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Archy O. de Berker

Researcher at University College London

Publications -  19
Citations -  1925

Archy O. de Berker is an academic researcher from University College London. The author has contributed to research in topics: Prefrontal cortex & Brain stimulation. The author has an hindex of 14, co-authored 18 publications receiving 1343 citations. Previous affiliations of Archy O. de Berker include University of Cambridge & Wellcome Trust Centre for Neuroimaging.

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A deep learning framework for neuroscience

TL;DR: It is argued that a deep network is best understood in terms of components used to design it—objective functions, architecture and learning rules—rather than unit-by-unit computation.
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Computations of uncertainty mediate acute stress responses in humans

TL;DR: It is demonstrated that subjective estimates of uncertainty predict the dynamics of subjective and physiological stress responses, and the finding that stress responses are tuned to environmental uncertainty provides new insight into their generation and likely adaptive function.
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Understanding the behavioural consequences of noninvasive brain stimulation.

TL;DR: It is argued that rational application of tES should occur in tandem with computational neurostimulation and appropriate physiological and behavioural assays to aid appreciation of the limitations and generate testable predictions of how tES expresses its effects on behaviour.
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Single-Unit Recordings in the Macaque Face Patch System Reveal Limitations of fMRI MVPA

TL;DR: In this paper, the authors performed multivariate pattern analysis (MVPA) of fMRI data and single-unit data in the same species, the macaque monkey, and concluded that fMRI MVPA cannot decode information contained in the weakly clustered neuronal responses responsible for coding the identity of human faces in the monkey brain.
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Triple dissociation of attention and decision computations across prefrontal cortex

TL;DR: These findings show how anatomically dissociable PFC representations evolve during attention-guided information search, supporting computations critical for value-guided choice.