Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Lance M. Optican

dblp:53/6913 · DBLP profile ↗
← Back
4ranked-venue papers
0as first author
0since 2021 · last 2008
0000-0003-3640-0762ORCID · corroborated

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

Artificial intelligence and machine learning · 3Applied, interdisciplinary, general and emerging computing · 1

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
1 paper
Image recognition and object detection · 50% 3D vision · 50%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
brain decoding
0.011991
Decoding of Neuronal Signals in Visual Pattern Recognition · NIPS 1991
Computer vision › Image recognition and object detection › object recognition
visual pattern recognition
0.011991
Decoding of Neuronal Signals in Visual Pattern Recognition · NIPS 1991
Bioinformatics and computational biology
computational neuroscience
0.011991
Decoding of Neuronal Signals in Visual Pattern Recognition · NIPS 1991
Bioinformatics and computational biology › computational neuroscience
neural decoding
0.011991
Decoding of Neuronal Signals in Visual Pattern Recognition · NIPS 1991

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

signal decoding · 0.0
YearPublicationVenuePosition
2008 Short-Term Memory Trace in Rapidly Adapting Synapses of Inferior Temporal Cortex
abstract
Visual short-term memory tasks depend upon both the inferior temporal cortex (ITC) and the prefrontal cortex (PFC). Activity in some neurons persists after the first (sample) stimulus is shown. This delay-period activity has been proposed as an important mechanism for working memory. In ITC neurons, intervening (nonmatching) stimuli wipe out the delay-period activity; hence, the role of ITC in memory must depend upon a different mechanism. Here, we look for a possible mechanism by contrasting memory effects in two architectonically different parts of ITC: area TE and the perirhinal cortex. We found that a large proportion (80%) of stimulus-selective neurons in area TE of macaque ITCs exhibit a memory effect during the stimulus interval. During a sequential delayed matching-to-sample task (DMS), the noise in the neuronal response to the test image was correlated with the noise in the neuronal response to the sample image. Neurons in perirhinal cortex did not show this correlation. These results led us to hypothesize that area TE contributes to short-term memory by acting as a matched filter. When the sample image appears, each TE neuron captures a static copy of its inputs by rapidly adjusting its synaptic weights to match the strength of their individual inputs. Input signals from subsequent images are multiplied by those synaptic weights, thereby computing a measure of the correlation between the past and present inputs. The total activity in area TE is sufficient to quantify the similarity between the two images. This matched filter theory provides an explanation of what is remembered, where the trace is stored, and how comparison is done across time, all without requiring delay period activity. Simulations of a matched filter model match the experimental results, suggesting that area TE neurons store a synaptic memory trace during short-term visual memory.
Yasuko Sugase-Miyamoto, Matthew C. Wiener, Lance M. Optican, Barry J. Richmond
PLoS Comput. Biol.4
1998 Distributed model of control of saccades by superior colliculus and cerebellum
Philippe Lefèvre, Christian Quaia, Lance M. Optican
Neural Networks3
1998 The maintenance of spatial accuracy by the perisaccadic remapping of visual receptive fields
Christian Quaia, Lance M. Optican, Michael E. Goldberg
Neural Networks2
1991 Decoding of Neuronal Signals in Visual Pattern Recognition
Emad N. Eskandar, Barry J. Richmond, John A. Hertz, Lance M. Optican, Troels W. Kjær
NIPS4