Comparison of Bootstrap Methods for Estimating Causality in Linear Dynamic Systems: A Review

In this study, we present a thorough comparison of the performance of four different bootstrap methods for assessing the significance of causal analysis in time series data. For this purpose, multivariate simulated data are generated by a linear feedback system. The methods investigated are uncorrelated Phase Randomization Bootstrap (uPRB), which generates surrogate data with no cross-correlation between variables by randomizing the phase in the frequency domain; Time Shift Bootstrap (TSB), which generates surrogate data by randomizing the phase in the time domain; Stationary Bootstrap (SB), which calculates standard errors and constructs confidence regions for weakly dependent stationary observations; and AR-Sieve Bootstrap (ARSB), a resampling method based on AutoRegressive (AR) models that approximates the underlying data-generating process. The uPRB method accurately identifies variable interactions but fails to detect self-feedback in some variables. The TSB method, despite performing worse than uPRB, is unable to detect feedback between certain variables. The SB method gives consistent causality results, although its ability to detect self-feedback decreases, as the mean block width increases. The ARSB method shows superior performance, accurately detecting both self-feedback and causality across all variables. Regarding the analysis of the Impulse Response Function (IRF), only the ARSB method succeeds in detecting both self-feedback and causality in all variables, aligning well with the connectivity diagram. Other methods, however, show considerable variations in detection performance, with some detecting false positives and others only detecting self-feedback

ニューロンの興奮性、抑制性結合と因果性の推定

Open House, ISM in Tachikawa, 2018.6.15統計数理研究所オープンハウス（立川）、H30.6.15ポスター発

統計値マッピングによる神経細胞の検出と可視化

Open House, ISM in Tachikawa, 2016.6.17統計数理研究所オープンハウス（立川）、H28.6.17ポスター発

ニューロンの規則、不規則活動と自励的同期現象

Open House, ISM in Tachikawa, 2017.6.16統計数理研究所オープンハウス（立川）、H29.6.16ポスター発

A statistical mapping strategy to identify inspiratory neurons among active cells in the pre-Bötzinger Complex

Open House, ISM in Tachikawa, 2015.6.19統計数理研究所オープンハウス（立川）、H27.6.19ポスター発

Rank of 3-tensors with 2 slices and Kronecker canonical forms

Tensor type data are becoming important recently in various application fields. We determine a rank of a tensor T so that A+T is diagonalizable for a given 3-tensor A with 2 slices over the complex and real number field.Comment: 12 pages, no figur

Cell Type-Dependent Activation Sequence During Rhythmic Bursting in the PreBötzinger Complex in Respiratory Rhythmic Slices From Mice

Spontaneous respiratory rhythmic burst activity can be preserved in the preBötzinger Complex (preBötC) of rodent medullary transverse slices. It is known, that the activation sequence of inspiratory neurons in the preBötC stochastically varies from cycle to cycle. To test whether the activation timing of an inspiratory neuron depends on its neurotransmitter, we performed calcium imaging of preBötC neurons using double-transgenic mice expressing EGFP in GlyT2+ neurons and tdTomato in GAD65+ neurons. Five types of inspiratory neurons were identified using the fluorescence protein expression and the maximum cross-correlation coefficient between neuronal calcium fluctuation and field potential. Regarding the activation sequence, irregular type putative excitatory (GlyT2-/GAD65-) neurons and irregular type glycinergic (GlyT2+/GAD65-) neurons tended to be activated early, while regular type putative excitatory neurons, regular type glycinergic neurons tended to be activated later. In conclusion, the different cell types define a general framework for the stochastically changing activation sequence of inspiratory neurons in the preBötC

ニューロンの同期現象を形成する最小ネットワークの推定

ISM Online Open House, 2020.10.27統計数理研究所オープンハウス（オンライン開催）、R2.10.27ポスター発

呼吸中枢に自励的同期現象を生成するニューロン・アストロサイト間の機能的結合の解明

Open House, ISM in Tachikawa, 2014.6.13統計数理研究所オープンハウス（立川）、H26.6.13ポスター発