M
Mark D. Humphries
Researcher at University of Nottingham
Publications - 91
Citations - 4599
Mark D. Humphries is an academic researcher from University of Nottingham. The author has contributed to research in topics: Population & Basal ganglia. The author has an hindex of 24, co-authored 88 publications receiving 4035 citations. Previous affiliations of Mark D. Humphries include École Normale Supérieure & University of Manchester.
Papers
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Network 'small-world-ness': a quantitative method for determining canonical network equivalence.
Mark D. Humphries,Kevin Gurney +1 more
TL;DR: A precise measure of ‘small-world-ness’ S is defined based on the trade off between high local clustering and short path length and several key properties of the metric are described and the use of WS canonical models is placed on a more secure footing.
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The brainstem reticular formation is a small-world, not scale-free, network
TL;DR: It is concluded that the medial RF is configured to create small-world (implying coherent rapid-processing capabilities), but not scale-free, type networks under assumptions which are amenable to quantitative measurement.
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The ventral basal ganglia, a selection mechanism at the crossroads of space, strategy, and reward
TL;DR: The ventral basal ganglia are revealed as a constellation of multiple functional systems for the learning and selection of flexible behaviours and of behavioural strategies, sharing the common operations of selection-by-disinhibition and of dopaminergic modulation.
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A physiologically plausible model of action selection and oscillatory activity in the basal ganglia.
TL;DR: A new spiking neuron model of the BG circuitry supports motor program selection and switching, which deteriorates under dopamine-depleted and dopamine-excessive conditions in a manner consistent with some pathologies associated with those dopamine states.
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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.