Publication
] Beyond the feedforward sweep: feedback computations in the visual cortex. Ann. N.Y. Acad. Sci. | Special Issue: The Year in Cognitive Neuroscience 1464, 222-241 (2020).
gk7812.pdf (1.93 MB)
gk7812.pdf (1.93 MB) Beyond the feedforward sweep: feedback computations in the visual cortex. Annals of the New York Academy of Sciences 1464, 222 - 241 (2020).
Biological and Computer Vision. (Cambridge University Press, 2021). doi:10.1017/9781108649995
Bottom-up and Top-down Input Augment the Variability of Cortical Neurons. Neuron 91(3), 540-547 (2016).
Can Deep Learning Recognize Subtle Human Activities?. CVPR 2020 (2020).
Cascade of neural processing orchestrates cognitive control in human frontal cortex. eLIFE (2016). doi:10.7554/eLife.12352 Manuscript (1.83 MB)
Manuscript (1.83 MB) Cascade of neural processing orchestrates cognitive control in human frontal cortex [code]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>
Cascade of neural processing orchestrates cognitive control in human frontal cortex [dataset]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>
Cognitive boundary signals in the human medial temporal lobe shape episodic memory representation. bioRxiv (2021).
Principles of neural coding (2013).
Corticocortical feedback increases the spatial extent of normalization. Frontiers in Systems Neuroscience 8, 105 (2014).
Single Neuron Studies of the Brain: Probing Cognition (2014).
Decrease in gamma-band activity tracks sequence learning. Frontiers in Systems Neuroscience 8, (2015). fnsys-08-00222.pdf (5.62 MB)
fnsys-08-00222.pdf (5.62 MB) Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. (2017). CBMM-Memo-064.pdf (3 MB)
CBMM-Memo-064.pdf (3 MB) Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. ICLR (2017). 1605.08104.pdf (2.9 MB)
1605.08104.pdf (2.9 MB) Development of automated interictal spike detector. 40th International Conference of the IEEE Engineering in Medicine and Biology Society - EMBC 2018 (2018). at <https://embc.embs.org/2018/>
Do computational models of vision need shape-based representations? Evidence from an individual with intriguing visual perceptions. Cognitive Neuropsychology 1 - 3 (2022). doi:10.1080/02643294.2022.2041588
On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering. arXiv (2022). doi:10.48550/arXiv.2201.03965 On_the_Efficacy_of_Co-Attention_Transformer_Layers.pdf (35.54 MB)
On_the_Efficacy_of_Co-Attention_Transformer_Layers.pdf (35.54 MB) Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass. Proceedings of the 39th International Conference on Machine Learning, PMLR 162, 4937-4955 (2022). dellaferrera22a.pdf (909.91 KB)
dellaferrera22a.pdf (909.91 KB) Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences. Cell 177, 1009 (2019). Author's last draft (20.26 MB)
Author's last draft (20.26 MB) Face neurons encode nonsemantic features. Proceedings of the National Academy of Sciences 119, (2022).
Finding any Waldo with zero-shot invariant and efficient visual search. Nature Communications 9, (2018).
Frivolous Units: Wider Networks Are Not Really That Wide. AAAI 2021 (2021). at <https://dblp.org/rec/conf/aaai/CasperBDGSVK21.html> 1912.04783.pdf (6.69 MB)
1912.04783.pdf (6.69 MB) Hypothesis-driven Online Video Stream Learning with Augmented Memory. arXiv (2021). doi:10.48550/arXiv.2104.02206 2104.02206.pdf (2.25 MB)
2104.02206.pdf (2.25 MB)