Acquisition and consolidation of spatial memory by hippocampal predicitve computation
| Project/Area Number |
15H04265
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Neurophysiology / General neuroscience
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| Research Institution | Institute of Physical and Chemical Research |
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Principal Investigator |
Fukai Tomoki 国立研究開発法人理化学研究所, 脳科学総合研究センター, チームリーダー (40218871)
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥17,680,000 (Direct Cost: ¥13,600,000、Indirect Cost: ¥4,080,000)
Fiscal Year 2017: ¥5,720,000 (Direct Cost: ¥4,400,000、Indirect Cost: ¥1,320,000)
Fiscal Year 2016: ¥5,720,000 (Direct Cost: ¥4,400,000、Indirect Cost: ¥1,320,000)
Fiscal Year 2015: ¥6,240,000 (Direct Cost: ¥4,800,000、Indirect Cost: ¥1,440,000)
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| Keywords | 自発発火 / 樹状突起 / 学習と記憶 / 神経回路モデル / シナプス可塑性 / ベイズ推定 / 回路構造 / カルシウムスパイク / 領野間連絡 / 海馬 / 正準相関解析 / ノンレム睡眠 / UP-DOWN状態遷移 / 計算論的モデル / 構造シナプス可塑性 / プレプレイ / リプレイ / カルシウム・スパイク / 領野間相互作用 / 空間学習 / シナプス構造可塑性 / 海馬場所細胞 / プリプレイ / 計算理論 |
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| Outline of Final Research Achievements |
We studied an inference task model to demonstrate that an adequate network structure naturally emerges from dual Hebbian learning for both synaptic weight plasticity and wiring plasticity. Especially in a sparsely connected network, wiring plasticity achieves reliable computation by enabling efficient information transmission. We constructed a cortical network model of UP-DOWN transitions to indicate the role of persistent UP states for the prolonged repetition of a selected set of cell assemblies during memory
consolidation. We proposed a network model of sequence learning which instantiates two synaptic pathways, one for proximal dendrite-somatic interactions to generate spontaneous sequences and the other for distal dendritic processing of extrinsic signals. The model performs robust one-shot learning of spatial memory. We showed that redundant synaptic connections between a neuron pair enable near-optimal learning by approximating a sample-based Bayesian filtering algorithm.
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Report
(4 results)
Research Products
(33 results)
