| Generative Adversarial Networks: An Overview A Creswell, T White, V Dumoulin, K Arulkumaran, B Sengupta, ... IEEE Signal Processing Magazine, 2018 | 4913 | 2018 |
| Knowing one's place: a free-energy approach to pattern regulation K Friston, M Levin, B Sengupta, G Pezzulo Journal of the Royal Society Interface 12 (105), 20141383, 2015 | 328 | 2015 |
| Action potential energy efficiency varies among neuron types in vertebrates and invertebrates B Sengupta, M Stemmler, SB Laughlin, JE Niven PLoS computational biology 6 (7), e1000840, 2010 | 309 | 2010 |
| Information and efficiency in the nervous system—a synthesis B Sengupta, MB Stemmler, KJ Friston PLoS computational biology 9 (7), e1003157, 2013 | 270 | 2013 |
| Towards a neuronal gauge theory B Sengupta, A Tozzi, GK Cooray, PK Douglas, KJ Friston PLoS Biology 14 (3), e1002400, 2016 | 171 | 2016 |
| Power consumption during neuronal computation B Sengupta, MB Stemmler Proceedings of the IEEE 102 (5), 738-750, 2014 | 125 | 2014 |
| Cognitive dynamics: From attractors to active inference K Friston, B Sengupta, G Auletta Proceedings of the IEEE 102 (4), 427-445, 2014 | 122 | 2014 |
| Balanced excitatory and inhibitory synaptic currents promote efficient coding and metabolic efficiency B Sengupta, SB Laughlin, JE Niven PLoS computational biology 9 (10), e1003263, 2013 | 102 | 2013 |
| The effect of cell size and channel density on neuronal information encoding and energy efficiency B Sengupta, AA Faisal, SB Laughlin, JE Niven Journal of Cerebral Blood Flow & Metabolism 33 (9), 1465-1473, 2013 | 97 | 2013 |
| Efficient gradient computation for dynamical models B Sengupta, KJ Friston, WD Penny NeuroImage 98, 521-527, 2014 | 72 | 2014 |
| Comparison of Langevin and Markov channel noise models for neuronal signal generation B Sengupta, SB Laughlin, JE Niven Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 81 (1 …, 2010 | 67 | 2010 |
| Consequences of converting graded to action potentials upon neural information coding and energy efficiency B Sengupta, SB Laughlin, JE Niven PLoS computational biology 10 (1), e1003439, 2014 | 64 | 2014 |
| Ten simple rules for effective computational research JM Osborne, MO Bernabeu, M Bruna, B Calderhead, J Cooper, ... PLoS Computational Biology 10 (3), e1003506, 2014 | 62 | 2014 |
| Neural dynamics under active inference: Plausibility and efficiency of information processing L Da Costa, T Parr, B Sengupta, K Friston Entropy 23 (4), 454, 2021 | 59* | 2021 |
| Hemispheric brain asymmetry differences in youths with attention-deficit/hyperactivity disorder PK Douglas, B Gutman, A Anderson, C Larios, KE Lawrence, K Narr, ... NeuroImage: Clinical 18, 744-752, 2018 | 56 | 2018 |
| Gradient-based MCMC samplers for dynamic causal modelling B Sengupta, KJ Friston, WD Penny NeuroImage 125, 1107-1118, 2016 | 56 | 2016 |
| Adversarial information factorization A Creswell, Y Mohamied, B Sengupta, AA Bharath arXiv preprint arXiv:1711.05175, 2017 | 51 | 2017 |
| Characterising seizures in anti-NMDA-receptor encephalitis with dynamic causal modelling GK Cooray, B Sengupta, P Douglas, M Englund, R Wickstrom, K Friston Neuroimage 118, 508-519, 2015 | 45 | 2015 |
| Functional analysis of ultra high information rates conveyed by rat vibrissal primary afferents AM Chagas, L Theis, B Sengupta, MC Stüttgen, M Bethge, C Schwarz Frontiers in Neural Circuits 7, 190, 2013 | 45 | 2013 |
| Dynamic causal modelling of electrographic seizure activity using Bayesian belief updating GK Cooray, B Sengupta, PK Douglas, K Friston Neuroimage 125, 1142-1154, 2016 | 44 | 2016 |
Karl FristonUniversity College LondonVerified email at ucl.ac.uk
