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Edmund T. Rolls

Researcher at University of Warwick

Publications -  645
Citations -  84442

Edmund T. Rolls is an academic researcher from University of Warwick. The author has contributed to research in topics: Orbitofrontal cortex & Visual cortex. The author has an hindex of 153, co-authored 612 publications receiving 77928 citations. Previous affiliations of Edmund T. Rolls include Fudan University & Newcastle University.

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Extensive Cortical Connectivity of the Human Hippocampal Memory System: Beyond the "What" and "Where" Dual Stream Model.

TL;DR: The human hippocampus is involved in forming new memories: damage impairs memory as discussed by the authors, and the hippocampus can then associate these inputs to form episodic memories of what happened where, which is called episodic memory.
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Reward Versus Nonreward Sensitivity of the Medial Versus Lateral Orbitofrontal Cortex Relates to the Severity of Depressive Symptoms

Chao Xie, +82 more
TL;DR: Activations in the lateral OFC relate to sensitivity to not winning, were associated with high depressive symptom scores, and at age 14 predicted the depressive symptoms at ages 16 and 19.
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Physiological mechanisms for thirst in the nonhuman primate.

TL;DR: In the rhesus monkey, cellular dehydration is an effective stimulus for thirst, and it is the primary determinant of drinking after water deprivation, used as an example of a natural thirst stimulus.
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Networks for memory, perception, and decision-making, and beyond to how the syntax for language might be implemented in the brain

TL;DR: In this article, the authors explored the possibility of syntax encoding in the cortex using sparse distributed place coding, where each cortical module 2-3 mm in diameter is formed of a local attractor neuronal network with a capacity in the order of 10,000 words.
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Spatial coordinate transforms linking the allocentric hippocampal and egocentric parietal primate brain systems for memory, action in space, and navigation

TL;DR: A new approach to coordinate transform learning in the brain is proposed, in which the traditional gain modulation is complemented by temporal trace rule competitive network learning.