Brain Networks Laboratory (Choe Lab)

Publications (By Topic)

By Year – By Medium

  1. Action Learning and Neural Semantics of Perception (selected)
  2. Predictive Dynamics and Consciousness (selected)
  3. Reactive agent's use of external markers for cognition (selected)
  4. Tool Use (selected)
  5. Action Learning and Neural Semantics of Perception (full)
  6. Sensorimotor Learning and Reinforcement Learning
  7. Evolutionary Perspectives on Brain Function
  8. Temporal Aspects of Brain Function
  9. Biologically Inspired Vision and Cortical Maps
  10. Deep Learning
  11. Thalamocortical Function
  12. Neural Synchrony and Perceptual Grouping
  13. Pattern Recognition etc.
  14. Neuroinformatics and Computational Neuroanatomy
  15. Big Data and Data Science
  16. General Topics on AI and Machine Learning
  17. System Biology and Health Informatics
  18. Dissertations (by students)
  19. Theses (by students)
  20. CS General

Action Learning and Neural Semantics of Perception (selected)

  1. Yoonsuck Choe and S. Kumar Bhamidipati. Autonomous acquisition of the meaning of sensory states through sensory-invariance driven action. In A. J. Ijspeert, M. Murata, and N. Wakamiya, editors, Biologically Inspired Approaches to Advanced Information Technology, Lecture Notes in Computer Science 3141, pages 176-188, Berlin, 2004. Springer.
  2. Yoonsuck Choe and Noah H. Smith. Motion-based autonomous grounding: Inferring external world properties from internal sensory states alone. In Yolanda Gil and Raymond Mooney, editors, Proceedings of the 21st National Conference on Artificial Intelligence(AAAI 2006), pages 936-941, 2006.
  3. Yoonsuck Choe, Huei-Fang Yang, and Daniel Chern-Yeow Eng. Autonomous learning of the semantics of internal sensory states based on motor exploration. International Journal of Humanoid Robotics, 4:211-243, 2007.
  4. Yoonsuck Choe, Huei-Fang Yang, and Navendu Misra. Motor system's role in grounding, receptive field development, and shape recognition. In Proceedings of the Seventh International Conference on Development and Learning, pages 67-72. IEEE, 2008.
  5. Yoonsuck Choe. Action-based autonomous grounding. In Joseph Modayil, Doina Precup, and Satinder Singh, editors, AAAI-11 Workshop on Lifelong Learning from Sensorimotor Experience, pages 56-57, Palo Alto, CA, 2011. AAAI Press. AAAI Workshop Technical Report WS-11-15.
  6. Amey Parulkar and Yoonsuck Choe. Motor-based autonomous grounding in a model of the fly optic flow system. In Proceedings of the International Joint Conference on Neural Networks, pages 4354-4361, 2016.

Predictive Dynamics and Consciousness (selected)

  1. Yoonsuck Choe, Jaerock Kwon, and Ji Ryang Chung. Time, consciousness, and mind uploading. International Journal on Machine Consciousness, 4:257-274, 2012.
  2. Yoonsuck Choe, Jaewook Yoo, and Jaerock Kwon. Prediction, resilience to change, and evolution of consciousness. In ICDL Workshop on Spatio-temporal Aspects of Embodied Predictive Processing 2021 (StEPP21), page TBA, 2021.
  3. Jaerock Kwon and Yoonsuck Choe. Internal state predictability as an evolutionary precursor of self-awareness and agency. In Proceedings of the Seventh International Conference on Development and Learning, pages 109-114. IEEE, 2008.
  4. Jaewook Yoo, Jaework Kwon, and Yoonsuck Choe. Predictable internal brain dynamics in EEG and its relation to conscious states. Frontiers in Neurorobotics, 8(00018), 2014.

Reactive agent's use of external markers for cognition (selected)

  1. Ji Ryang Chung and Yoonsuck Choe. Emergence of memory-like behavior in reactive agents using external markers. In Proceedings of the 21st International Conference on Tools with Artificial Intelligence, 2009. ICTAI '09, pages 404-408, 2009.
  2. Ji Ryang Chung and Yoonsuck Choe. Emergence of memory in reactive agents equipped with environmental markers. IEEE Transactions on Autonomous Mental Development, 3:257-271, 2011.

Tool Use (selected)

  1. Michael Freitag and Yoonsuck Choe. Analysis of tool use strategies in evolved neural circuits controlling an articulated limb. In Proceedings of the International Joint Conference on Neural Networks, pages 4331-4338, 2016.
  2. Qinbo Li and Yoonsuck Choe. Construction and use of tools through hierarchical deep reinforcement learning. In 2021 IEEE/RSJ IROS Workshop on Human-like Behavior and Cognition in Robots, page TBA, 2021.
  3. Qinbo Li and Yoonsuck Choe. Emergence of tool construction and tool use through hierarchical reinforcement learning. In Carlo F. Morabito, Cesare Alippi, Yoonsuck Choe, and Robert Kozma, editors, Artificial Intelligence in the Age of Neural Networks and Brain Computing, page In press. Academic Press, Cambridge, MA, second edition, 2024.
  4. Qinbo Li, Jaewook Yoo, and Yoonsuck Choe. Emergence of tool use in an articulated limb controlled by evolved neural circuits. In Proceedings of the International Joint Conference on Neural Networks, 2015. DOI: 10.1109/IJCNN.2015.7280564.
  5. Khuong Nguyen and Yoonsuck Choe. Emergence of different modes of tool use in a reaching and dragging task. In 2021 International Joint Conference on Neural Networks (IJCNN), 2021. In press.
  6. Khuong N Nguyen, Jaewook Yoo, and Yoonsuck Choe. Speeding up affordance learning for tool use, using proprioceptive and kinesthetic inputs. In 2019 International Joint Conference on Neural Networks (IJCNN), pages 1-8. IEEE, 2019.
  7. Randall Reams and Yoonsuck Choe. Emergence of tool construction in an articulated limb controlled by evolved neural circuits. In Proceedings of the International Joint Conference on Neural Networks, pages 642-649, 2017.
  8. Han Wang, Jaewook Yoo, Qinbo Li, and Yoonsuck Choe. Dynamical analysis of recurrent neural circuits in articulated limb controllers for tool use. In Proceedings of the International Joint Conference on Neural Networks, pages 4339-4345, 2016.

Action Learning and Neural Semantics of Perception (full)

