Dr Aditya Gilra
PhD
Department of Computer Science
Lecturer in Machine Learning
Member of the Machine Learning research group
+44 114 222 1949
Full contact details
Department of Computer Science
Regent Court (DCS)
211 Portobello
Sheffield
S1 4DP
- Profile
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Dr. Aditya Gilra is a Lecturer (=Assistant Professor) in the Machine Learning Group at the Department of Computer Science, University of Sheffield, UK, since January 2020.
Earlier, he was a post-doctoral researcher in the groups of: Prof. Gašper Tkačik at the Institute of Science and Technology, Austria, in 2019; Prof Raoul-Martin Memmesheimer at the University of Bonn, Germany, in 2018; and Prof Wulfram Gerstner at the École Polytechnique Fédérale de Lausanne, Switzerland, in 2015-2017.
He has a background in Computational Neuroscience (PhD - National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India with Prof Upinder S. Bhalla), Physics (Master's - Tata Institute of Fundamental Research, Mumbai, India) and Electrical & Electronics Engineering (Bachelor's - Indian Institute of Technology Madras, Chennai, India).
- Research interests
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Dr. Aditya Gilra is interested in how the brain learns motor and cognitive tasks. His research lies at the interface of computational neuroscience, control theory and machine learning. Check out key publications and related code.
- Publications
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Journal articles
- Dynamical Learning of Dynamics. Physical review letters, 125(8), 088103.
- Thalamic regulation of switching between cortical representations enables cognitive flexibility. Nature Neuroscience, 21(12), 1753-1763.
- Multi-timescale memory dynamics extend task repertoire in a reinforcement learning network with attention-gated memory. Frontiers in Computational Neuroscience, 12.
- Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network. eLife, 6.
- Bulbar microcircuit model predicts connectivity and roles of interneurons in odor coding. PLoS ONE, 10(5).
Conference proceedings papers
- Non-linear motor control by local learning in spiking neural networks. 35th International Conference on Machine Learning, ICML 2018, Vol. 4 (pp 2855-2864)
- Quantum phases due to cosmic gravity and the spin-statistics connection. Journal of Physics: Conference Series, Vol. 174 (pp 012040-012040)
- New tests and clarification of some conceptual issues in the superposition of monochromatic light fields. The Nature of Light: What Are Photons?
- Dynamical Learning of Dynamics. Physical review letters, 125(8), 088103.