Maneesh Sahani
Maneesh Sahani studies theoretical neuroscience and machine learning. His research uses latent-variable models to analyse neural data and investigate computation. Sahani received his Caltech doctorate in computation and neural systems in May 1999. His dissertation studied latent-variable models for neural-data analysis with advisers Richard Andersen and John Hopfield. He held a UCL lectureship from May 2004, a readership from October 2009 and a theoretical-neuroscience and machine-learning professorship from October 2013. With Lea Duncker, Laura Driscoll, Krishna Shenoy and David Sussillo, Sahani co-authored a NeurIPS 2020 paper on organizing recurrent-network dynamics by task computation to enable continual learning.
- Works
- 295
- Citations
- 10,948
- h-index
- 49
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
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1999
Latent-variable doctoral research
Sahani received his Caltech doctorate in computation and neural systems in May 1999. His dissertation studied latent-variable models for neural-data analysis with advisers Richard Andersen and John Hopfield.
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2004–2013
Gatsby faculty career
He held a UCL lectureship from May 2004, a readership from October 2009 and a theoretical-neuroscience and machine-learning professorship from October 2013.
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2020
Continual learning in recurrent networks
With Lea Duncker, Laura Driscoll, Krishna Shenoy and David Sussillo, Sahani co-authored a NeurIPS 2020 paper on organizing recurrent-network dynamics by task computation to enable continual learning.
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As of October 2026
Models of neural population activity
His research account describes joint work on latent variables and Gaussian-process methods for neural-population dynamics. It also studies models of auditory responses and learning.
Sources and credits
Photo: simonsfoundation.org; Maneesh Sahani; creator not stated. License: Not stated by source. Source: simonsfoundation.org. Cropped to a square and resized. Ask us to remove or replace this photo.