| Project/Area Number |
12680382
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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 | Kyoto University |
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Principal Investigator |
SHINOMOTO Shigeru Kyoto Univ., Department of Physics, Associate Professor, 大学院・理学研究科, 助教授 (60187383)
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| Project Period (FY) |
2000 – 2003
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| Project Status |
Completed (Fiscal Year 2003)
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| Budget Amount *help |
¥3,400,000 (Direct Cost: ¥3,400,000)
Fiscal Year 2003: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2002: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2001: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2000: ¥1,000,000 (Direct Cost: ¥1,000,000)
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| Keywords | spike signal / current injection / neurospiking model / local variation / 時間依存ポアソンモデル |
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| Research Abstract |
In order to understand the way in which information is transferred, stored and processed in the brain by means of the sequences of neuronal spikes, we took the following two strategies: (A) Select mathematical models that could predict spike sequences of biological neurons for a variety of input currents. For this purpose, we first performed intracellular current injection experiments on rat cortical neurons, using temporally fluctuating currents. We have constructed an experimental setup in the medical school of our university and made efforts at achieving experimental skills for four years. Second, we started to select models according to the data we have obtained: We determined the parameter values with which each model is able to mimic the response of each neuron. (B) Establish a new method of analyzing spike sequences recorded from the cortical neurons in vivo. Recently we found that cortical neurons can be categorized into two groups in terms of their firing statistics. Two groups of neurons classified according to the spiking irregularity exhibit different responses to the same stimulus. This suggests that neurons in each area can be classified into different groups possessing unique spiking statistics and corresponding functional properties.
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