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From Surface to Depth: using deep learning to predict striatal fMRI reward signaling in the EEG

N. Herzog, L. Vähäsarja, P. Reinfeld, N. Scherf, S. Hofmann, C. Andreou, A. Villringer, and C. Mulert. (2024)

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A pattern-based approach to a cell tracking ontologyP. Burek, N. Scherf, and H. Herre. Procedia Comput. Sci., (2019)Multi-scale imaging and analysis identify pan-embryo cell dynamics of germlayer formation in zebrafishG. Shah, K. Thierbach, B. Schmid, J. Waschke, A. Reade, M. Hlawitschka, I. Roeder, N. Scherf, and J. Huisken. Nat. Commun., 10 (1): 5753 (December 2019)Open Problem: Active Representation LearningN. Milosevic, G. M�ller, J. Huisken, and N. Scherf. accepted presented at ARLET workshop at ICLR 2024, (2024)From Surface to Depth: using deep learning to predict striatal fMRI reward signaling in the EEGN. Herzog, L. Vähäsarja, P. Reinfeld, N. Scherf, S. Hofmann, C. Andreou, A. Villringer, and C. Mulert. (2024)Human-aligned deep and sparse encoding models of dynamic 3D face similarity perceptionS. Hofmann, A. Ciston, A. Koushik, F. Klotzsche, M. Hebart, K. Müller, A. Villringer, N. Scherf, A. Hilsmann, V. Nikulin and 1 other author(s). (2024)Ontology patterns for the representation of quality changes of cells in timeP. Burek, N. Scherf, and H. Herre. J. Biomed. Semantics, 10 (1): 16 (October 2019)Automatic lung segmentation and quantification of aeration in computed tomography of the chest using 3D transfer learningL. Maiello, L. Ball, M. Micali, F. Iannuzzi, N. Scherf, R. Hoffmann, M. Gama de Abreu, P. Pelosi, and R. Huhle. Front. Physiol., (2021)Domain-specific cues improve robustness of deep learning-based segmentation of CT volumesM. Kloenne, S. Niehaus, L. Lampe, A. Merola, J. Reinelt, I. Roeder, and N. Scherf. Sci. Rep., 10 (1): 10712 (July 2020)Benchmarking Large Language Models for Bio-Image Analysis Code GenerationR. Haase, C. Tischer, J. Hériché, and N. Scherf. bioRxiv, (2024)Automatic Lung Segmentation and Quantification of Aeration in Computed Tomography of the Chest Using 3D Transfer LearningL. Maiello, L. Ball, M. Micali, F. Iannuzzi, N. Scherf, R. Hoffmann, M. Gama de Abreu, P. Pelosi, and R. Huhle. Frontiers in Physiology, (February 2022)

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From Surface to Depth: using deep learning to predict striatal fMRI reward signaling in the EEG

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML From Surface to Depth: using deep learning to predict striatal fMRI reward signaling in the EEG

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From Surface to Depth: using deep learning to predict striatal fMRI reward signaling in the EEG