Demo Data from EDBT 2026 / reviewers in the wild / expert
Jim Wielaard
dblp:77/3840
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Deep learning architectures and training › convolutional neural network
receptive field |
0.1 | 1 | 2005 | Neural mechanisms of contrast dependent receptive field size in V1 · NIPS 2005 |
Bioinformatics and computational biology
computational neuroscience |
0.1 | 1 | 2005 | Neural mechanisms of contrast dependent receptive field size in V1 · NIPS 2005 |
Bioinformatics and computational biology › computational neuroscience › visual cortex
visual cortex modeling |
0.1 | 1 | 2005 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | Neural mechanisms of contrast dependent receptive field size in V1abstractBased 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 |
NIPS | 1 |