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Kevin Gurney
Researcher at University of Sheffield
Publications - 163
Citations - 11697
Kevin Gurney is an academic researcher from University of Sheffield. The author has contributed to research in topics: Action selection & Artificial neural network. The author has an hindex of 35, co-authored 160 publications receiving 10918 citations. Previous affiliations of Kevin Gurney include University of the West of England & Brunel University London.
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A Probabilistic, Distributed, Recursive Mechanism for Decision-making in the Brain
TL;DR: This work characterize its essential composition, using as a framework a novel recursive Bayesian algorithm that makes decisions based on spike-trains with the statistics of those in sensory cortex (MT), and demonstrates it quantitatively replicates the choice behaviour of monkeys, whilst predicting losses of otherwise usable information from MT.
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Object-based biasing for attentional control of gaze: a comparison of biologically plausible mechanisms
TL;DR: Here, by simulating models trained and naive to the target stimulus, it is shown that subsequent learning of a combined representation of an untrained target stimulus can explain the experimentally observed decrease in the slope of reaction time against number of distractors for that target.
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Efficient current-based optimization techniques for parameter estimation in multi-compartment neuronal models
TL;DR: This work automates theimation of the maximal ion channel conductances in Hodgkin-Huxley models from patch clamp data and presents a general neuronal parameter-fitting algorithm that is both computationally efficient and robust.
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Evidence for a probabilistic, brain-distributed, recursive mechanism for decision-making
TL;DR: This work introduces a recursive Bayesian algorithm that makes decisions based on spike trains, and shows that the dynamics of its mapped computations match those of neural activity in the sensory-motor cortex and striatum during decisions, and forecast those of basal ganglia output and thalamus.
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Decision-making out of neural events: from discrimination information to psychometric power laws
TL;DR: The results suggest that the mean decision sample decreases with increasing fa to fb KLD and, crucially, this follows a power law, and the universality of Pieron's law indicates that it can inform us of something fundamental about sensorimotor decision-making.