Advances in Neural Information Processing Systems, vol. 25, 2012.
L Whiteley and M Sahani. Attention in a Bayesian framework. Frontiers in Human Neuroscience, 6:100, 2012.
MB 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.
RE Turner and M Sahani. Probabilistic amplitude and frequency demodulation. In J Shawe-Taylor, RS Zemel, P Bartlett, FCN Pereira, and KQ Weinberger, eds., Advances in Neural Information Processing Systems, vol. 24, pp. 981–989, Red Hook, New York, 2011. Curran Associates, Inc.