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Jim Wielaard

dblp:77/3840 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2005
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%
Artificial intelligence
1 paper
Deep learning architectures and training · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Deep learning architectures and training › convolutional neural network
receptive field
0.112005
Neural mechanisms of contrast dependent receptive field size in V1 · NIPS 2005
Bioinformatics and computational biology
computational neuroscience
0.112005
Neural mechanisms of contrast dependent receptive field size in V1 · NIPS 2005
Bioinformatics and computational biology › computational neuroscience › visual cortex
visual cortex modeling
0.112005
Neural mechanisms of contrast dependent receptive field size in V1 · NIPS 2005

Methods — techniques the papers use, named apart from their topics

spiking neuron model · 0.1large-scale simulation · 0.1
YearPublicationVenuePosition
2005 Neural mechanisms of contrast dependent receptive field size in V1
abstract
Based on a large scale spiking neuron model of the input layers 4C and of macaque, we identify neural mechanisms for the observed contrast dependent receptive field size of V1 cells. We observe a rich variety of mechanisms for the phenomenon and analyze them based on the relative gain of excitatory and inhibitory synaptic inputs. We observe an average growth in the spatial extent of excitation and inhibition for low contrast, as predicted from phenomenological models. However, contrary to phenomenological models, our simulation results suggest this is neither sufficient nor necessary to explain the phenomenon.
Jim Wielaard, Paul Sajda
NIPS1