| A new look at state-space models for neural data L Paninski, Y Ahmadian, DG Ferreira, S Koyama, K Rahnama Rad, ... Journal of computational neuroscience 29, 107-126, 2010 | 258 | 2010 |
| Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains JW Pillow, Y Ahmadian, L Paninski Neural computation 23 (1), 1-45, 2011 | 173 | 2011 |
| The dynamical regime of sensory cortex: stable dynamics around a single stimulus-tuned attractor account for patterns of noise variability G Hennequin, Y Ahmadian, DB Rubin, M Lengyel, KD Miller Neuron 98 (4), 846-860. e5, 2018 | 149 | 2018 |
| Analysis of the stabilized supralinear network Y Ahmadian, DB Rubin, KD Miller Neural computation 25 (8), 1994-2037, 2013 | 143 | 2013 |
| Modeling the impact of common noise inputs on the network activity of retinal ganglion cells M Vidne, Y Ahmadian, J Shlens, JW Pillow, J Kulkarni, AM Litke, ... Journal of computational neuroscience 33, 97-121, 2012 | 132 | 2012 |
| Efficient Markov chain Monte Carlo methods for decoding neural spike trains Y Ahmadian, JW Pillow, L Paninski Neural computation 23 (1), 46-96, 2011 | 99* | 2011 |
| Properties of networks with partially structured and partially random connectivity Y Ahmadian, F Fumarola, KD Miller Physical Review E 91 (1), 012820, 2015 | 96 | 2015 |
| What is the dynamical regime of cerebral cortex? Y Ahmadian, KD Miller Neuron 109 (21), 3373-3391, 2021 | 87 | 2021 |
| Heading direction with respect to a reference point modulates place-cell activity PE Jercog, Y Ahmadian, C Woodruff, R Deb-Sen, LF Abbott, ER Kandel Nature communications 10 (1), 2333, 2019 | 71 | 2019 |
| Designing optimal stimuli to control neuronal spike timing Y Ahmadian, AM Packer, R Yuste, L Paninski Journal of neurophysiology 106 (2), 1038-1053, 2011 | 65 | 2011 |
| Sniff-synchronized, gradient-guided olfactory search by freely moving mice TM Findley, DG Wyrick, JL Cramer, MA Brown, B Holcomb, R Attey, D Yeh, ... Elife 10, e58523, 2021 | 58 | 2021 |
| Deviation from the matching law reflects an optimal strategy involving learning over multiple timescales K Iigaya, Y Ahmadian, LP Sugrue, GS Corrado, Y Loewenstein, ... Nature communications 10 (1), 1466, 2019 | 58 | 2019 |
| Somatostatin-expressing interneurons in the auditory cortex mediate sustained suppression by spectral surround AA Lakunina, MB Nardoci, Y Ahmadian, S Jaramillo Journal of Neuroscience 40 (18), 3564-3575, 2020 | 47 | 2020 |
| Incorporating naturalistic correlation structure improves spectrogram reconstruction from neuronal activity in the songbird auditory midbrain AD Ramirez, Y Ahmadian, J Schumacher, D Schneider, SMN Woolley, ... Journal of Neuroscience 31 (10), 3828-3842, 2011 | 34 | 2011 |
| Sniff invariant odor coding R Shusterman, YB Sirotin, MC Smear, Y Ahmadian, D Rinberg eneuro 5 (6), 2018 | 32 | 2018 |
| Acoustic phonon scattering in a low density, high mobility AlGaN∕ GaN field-effect transistor EA Henriksen, S Syed, Y Ahmadian, MJ Manfra, KW Baldwin, AM Sergent, ... Applied Physics Letters 86 (25), 2005 | 29 | 2005 |
| Inferring functional connectivity in an ensemble of retinal ganglion cells sharing a common input M Vidne, J Kulkarni, Y Ahmadian, J Pillow, J Shlens, E Chichilnisky, ... COSYNE, 2009 | 18 | 2009 |
| Learning fast and slow: deviations from the matching law can reflect an optimal strategy under uncertainty K Iigaya, Y Ahmadian, LP Sugrue, GS Corrado, Y Loewenstein, ... bioRxiv, 141309, 2017 | 15 | 2017 |
| Capturing the diversity of biological tuning curves using generative adversarial networks T Arakaki, G Barello, Y Ahmadian arXiv preprint arXiv:1707.04582, 2017 | 9 | 2017 |
| Learning unbelievable probabilities Z Pitkow, Y Ahmadian, K Miller Advances in neural information processing systems 24, 2011 | 8 | 2011 |
Liam PaninskiColumbia UniversityVerified email at stat.columbia.edu
