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Filters: Author is DiCarlo, James J. [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).
Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs. NeurIPS 2021 (2021). at <https://nips.cc/Conferences/2021/ScheduleMultitrack?event=41268>
Computational models of category-selective brain regions enable high-throughput tests of selectivity. Nature Communications 12, (2021).
Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition. Neuron 109, 164 - 176.e5 (2021).
Evidence that recurrent pathways between the prefrontal and inferior temporal cortex is critical during core object recognition . COSYNE (2020).
Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition. Neuron (2020). doi:10.1016/j.neuron.2020.09.035
Hierarchical neural network models that more closely match primary visual cortex tend to better explain higher level visual cortical responses . COSYNE (2020).
The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys. Nature Communications 11, (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>
Are topographic deep convolutional neural networks better models of the ventral visual stream?. Conference on Cognitive Computational Neuroscience (2019).
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).
Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?. bioRxiv preprint (2018). doi:10.1101/407007