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Evidence that recurrent pathways between the prefrontal and inferior temporal cortex is critical during core object recognition . COSYNE (2020).
Hierarchical neural network models that more closely match primary visual cortex tend to better explain higher level visual cortical responses . COSYNE (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).
Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior. Nature Neuroscience (2019). doi:10.1038/s41593-019-0392-5
Evidence that recurrent pathways between the prefrontal and inferior temporal cortex is critical during core object recognition . Society for Neuroscience (2019).
A meta-analysis of ANNs as models of primate V1 . Bernstein (2019).
Neural Population Control via Deep Image Synthesis. Science 364, (2019).