Demo Data from EDBT 2026 / reviewers in the wild / expert
Stephen G. Lisberger
dblp:87/2233
· DBLP profile ↗
4ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0001-7859-4361ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 4 · 1 first-author · 1 since 2021
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.
| Artificial intelligence
2 papers |
Deep learning architectures and training · 68% Face, body and person analysis · 32% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Deep learning architectures and training
biologically plausible learning |
0.0 | 1 | 1992 | Biologically Plausible Local Learning Rules for the Adaptation of the Vestibulo-Ocular Reflex · NIPS 1992 |
Bioinformatics and computational biology
computational neuroscience |
0.0 | 1 | 1992 | Biologically Plausible Local Learning Rules for the Adaptation of the Vestibulo-Ocular Reflex · NIPS 1992 |
Bioinformatics and computational biology › computational neuroscience
vestibulo-ocular reflex |
0.0 | 1 | 1992 | Biologically Plausible Local Learning Rules for the Adaptation of the Vestibulo-Ocular Reflex · NIPS 1992 |
Computer vision › Face, body and person analysis
eye tracking |
0.0 | 1 | 1991 | Recurrent Eye Tracking Network Using a Distributed Representation of Image Motion · NIPS 1991 |
Machine learning › Deep learning architectures and training
recurrent neural network |
0.0 | 1 | 1991 | Recurrent Eye Tracking Network Using a Distributed Representation of Image Motion · NIPS 1991 |
Methods — techniques the papers use, named apart from their topics
hebbian learning · 0.0local learning rules · 0.0local learning rule · 0.0distributed representation of image motion · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Toward a Biomimetic Neural Circuit Model of Sensory-Motor ProcessingabstractComputational models have been a mainstay of research on smooth pursuit eye movements in monkeys. Pursuit is a sensory-motor system that is driven by the visual motion of small targets. It creates a smooth eye movement that accelerates up to target speed and tracks the moving target essentially perfectly. In this review of my laboratory's research, I trace the development of computational models of pursuit eye movements from the early control-theory models to the most recent neural circuit models. I outline a combined experimental and computational plan to move the models to the next level. Finally, I explain why research on nonhuman primates is so critical to the development of the neural circuit models I think we need. Stephen G. Lisberger |
Neural Comput. | 1 |
| 1992 | Biologically Plausible Local Learning Rules for the Adaptation of the Vestibulo-Ocular Reflex
Olivier J. M. D. Coenen, Terrence J. Sejnowski, Stephen G. Lisberger |
NIPS | 3 |
| 1991 | Recurrent Eye Tracking Network Using a Distributed Representation of Image Motion
Paul A. Viola, Stephen G. Lisberger, Terrence J. Sejnowski |
NIPS | 2 |
| 1989 | A Control Systems Model of Smooth Pursuit Eye Movements with Realistic Emergent PropertiesabstractVisual tracking of objects in a noisy environment is a difficult problem that has been solved by the primate oculomotor system, but remains unsolved in robotics. In primates, smooth pursuit eye movements match eye motion to target motion to keep the eye pointed at smoothly moving targets. We have used computer models as a tool to investigate possible computational strategies underlying this behavior. Here, we present a model based upon behavioral data from monkeys. The model emphasizes the variety of visual signals available for pursuit and, in particular, includes a sensitivity to the acceleration of retinal images. The model was designed to replicate the initial eye velocity response observed during pursuit of different target motions. The strength of the model is that it also exhibits a number of emergent properties that are seen in the behavior of both humans and monkeys. This suggests that the elements in the model capture important aspects of the mechanism of visual tracking by the primate smooth pursuit system. Richard J. Krauzlis, Stephen G. Lisberger |
Neural Comput. | 2 |