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Idan Grosbard

As seen in: Wiley Online Library, BioRxiv
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Articles

Electroencephalography-Based Clustering Reveals Robust Neurophysiological Subtypes in Parkinson's Disease

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By Idan Grosbard, Daniel Vered BSc, Zoya Katzir MSc
| Wiley Online Library Verified
The clinical and pathophysiological heterogeneity of Parkinson's disease (PD) is well recognized, with substantial variability in motor and cognitive manifestations across individuals.1 Efforts to capture this variability have included both a priori subtype classifications based on features such as age at onset or motor phenotype (TD, tremor dominance or PIGD, postural instability gait difficulty), and data-driven clustering approaches integrating broader clinical, motor, and non-motor...
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[2505.24244] Mamba Knockout for Unraveling Factual Information Flow

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By Idan Grosbard, Idan Daniel, Yuval Ran-Milo, Yonatan Slutzky
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Self-supervision deep learning models are better models of human high-level visual cortex: The roles of multi-modality and dataset training size

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By Idan Grosbard, Galit Yovel
| BioRxiv
Abstract With the rapid development of Artificial Neural Network based visual models, many studies have shown that these models show unprecedented potence in predicting neural responses to images in visual cortex. Lately, advances in computer vision have introduced self-supervised models, where a model is trained using supervision from natural properties of the training set.
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