Sandia Neuroscience

Craig Vineyard, Ph.D.

Computer Engineering

Craig VineyardCraig Vineyard is a Senior Member of the Technical Staff in the Data-driven and Neural Computing group (1462) at Sandia National Laboratories. He has worked on neural network modeling and simulation, the development of information theoretic analysis techniques, complex systems modeling, neuromorphic computing architecture development, machine learning algorithms, and game theory research.

Craig received the B.S., M.S., and PhD. degrees in computer engineering from the University of New Mexico with a concentration in Computational Intelligence. His thesis work focused upon algorithmic game theory applied to machine learning for functional as well as descriptive insights. In particular he applied game theoretic mechanism design to support vector machines (SVM) and a classification setting for adaptive machine learning.

Contact Information

cmviney@sandia.gov

Research Areas

Computational Neuroscience

  • Information theoretic analysis of neural networks
  • Computational studies of the hippocampus

Neural Computing

  • Assessing and designing novel neural-inspired computing architectures
  • Identifying areas for neural algorithms to impact cyber security and image processing applications
  • Developing adaptive machine learning algorithms and learning theory

Neural Game Theory

  • The intersection of neuroscience and game theory research to study neural dynamics from a strategic interaction approach for functional and descriptive insight

 

Publications

1. Vineyard, C.M., Verzi, S.J., James, C.D., & Aimone, J.B. (2016) Quantifying Neural Information Content: A Case Study of the Impact of Hippocampal Adult Neurogenesis (IJCNN 2016)

2. Vineyard, C.M., Verzi, S.J., James, C.D., Aimone, J.B., & Heileman, G.L. (2015) MapReduce SVM Game (INNS 2015)

3. Vineyard, C.M., Verzi, S.J., James, C.D., Aimone, J.B., & Heileman, G.L. (2015) Repeated Play of the SVM Game as a Means of Adaptive Classification (IJCNN 2015)

4. Vineyard, C.M., Verzi, S.J., Caudell, T.P., Bernard, M.L., & Aimone, J.B. (2013) Adult Neurogenesis: Implications on Human and Computational Decision Making. Foundations of Augmented Cognition (531-540). Springer Berlin Heidelberg.

5. Vineyard, C. M., Verzi, S. J., Bernard, M. L., Taylor, S. E., Dubicka, I., & Caudell, T. P. (2012). A multi-modal network architecture for knowledge discovery. Security Informatics, 1(1), 1-12.

6. Vineyard, C. M., Emmanuel, G. R., Verzi, S. J., & Heileman, G. L. (2013). A Game Theoretic Model of Neurocomputation. In Biologically Inspired Cognitive Architectures 2012 (pp. 373-374). Springer Berlin Heidelberg.

7. Vineyard, C. M., Aimone, J. B., & Emmanuel, G. R. (2013). Neurogenesis in a High Resolution Dentate Gyrus Model. In Biologically Inspired Cognitive Architectures 2012 (pp. 371-372). Springer Berlin Heidelberg.

8. Vineyard, C. M., Heileman, G. L., Verzi, S. J., & Jordan, R. (2012, May). Game theoretic mechanism design applied to machine learning classification. In Cognitive Information Processing (CIP), 2012 3rd International Workshop on (pp. 1-5). IEEE.

9. Vineyard, C. M., Lakkaraju, K., Collard, J., & Verzi, S. J. (2012). The impact of attitude resolve on population wide attitude change. In Social Computing, Behavioral-Cultural Modeling and Prediction (pp. 322-330). Springer Berlin Heidelberg.

10. Vineyard, C.M., Verzi, S.J., Bernard, M.L., Caudell, T.P. "A Multimodal Hypertensor Architecture for Association Formation." Biologically Inspired Cognitive Architectures 2011: Proceedings of the Second Annual Meeting of the BICA Society. Vol. 233. IOS Press, 2011.

11. Vineyard, C. M., Verzi, S. J., Bernard, M. L., Taylor, S. E., Shaneyfelt, W. L., Dubicka, I., ... & Caudell, T. P. (2011, July). A neurophysiologically inspired hippocampus based associative-ART Artificial neural network architecture. In Neural Networks (IJCNN), The 2011 International Joint Conference on (pp. 2100-2105). IEEE.

12. Vineyard, C. M., Bernard, M. L., Taylor, S. E., Caudell, T. P., Watson, P., Verzi, S., ... & Eichenbaum, H. (2010, August). A Neurologically Plausible Artificial Neural Network Computational Architecture of Episodic Memory and Recall. In Biologically Inspired Cognitive Architectures 2010: Proceedings of the First Annual Meeting of the BICA Society (Vol. 221, p. 175). IOS Press, Incorporated.

13. Vineyard, C. M., Taylor, S. E., Bernard, M. L., Verzi, S. J., Caudell, T. P., Heileman, G. L., & Watson, P. (2010). A Cortical-Hippocampal Neural Architecture for Episodic Memory with Information Theoretic Model Analysis. In World Multi-Conference on Systemics, Cybernetics and Informatics (pp. 281-285).

14. Vineyard, C. M., Taylor, S. E., Bernard, M. L., Verzi, S. J., Morrow, J. D., Watson, P., ... & Cohen, N. J. (2009). Episodic memory modeled by an integrated cortical-hippocampal neural architecture. In Human Behavior-computational Intelligence Modeling Conference 2009.

15. Taylor, S. E., Bernard, M. L., Verzi, S. J., Morrow, J. D., Vineyard, C. M., Healy, M. J., & Caudell, T. P. (2009, June). Temporal semantics: An adaptive resonance theory approach. In Neural Networks, 2009. IJCNN 2009. International Joint Conference on (pp. 3111-3117). IEEE.

16. Taylor, S. E., Vineyard, C. M., Healy, M. J., Caudell, T. P., Cohen, N. J., Watson, P., ... & Eichenbaum, H. (2009, March). Memory in silico: Building a neuromimetic episodic cognitive model. In Computer Science and Information Engineering, 2009 WRI World Congress on (Vol. 5, pp. 733-737). IEEE.