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Yoonsuck Choe
Computational Neuroscience
Brain Imaging And Analysis
Neuroevolution
Temporal Brain Dynamics
Sensorimotor Learning And Grounding
About
I am a professor in the Department of Computer Science and Engineering at Texas A&M University. My research is broadly in computational neuroscience, brain imaging and analysis, neuroevolution, and neural networks (deep learning). I am especially interested in the temporal aspects of brain function (delay, internal brain dynamics, prediction, consciousness), sensorimotor learning and grounding (visual, tactile, proprioceptive, semantics, meaning), agent-environment interaction (including tool construction and tool use), and evolution of brain structure and function
Research Performance Summary
1995-2025
Active Research Span
First Recorded Paper
Laterally interconnected self-organizing maps in hand-written digit recognition
Year:
1995
Citations:
19
Venue:
Advances in neural information processing systems 8
Latest Recorded Paper
Saliency Thresholds in Neural Code and its Relation to the Power-Law, Gaussian, and Lambert W Function
Year:
2025
Citations:
0
Venue:
NeurIPS 2025 Workshop on Symmetry and Geometry in Neural Representations
Last 10 Years Publication Activity
This timeline shows the professor's yearly publication activity.
Publication Venues and Collaboration
Journal, Conference, and Book Publication Breakdown
Top Coauthors
Choe
Miikkulainen
Kwon
Research Impact by Period
Papers
3
Citations
2
Avg. Citations / Paper
0.7
H-Index
1
Papers
24
Citations
269
Avg. Citations / Paper
11.2
H-Index
7
Papers
44
Citations
489
Avg. Citations / Paper
11.1
H-Index
9
Papers
50
Citations
474
Avg. Citations / Paper
9.5
H-Index
13
Papers
75
Citations
979
Avg. Citations / Paper
13.1
H-Index
15
Papers
26
Citations
427
Avg. Citations / Paper
16.4
H-Index
12
Papers
11
Citations
250
Avg. Citations / Paper
22.7
H-Index
6
Contact and Professional Links
Detected Research Keywords
Knife Edge Scanning
Edge Scanning Microscope
Edge Scanning Microscopy
Dynamics Delay Compensation
Organizing Neural Network
Neural Network Model
Model Primary Visual
Primary Visual Cortex
Map Spiking Neurons
Internal Sensory States