| Project/Area Number |
19200014
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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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 | Tamagawa University |
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Principal Investigator |
TSUKADA Minoru Tamagawa University, 脳科学研究所, 教授 (80074392)
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| Co-Investigator(Kenkyū-buntansha) |
小島 比呂志 玉川大学, 工学部, 教授 (50281671)
大森 隆司 玉川大学, 工学部, 教授 (50143384)
岡田 浩之 玉川大学, 工学部, 教授 (10349326)
酒井 裕 玉川大学, 脳科学研究所, 准教授 (70323376)
奥田 次郎 玉川大学, 脳科学研究, 嘱託教員 (80384725)
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| Co-Investigator(Renkei-kenkyūsha) |
KOJIMA Hiroshi 玉川大学, 工学部, 教授 (50281671)
KOJIMA Hiroshi 玉川大学, 工学部, 教授 (50143384)
OKADA Hiroyuki 玉川大学, 工学部, 教授 (10349326)
SAKAI Yutaka 玉川大学, 脳科学研究所, 准教授 (70323376)
OKUDA Jiro 京都産業大学, コンピュータ理工学科, 准教授 (80384725)
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| Project Period (FY) |
2007 – 2009
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| Project Status |
Completed (Fiscal Year 2009)
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| Budget Amount *help |
¥49,140,000 (Direct Cost: ¥37,800,000、Indirect Cost: ¥11,340,000)
Fiscal Year 2009: ¥12,350,000 (Direct Cost: ¥9,500,000、Indirect Cost: ¥2,850,000)
Fiscal Year 2008: ¥17,940,000 (Direct Cost: ¥13,800,000、Indirect Cost: ¥4,140,000)
Fiscal Year 2007: ¥18,850,000 (Direct Cost: ¥14,500,000、Indirect Cost: ¥4,350,000)
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| Keywords | 時空間学習則 / ヘブ学習則 / 記憶の情報表現 / 計算論モデル / 海馬培養錐体細胞 / 変数選択問題 / 非侵襲脳計測 / カントールコーディング |
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| Research Abstract |
In storing memory, sensory information (bottom-up) and awareness, consciousness and forecast information (top-down) interact on weight space of neural network. Our research revealed that spatio-temporal learning rule(STLR) and Hebb rule coexist in single pyramidal neurons of the hippocampal CA1 area. In STLR mechanism, synaptic weight changes on dendrite are determined by local association of input neurons (bottom-up) without soma firing whereas in Hebb mechanism the soma fires by top-down information such as awareness, consciousness and forecast (top-down). The coexistence of STLR (local) and Hebb (global) on the neuronal level may support this dynamic process that repeats itself until the internal model fits the external environment. These results were effectively applied to the artificial model.
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