  1. S. Kumar Bhamidipati. Sensory invariance driven action (SIDA) framework for understanding the meaning of neural spikes. Master's thesis, Department of Computer Science, Texas A&M University, 2004.
  2. Yoonsuck Choe and S. Kumar Bhamidipati. Learning the meaning of neural spikes through sensory-invariance driven action. Technical Report 2003-8-3, Department of Computer Science, Texas A&M University, 2003.
  3. Yoonsuck Choe and S. Kumar Bhamidipati. Autonomous acquisition of the meaning of sensory states through sensory-invariance driven action. In A. J. Ijspeert, M. Murata, and N. Wakamiya, editors, Biologically Inspired Approaches to Advanced Information Technology, Lecture Notes in Computer Science 3141, pages 176-188, Berlin, 2004. Springer.
  4. Yoonsuck Choe and Noah H. Smith. Motion-based autonomous grounding: Inferring external world properties from internal sensory states alone. In Yolanda Gil and Raymond Mooney, editors, Proceedings of the 21st National Conference on Artificial Intelligence(AAAI 2006), pages 936-941, 2006.
  5. Yoonsuck Choe and Huei-Fang Yang. Co-development of visual receptive fields and their motor primitive-based decoding scheme. In Society for Neuroscience Abstracts. Washington, DC: Society for Neuroscience, 2006. Program No. 734.6. Online.
  6. Yoonsuck Choe and Huei-Fang Yang. Decoding spikes without stimulus information: Its implications on receptive-field learning. In Proceedings of the 5th Computational and Systems Neuroscience Meeting (COSYNE 2008 Abstracts), page 267, 2008.
  7. Yoonsuck Choe and Huei-Fang Yang. Action-based grounding: Beyond encoding/decoding in neural code. BMC Neuroscience, 17(Suppl 1):P28, 2016. Computational Neuroscience meeting abstract.
  8. Yoonsuck Choe, Huei-Fang Yang, and Daniel Chern-Yeow Eng. Autonomous learning of the semantics of internal sensory states based on motor exploration. International Journal of Humanoid Robotics, 4:211-243, 2007.
  9. Yoonsuck Choe, Huei-Fang Yang, and Navendu Misra. Motor system's role in grounding, receptive field development, and shape recognition. In Proceedings of the Seventh International Conference on Development and Learning, pages 67-72. IEEE, 2008.
  10. Yoonsuck Choe. A deeper semantic role for the mirror system. Behavioral and Brain Sciences, 28, 2005. (On-line supplemental commentary on Arbib (2005) Behavioral and Brain Sciences, 28:105-167.).
  11. Yoonsuck Choe. Action-based autonomous grounding. In Joseph Modayil, Doina Precup, and Satinder Singh, editors, AAAI-11 Workshop on Lifelong Learning from Sensorimotor Experience, pages 56-57, Palo Alto, CA, 2011. AAAI Press. AAAI Workshop Technical Report WS-11-15.
  12. Heeyoul Choi and Yoonsuck Choe. Simultaneous grounding and receptive field learning in visuomotor agents. BMC Neuroscience, 11(Suppl 1):P89, 2010. Nineteenth Annual Computational Neuroscience Meeting: CNS*2010.
  13. Timothy A. Mann and Yoonsuck Choe. Grounding the meaning of nonprototypical smiles on motor behavior. Behavioral and Brain Sciences, 33:453-454, 2010. Commentary on Niedenthal et al. (same volume).
  14. Navendu Misra and Yoonsuck Choe. Shape recognition through dynamic motor representations. In Robert Kozma and Leonid Perlovsky, editors, Neurodynamics of Higher-Level Cognition and Consciousness, pages 185-210. Springer, Berlin, 2007.
  15. Navendu Misra. Comparison of motor-based versus visual representations in object recognition tasks. Master's thesis, Department of Computer Science, Texas A&M University, College Station, Texas, 2005.
  16. Khuong N Nguyen, Xi Liu, Oleg Komogortsev, Ricardo Gutierrez-Osuna, and Yoonsuck Choe. Explanation of the perceptual oblique effect based on the fidelity of oculomotor control during saccades. In Proceedings of the International Conference on Development and Learning, pages 15-20, 2017.
  17. Amey Parulkar and Yoonsuck Choe. Motor-based autonomous grounding in a model of the fly optic flow system. In Proceedings of the International Joint Conference on Neural Networks, pages 4354-4361, 2016.
  18. Huei-Fang Yang and Yoonsuck Choe. Co-development of visual receptive fields and their motor-primitive-based decoding scheme. In Proceedings of the International Joint Conference on Neural Networks 2007 Post conference Workshop on Biologically-inspired Computational Vision (BCV) 2007, 2007.

Sensorimotor Learning and Reinforcement Learning

  1. S. Kumar Bhamidipati. Sensory invariance driven action (SIDA) framework for understanding the meaning of neural spikes. Master's thesis, Department of Computer Science, Texas A&M University, 2004.
  2. Yoonsuck Choe and S. Kumar Bhamidipati. Learning the meaning of neural spikes through sensory-invariance driven action. Technical Report 2003-8-3, Department of Computer Science, Texas A&M University, 2003.
  3. Yoonsuck Choe and S. Kumar Bhamidipati. Autonomous acquisition of the meaning of sensory states through sensory-invariance driven action. In A. J. Ijspeert, M. Murata, and N. Wakamiya, editors, Biologically Inspired Approaches to Advanced Information Technology, Lecture Notes in Computer Science 3141, pages 176-188, Berlin, 2004. Springer.
  4. Yoonsuck Choe and Noah H. Smith. Motion-based autonomous grounding: Inferring external world properties from internal sensory states alone. In Yolanda Gil and Raymond Mooney, editors, Proceedings of the 21st National Conference on Artificial Intelligence(AAAI 2006), pages 936-941, 2006.
  5. Yoonsuck Choe and Huei-Fang Yang. Co-development of visual receptive fields and their motor primitive-based decoding scheme. In Society for Neuroscience Abstracts. Washington, DC: Society for Neuroscience, 2006. Program No. 734.6. Online.
  6. Yoonsuck Choe and Huei-Fang Yang. Decoding spikes without stimulus information: Its implications on receptive-field learning. In Proceedings of the 5th Computational and Systems Neuroscience Meeting (COSYNE 2008 Abstracts), page 267, 2008.
  7. Yoonsuck Choe, Huei-Fang Yang, and Daniel Chern-Yeow Eng. Autonomous learning of the semantics of internal sensory states based on motor exploration. International Journal of Humanoid Robotics, 4:211-243, 2007.
  8. Yoonsuck Choe, Huei-Fang Yang, and Navendu Misra. Motor system's role in grounding, receptive field development, and shape recognition. In Proceedings of the Seventh International Conference on Development and Learning, pages 67-72. IEEE, 2008.
  9. Yoonsuck Choe. A deeper semantic role for the mirror system. Behavioral and Brain Sciences, 28, 2005. (On-line supplemental commentary on Arbib (2005) Behavioral and Brain Sciences, 28:105-167.).
  10. Yoonsuck Choe. Action-based autonomous grounding. In Joseph Modayil, Doina Precup, and Satinder Singh, editors, AAAI-11 Workshop on Lifelong Learning from Sensorimotor Experience, pages 56-57, Palo Alto, CA, 2011. AAAI Press. AAAI Workshop Technical Report WS-11-15.
  11. Heeyoul Choi and Yoonsuck Choe. Simultaneous grounding and receptive field learning in visuomotor agents. BMC Neuroscience, 11(Suppl 1):P89, 2010. Nineteenth Annual Computational Neuroscience Meeting: CNS*2010.
  12. Kyungrak Choi, Yoonsuck Choe, and Hangue Park. Reinforcement learning may demystify the limited human motor learning efficacy due to visual-proprioceptive mismatch. International Journal of Neural Systems, 2024.
  13. Qinbo Li, Jaewook Yoo, and Yoonsuck Choe. Emergence of tool use in an articulated limb controlled by evolved neural circuits. In Proceedings of the International Joint Conference on Neural Networks, 2015. DOI: 10.1109/IJCNN.2015.7280564.
  14. Timothy A. Mann and Yoonsuck Choe. Grounding the meaning of nonprototypical smiles on motor behavior. Behavioral and Brain Sciences, 33:453-454, 2010. Commentary on Niedenthal et al. (same volume).
  15. Timothy A. Mann and Yoonsuck Choe. Prenatal to postnatal transfer of motor skills through motor-compatible sensory representations. In Proceedings of the Nineth International Conference on Development and Learning, pages 185-190, 2010.
  16. Timothy A. Mann and Yoonsuck Choe. Scaling up reinforcement learning through targeted exploration. In Proceedigs of the Twenty-Fifth AAAI Conference on Artificial Intelligence, pages 435-440, 2011.
  17. Timothy A. Mann and Yoonsuck Choe. Directed exploration in reinforcement learning with transferred knowledge. Journal of Machine Learning Research: Workshop and Conference Proceedings, 24:59-76, 2012.
  18. Timothy A. Mann and Yoonsuck Choe. Directed exploration in reinforcement learning with transferred knowledge. In Proceedings of the 10th European Workshop on Reinforcement Learning, 2012.
  19. Timothy A. Mann, Yunjung Park, Sungmoon Jeong, Minho Lee, and Yoonsuck Choe. Autonomously improving binocular depth estimation. In The 21st Annual Conference of the Japanese Neural Network Society, 2011. P2-15 [online].
  20. Navendu Misra and Yoonsuck Choe. Shape recognition through dynamic motor representations. In Robert Kozma and Leonid Perlovsky, editors, Neurodynamics of Higher-Level Cognition and Consciousness, pages 185-210. Springer, Berlin, 2007.
  21. Navendu Misra. Comparison of motor-based versus visual representations in object recognition tasks. Master's thesis, Department of Computer Science, Texas A&M University, College Station, Texas, 2005.
  22. Hari Raghav, Shuo-Hsiu James Chang, Yoonsuck Choe, and Hangue Park. Proportional sway-based electrotactile feedback improves lateral standing balance. Frontiers in Neuroscience, 18:1249783, 2024.
  23. Takashi Yamauchi, Hwaryong Seo, Yoonsuck Choe, Casady Bowman, and Kunchen Xiao. Assessing emotions by cursor motions: An affective computing approach. In Proceedings of the 36th Annual Conference of the Cognitive Science Society, pages 2721-2726, 2015.
  24. Huei-Fang Yang and Yoonsuck Choe. Co-development of visual receptive fields and their motor-primitive-based decoding scheme. In Proceedings of the International Joint Conference on Neural Networks 2007 Post conference Workshop on Biologically-inspired Computational Vision (BCV) 2007, 2007.

