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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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Book ChapterDOI

Visual Functions of the Orbitofrontal Cortex

TL;DR: The human orbitofrontal cortex is activated by visual stimuli that show how much monetary reward has been obtained; and by mismatches in a visual discrimination reversal task between the face expression expected, and that obtained.
Journal ArticleDOI

Chemosensory learning and memory

TL;DR: This issue presents an updated view of present knowledge and questions raised in the rapidly expanding field of chemosensory (taste and olfactory) learning, and pays tribute to Jan Bures, who passed away on August 24, 2012, in Prague.
Proceedings Article

Connected Cortial Recurrent Networks

TL;DR: A model of an associative memory composed of many modules working as attractor neural networks with features of biological realism is proposed and analyzed using standard statistical physics techniques and it is found that, if it is large, global retrieval phases can be found in which selective sustained actibities induced in modules which have not been stimulated.
Journal ArticleDOI

Intersecting distributed networks support convergent linguistic functioning across different languages in bilinguals

TL;DR: In this article , the authors scan Chinese-English bilinguals during an implicit reading task involving Chinese words, English words, and Chinese pinyin and observe broad brain cortical regions wherein inter-digitated distributed neural populations supported the same cognitive components of different languages.
Book ChapterDOI

Connected cortical recurrent networks

TL;DR: In this article, a model of an associative memory composed of many modules working as attractor neural networks with features of biological realism is proposed and analyzed using standard statistical physics techniques, and the form of the associations is such that, in the case of a tri-modular network studied, results from a psychophysical experiment on the simultaneous processing of contradictory information can be qualitatively reproduced within the limitations imposed by the simplicity of the model.