Mohammad Bashiri

Graduate Student

system identification
Mohammad Bashiri

My interest lies in identifying regularities underlying neural computations that contribute to data-efficient learning and robust inference. These regularities are commonly framed as tuning where the activity of many neurons is sensitive to a common axis (aka tuning axis) in the input space. For instance, in the context of the visual system, many neurons have similar receptive fields but each is rotated differently, which makes the rotation angle the tuning axis. The questions I am currently working on are: 1) What other regularities are there in the computations performed by cortical neuronal populations? 2) Can we develop methods that automatically discover tuning in a given multiple-input and multiple-output (MIMO) system? And 3) How can we use models to design experiments such that we can control the activity of neuronal populations and ultimately control learning and inference. To answer these questions I use deep neural networks as models of the visual system and work with large scale neuro-physiological and -anatomical data.


Publications

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2024

Mohammad Bashiri PhD Thesis, International Max Planck Research School for Intelligent Systems, Tübingen

2023

Konstantin F. Willeke, Paul G. Fahey, Mohammad Bashiri, Laura Hansel, Christoph Blessing, Konstantin-Klemens Lurz, Max F. Burg, Santiago A. Cadena, Zhiwei Ding, Kayla Ponder, Taliah Muhammad, Saumil S. Patel, Kaiwen Deng, Yuanfang Guan, Yiqin Zhu, Kaiwen Xiao, Xiao Han, Simone Azeglio, Ulisse Ferrari, Peter Neri, Olivier Marre, Adrian Hoffmann, Kirill Fedyanin, Kirill Vishniakov, Maxim Panov, Subash Prakash, Kishan Naik, Kantharaju Narayanappa, Alexander S. Ecker, Andreas S. Tolias, Fabian H. Sinz Retrospective on the SENSORIUM 2022 competition Proceedings of Machine Learning Research
Polina Turishcheva, Paul G. Fahey, Laura Hansel, Rachel Froebe, Kayla Ponder, Michaela Vystrčilová, Konstantin F. Willeke, Mohammad Bashiri, Eric Wang, Zhiwei Ding, Andreas S. Tolias, Fabian H. Sinz, Alexander S. Ecker The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos arXiv
Paweł A. Pierzchlewicz, Mohammad Bashiri, R. James Cotton, Fabian H. Sinz Optimizing MPJPE promotes miscalibration in multi-hypothesis human pose lifting ICLR - tiny paper
conference paper · pose · openreview
Konstantin-Klemens Lurz1, Mohammad Bashiri1, Edgar Y. Walker, Fabian H. Sinz Bayesian Oracle for bounding information gain in neural encoding models ICLR 2023 (accepted) , equal contribution: 1

2022

Paweł A. Pierzchlewicz, R. James Cotton, Mohammad Bashiri, Fabian H. Sinz Multi-hypothesis 3D human pose estimation metrics favor miscalibrated distributions arXiv
preprint · pose · arXiv
Konstantin Lurz, Mohammad Bashiri, Fabian Sinz Bayesian Oracle for bounding information gain in neural encoding models Neurips 2022 Workshop InfoCog
workshop paper · system identification ·
Luca Baroni1, Mohammad Bashiri1, Konstantin Friedrich Willeke, Ján Antolík, Fabian Sinz Learning Invariance Manifolds of Visual Sensory Neurons Neurips 2022 Workshop NeurReps , equal contribution: 1
Konstantin F. Willeke, Paul G. Fahey, Mohammad Bashiri, Laura Pede, Max F. Burg, Christoph Blessing, Santiago A. Cadena, Zhiwei Ding, Konstantin-Klemens Lurz, Kayla Ponder, Taliah Muhammad, Saumil S. Patel, Alexander S. Ecker, Andreas S. Tolias, Fabian H. Sinz The Sensorium competition on predicting large-scale mouse primary visual cortex activity arXiv

2021

Mohammad Bashiri, Edgar Y. Walker, Konstantin-Klemens Lurz, Akshay Kumar Jagadish, Taliah Muhammad, Zhiwei Ding, Zhuokun Ding, Andreas S. Tolias, Fabian H. Sinz A flow-based latent state generative model of neural population responses to natural images NeurIPS (spotlight)
Konstantin-Klemens Lurz, Mohammad Bashiri, Konstantin Friedrich Willeke, Akshay Kumar Jagadish, Eric Wang, Edgar Y Walker, Santiago Cadena, Taliah Muhammad, Eric Cobos, Andreas Tolias, Alexander Ecker, Fabian Sinz Generalization in data-driven models of primary visual cortex ICLR (spotlight)