2006 Fiscal Year Final Research Report Summary
Study on Emergent Functional Evolution and Heuristic Knowledge Acquisition by Neural Structural Development
| Project/Area Number |
17500083
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Tohoku University |
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Principal Investigator |
HOMMA Noriyasu Tohoku University, School of Medicine, Associate Professor, 医学部, 助教授 (30282023)
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| Co-Investigator(Kenkyū-buntansha) |
YOSHIZAWA Makoto Information Synergy Center, Professor, 情報シナジーセンター, 教授 (60166931)
SAKAI Masao Tohoku University, Center for the Advancement of Higher Education, Lecturer, 高等教育開発促進センター, 講師 (30344740)
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| Project Period (FY) |
2005 – 2006
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| Keywords | Neural networks / Functional formation / Heuristic methods |
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| Research Abstract |
In this research project, we analyze neural spike dynamics of a double feedback neural unit (DFNU) to clarify physiological microscopic mechanism of neural spike communication and functional formation. An essential emphasis of the analysis is on use of the DFNU's simple formulations that can provide quantitative analytic results. Comparing dynamics of Hodgkin-Huxley model to that of the DFNU, it is shown that dynamics of the DFNU is also physiologically plausible under a condition. The results suggest that high-frequency firings are relatively appropriate for a neural informational carrier due to the reliability and robustness to noisy inputs. To realize such reliable spike communication, we improved the DFNU's performance by using extra noisy inputs with appropriate amplitudes. Simulation studies show that there is optimal region of the amplitude that makes the DFNU possess the noise-enhanced reliable communication ability as similar to stochastic resonance phenomena.
In addition to the microscopic analysis, we further conduct experimental analysis to investigate a key mechanism of emergent functional evolution from a viewpoint of macroscopic brain sciences. A phased reinforcement learning algorithm for controlling nonholonomic systems is proposed for this purpose. The key element of the proposed algorithm is a shaping function defined on a novel position-direction space. The shaping function is started to be constructed once the goal is reached and constrains the exploration strategy. The proposed method is applied to the positioning tasks of a 2-link planer underactuated manipulator. This manipulator has one passive joint and is difficult to control. As the result, the efficiency of the proposed shaping function was confirmed in learning speed and best policy.
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Research Products
(23 results)
