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Cameron Higgins

Researcher at University of Oxford

Publications -  9
Citations -  105

Cameron Higgins is an academic researcher from University of Oxford. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 3, co-authored 5 publications receiving 30 citations.

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Replay bursts in humans coincide with activation of the default mode and parietal alpha networks

TL;DR: Investigating whether replay coincided with spontaneous patterns of whole-brain activity found that replay sequences were packaged into transient bursts occurring selectively during activation of the default mode network (DMN) and parietal alpha networks.
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Temporally delayed linear modelling (TDLM) measures replay in both animals and humans

TL;DR: In this article, an analysis toolkit called temporal delayed linear modelling (TDLM) was developed to find neural sequences that respect a pre-specified transition graph, which combines nonlinear classification and linear temporal modelling to test for statistical regularities in sequences of taskrelated reactivations.
Posted ContentDOI

Replay bursts coincide with activation of the default mode and parietal alpha network

TL;DR: The data show a tight correspondence between two widely studied phenomena of neural physiology and suggest the DMN may coordinate replay bursts in a manner that minimizes interference with ongoing cognition.
Posted ContentDOI

Brain stimulation boosts perceptual learning by altering sensory GABAergic plasticity and functional connectivity

TL;DR: In this paper, anodal direct current stimulation (tDCS) was combined with multi-modal brain measures to modulate cortical excitability during training on a signal-in-noise task and test directly the link between processing in visual cortex and its interactions with decision-related areas (i.e. posterior parietal cortex).
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Mixtures of large-scale dynamic functional brain network modes

TL;DR: In this article , the authors proposed DyNeMo, a new generative model for functional connectivity as a time-varying linear mixture of spatially distributed statistical "modes".