Evolutionary Perspectives on Brain Function

  1. Y. Choe and J. Kwon. Internal state predictability as an evolutionary precursor of self-awareness and agency. In Neuroscience Meeting Planner, Washington, DC: Society for Neuroscience, 2008. Program No. 738.14. Online.
  2. Ji Ryang Chung and Yoonsuck Choe. Emergence of memory-like behavior in reactive agents using external markers. In Proceedings of the 21st International Conference on Tools with Artificial Intelligence, 2009. ICTAI '09, pages 404-408, 2009.
  3. Ji Ryang Chung, Jaerock Kwon, and Yoonsuck Choe. Evolution of recollection and prediction in neural networks. In Proceedings of the International Joint Conference on Neural Networks, pages 571-577, Piscataway, NJ, 2009. IEEE Press.
  4. Ji Ryang Chung, Jaerock Kwon, Timothy A. Mann, and Yoonsuck Choe. Evolution of time in neural networks: From the present to the past, and forward to the future. In A. Ravishankar Rao and Guillermo A. Cecchi, editors, The Relevance of the Time Domain to Neural Network Models, Springer Series in Cognitive and Neural Systems 3, pages 99-116. Springer, New York, 2012.
  5. Michael Freitag and Yoonsuck Choe. Analysis of tool use strategies in evolved neural circuits controlling an articulated limb. In Proceedings of the International Joint Conference on Neural Networks, pages 4331-4338, 2016.
  6. Jin Huang and Yoonsuck Choe. Evolution of proxy use in neural network controllers for crowd modeling. In Proceedings of the International Joint Conference on Neural Networks, 2023.
  7. Jaerock Kwon and Yoonsuck Choe. Enhanced facilitatory neuronal dynamics for delay compensation. In Proceedings of the International Joint Conference on Neural Networks, pages 2040-2045, Piscataway, NJ, 2007. IEEE Press.
  8. Jaerock Kwon and Yoonsuck Choe. Internal state predictability as an evolutionary precursor of self-awareness and agency. In Proceedings of the Seventh International Conference on Development and Learning, pages 109-114. IEEE, 2008.
  9. Jaerock Kwon and Yoonsuck Choe. Facilitating neural dynamics for delay compensation: A road to predictive neural dynamics?. Neural Networks, 22:267-276, 2009.
  10. Jaerock Kwon and Yoonsuck Choe. Predictive internal neural dynamics for delay compensation. In Second World Congress on Nature and Biologically Inspired Computing (NaBIC2010), pages 443-448, 2010.
  11. Qinbo Li, Jaewook Yoo, and Yoonsuck Choe. Emergence of tool use in an articulated limb controlled by evolved neural circuits. In Proceedings of the International Joint Conference on Neural Networks, 2015. DOI: 10.1109/IJCNN.2015.7280564.
  12. Heejin Lim and Yoonsuck Choe. Compensating for neural transmission delay using extrapolatory neural activation in evolutionary neural networks. Neural Information Processing-Letters and Reviews, 10:147-161, 2006.
  13. Heejin Lim and Yoonsuck Choe. Facilitating neural dynamics for delay compensation and prediction in evolutionary neural networks. In Maarten Keijzer, editor, Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO-2006, pages 167-174, 2006.
  14. Timothy A. Mann and Yoonsuck Choe. Neural conduction delay forces the emergence of predictive function in an evolving simulation. BMC Neuroscience, 11(Suppl 1):P62, 2010. Nineteenth Annual Computational Neuroscience Meeting: CNS*2010.
  15. Amey Parulkar and Yoonsuck Choe. Motor-based autonomous grounding in a model of the fly optic flow system. In Proceedings of the International Joint Conference on Neural Networks, pages 4354-4361, 2016.
  16. Han Wang, Jaewook Yoo, Qinbo Li, and Yoonsuck Choe. Dynamical analysis of recurrent neural circuits in articulated limb controllers for tool use. In Proceedings of the International Joint Conference on Neural Networks, pages 4339-4345, 2016.

Temporal Aspects of Brain Function

  1. Y. Choe and J. Kwon. Internal state predictability as an evolutionary precursor of self-awareness and agency. In Neuroscience Meeting Planner, Washington, DC: Society for Neuroscience, 2008. Program No. 738.14. Online.
  2. Yoonsuck Choe and Risto Miikkulainen. Contour integration and segmentation in a self-organizing map of spiking neurons. Biological Cybernetics, 90:75-88, 2004.
  3. Yoonsuck Choe, Jaerock Kwon, and Ji Ryang Chung. Time, consciousness, and mind uploading. International Journal on Machine Consciousness, 4:257-274, 2012.
  4. Yoonsuck Choe, Jaewook Yoo, and Jaerock Kwon. Prediction, resilience to change, and evolution of consciousness. In ICDL Workshop on Spatio-temporal Aspects of Embodied Predictive Processing 2021 (StEPP21), page TBA, 2021.
  5. Yoonsuck Choe. The role of temporal parameters in a thalamocortical model of analogy. IEEE Transactions on Neural Networks, 15:1071-1082, 2004.
  6. Yoonsuck Choe. Anti-Hebbian learning. In Dieter Jaeger and Ranu Jung, editors, Encyclopedia of Computational Neuroscience, pages 191-193. Springer, New York, 1st edition, 2015.
  7. Yoonsuck Choe. Hebbian learning. In Dieter Jaeger and Ranu Jung, editors, Encyclopedia of Computational Neuroscience, pages 1305-1309. Springer, New York, 1st edition, 2015.
  8. Ji Ryang Chung and Yoonsuck Choe. Emergence of memory in reactive agents equipped with environmental markers. IEEE Transactions on Autonomous Mental Development, 3:257-271, 2011.
  9. Ji Ryang Chung, Jaerock Kwon, and Yoonsuck Choe. Evolution of recollection and prediction in neural networks. In Proceedings of the International Joint Conference on Neural Networks, pages 571-577, Piscataway, NJ, 2009. IEEE Press.
  10. Ji Ryang Chung, Jaerock Kwon, Timothy A. Mann, and Yoonsuck Choe. Evolution of time in neural networks: From the present to the past, and forward to the future. In A. Ravishankar Rao and Guillermo A. Cecchi, editors, The Relevance of the Time Domain to Neural Network Models, Springer Series in Cognitive and Neural Systems 3, pages 99-116. Springer, New York, 2012.
  11. Bum Soon Jang, Timothy Mann, and Yoonsuck Choe. Effects of varying the delay distribution in random, scale-free, and small-world networks. In Proceedings of the 2008 IEEE International Conference on Granular Computing, pages 316-321, 2008.
  12. Bum Soon Jang. Effect of varying the delay distribution in different classes of networks: Random, scale-free, and small-world. Master's thesis, Department of Computer Science, Texas A&M University, 2007.
  13. Jaerock Kwon and Yoonsuck Choe. Enhanced facilitatory neuronal dynamics for delay compensation. In Proceedings of the International Joint Conference on Neural Networks, pages 2040-2045, Piscataway, NJ, 2007. IEEE Press.
  14. Jaerock Kwon and Yoonsuck Choe. Internal state predictability as an evolutionary precursor of self-awareness and agency. In Proceedings of the Seventh International Conference on Development and Learning, pages 109-114. IEEE, 2008.
  15. Jaerock Kwon and Yoonsuck Choe. Facilitating neural dynamics for delay compensation: A road to predictive neural dynamics?. Neural Networks, 22:267-276, 2009.
  16. Jaerock Kwon and Yoonsuck Choe. Predictive internal neural dynamics for delay compensation. In Second World Congress on Nature and Biologically Inspired Computing (NaBIC2010), pages 443-448, 2010.
  17. Heejin Lim and Yoonsuck Choe. Extrapolative role of facilitating synapses in the compensation of neural delay. In Society for Neuroscience Abstracts. Washington, DC: Society for Neuroscience, 2005. Program No. 41.19. Online.
  18. Heejin Lim and Yoonsuck Choe. Facilitatory neural activity compensating for neural delays as a potential cause of the flash-lag effect. In Proceedings of the International Joint Conference on Neural Networks, pages 268-273, Piscataway, NJ, 2005. IEEE Press.
  19. Heejin Lim and Yoonsuck Choe. Compensating for neural transmission delay using extrapolatory neural activation in evolutionary neural networks. Neural Information Processing-Letters and Reviews, 10:147-161, 2006.
  20. Heejin Lim and Yoonsuck Choe. Delay compensation through facilitating synapses and STDP: A neural basis for orientation flash-lag effect. In Proceedings of the International Joint Conference on Neural Networks, pages 8385-8392, Piscataway, NJ, 2006. IEEE Press.
  21. Heejin Lim and Yoonsuck Choe. Facilitating neural dynamics for delay compensation and prediction in evolutionary neural networks. In Maarten Keijzer, editor, Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO-2006, pages 167-174, 2006.
  22. Heejin Lim and Yoonsuck Choe. Extrapolative delay compensation through facilitating synapses and its relation to the flash-lag effect. IEEE Transactions on Neural Networks, 19:1678-1688, 2008.
  23. Heejin Lim. Facilitatory Neural Dynamics for Extrapolatory Prediction. PhD thesis, Department of Computer Science, Texas A&M University, 2006.
  24. Michail Maniadakis, Marc Wittmann, Sylvie Droit-Volet, and Yoonsuck Choe. Toward embodied artificial cognition: Time is on my side. Frontiers in Neurorobotics, 8:25, 2014.
  25. Timothy A. Mann and Yoonsuck Choe. Neural conduction delay forces the emergence of predictive function in an evolving simulation. BMC Neuroscience, 11(Suppl 1):P62, 2010. Nineteenth Annual Computational Neuroscience Meeting: CNS*2010.
  26. Khuong Nguyen and Yoonsuck Choe. Dynamic control using feedforward networks with adaptive delay and facilitating neural dynamics. In Proceedings of the International Joint Conference on Neural Networks, pages 2987-2994, 2017.
  27. Jaewook Yoo, Jaework Kwon, and Yoonsuck Choe. Predictable internal brain dynamics in EEG and its relation to conscious states. Frontiers in Neurorobotics, 8(00018), 2014.

