Computational Neuroscience
The human brain is a complex network, consisting of functionally interconnected regions whose coordinated effort gives rise to different functions. Understanding what these regions are, how they interact, and how this interaction forms a wide range of behavior has long been an essential question for human neuroscience. Neuroimaging techniques have provided a unique opportunity to tackle this question in a data-driven way. Advances in neuroimaging techniques such as functional Magnetic Resonance Imaging (fMRI), have allowed us to approximately measure the neural activity in the brain. However, fMRI data are not only massive in size but also spatially and temporally complex. One of the research directions in our group is to develop advanced machine learning techniques to study brain function and its link to behavior.
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arxiv 2022 Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for any atlas when raw data is not available Javid Dadashkarimi, Amin Karbasi, and Dustin Scheinost -
MICCAI 2022 Combining multiple atlases to estimate data-driven mappings between functional connectomes using optimal transport | Top 13 % Javid Dadashkarimi, Amin Karbasi, and Dustin Scheinost -
MICCAI 2021 Data-driven mapping between functional connectomes using optimal transport Javid Dadashkarimi, Amin Karbasi, and Dustin Scheinost -
NeuroImage 2020 There is no single functional atlas even for a single individual: Functional parcel definitions change with task | Facebook Main Award M. Salehi , A. S. Greene , A. Karbasi , X. Shen , D. Scheinost, and R. T. Constable -
NeuroImage 2020 Individualized functional networks reconfigure with cognitive state M. Salehi, A. Karbasi, D. S. Barron, D. Scheinost, R. T. Constable -
PhD Thesis 2019 Individualized and Task-Specific Functional Brain Mapping | Ivy 3-Minute Thesis Competition Award Mehraveh Salehi, Todd Constable , and Amin Karbasi -
Journal of Computational Neuroscience 2018 Learning neural connectivity from firing activity: efficient algorithms with provable guarantees on topology. Amin Karbasi, Amir Hesam Salavati, Martin Vetterli -
Journal of Computational Neuroscience 2018 An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks. Mehraveh Salehi, Amin Karbasi, Xilin Shen, Dustin Scheinost, R. Todd Constable: -
arXiv 2014 Convolutional Neural Associative Memories: Massive Capacity with Noise Tolerance. Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi -
Neural Computation 2014 Noise Facilitation in Associative Memories of Exponential Capacity | IEEE Data Storage Best Student Paper Award Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi, Lav R. Varshney: -
ICML 2013 Iterative Learning and Denoising in Convolutional Neural Associative Memories. Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi -
ITW 2013 Coupled neural associative memories. Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi -
NeurIPS 2013 Noise-Enhanced Associative Memories Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi, and Lav R. Varshney
