| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2019: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Outline of Final Research Achievements |
Information in the nervous system is transmitted by spike trains, but it's unclear which statistic acts as the carrier. Neural network theory suggests the inputs to elements mirror the network structure and the carrier relies on the activation function. Neurons are noisy, nonlinear elements; spike generation depends on past activity. Hence, it's crucial to explore this dependency structure. We have developed a linear operator to depict statistical behavior and scrutinize the stochastic neuron model's input-output traits. This operator illuminates the model's behavior. However, deriving the transitional probability density function for the operator is challenging. Our research aims to overcome these hurdles.
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