Neural and Cognitive Modeling

Computational or information processing models offer an attractive approach to understanding memory, learning, and behavior in biological systems and provide a rich source of ideas for realizing similar capabilities in engineered systems. Honavar's research on cognitive and neural modeling is focused on:

• Neural architectures and algorithms for associative memory storage and retrieval
• Neural architectures and algorithms for learning
• Neural architectures and algorithms for syntax analysis
• Neural architectures and algorithms for spatial localization, spatial learning, and navigation under uncertainty

References

1. Caragea, D., Silvescu, A., and Honavar, V. (2001). Invited Chapter. Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources. In: Emerging Neural Architectures Based on Neuroscience. Berlin: Springer-Verlag.

2. Polikar, R., Udpa, L., Udpa, S., and Honavar, V. (2001). Learn++: An Incremental Learning Algorithm for Multi-Layer Perceptron Networks. IEEE Transactions on Systems, Man, and Cybernetics. Vol. 31, No. 4. pp. 497-508.

3. Polikar, R., Shinar, R., Honavar, V., Udpa, L., and Porter, M. (2001). Detection and Identification of Odorants Using an Electronic Nose. In: Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing.

4. Balakrishnan, K., Bousquet, O. and Honavar, V. (2000). Spatial Learning and Localization in Animals: A Computational Model and Its Implications for Mobile Robots, Adaptive Behavior. Vol. 7. no. 2. pp. 173-216

5. Parekh, R., Yang, J., and Honavar, V. (2000). Constructive Neural Network Learning Algorithms for Multi-Category Pattern Classification. IEEE Transactions on Neural Networks. Vol. 11. No. 2. pp. 436-451.

6. Yang, J., Parekh, R. & Honavar, V. (2000). Comparison of Performance of Variants of Single-Layer Perceptron Algorithms on Non-Separable Data. Neural, Parallel, and Scientific Computation. Vol. 8. pp. 415-438.

7. Chen, C-H. & Honavar, V. (1999). A Neural Architecture for Syntax Analysis. IEEE Transactions on Neural Networks. Vol. 10 pp. 94-114.

8. Chen, C-H. & Honavar, V. (1999). A Neural Architecture for Information Retrieval and Query Processing. Invited chapter. In: Handbook of Natural Language Processing. Dale, Moisl & Somers (Ed). New York: Marcel Dekker.

9. Chen, C-H. and Honavar, V. (1995). A Neural Memory Architecture for Content as well as Address-Based Storage and Recall: Theory and Applications Connection Science. vol. 7. pp. 293-312.

10. Honavar, V. (1994). Symbolic Artificial Intelligenc e and Numeric Artificial Neural Networks: Toward a Resolution of the Dichotomy. Invited chapter. In: Computational Architectures Integrating Symbolic and Neural Processes. pp. 351-388. Sun, R. and Bookman, L. (Ed.) New York: Kluwer.

11. Honavar, V. and Uhr, L. (1990). Coordination and Control Structures and Processes: Possibilities for Connectionist Networks. Journal of Experimental and Theoretical Artificial Intelligence 2: 277-302.

12. Honavar, V. and Uhr, L. (1989). Brain-Structured Connectionist Networks that Perceive and Learn. Connection Science 1: 139-160.