K
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.
Papers
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Journal ArticleDOI
From model specification to simulation of biologically constrained networks of spiking neurons.
TL;DR: A declarative extensible markup language (SpineML) for describing the dynamics, network and experiments of large-scale spiking neural network simulations is described which builds upon the NineML standard.
Proceedings ArticleDOI
Naive Bayes novelty detection for a moving robot with whiskers
Nathan F. Lepora,Martin J. Pearson,Ben Mitchinson,Mat Evans,Charles Fox,Anthony Pipe,Kevin Gurney,Tony J. Prescott +7 more
TL;DR: A biomimetic robot inspired by the rat whisker system was used to examine the performance of a novelty detection algorithm based on a “naive” implementation of Bayes rule.
Book ChapterDOI
A real-time, FPGA based, biologically plausible neural network processor
Martin J. Pearson,Ian Gilhespy,Kevin Gurney,Chris Melhuish,Benjamin Mitchinson,Mokhtar Nibouche,Anthony G. Pipe +6 more
TL;DR: A real-time, large scale, leaky-integrate-and-fire neural network processor realized using FPGA is presented, designed to investigate and implement biologically plausible models of the rodent vibrissae based somatosensory system to control a robot.
Book ChapterDOI
Action Discovery and Intrinsic Motivation: A Biologically Constrained Formalisation
TL;DR: It is argued that action discovery requires an interplay between separate internal forward models of prediction and inverse models mapping outcomes to actions, and a biologically motivated, formal framework or “ontology” is introduced for dealing with many aspects of action discovery.
Journal ArticleDOI
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.