2019 Fiscal Year Final Research Report
Constructing state-space models that fit hierarchical networks to multitude of event sequences
| Project/Area Number |
26280007
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (B)
|
| Allocation Type | Partial Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Research Field |
Statistical science
|
|---|
| Research Institution | Kyoto University |
|---|
Principal Investigator |
|
|---|
| Project Period (FY) |
2014-04-01 – 2020-03-31
|
| Keywords | 神経スパイク / 状態空間法 / 大規模データ |
|---|
| Outline of Final Research Achievements |
In this project, we have tackled the problem of reconstructing neuronal circuitry from a large number of parallel spike trains recorded from cortical neurons in vivo. The original idea of inferring neuronal synaptic connections from parallel spike trains was proposed by Perkel, Gerstein, and Moore in more than 50 years ago. The existing methods can give plausible inferences about connections but also have produced a lot of false inferences due to large fluctuations in neuronal activity. We constructed a new analytical method by applying a Generalized Linear Model to a cross-correlation between spike trains for a pair of neurons. The new method has the potential to capture the difference in neuronal computation in different brain regions. It took us 6 years to complete the study before we published the result in Nature Communications in 2019. We have also obtained a lot of other studies during this period.
|
| Free Research Field |
計算論的神経科学
|
| Academic Significance and Societal Importance of the Research Achievements |
近年は計測技術の急速な進展によって膨大な神経スパイク信号が得られるようになっているので，同時計測された信号列から神経回路に関する知見が得られるようになると期待される．また解析手法は一般的なものなので神経信号のみならず，社会データにも適用することによって社会の構造に対する知見が得られるようになると考えられる．
|