Biologically Inspired Vision and Cortical Maps

  1. Yoon H. Bai, Choonseog Park, and Yoonsuck Choe. Relative advantage of touch over vision in the exploration of texture. In Proceedings of the 19th International Conference on Pattern Recognition (ICPR 2008), pages 1-4, 10.1109/ICPR.2008.4760961, 2008. Best Scientific Paper Award.
  2. Yoonsuck Choe and Risto Miikkulainen. Self-organization and segmentation with laterally connected maps of spiking neurons. In Workshop on Self-Organizing Maps, pages 20-31, Espoo, Finland, 1997. Helsinki University of Technology.
  3. Yoonsuck Choe and Risto Miikkulainen. Self-organization and segmentation with laterally connected spiking neurons. In Proceedings of the 15th International Joint Conference on Artificial Intelligence, pages 1120-1125. San Francisco, CA: Morgan Kaufmann, 1997.
  4. Yoonsuck Choe and Risto Miikkulainen. Self-organization and segmentation in a laterally connected orientation map of spiking neurons. Neurocomputing, 21:139-157, 1998.
  5. Yoonsuck Choe and Risto Miikkulainen. Contour integration and segmentation with self-organized lateral connections. Technical Report AI2000-286, Department of Computer Sciences, The University of Texas at Austin, 2000.
  6. Yoonsuck Choe and Risto Miikkulainen. A self-organizing neural network for contour integration through synchronized firing. In Proceedings of the 17th National Conference on Artificial Intelligence, pages 123-128. Cambridge, MA: MIT Press, 2000.
  7. Yoonsuck Choe and Risto Miikkulainen. Contour integration and segmentation in a self-organizing map of spiking neurons. Biological Cybernetics, 90:75-88, 2004.
  8. Yoonsuck Choe and Subramonia Sarma. Relationship between suspicious coincidence in natural images and oriented filter response distributions. Technical Report 2003-8-4, Department of Computer Science, Texas A&M University, 2003.
  9. Yoonsuck Choe, Joseph Sirosh, and Risto Miikkulainen. Laterally interconnected self-organizing maps in hand-written digit recognition. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, Advances in Neural Information Processing Systems 8, pages 736-742. Cambridge, MA: MIT Press, 1996.
  10. Yoonsuck Choe, Risto Miikkulainen, and Lawrence K. Cormack. Effects of presynaptic and postsynaptic resource redistribution in Hebbian weight adaptation. Neurocomputing, 32-33:77-82, 2000.
  11. Yoonsuck Choe. Laterally interconnected self-organizing feature map in handwritten digit recognition. Master's thesis, Department of Computer Sciences, The University of Texas at Austin, 1995. Technical Report AI95-236.
  12. Yoonsuck Choe. Perceptual Grouping in a Self-Organizing Map of Spiking Neurons. PhD thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, 2001. Technical Report AI01-292.
  13. Heeyoul Choi, Seungjin Choi, and Yoonsuck Choe. Manifold integration with markov random walks. In Proceedings of the 23rd National Conference on Artificial Intelligence(AAAI 2008), pages 424-429, 2008.
  14. Hyeon-Cheol Lee and Yoonsuck Choe. Detecting salient contours using orientation energy distribution. In Proceedings of the International Joint Conference on Neural Networks, pages 206-211. IEEE, 2003.
  15. Timothy A. Mann, Yunjung Park, Sungmoon Jeong, Minho Lee, and Yoonsuck Choe. Autonomous and interactive improvement of binocular visual depth estimation through sensorimotor interaction. IEEE Transactions on Autonomous Mental Development, 5:74-84, 2013.
  16. Risto Miikkulainen, James A. Bednar, Yoonsuck Choe, and Joseph Sirosh. Self-organization, plasticity, and low-level visual phenomena in a laterally connected map model of the primary visual cortex. In R. L. Goldstone, P. G. Schyns, and D. L. Medin, editors, Perceptual Learning, volume 36 of Psychology of Learning and Motivation, pages 257-308. Academic Press, San Diego, CA, 1997.
  17. Risto Miikkulainen, James A. Bednar, Yoonsuck Choe, and Joseph Sirosh. A self-organizing neural network model of the primary visual cortex. In Shiro Usui and Takashi Omori, editors, Proceedings of the Fifth International Conference on Neural Information Processing, volume 2, pages 815-818. Tokyo; Burke, VA; Amsterdam: IOS Press, 1998.
  18. Risto Miikkulainen, James A. Bednar, Yoonsuck Choe, and Joseph Sirosh. Modeling self-organization in the visual cortex. In Erkki Oja and Samuel Kaski, editors, Kohonen Maps, New York, 1999. Elsevier.
  19. Risto Miikkulainen, James A. Bednar, and Yoonsuck Choe. Sparse, redundancy-reduced visual coding through self-organized lateral connections. In Society for Neuroscience Abstracts. Washington, DC: Society for Neuroscience, 2004. Program No. 490.3. Online.
  20. Risto Miikkulainen, James A. Bednar, Yoonsuck Choe, and Joseph Sirosh. Computational Maps in the Visual Cortex. Springer, Berlin, 2005. URL: http://www.computationalmaps.org. 538 pages.
  21. Sejong Oh and Yoonsuck Choe. Texture segmentation in 2D vs. 3D: Did 3D developmentally precede 2D?. In J. Triesh and T. Jebara, editors, Proceedings of the 2004 International Conference on Development and Learning [electronic], pages 175-182. UCSD Institute for Neural Computation, 2004.
  22. Sejong Oh and Yoonsuck Choe. Segmentation of textures defined on flat vs. layered surfaces using neural networks: Comparison of 2D vs. 3D representations. Neurocomputing, 70:2245-2255, 2007.
  23. Sejong Oh. Learning to segment texture in 2D vs. 3D: A comparative study. Master's thesis, Department of Computer Science, Texas A&M University, 2004.
  24. Choonseog Park, Yoon H. Bai, and Yoonsuck Choe. Tactile or visual?: Stimulus characteristics determine receptive field type in a self-organizing map model of cortical development. In Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Multimedia Signal and Vision Processing, pages 6-13, 2009. Best Student Paper Award.
  25. Choonseog Park, Heeyoul Choi, and Yoonsuck Choe. Self-organization of tactile receptive fields: Exploring their textural origin and their representational properties. In Advances in Self-Organizing Maps: Proceedings of the 7th International Workshop, WSOM 2009. LNCS 5629, pages 228-236, Heidelberg, 2009. Springer.
  26. Choonseog Park, Heeyoul Choi, and Yoonsuck Choe. Textural-input-driven self-organization of tactile receptive fields. BMC Neuroscience, Suppl 1:P62, 2009. Eighteenth Annual Computational Neuroscience Meeting: CNS*2009.
  27. J. Park, S. Kim, S. I. Park, Y. Choe, J. Li, and A. Han. A microchip for quantitative analysis of CNS axon growth under localized biomolecular treatments. Journal of Neuroscience Methods, 221:166-174, 2013.
  28. Choonseog Park. Performance, Development, and Analysis of Tactile vs. Visual Receptive Fields in Texture Tasks. PhD thesis, Department of Computer Science, Texas A&M University, 2009.
  29. Subramonia Sarma and Yoonsuck Choe. Salience in orientation-filter response measured as suspicious coincidence in natural images. In Yolanda Gil and Raymond Mooney, editors, Proceedings of the 21st National Conference on Artificial Intelligence(AAAI 2006), pages 193-198, 2006.
  30. Joseph Sirosh, Risto Miikkulainen, and Yoonsuck Choe, editors. Lateral Interactions in the Cortex: Structure and Function. The UTCS Neural Networks Research Group, Austin, TX, 1996. Electronic book, ISBN 0-9647060-0-8, http://nn.cs.utexas.edu/web-pubs/htmlbook96/.
  31. Jaewook Yoo, Jinho Choi, and Yoonsuck Choe. Development of target reaching gesture map in the cortex and its relation to the motor map: A simulation study. In Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July 2-4, 2014, pages 187-197. Springer, Heidelberg, 2014.
  32. Yingwei Yu and Yoonsuck Choe. Modeling disinhibition in the early visual pathway. Technical Report 2003-8-6, Department of Computer Science, Texas A&M University, 2003.
  33. Yingwei Yu and Yoonsuck Choe. Angular disinhibition effect in a modified Poggendorff illusion. In Kenneth D. Forbus, Dedre Gentner, and Terry Regier, editors, Proceedings of the 26th Annual Conference of the Cognitive Science Society, pages 1500-1505, 2004.
  34. Yingwei Yu and Yoonsuck Choe. A neural model of the scintillating grid illusion: Disinhibition and self-inhibition in early vision. Neural Computation, 18:521-544, 2006.
  35. Yingwei Yu and Yoonsuck Choe. Neural model of disinhibitory interactions in modified Poggendorff illusion. Biological Cybernetics, 98:75-85, 2008.
  36. Yingwei Yu, Takashi Yamauchi, and Yoonsuck Choe. Explaining low-level brightness-contrast illusions using disinhibition. In A. J. Ijspeert, M. Murata, and N. Wakamiya, editors, Biologically Inspired Approaches to Advanced Information Technology, Lecture Notes in Computer Science 3141, pages 166-175, Berlin, 2004. Springer.
  37. Yingwei Yu. Computational Role of Disinhibition in Brain Function. PhD thesis, Department of Computer Science, Texas A&M University, 2006.

