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
More filters
Journal ArticleDOI
What is reinforced by phasic dopamine signals
TL;DR: The 'time-stamp' nature of thephasic response, in conjunction with the other signals likely to be present in the basal ganglia at the time of phasic DA input, suggests it may reinforce the discovery of unpredicted sensory events for which the organism is responsible.
Journal ArticleDOI
Layered control architectures in robots and vertebrates
TL;DR: It is argued that, in addition to subsumption- like conflict resolution mechanisms, the vertebrate nervous system employs specialized selection mechanisms located in a group of central brain structures termed the basal ganglia to provide effective and flexible action selection.
Journal ArticleDOI
A robot model of the basal ganglia: Behavior and intrinsic processing
TL;DR: This work describes a robot architecture into which a computational model of the basal ganglia to generate integrated selection sequences in an autonomous agent is embedded, and demonstrates effective action selection by the embedded model under a wide range of sensory and motivational conditions.
Journal ArticleDOI
Dopaminergic Control of the Exploration-Exploitation Trade-Off via the Basal Ganglia.
TL;DR: These models support the hypothesis that changes in tonic dopamine within the striatum can alter the exploration-exploitation trade-off by modulating the output of the basal ganglia.
Journal ArticleDOI
Computational models of the basal ganglia: from robots to membranes.
TL;DR: This viewpoint presents a framework for understanding the aims, limitations and methods for testing of computational models across all structural levels, and identifies distinct modelling strategies that can deliver important and complementary insights into the nature of problems the basal ganglia have evolved to solve.