Person: Tang, Hanlin
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Tang
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Tang, Hanlin
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Publication Decrease in gamma-band activity tracks sequence learning(Frontiers Media S.A., 2015) Madhavan, Radhika; Millman, Daniel; Tang, Hanlin; Crone, Nathan E.; Lenz, Fredrick A.; Tierney, Travis S.; Madsen, Joseph; Kreiman, Gabriel; Anderson, William S.Learning novel sequences constitutes an example of declarative memory formation, involving conscious recall of temporal events. Performance in sequence learning tasks improves with repetition and involves forming temporal associations over scales of seconds to minutes. To further understand the neural circuits underlying declarative sequence learning over trials, we tracked changes in intracranial field potentials (IFPs) recorded from 1142 electrodes implanted throughout temporal and frontal cortical areas in 14 human subjects, while they learned the temporal-order of multiple sequences of images over trials through repeated recall. We observed an increase in power in the gamma frequency band (30–100 Hz) in the recall phase, particularly in areas within the temporal lobe including the parahippocampal gyrus. The degree of this gamma power enhancement decreased over trials with improved sequence recall. Modulation of gamma power was directly correlated with the improvement in recall performance. When presenting new sequences, gamma power was reset to high values and decreased again after learning. These observations suggest that signals in the gamma frequency band may play a more prominent role during the early steps of the learning process rather than during the maintenance of memory traces.Publication Role of Recurrent Computations in Object Completion(2016-01-11) Tang, Hanlin; Gershman, Samuel J.; Livingstone, Margaret S.; Nakayama, Ken; Hogle, JamesExisting models of visual object recognition posit that recognition is orchestrated by a hierarchy of processing layers. In these models, neural computation proceeds in a largely feed-forward path up this hierarchy, without substantial feedback or recurrent processing. These feed-forward models provide a parsimonious account of experimental data, and have given rise to deep convolutional networks in computer vision that outperform previous approaches to object recognition. In this dissertation, we challenge these feed-forward theories by considering the problem of occlusion. In natural vision, objects are often partially visible, either due to occlusion, limited viewing angles, or poor illumination. The vast majority of previous neurophysiological studies focus on the completion of simple contours, geometric shapes, or line drawings. These studies typically contrast neural responses to occluded objects against responses to unrecognizable scrambled counterparts, thus confounding object completion mechanisms with neural activity linked to perceptual awareness. We set out to provide conceptual advances by using naturalistic objects and measuring the selectivity and tolerance of neural responses when objects are only partially visible. While we know that feedback and recurrent connections are prevalent throughout visual cortex, their underlying roles are unclear. We present three lines of evidence for the role of recurrence in recognition of occluded objects. We first recorded intracranial field potentials from electrodes surgically implanted in epilepsy patients and measured neural responses to whole and occluded objects. Responses along the ventral visual stream remained selective despite heavy occlusion. However, these visually selective signals emerged ~100 ms later than responses to whole objects. The processing delays were particularly pronounced in higher visual areas within the ventral stream, suggesting the involvement of additional recurrent processing. Second, we conducted psychophysical experiments to demonstrate that disrupting this recurrence with backward masking after ~75 ms significantly impaired recognition of occluded, but not whole, objects. Lastly, we augmented a computational model with recurrence that significantly outperformed existing feed-forward models and matched human performance.Publication Face Recognition: Vision and Emotions beyond the Bubble(Elsevier BV, 2011) Tang, Hanlin; Kreiman, Gabriel