Deep Learning

  1. Hyun-Joo Jung, Jaedeok Kim, and Yoonsuck Choe. How compact?: Assessing compactness of representations through layer-wise pruning. In AAAI 2019 Workshop on Network Interpretability for Deep Learning, 2019.
  2. Jaedeok Kim, Chiyoun Park, Hyun-Joo Jung, and Yoonsuck Choe. Plug-in, trainable gate for streamlining arbitrary neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 4452-4459, 2020.
  3. Woo Seok Kim, Jianfeng Liu, Qinbo Li, Sungcheol Hong, Kezhuo Qi, Rahul Cherukuri, Byung-Jun Yoon, Justin Moscarello, Yoonsuck Choe, Stephen Maren, et al. Ai-driven high-throughput automation of behavioral analysis and dual-channel wireless optogenetics for multiplexing brain dynamics. bioRxiv, pages 2021-09, 2021.
  4. Seung-Geon Lee, Jaedeok Kim, Hyun-Joo Jung, and Yoonsuck Choe. Comparing sample-wise learnability across deep neural network models. In Proceedings of the AAAI Conference on Artificial Intelligence (Student Abstract), volume 33, pages 9961-9962, 2019.
  5. Qinbo Li, Qing Wan, Sang-Heon Lee, and Yoonsuck Choe. Video face recognition with audio-visual aggregation network. In International Conference on Neural Information Processing (ICONIP 2021), pages 150-161. Springer, 2021.
  6. Francesco Carlo Morabito, Robert Kozma, Cesare Alippi, and Yoonsuck Choe. Advances in ai, neural networks, and brain computing: An introduction. In Artificial Intelligence in the Age of Neural Networks and Brain Computing, pages 1-8. Academic Press, Cambridge, MA, second edition, 2024.
  7. Maryam Savari and Yoonsuck Choe. Online virtual training in soft actor-critic for autonomous driving. In 2021 International Joint Conference on Neural Networks (IJCNN), 2021. In press.
  8. Maryam Savari and Yoonsuck Choe. Utilizing human feedback in autonomous driving: Discrete vs. continuous. Machines, 10:609, 2022.
  9. Maryam Savari. Utilizing Human Feedback in the Soft Actor-Critic Algorithm for Autonomous Driving. PhD thesis, Department of Computer Science and Engineering, Texas A&M University, 2022.
  10. Chul Sung, Chunhui Higgins, Bo Zhang, and Yoonsuck Choe. Evaluating deep learning in churn prediction for everything-as-a-service in the cloud. In Proceedings of the International Joint Conference on Neural Networks, pages 3664-3669, 2017.
  11. Qing Wan and Yoonsuck Choe. Action recognition and state change prediction in a recipe understanding task using a lightweight neural network model. In Proceedings of the AAAI Conference on Artificial Intelligence (Student Abstract), pages 13945-13946, 2020.
  12. Qing Wan and Yoonsuck Choe. AdjointBackMap: Reconstructing effective decision hypersurfaces from CNN layers using adjoint operators. Neural Networks, 154:78-98, 2022.
  13. Qing Wan, Siu Wun Cheung, and Yoonsuck Choe. AdjointBackMapV2: Precise reconstruction of arbitrary CNN unit's activation via adjoint operators. Neural Networks, 2024.
  14. Ye Wang, Han Wang, Xinxiang Zhang, Theodora Chaspari, Yoonsuck Choe, and Mi Lu. An attention-aware bidirectional multi-residual recurrent neural network (abmrnn): A study about better short-term text classification. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3582-3586. IEEE, 2019.
  15. Ye Wang, Xinxiang Zhang, Mi Lu, Han Wang, and Yoonsuck Choe. Attention augmentation with multi-residual in bidirectional LSTM. Neurocomputing, 385:340-347, 2019.

Thalamocortical Function

  1. Yoonsuck Choe. Active representations: A primitive for analogical computing in the brain. Technical Report 2002-1-3, Department of Computer Science, Texas A&M University, 2002. 6 pages.
  2. Yoonsuck Choe. Second order isomorphism: A reinterpretation and its implications in brain and cognitive sciences. In Wayne D. Gray and Christian D. Schunn, editors, Proceedings of the 24th Annual Conference of the Cognitive Science Society, pages 190-195. Erlbaum, 2002.
  3. Yoonsuck Choe. Analogical cascade: A theory on the role of the thalamo-cortical loop in brain function. Neurocomputing, 52-54:713-719, 2003.
  4. Yoonsuck Choe. Processing of analogy in the thalamocortical circuit. In Proceedings of the International Joint Conference on Neural Networks, pages 1480-1485. IEEE, 2003.
  5. Yoonsuck Choe. The role of temporal parameters in a thalamocortical model of analogy. IEEE Transactions on Neural Networks, 15:1071-1082, 2004.
  6. Yoonsuck Choe. How neural is the neural blackboard architecture?. Behavioral and Brain Sciences, 29:72-73, 2005. (Commentary on van der Velde and de Kamps (2006) Behavioral and Brain Sciences, 29:37-108.) 2 pages.
  7. Yingwei Yu and Yoonsuck Choe. Asymptotic stability analysis of the thalamocortical circuit. In Society for Neuroscience Abstracts. Washington, DC: Society for Neuroscience, 2005. Program No. 274.23. Online.
  8. Yingwei Yu and Yoonsuck Choe. Selective attention in time: An extended model of stimulus onset asynchrony (SOA) in Stroop effect. In Proceedings of the Fifth International Conference on Development and Learning ICDL 2006 [electronic], Bloomington, IN, 2006. Department of Psychological and Brain Sciences, Indiana University.
  9. Yingwei Yu. Computational Role of Disinhibition in Brain Function. PhD thesis, Department of Computer Science, Texas A&M University, 2006.

Neural Synchrony and Perceptual Grouping

  1. Yoonsuck Choe and Risto Miikkulainen. The role of postsynaptic potential decay rate in neural synchrony. Neurocomputing, 52-54:707-712, 2003.
  2. Risto Miikkulainen, James A. Bednar, Yoonsuck Choe, and Joseph Sirosh. Computational Maps in the Visual Cortex. Springer, Berlin, 2005. URL: http://www.computationalmaps.org. 538 pages.

