| 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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| 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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