Maneesh Sahani, Ph. D.

Gatsby Computational Neuroscience Unit
University College London
25 Howland Street, London, W1T 4JG
Email: maneesh [at] gatsby.ucl.ac.uk

Academic Positions

Gatsby Computational Neuroscience Unit, University College, London
Director    10/17  – 
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 Professor     9/13  –  8/14
Visiting Associate Professor     8/10  –  8/13
Visiting Assistant Professor     8/04  –  7/10

 
University of California, San Francisco, California
Postdoctoral Fellow     8/02  –  4/04

 
Gatsby Computational Neuroscience Unit, University College, London
Senior Research Fellow     6/99  –  8/02

 
 

Education

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.

 
B.S. Physics        6/93
 

Selected Memberships, Professional Activities and Service

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
Member, IEEE.        10
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
 

Selected Publications

H Soulat, S Keshavarzi, TW Margrie, and M Sahani. Probabilistic tensor decomposition of neural population spiking activity. In Advances in Neural Information Processing Systems 33, 2021.

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.