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Filters: Author is Schrimpf, M. [Clear All Filters]
Chemogenetic suppression of macaque V4 neurons produces retinotopically specific deficits in downstream IT neural activity patterns and core object recognition behavior. Journal of Vision 21, (2021).
Frivolous Units: Wider Networks Are Not Really That Wide. AAAI 2021 (2021). at <https://dblp.org/rec/conf/aaai/CasperBDGSVK21.html>
Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior. bioRxiv (2021).
The neural architecture of language: Integrative modeling converges on predictive processing. Proceedings of the National Academy of Sciences 118, e2105646118 (2021).
Hierarchical neural network models that more closely match primary visual cortex tend to better explain higher level visual cortical responses . COSYNE (2020).
Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence. Neuron 108, 413 - 423 (2020).
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations. Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020) (2020). at <https://proceedings.neurips.cc/paper/2020/hash/98b17f068d5d9b7668e19fb8ae470841-Abstract.html>
Temporal information for action recognition only needs to be integrated at a choice level in neural networks and primates . COSYNE (2020).
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. arXiv (2020). at <https://arxiv.org/abs/2007.04954>
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (2019).
Large-scale hyperparameter search for predicting human brain responses in the Algonauts challenge. The Algonauts Project: Explaining the Human Visual Brain Workshop 2019 (2019). doi:10.1101/689844
Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?. bioRxiv preprint (2018). doi:10.1101/407007
Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences (2018). doi:10.1073/pnas.1719397115