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Stephen G. Lisberger

dblp:87/2233 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Machine learning › Deep learning architectures and training
biologically plausible learning
0.011992
Biologically Plausible Local Learning Rules for the Adaptation of the Vestibulo-Ocular Reflex · NIPS 1992
Bioinformatics and computational biology
computational neuroscience
0.011992
Biologically Plausible Local Learning Rules for the Adaptation of the Vestibulo-Ocular Reflex · NIPS 1992
Bioinformatics and computational biology › computational neuroscience
vestibulo-ocular reflex
0.011992
Biologically Plausible Local Learning Rules for the Adaptation of the Vestibulo-Ocular Reflex · NIPS 1992
Computer vision › Face, body and person analysis
eye tracking
0.011991
Recurrent Eye Tracking Network Using a Distributed Representation of Image Motion · NIPS 1991
Machine learning › Deep learning architectures and training
recurrent neural network
0.011991
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
YearPublicationVenuePosition
2023 Toward a Biomimetic Neural Circuit Model of Sensory-Motor Processing
abstract
Computational 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
NIPS3
1991 Recurrent Eye Tracking Network Using a Distributed Representation of Image Motion
Paul A. Viola, Stephen G. Lisberger, Terrence J. Sejnowski
NIPS2
1989 A Control Systems Model of Smooth Pursuit Eye Movements with Realistic Emergent Properties
abstract
Visual 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