A new look at state-space models for neural data. - Liam Paninski; Yashar Ahmadian; Daniel Gil Ferreira; Shinsuke Koyama; Kamiar Rahnama Rad; Michael Vidne; Joshua T. Vogelstein; Wei Wu 006 (J. Comput. Neurosci., 2010)

Learning unbelievable probabilities. - Xaq Pitkow; Yashar Ahmadian; Kenneth D. Miller (NIPS, 2011)

Learning unbelievable marginal probabilities - Xaq Pitkow; Yashar Ahmadian; Kenneth D. Miller (CoRR, 2011)

Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains. - Yashar Ahmadian; Jonathan W. Pillow; Liam Paninski (Neural Comput., 2011)

Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains. - Jonathan W. Pillow; Yashar Ahmadian; Liam Paninski (Neural Comput., 2011)

Modeling the impact of common noise inputs on the network activity of retinal ganglion cells. - Michael Vidne; Yashar Ahmadian; Jonathon Shlens; Jonathan W. Pillow; Jayant Kulkarni; Alan M. Litke; E. J. Chichilnisky; Eero P. Simoncelli; Liam Paninski (J. Comput. Neurosci., 2012)

Analysis of the Stabilized Supralinear Network. - Yashar Ahmadian; Daniel B. Rubin; Kenneth D. Miller (Neural Comput., 2013)

Capturing the diversity of biological tuning curves using generative adversarial networks. - Takafumi Arakaki; Gregory Barello; Yashar Ahmadian (CoRR, 2017)

Inferring neural circuit structure from datasets of heterogeneous tuning curves. - Takafumi Arakaki; Gregory Barello; Yashar Ahmadian (PLoS Comput. Biol., 2019)

The stabilized supralinear network accounts for the contrast dependence of visual cortical gamma oscillations. - Caleb J. Holt; Kenneth D. Miller; Yashar Ahmadian (PLoS Comput. Biol., 2024)