Pattern Recognition etc.

  1. Heeyoul Choi, Seungjin Choi, and Yoonsuck Choe. Manifold integration with markov random walks. In Proceedings of the 23rd National Conference on Artificial Intelligence(AAAI 2008), pages 424-429, 2008.
  2. Heeyoul Choi, Ricardo Gutierrez-Osuna, Seungjin Choi, and Yoonsuck Choe. Kernel oriented discriminant analysis for speaker-independent phoneme spaces. In Proceedings of the 19th International Conference on Pattern Recognition, pages 1-4, 2008.
  3. Heeyoul Choi, Anup Katake, Seungjin Choi, Yoonseop Kang, and Yoonsuck Choe. Probabilistic combination of multiple evidence. In Proceedings of the International Conference on Neural Information Processing (Part I, Lecture Notes in Computer Science 5863), pages 302-311, 2009.
  4. Heeyoul Choi, Seungjin Choi, Anup Katake, and Yoonsuck Choe. Learning alpha-integration with partially labeled data. In Proceedings of the 2010 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010), pages 2058-2061, 2010.
  5. Heeyoul Choi, Seungjin Choi, Anup Katake, Yoonseop Kang, and Yoonsuck Choe. Manifold alpha-integration. In B.-T. Zhang and M. A. Orgun, editors, Lecture Notes in Computer Science, PRICAI 2010: Trends in Artificial Intelligence. 11th Pacific Rim International Conference on Artificial Intelligence, pages 397-408. Springer, Berlin, 2010.
  6. Heeyoul Choi, Anup Katake, Seungjin Choi, and Yoonsuck Choe. Alpha-integration of multiple evidence. In Proceedings of the 2010 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010), pages 2210-2213, 2010.
  7. Heeyoul Choi, Seungjin Choi, and Yoonsuck Choe. Parameter learning for alpha-integration. Neural Computation, 25:1585-1604, 2013.
  8. Heeyoul Choi. Manifold Integration: Data Integration on Multiple Manifolds. PhD thesis, Department of Computer Science, Texas A&M University, 2010.
  9. Yingwei Yu, Yoonsuck Choe, and Ricardo Gutierrez-Osuna. Preserving class discriminatory information by context-sensitive intra-class clustering algorithm. Technical Report 2005-2-3, Department of Computer Science, Texas A&M University, 2005.
  10. Yingwei Yu, Ricardo Gutierrez-Osuna, and Yoonsuck Choe. Context-sensitive intra-class clustering. Pattern Recognition Letters, 37:85-93, 2014.

