A Functional Model of Limbic System of Brain - Brain Informatics - page 146

4 Conclusion
A model of limbic system is proposed by combining a conventional hippocampus-
neocortex model, chaotic neural works and an amygdala model. The proposed
mathematical model can realize mutual association and long-term memory of multiple
time series patterns with higher performance comparing with the conventional mod-
els. Different learning rules, such as Hebbian competition rule and reinforcement
learning rule, are functionally adopted in the proposed model. This integration of
memory models and emotion models gives an evidence of the realization probability
of the computational artificial brain in the future.
References
1. Adachi, M., Aihara, K.: Associative Dynamics in Chaotic Neural Network. Neural Net-
works (10), 83-98 (1997)
2. Aihara, K., Takabe, T., Toyoda, M.: Chaotic Neural Networks. Physics Letters A. 144(6-
7), 333-340 (1990)
3. Balkenius, C., Morén, J.: Emotional Learning: A Computational Model the Amygdala.
Cybernetics and Systems 32(6), 611-636 (2000)
4. Ito, M., Kuroiwa, J., Sawada, Y., Miyake, S.: A model of hippocampus-neocortex for episodic
memory. In: Proc. 5 th Intern. Conf. Neural Information Processing, 1P-16P, pp. 431-434 (1998)
5. Ito, M., Miyake, S., Inawashiro, S., Kuroiwa, J., Sawada, Y.: Long term memory of tempo-
ral patterns in a hippocampus-cortex model. Technical Report of IEICE. In: NLP 2000,
Vol.18, pp. 25-32 (2000) (in Japanese)
6. Kajiwara, R., Takashima, I., Mimura, Y., Witter, M.P., Iijima, T.: Amygdala Input Pro-
motes Spread of Excitatory Neural Activity from Perirhinal Cortex to the Entorhinal-
Hippocampal circuit. J.europhysiology 89, 2176-2184 (2003)
7. Kuremoto, T., Eto, T., Kobayashi, K., Obayashi, M.: A multilayered chaotic neural net-
work for associative memory. In: Proc. of SICE Annual Conf., pp. 1020-1023 (2005)
8. Kuremoto, T., Eto, T., Kobayashi, K., Obayashi, M.: A chaotic model of hippocampus-
neocortex. In: Wang, L., Chen, K., Ong, Y.S., (eds.) ICNC 2005. LNCS, vol. 3610, pp.
439-448. Springer, Heidelberg (2005)
9. Kuremoto, T., Eto, T., Kobayashi, K., Obayashi, M.: A Hippocampus-Neocortex Model
for Chaotic Association. In: Chen, K., Wang, L. (eds.) Trends in Neural Computation
(Studies in Computational Intelligence), vol. 35, pp. 111-133 (2006)
10. Kuremoto, T., Ohta, T., Obayashi, M., Kobayashi, K.: A Dynamic Associative System by
Adopting an Amygdala Model. Artif. Life and Robotics 13(2), 478-482 (2009)
11. MaGaugh, J.L., Cahill, L., Roozendaal, B.: Involvement of the Amygdala in Memory
Storage: Interation with Other Brain Systems. In: Proc. Natl. Acad. Sci. USA, vol. 93, pp.
13508-13514 (1996)
12. Mizutani, S., Sano, T., Uchiyama, T., Sonehara, N.: Controlling Chaos in Chaotic Neural
Networks. Electronics and Communications in Japan, Part III: Fundamental Electronics
Science 81(8), 73-82 (1998)
13. Morén, J., Balkenius, C.: A Computational Model of Emotional Learning in the Amygdala.
In: Proc. of the 6th Intern. Conf. on the Simulation of Adaptive Behavior. MIT Press,
Cambridge (2000)
14. Rouhani, H., Jalili, M., Araabi, B.N., Eppler, W., Luscas, W.: Brain Emotional Learning
Based Intelligent Controller Applied to Neurofuzzy Model of Micro-Heat Exchanger. Ex-
pert Sys. with Appli. 32, 911-918 (2007)