Memory dynamics in attractor networks with saliency weights

Neural Computation

Volumn 22, Issue 7, 2010, Pages 1899-1926

  Tang, Huajin ^a   Li, Haizhou ^a,b   Yan, Rui ^a  

^a INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

^b UNIVERSITY OF EASTERN FINLAND   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ANIMAL; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; HUMAN; LETTER; MEMORY; NERVE CELL; NERVE CELL NETWORK; NERVE CELL PLASTICITY; NONLINEAR SYSTEM; PHYSIOLOGY; SYNAPTIC TRANSMISSION;

ACTION POTENTIALS; ANIMALS; COMPUTER SIMULATION; HUMANS; MEMORY; NERVE NET; NEURAL NETWORKS (COMPUTER); NEURONAL PLASTICITY; NEURONS; NONLINEAR DYNAMICS; SYNAPTIC TRANSMISSION;

EID: 77955990471     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2010.07-09-1050     Document Type: Letter

References (34)

1. Amit, D. J., Gutfreund, H., & Sompolinsky, H. (1985a). Spin glass models of neural networks. Phys. Rev. A., 32(2), 1007-1018.
2. Amit, D. J., Gutfreund, H., & Sompolinsky, H. (1985b). Storing infinite number of patterns in a spin glass model for neural networks. Phys. Rev. Lett., 55(4), 1530-1533.
3. Amit, D. J., & Mongillo, G. (2003). Spike-driven synaptic dynamics generating working memory states. Neural Computation, 15, 565-596.
4. Bakker, A., Kirwan, C. B., Miller, M., & Stark, C. E. L. (1999). Pattern separation in the human hippocampal CA3 and dentate gyrus. Science, 319, 1640-1642.
5. Bird, C. M., & Burgess, N. (2008). The hippocampus and memory: Insights from spatial processing. Nature Reviews Neuroscience, 9(3), 182-194.
6. Blumenfeld, B., Preminger, S., Sagi, D., & Tsodyks, M. (2006). Dynamics of memory representations in networks with novelty-facilitated synaptic plasticity. Neuron, 52, 383-394.
7. Chechik, G., Meilijson, I., & Ruppin, E. (2001). Effective neuronal learning with ineffective Hebbian learning rules. Neural Computation, 13, 817-840.
8. Dayan, P., & Willshaw, D. J. (1991). Optimizing synaptic learning rules in linear associative memories. Biol. Cyber., 65(4), 253-265.
9. Ericson, C. A., & Desimone, R. (1999). Responses of macaque perirhinal neurons during and after visual stimulus association learning. J. Neurosci., 19, 10404-10416.
10. Hahnloser, R. H. R. (1998). On the piecewise analysis of networks of linear threshold neurons. Neural Networks, 11, 691-697.
11. Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. New York: Wiley.
12. Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA, 79, 2554-2558.
13. Jensen, O., & Lisman, J. E. (2004). Hippocampal sequence-encoding driven by a cortical multi-item working memory buffer. Trends in Neuroscience, 28(2), 67-72.
14. Leutgeb, J. K., Leutgeb, S., Treves, A., Meyer, R., Barnes, C. A., McNaughton, B. L., et al. (2005). Progressive transformation of hippocampal neuronal representations in morphed environments. Neuron, 48, 345-358.
15. Lisman, J. E. (1999). Relating hippocampal circuitry to function: Recall of memory sequences by reciprocal dentate-CA3 interactions. Neuron, 22, 233-242.
16. Miyashita, Y. (1988). Neuronal correlate of visual associative long-term memory in the primate temporal cortex. Nature, 335, 817-820.
17. Moser, E. I., Kropff, E., & Moser, M.-B. (2008). Place cells, grid cells, and the brain's spatial representation system. Annu. Rev. Neurosci., 31, 69-89.
18. Mueller, R., & Herz, A. V. M. (1999). Content-addressable memory with spiking neurons. Physical Review E, 59(3), 3330-3338.
19. Pantic, L., Torres, J. J., Kappen, H. J., & Gielen, S. C. A. M. (2002). Associative memory with dynamic synapses. Neural Computation, 14, 2903-2923.
20. Poucet, B., & Save, E. (2005). Attractors in memory. Science, 308, 799-800.
21. Roudi, Y., & Treves, A. (2003). Disappearance of spurious states in analog associative memories. Physical Review E, 67, 041906.
22. Siegelmann, H. (2008). Analog-symbolic memory that tracks via reconsolidation. Physica D, 237, 1207-1214.
23. Tang, H., Tan, K. C., & Teoh, E. J. (2006). Dynamics analysis and analog associative memory of networks with LT neurons. IEEE Trans. on Neural Networks, 17(2), 409-418.
24. Tang, H., Tan, K. C., & Zhang, W. (2005). Analysis of cyclic dynamics for networks of linear threshold neurons. Neural Computation, 17, 97-114.
25. Treves, A. (1990a). Graded-response neurons and information encoding in autoassociative memories. Physical Review A, 42(4), 2418-2430.
26. Treves, A. (1990b). Threshold-linear formal neurons in auto-associative nets. J. Phys. A: Math. Gen., 23, 2631-2650.
27. Treves, A., & Rolls, E. T. (1991). What determines the capacity of autoassociative memories in the brain? Network, 2, 371-397.
28. Treves, A., & Rolls, E. T. (1994). Computational analysis of the role of the hippocampus in memory. Hippocampus, 4, 374-391.
29. Tsodyks, M. (2005). Attractor neural networks and spatial maps in hippocampus. Neuron, 48(2), 168-169.
30. Waugh, F. R., Marcus, C. M., & Westervelt, R. M. (1990). Fixed-point attractors in analog neural computation. Physical Review Letters, 64(16), 1986-1989.
31. Wersing, H., Beyn, W. J., & Ritter, H. (2001). Dynamical stability conditions for recurrent neural networks with unsaturating piecewise linear transfer functions. Neural Computation, 13(8), 1811-1825.
32. Wills, T. J., Lever, C., Cacucci, F., Burgess, N., & O'Keefe, J. (2005). Attractor dynamics in the hippocampal representation of the local environment. Science, 308(3), 873-876.
33. Yi, Z., Tan, K. K., & Lee, T. H. (2003). Multistability analysis for recurrent neural networks with unsaturating piecewise linear transfer functions. Neural Computation, 15(3), 639-662.
34. Zemel, R. S., & Mozer, M. C. (2001). Localist attractor networks. Neural Computation, 13, 1045-1064.