Neuroinformatics and Computational Neuroanatomy

  1. James A. Bednar, Yoonsuck Choe, Judah De Paula, Risto Miikkulainen, Jefferson Provost, and Tal Tversky. Modeling cortical maps with Topographica. Neurocomputing, 58-60:1129-1135, 2004.
  2. J. A. Bednar, Y. Choe, J. De Paula, R. Miikkulainen, and J. Provost. Modeling the visual cortex using the topographica cortical map simulator. In Society for Neuroscience Abstracts. Washington, DC: Society for Neuroscience, 2005. Program No. 508.1.
  3. Y. Choe, L. C. Abbott, J. Keyser, J. Kwon, D. M. Mayerich, Zeki Melek, and B. H. McCormick. Enhanced microvascular staining and tracing in large volumes of mouse brain tissue. In Neuroscience Meeting Planner, San Diego, CA: Society for Neuroscience, 2007. Program No. 845.14. Online.
  4. Y. Choe, D. Han, P.-S. Huang, J. Keyser, J. Kwon, D. Mayerich, and L. C. Abbott. Complete submicrometer scans of mouse brain microstructure: Neurons and vasculatures. In Neuroscience Meeting Planner, Chicago, IL: Society for Neuroscience, 2009. Program No. 389.10. Online.
  5. Yoonsuck Choe, Louise C. Abbott, Donghyeop Han, Pei-San Huang, John Keyser, Jaerock Kwon, David Mayerich, Zeki Melek, and Bruce H. McCormick. Knife-edge scanning microscopy: High-throughput imaging and analysis of massive volumes of biological microstructures. In A. Ravi Rao and Guillermo Cecchi, editors, High-Throughput Image Reconstruction and Analysis: Intelligent Microscopy Applications, pages 11-37. Artech House, Boston, MA, 2009.
  6. Yoonsuck Choe, Louise C. Abbott, Giovanna Ponte, John Keyser, Jaerock Kwon, David Mayerich, Daniel Miller, Donghyeop Han, Anna Maria Grimaldi, Graziano Fiorito, David B. Edelman, and Jeffrey L. McKinstry. Charting out the octopus connectome at submicron resolution using the knife-edge scanning microscope. BMC Neuroscience, 11(Suppl 1):P136, 2010. Nineteenth Annual Computational Neuroscience Meeting: CNS*2010.
  7. Yoonsuck Choe, David Mayerich, Jaerock Kwon, Daniel E. Miller, Ji Ryang Chung, Chul Sung, John Keyser, and Louise C. Abbott. Knife-edge scanning microscopy for connectomics research. In Proceedings of the International Joint Conference on Neural Networks, pages 2258-2265, Piscataway, NJ, 2011. IEEE Press.
  8. Yoonsuck Choe, David Mayerich, Jaerock Kwon, Daniel E. Miller, Chul Sung, Ji Ryang Chung, Todd Huffman, John Keyser, and Louise C. Abbott. Specimen preparation, imaging, and analysis protocols for knife-edge scanning microscopy. Journal of Visualized Experiments, 58:e3248, 2011. doi: 10.3791/3248.
  9. Yoonsuck Choe, Jaerock Kwon, David Mayerich, and Louise C. Abbott. Connectome, mouse. In Dieter Jaeger and Ranu Jung, editors, Encyclopedia of Computational Neuroscience, pages 807-810. Springer, New York, 1st edition, 2015.
  10. Yoonsuck Choe. Anti-Hebbian learning. In Dieter Jaeger and Ranu Jung, editors, Encyclopedia of Computational Neuroscience, pages 191-193. Springer, New York, 1st edition, 2015.
  11. Yoonsuck Choe. Brain atlases. In Dieter Jaeger and Ranu Jung, editors, Encyclopedia of Computational Neuroscience, page 434. Springer, New York, 1st edition, 2015.
  12. Yoonsuck Choe. Computational neuroanatomy: Overview. In Dieter Jaeger and Ranu Jung, editors, Encyclopedia of Computational Neuroscience, pages 24-26. Springer, New York, 1st edition, 2015.
  13. Yoonsuck Choe. Connectome, general. In Dieter Jaeger and Ranu Jung, editors, Encyclopedia of Computational Neuroscience, pages 798-806. Springer, New York, 1st edition, 2015.
  14. Yoonsuck Choe. Hebbian learning. In Dieter Jaeger and Ranu Jung, editors, Encyclopedia of Computational Neuroscience, pages 1305-1309. Springer, New York, 1st edition, 2015.
  15. Yoonsuck Choe. Physical sectioning microscopy. In Dieter Jaeger and Ranu Jung, editors, Encyclopedia of Computational Neuroscience, pages 2376-2379. Springer, New York, 1st edition, 2015.
  16. J. R. Chung, C. Sung, D. Mayerich, J. Kwon, D. E. Miller, T. Huffman, L. C. Abbott, J. Keyser, and Y. Choe. Multiscale exploration of mouse brain microstructures using the knife-edge scanning microscope brain atlas. Frontiers in Neuroinformatics, 5:29, 2011.
  17. Daniel Chern-Yeow Eng and Yoonsuck Choe. Stereo pseudo 3D rendering for web-based display of scientific volumetric data. In Proceedings of the IEEE/EG International Symposium on Volume Graphics, 2008.
  18. Daniel C.-Y. Eng. Web-based stereo rendering for visualization and annotation of scientific volumetric data. Master's thesis, Department of Computer Science, Texas A&M University, 2008.
  19. Jyothi S. Guntupalli. Physical sectioning in 3D biological microscopy. Master's thesis, Department of Computer Science, Texas A&M University, 2007.
  20. Donghyeop Han, Heeyoul Choi, Choonseog Park, and Yoonsuck Choe. Fast and accurate retinal vasculature tracing and kernel-isomap-based feature selection. In Proceedings of the International Joint Conference on Neural Networks, pages 1075-1082, Piscataway, NJ, 2009. IEEE Press.
  21. Donghyeop Han, John Keyser, and Yoonsuck Choe. A local maximum intensity projection tracing of vasculature in Knife-Edge Scanning Microscope volume data. In Proceedings of the IEEE International Symposium on Biomedical Imaging, pages 1259-1262, 2009.
  22. Jaerock Kwon, David Mayerich, Yoonsuck Choe, and Bruce H. McCormick. Lateral sectioning for knife-edge scanning microscopy. In Proceedings of the IEEE International Symposium on Biomedical Imaging, pages 1371-1374, 2008.
  23. Jaerock Kwon, David Mayerich, and Yoonsuck Choe. Automated cropping and artifact removal for knife-edge scanning microscopy. In Proceedings of the IEEE International Symposium on Biomedical Imaging, pages 1366-1369, 2011.
  24. Junseok Lee, Wookyung An, and Yoonsuck Choe. Mapping the full vascular network in the mouse brain at submicrometer resolution. In Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE, pages 3309-3312. IEEE, 2017.
  25. Junseok Lee, Jaewook Yoo, and Yoonsuck Choe. Tracing and analysis of the whole mouse brain vasculature with systematic cleaning to remove and consolidate erroneous images. In Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 143-146, 2018.
  26. Jung H. Lee, Yoonsuck Choe, Salva Ardid, Reza Abbasi-Asl, Michelle McCarthy, and Brian Hu. Editorial: Functional microcircuits in the brain and in artificial intelligent systems. Frontiers in Computational Neuroscience, 2023.
  27. Sungjun Lim, Michael Nowak, and Yoonsuck Choe. Automated neurovascular tracing and analysis of the knife-edge scanning microscope rat nissl data set using a computing cluster. In Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 6445-6448, 2016.
  28. D. M. Mayerich, L. C. Abbott, Y. Choe, D. Han, J. Keyser, Zeki Melek, and B. H. McCormick. Efficient methods for tracing and visualization of neural morphology in microscopy image stacks. In Neuroscience Meeting Planner, San Diego, CA: Society for Neuroscience, 2007. Program No. 845.2. Online.
  29. D. Mayerich, J. Kwon, Y. Choe, L. Abbott, and J. Keyser. Constructing high-resolution microvascular models. In Proceedings of the 3rd International Workshop on Microscopic Image Analysis with Applications in Biology (MIAAB 2008), 2008. Online.
  30. D. Mayerich, J. Kwon, C. Sung, L. C. Abbott, J. Keyser, and Y. Choe. Fast macro-scale transmission imaging of microvascular networks using KESM. Biomedical Optics Express, 2:2888-2896, 2011.
  31. David Mayerich, Jaerock Kwon, Aaron Panchal, John Keyser, and Yoonsuck Choe. Fast cell detection in high-throughput imagery using gpu-accelerated machine learning. In Proceedings of the IEEE International Symposium on Biomedical Imaging, pages 719-723, 2011.
  32. David Mayerich, Yoonsuck Choe, and John Keyser. Reconstruction, techniques and validation. In Dieter Jaeger and Ranu Jung, editors, Encyclopedia of Computational Neuroscience, pages 2591-2593. Springer, New York, 1st edition, 2015.
  33. B. H. McCormick, Y. Choe, W. Koh, L. C. Abbott, J. Keyser, Z. Melek, P. Doddapaneni, and D. Mayerich. Construction of anatomically correct models of mouse brain networks. Neurocomputing, 58-60:379-386, 2004.
  34. B. H. McCormick, B. L. Busse, D. M. Mayerich, L. C. Abbott, Y. Choe, J. Keyser, S. J. Smith, and W. Denk. Biologically accurate modeling of mouse brain requires biologically accurate networks. Microscopy and Microanalysis, 11 (Supplement 2):66-67, 2005.
  35. B. H. McCormick, D. M. Mayerich, B. L. Busse, Z. Melek, W. Koh, L. C. Abbott, Y. Choe, and E.-J. Kim. The whole mouse brain: The spatial distribution and morphology of its neurons. Microscopy and Microanalysis, 11 (Supplement 2):640-641, 2005.
  36. B. H. McCormick, L. C. Abbott, D. M. Mayerich, , J. Keyser, Jaerock Kwon, Zeki Melek, and Y. Choe. Full-scale submicron neuroanatomy of the mouse brain. In Society for Neuroscience Abstracts. Washington, DC: Society for Neuroscience, 2006. Program No. 694.5. Online.
  37. Daniel E. Miller, Raj Shah, Wencong Zhang, Jaewook Yoo, Jaerock Kwon, David Mayerich, John Keyser, Louise C. Abbott, and Yoonsuck Choe. Fast submicrometer-scale imaging of whole zebrafish using the knife-edge scanning microscope. In Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 5901-5904, 2016.
  38. Michael Nowak and Yoonsuck Choe. Learning to distinguish cerebral vasculature data from mechanical chatter in india-ink images acquired using knife-edge scanning microscopy. In Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 3989-3992, 2016.
  39. Michael Nowak and Yoonsuck Choe. Data-driven synthetic cerebrovascular models for validation of segmentation algorithms. In Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 5154-5157, 2018.
  40. Michael Nowak and Yoonsuck Choe. Towards an open-source framework for the analysis of cerebrovasculature structure. In Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 570-573, 2018.
  41. Michael Nowak, Alexander Lozovskiy, Dimitri Dobroskok, and Yoonsuck Choe. Knife-edge scanning microscopy for in silico study of cerebral blood flow: from biological imaging data to flow simulations. In Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 5957-5960, 2016.
  42. Michael R Nowak, Junseok Lee, and Yoonsuck Choe. A queryable graph representation of vascular connectivity in the whole mouse brain. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 256-260. IEEE, 2019.
  43. Huei-Fang Yang and Yoonsuck Choe. 3D volume extraction of densely packed cells in EM data stack by forward and backward graph cuts. In Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Multimedia Signal and Vision Processing, pages 47-52, 2009.
  44. Huei-Fang Yang and Yoonsuck Choe. Cell tracking and segmentation in electron microscopy images using graph cuts. In Proceedings of the IEEE International Symposium on Biomedical Imaging, pages 306-309, 2009.
  45. Huei-Fang Yang and Yoonsuck Choe. Electron microscopy image segmentation with estimated symmetric three-dimensional shape prior. In Proceedings of the 6th International Symposium on Visual Computing, 2010.
  46. Huei-Fang Yang and Yoonsuck Choe. Ground truth estimation by maximizing topological agreements in electron microscopy data. In Proceedings of the 7th International Symposium on Visual Computing (LNCS 6938), pages 371-380, 2011.
  47. Huei-Fang Yang and Yoonsuck Choe. An interactive editing framework for electron microscopy image segmentation. In Proceedings of the 7th International Symposium on Visual Computing (LNCS 6938), pages 400-409, 2011.

Big Data and Data Science

  1. Jisung Kim, Yoonsuck Choe, and Klaus Mueller. Extracting clinical relations in electronic health records using enriched parse trees. Procedia Computer Science, 53:274-283, 2015.
  2. Chul Sung, Bo Zhang, Chunhui Higgins, and Yoonsuck Choe. Data-driven sales leads prediction for everything-as-a-service in the cloud. In Proceedings of the 3rd IEEE International Conference on Data Science and Advanced Analytics, pages 557-563, 2016.

General Topics on AI and Machine Learning

  1. Yoonsuck Choe and Timothy A. Mann. From problem solving to problem posing. Brain-Mind Magazine, 1:7-8, 2012.
  2. Yoonsuck Choe. Meaning vs. information, prediction vs. memory, and question vs. answer. In Carlo F. Morabito, Cesare Alippi, Yoonsuck Choe, and Robert Kozma, editors, Artificial Intelligence in the Age of Neural Networks and Brain Computing, pages 281-292. Academic Press, Cambridge, MA, 2019.
  3. Yoonsuck Choe. Meaning vs. information, prediction vs. memory, and question vs. answer. In Carlo F. Morabito, Cesare Alippi, Yoonsuck Choe, and Robert Kozma, editors, Artificial Intelligence in the Age of Neural Networks and Brain Computing, page In press. Academic Press, Cambridge, MA, second edition, 2024.

System Biology and Health Informatics

  1. Jisung Kim, Yoonsuck Choe, and Klaus Mueller. Extracting clinical relations in electronic health records using enriched parse trees. Procedia Computer Science, 53:274-283, 2015.
  2. Hao Xiong and Yoonsuck Choe. Significantly different dynamic behaviors of biological networks between normal and abnormal cells in response to perturbation of environmental stressors and drugs. In Engineering Principles in Biological Systems (Cold Spring Harbor Laboratory, New York, December 3-6, 2006), page 52, 2006.
  3. Hao Xiong and Yoonsuck Choe. Constrained estimation of genetic networks. In BIOCOMP'07, Proceedings of the 2007 International Conference on Bioinformatics and Computational Biology, pages 51-57, 2007.
  4. Hao Xiong and Yoonsuck Choe. Dynamic pathway analysis. BMC Systems Biology, 2:9, 2008. 17 pages (online open-access journal).
  5. Hao Xiong and Yoonsuck Choe. Structural systems identification of genetic regulatory networks. Bioinformatics, 24:553-560, 2008.

