Memory model with unsupervised sequential learning: The effect of threshold self-adjustment

Proceedings of the 37th Annual Southeast Regional Conference, ACM-SE 1999

Volumn , Issue , 1999, Pages

  Gorchetchnikov, Anatoli ^a  

^a MIDDLE TENNESSEE STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATIVE PROCESSING;

ACTIVATION DYNAMICS; AI SYSTEMS; BI-DIRECTIONAL ASSOCIATIVE MEMORY; MEMORY MODELING; SELF ADJUSTMENT; SEQUENTIAL LEARNING;

NEURAL NETWORKS;

EID: 0342841446     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/306363.306385     Document Type: Conference Paper

References (15)

1. Cohen, M., Grossberg, S. (1983). Absolute stability of global formation and parallel memory storage by competitive neural networks. IEEE Transactions on Systems, Man, and Cybernetics, 13, pp. 815-826.
2. French, R. (1991). Using semi-distributed representations to overcome catastrophic forgetting in connectionist networks. Proceedings of the 13th Annual Cognitive Science Society Conference, NJ: LEA, 173-178.
3. French, R. (1994). Dynamically constraining connectionist networks to produce distributed, orthogonal representations to reduce catastrophic interference. Proceedings of the 16th Annual Cognitive Science Society Conference, NJ: LEA, 335-340.
4. French, R. (1995). Interactive tandem networks and the sequential learning problem. Proceedings of the 17th Annual Cognitive Science Society Conference, NJ: LEA, 222-227.
5. French, R. (1997). Pseudo-recurrent connectionist networks: an approach to "sensitivity-stability" dilemma. Connection Science, 9 (4), 353-379.
6. Gluck, M., Myers, C. (1997). Psychobiological models of hippocampal function in learning and memory. Annual review of Psychology, 48, 481-514.
7. Grossberg, S. (1988). Nonlinear neural networks: Principles, mechanisms, and architectures. Neural Networks, 1(1), 17-61.
8. Hasselmo, M., Cekic, M. (1996). Suppression of synaptic transmission may allow combination of associative feedback and self-organizing feedforward connections in the neocortex. Behavioral Brain Research, 79, 153-161.
9. Kosko, B. (1992). Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice-Hall, Englewood Cliffs, NJ.
10. McClelland, J., McNaughton, B., O'Reilly, R. (1995). Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological review, 102, 419-457.
11. McKenna, T., Davis, J., Zornetzer, S. (Eds.). (1992). Single Neuron Computation, Academic Press, San Diego, CA.
12. Robins, A. (1995). Catastrophic forgetting, rehearsal, and pseudorehearsal. Connection Science, 7, 123-146.
13. Robins, A. (1998). Solutions to the catastrophic forgetting problem. Proceedings of the 20th Annual Conference of the Cognitive Science Society, NJ: LEA, 899-904.
14. Robins, A., McCallum, S. (1998). Catastrophic forgetting and pseudorehearsal solution in Hopfield type networks. Connection Science, in press.
15. Tam, D. (1992). Signal processing in multithreshold neurons. In McKenna, T., Davis, J., Zornetzer, S. (Eds.), Single Neuron Computation, (pp. 481-502). Academic Press, San Diego, CA.