Martin Schrimpf - Publications

Affiliations: 
2011-2014 Informatik TU Munich, München, Bayern, Germany 
 2014-2017 TU Munich, München, Bayern, Germany 
 2014-2017 LMU Munich, München, Bayern, Germany 
 2014-2017 University of Augsburg 
 2015-2015 Siemens AG 
 2015-2016 Labs Oracle 
 2016-2016 Medical School Harvard University, Cambridge, MA, United States 
 2017- Brain and Cognitive Sciences Massachusetts Institute of Technology, Cambridge, MA, United States 
 2017-2017 Einstein AI Research Salesforce 
Area:
computational neuroscience, vision, deep learning, artificial neural networks
Website:
http://mschrimpf.com/

9 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2024 Hosseini EA, Schrimpf M, Zhang Y, Bowman S, Zaslavsky N, Fedorenko E. Artificial Neural Network Language Models Predict Human Brain Responses to Language Even After a Developmentally Realistic Amount of Training. Neurobiology of Language (Cambridge, Mass.). 5: 43-63. PMID 38645622 DOI: 10.1162/nol_a_00137  0.315
2024 DiCarlo JJ, Yamins DLK, Ferguson ME, Fedorenko E, Bethge M, Bonnen T, Schrimpf M. Let's move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human vision - CORRIGENDUM. The Behavioral and Brain Sciences. 47: e66. PMID 38305315 DOI: 10.1017/S0140525X23003564  0.547
2024 Tuckute G, Sathe A, Srikant S, Taliaferro M, Wang M, Schrimpf M, Kay K, Fedorenko E. Driving and suppressing the human language network using large language models. Nature Human Behaviour. PMID 38172630 DOI: 10.1038/s41562-023-01783-7  0.321
2023 DiCarlo JJ, Yamins DLK, Ferguson ME, Fedorenko E, Bethge M, Bonnen T, Schrimpf M. Let's move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human vision. The Behavioral and Brain Sciences. 46: e390. PMID 38054303 DOI: 10.1017/S0140525X23001607  0.602
2023 Tuckute G, Sathe A, Srikant S, Taliaferro M, Wang M, Schrimpf M, Kay K, Fedorenko E. Driving and suppressing the human language network using large language models. Biorxiv : the Preprint Server For Biology. PMID 37090673 DOI: 10.1101/2023.04.16.537080  0.316
2021 Schrimpf M, Blank IA, Tuckute G, Kauf C, Hosseini EA, Kanwisher N, Tenenbaum JB, Fedorenko E. The neural architecture of language: Integrative modeling converges on predictive processing. Proceedings of the National Academy of Sciences of the United States of America. 118. PMID 34737231 DOI: 10.1073/pnas.2105646118  0.465
2021 Zhuang C, Yan S, Nayebi A, Schrimpf M, Frank MC, DiCarlo JJ, Yamins DLK. Unsupervised neural network models of the ventral visual stream. Proceedings of the National Academy of Sciences of the United States of America. 118. PMID 33431673 DOI: 10.1073/pnas.2014196118  0.565
2020 Schrimpf M, Kubilius J, Lee MJ, Ratan Murty NA, Ajemian R, DiCarlo JJ. Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence. Neuron. PMID 32918861 DOI: 10.1016/J.Neuron.2020.07.040  0.556
2018 Tang H, Schrimpf M, Lotter W, Moerman C, Paredes A, Ortega Caro J, Hardesty W, Cox D, Kreiman G. Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences of the United States of America. PMID 30104363 DOI: 10.1073/Pnas.1719397115  0.535
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