Dissertations (by students)

  1. Heeyoul Choi. Manifold Integration: Data Integration on Multiple Manifolds. PhD thesis, Department of Computer Science, Texas A&M University, 2010.
  2. Ji Ryang Chung. Evolution of Memory in Reactive Artificial Neural Networks. PhD thesis, Department of Computer Science, Texas A&M University, 2011.
  3. Donghyeop Han. Rapid 3D Tracing of the Mouse Brain Neurovasculature with Local Maximum Intensity Projection and Moving Windows. PhD thesis, Department of Computer Science, Texas A&M University, 2009.
  4. Jae-Rock Kwon. Acquisition and Mining of the Whole Mouse Brain Microstructure. PhD thesis, Department of Computer Science, Texas A&M University, 2009.
  5. Junseok Lee. Mapping and Analyzing the Full Vascular Network in the Mouse Brain at Submicrometer Resolution. PhD thesis, Department of Computer Science and Engineering, Texas A&M University, 2018.
  6. Qinbo Li. Exploring Multimodal Information in Deep Learing. PhD thesis, Department of Computer Science and Engineering, Texas A&M University, 2022.
  7. Heejin Lim. Facilitatory Neural Dynamics for Extrapolatory Prediction. PhD thesis, Department of Computer Science, Texas A&M University, 2006.
  8. Timothy A. Mann. Scaling Up Reinforcement Learning Without Sacrificing Optimality by Constraining Exploration. PhD thesis, Department of Computer Science, Texas A&M University, 2012.
  9. Khuong Nguyen. Robot-based evaluation of bluetooth fingerprinting. PhD thesis, Department of Computer Science and Engineering, Texas A&M University, 2019.
  10. Michael Nowak. Whole-Mouse Brain Vascular Analysis Framework: Synthetic Model-Based Validation, Informatics Platform, and Queryable Database. PhD thesis, Department of Computer Science and Engineering, Texas A&M University, 2019.
  11. Choonseog Park. Performance, Development, and Analysis of Tactile vs. Visual Receptive Fields in Texture Tasks. PhD thesis, Department of Computer Science, Texas A&M University, 2009.
  12. Maryam Savari. Utilizing Human Feedback in the Soft Actor-Critic Algorithm for Autonomous Driving. PhD thesis, Department of Computer Science and Engineering, Texas A&M University, 2022.
  13. Chul Sung. Exploration, Registration, and Analysis of High-Throughput 3D Microscopy Data from the Knife-Edge Scanning Microscope. PhD thesis, Department of Computer Science and Engineering, Texas A&M University, 2013.
  14. Qing Wan. Reconstructing and Analyzing Effective Hypersurfaces From Convolutional Neural Network Layers Using AdjointBackMap. PhD thesis, Department of Computer Science and Engineering, Texas A&M University, 2022.
  15. Han Wang. Dynamic Analysis of Recurrent Neural Networks. PhD thesis, Department of Computer Science and Engineering, Texas A&M University, 2020.
  16. Hao Xiong. Systems Identification, Dynamic Analysis, and Optimal Control of Biological Networks. PhD thesis, Department of Computer Science, Texas A&M University, 2008.
  17. Huei-Fang Yang. Reconstruction of 3D Neuronal Structures from Densely Packed Electron Microscopy Data Stacks. PhD thesis, Department of Computer Science and Engineering, Texas A&M University, 2011.
  18. Jaewook Yoo. Sensorimotor Aspects of Brain Function: Development, Internal Dynamics, and Tool Use. PhD thesis, Department of Computer Science and Engineering, Texas A&M University, 2018.
  19. Yingwei Yu. Computational Role of Disinhibition in Brain Function. PhD thesis, Department of Computer Science, Texas A&M University, 2006.

Theses (by students)

  1. Wookyung An. Automated reconstruction of neurovascular networks in knife-edge scanning microscope mouse and rat nissl stained data sets. Master's thesis, Department of Computer Science and Engineering, Texas A&M University, 2016.
  2. Yoon H. Bai. Relative advantage of touch over vision in the exploration of texture. Master's thesis, Department of Computer Science, Texas A&M University, College Station, Texas, 2008.
  3. S. Kumar Bhamidipati. Sensory invariance driven action (SIDA) framework for understanding the meaning of neural spikes. Master's thesis, Department of Computer Science, Texas A&M University, 2004.
  4. Jinho Choi. Knife-edge scanning microscope mouse brain atlas in vector graphics for enhanced performance. Master's thesis, Department of Computer Science and Engineering, Texas A&M University, 2013.
  5. Ananth Dileepkumar. Semi-automated reconstruction of vascular networks in knife-edge scanning microscope mosue brain data. Master's thesis, Department of Computer Science and Engineering, Texas A&M University, 2014.
  6. Daniel C.-Y. Eng. Web-based stereo rendering for visualization and annotation of scientific volumetric data. Master's thesis, Department of Computer Science, Texas A&M University, 2008.
  7. Jyothi S. Guntupalli. Physical sectioning in 3D biological microscopy. Master's thesis, Department of Computer Science, Texas A&M University, 2007.
  8. Bum Soon Jang. Effect of varying the delay distribution in different classes of networks: Random, scale-free, and small-world. Master's thesis, Department of Computer Science, Texas A&M University, 2007.
  9. Dongkun Kim. Automatic seedpoint selection and tracing of microstructures in the knife-edge scanning microscope mouse brain data set. Master's thesis, Department of Computer Science, Texas A&M University, College Station, Texas, 2011.
  10. Shashwat Lal Das. Cell detection in Knife-Edge Scanning Microscopy images of nissl-stained mouse and rat brain samples using random forests. Master's thesis, Department of Computer Science and Engineering, Texas A&M University, 2014.
  11. Sungjune Lim. Automated neurovascular tracing and analysis of the knife-edge scanning microscope rat nissl data set using a computing cluster. Master's thesis, Department of Computer Science and Engineering, Texas A&M University, 2015.
  12. Navendu Misra. Comparison of motor-based versus visual representations in object recognition tasks. Master's thesis, Department of Computer Science, Texas A&M University, College Station, Texas, 2005.
  13. Hari Shankar Muddana. Integrated biomechanical model of cells embedded in extracellular matrix. Master's thesis, Department of Computer Science, Texas A&M University, 2006.
  14. Sejong Oh. Learning to segment texture in 2D vs. 3D: A comparative study. Master's thesis, Department of Computer Science, Texas A&M University, 2004.
  15. Amey Parulkar. Autonomous grounding of the optical flow detectors in a simulated visuomotor system of the fly using behaviorally meaningful actions. Master's thesis, Department of Computer Science, Texas A&M University, 2015.
  16. Subramonia P. Sarma. Relationship between suspicious coincidence in natural images and contour-salience in oriented filter responses. Master's thesis, Department of Computer Science, Texas A&M University, 2003.
  17. Raj S. Shah. Reducing chatter in knife-edge scanning microscopy. Master's thesis, Department of Computer Science and Engineering, Texas A&M University, 2014.
  18. Ankur Singhal. Skeletonization-based automated tracing and reconstruction of neurovascular networks in knife-edge scanning microscope mouse brain india ink data. Master's thesis, Department of Computer Science and Engineering, Texas A&M University, 2015.
  19. Manisha Srivastava. Knife-edge scanning microscope brain atlas interface for tracing and analysis of vasculature data. Master's thesis, Department of Computer Science and Engineering, Texas A&M University, 2015.
  20. Wenjie Yang. Automated neurovascular tracing and analysis of the Knife-Edge Scanning Microscope India ink data set. Master's thesis, Department of Computer Science and Engineering, Texas A&M University, 2014.
  21. Wencong Zhang. Real-time image error detection in Knife-Edge Scanning Microscope. Master's thesis, Department of Computer Science and Engineering, Texas A&M University, 2014.

CS General

  1. Kwanyong Lee and Yoonsuck Choe. Elements of Computer Science. KNOU Press, Seoul, Korea, 2011. In Korean. 393 pages.
Yoonsuck Choe, Professor
Department of Computer Science and Engineering
Texas A&M University
choe@tamu.edu      google scholar