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Development of automated interictal spike detector. 40th International Conference of the IEEE Engineering in Medicine and Biology Society - EMBC 2018 (In Press). at <https://embc.embs.org/2018/>
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)

Finding any Waldo with zero-shot invariant and efficient visual search. Nature Communications 9, (2018).
Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition. arXiv | Cornell University arXiv:1803.01967, (2018).
Neural Interactions Underlying Visuomotor Associations in the Human Brain. Cerebral Cortex 1–17, (2018).
A neural network trained to predict future videoframes mimics critical properties of biologicalneuronal responses and perception. ( arXiv | Cornell University, 2018). at <https://arxiv.org/pdf/1805.10734.pdf>
1805.10734.pdf (9.59 MB)

Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences (2018). doi:10.1073/pnas.1719397115
1719397115.full_.pdf (1.1 MB)

Spatiotemporal interpretation features in the recognition of dynamic images. (2018).
CBMM-Memo-094.pdf (1.21 MB)
CBMM-Memo-094-dynamic-figures.zip (1.8 MB)
fig1.ppsx (147.67 KB)
fig2.ppsx (419.72 KB)
fig4.ppsx (673.41 KB)
figS1.ppsx (587.88 KB)
figS2.ppsx (281.56 KB)







What is changing when: decoding visual information in movies from human intracranial recordings. NeuroImage 180, Part A, 147-159 (2018).
Human neurophysiological responses during movies (2.78 MB)

Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. (2017).
CBMM-Memo-064.pdf (3 MB)

Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. ICLR (2017).
1605.08104.pdf (2.9 MB)

A null model for cortical representations with grandmothers galore. Language, Cognition and Neuroscience 274 - 285 (2017). doi:10.1080/23273798.2016.1218033
Computational and Cognitive Neuroscience of Vision (Springer Singapore, 2017). at <http://www.springer.com/us/book/9789811002113>
On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations. (2017).
CBMM-Memo-065.pdf (687.76 KB)

What is changing when: Decoding visual information in movies from human intracranial recordings. Neuroimage (2017). doi:https://doi.org/10.1016/j.neuroimage.2017.08.027
Bottom-up and Top-down Input Augment the Variability of Cortical Neurons. Neuron 91(3), 540-547 (2016).
Cascade of neural processing orchestrates cognitive control in human frontal cortex [dataset]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>
Cascade of neural processing orchestrates cognitive control in human frontal cortex. eLIFE (2016). doi:10.7554/eLife.12352
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>
A machine learning approach to predict episodic memory formation. 2016 Annual Conference on Information Science and Systems (CISS) 539 - 544 (2016). doi:10.1109/CISS.2016.7460560
Neural Information Processing Systems (NIPS) 2015 Review. (2016).
Read the Views & Review article by Gabriel Kreiman (443.87 KB)

Predicting episodic memory formation for movie events. Scientific Reports (2016). doi:10.1038/srep30175