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.
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Journal ArticleDOI
Striatal Neuropeptides Enhance Selection and Rejection of Sequential Actions.
TL;DR: It is demonstrated that diffuse neuropeptide connectivity enhanced the selection of unordered action requests, and that for true action sequences, a patterning of the SP connectivity reflecting this ordering enhanced selection of actions presented in the correct sequential order and suppressed incorrect ordering.
Proceedings ArticleDOI
Implementation of multi-layer leaky integrator networks on a cellular processor array
TL;DR: An application of a massively parallel processor array VLSI circuit to the implementation of neural networks in complex architectural arrangements, providing a high-speed, low-power, compact hardware platform for possible embedded robotic applications.
Journal ArticleDOI
SpineCreator: a Graphical User Interface for the Creation of Layered Neural Models
TL;DR: A new graphical software tool, SpineCreator, is described, which facilitates the creation and visualisation of layered models of point spiking neurons or rate coded neurons without requiring the need for programming.
Journal ArticleDOI
Spiking neural network simulation: memory-optimal synaptic event scheduling
Robert D. Stewart,Kevin Gurney +1 more
TL;DR: This work introduces novel scheduling algorithms for both discrete and continuous event delivery, where the memory requirement scales instead with the number of neurons.
Journal ArticleDOI
A model for the spatial integration and differentiation of velocity signals.
Kevin Gurney,Michael J. Wright +1 more
TL;DR: A model of optic flow processing which is able to reconcile the integrative, cooperative phenomena of motion capture and coherence with the differentiation of velocity signals in motion segmentation and transparency is presented.