Statistical Machine Learning Methods for High-dimensional Neural Population Data Analysis

Advances in techniques have been producing increasingly complex neural recordings, posing significant challenges for data analysis. This thesis discusses novel statistical methods for analyzing high-dimensional neural data. Part one discusses two extensions of state space models tailored to neural data analysis. First, we propose using a flexible count data distribution family in the observation model to faithfully capture over-dispersion and under-dispersion of the neural observations. Second, we incorporate nonlinear observation models into state space models to improve the flexibility of the model and get a more concise representation of the data. For both extensions, novel variational inference techniques are developed for model fitting, and simulated and real experiments show the advantages of our extensions. Part two discusses a fast region of interest (ROI) detection method for large-scale calcium imaging data based on structured matrix factorization. Part three discusses a method for sampling from a maximum entropy distribution with complicated constraints, which is useful for hypothesis testing for neural data analysis and many other applications related to maximum entropy formulation. We conclude the thesis with discussions and future works

Classification of coal-bearing strata abnormal structure based on POA–ELM

In order to identify and classify the abnormal structures in coal-bearing strata more accurately, a POA−ELM model based on the pelican optimization algorithm (POA) and the extreme learning machine (ELM) is proposed. The performance of extreme learning machine is unstable because the input weights and hidden layer bias are generated randomly. The POA can be used to optimize the input weights and hidden layer bias of extreme learning machine, so as to improve the performance of extreme learning machine model. The POA−ELM model is applied to identify and classify the abnormal structures in coal-bearing strata. Firstly, three coal-bearing strata simulation models of small fault, scour zone and collapse column are established with the COMSOL Multiphysics5.5. The Ricker wave is the source signal. The in-seam wave signals are collected by wave transmission method, and the in-seam wave data set is established. Then the z-score method is used to standardize the in-seam wave data and the principal component analysis (PCA) is used to reduce the dimension. Secondly, the POA is used to optimize the extreme learning machine, and the POA−ELM classification model is constructed with MATLAB. The POA−ELM model is used to classify small fault, scour zone and collapse column. The classification performance of ELM and POA−ELM is evaluated and compared by cross-validation method and evaluation indices such as accuracy, precision and recall rate. The results show that the POA can effectively optimize the ELM, and the POA−ELM model has higher classification accuracy and better stability. The classification accuracy of POA−ELM for abnormal structures can reach more than 99%. Thirdly, in order to verify the classification effect of POA−ELM in practical applications, after wavelet de-noising, z-score standardization and PCA dimensionality reduction, the real fault in-seam wave data are used as the test set and imported into the POA−ELM model for classification. The results show that the identification accuracy of POA−ELM model for real fault can reach more than 97%. Finally, based on the same data set, the classification effects of POA−ELM, ELM, support vector machine (SVM) and BP neural network are compared. The results show that the identification and classification accuracy of POA−ELM model is the highest. Through research and analysis, the POA can effectively optimize the ELM, and the POA−ELM model can accurately classify different geological structures and effectively identify real faults, which is better than other methods

Intellectual capital and firm performance in the context of venture-capital syndication background in China

This paper is intended to investigate the role of Venture-Capital Syndication (VCS) background in the relationship between intellectual capital (IC) and portfolio firm performance (PFP); specifically, this article examines the moderating effect of VCS’s leading firm background and member heterogeneity on the effect of IC on PFP. This study used a modified VAIC model to measure IC to compose a 4-component variable including human capital, structural capital, relational capital, and innovation capital. The data were collected from VCS-backed and listed firms in China during 2014 to 2018 applying the pooled OLS model for hypotheses test, Generalized Method of Moments (GMMs) to reduce endogeneity and unobserved factor control, and also return on equity (ROE) instead of ROA for the robustness test. Empirical results showed that IC and its components can improve PFP for VCS-backed firms in China; in detail, IC showed greater impact on performance of firms invested by foreign lead investors than in private or government VCS, specially reflected in the impact of innovation capital on PFP. Furthermore, IC showed weaker impact on PFP of mixed VCS-backed firms compared to pure VCS-backed firms and showed diminished effect on higher VCS member heterogeneity mainly reflected in the impact of relational capital on firm performance. These findings propose a new way of combining IC and VC to improve firm performance and are beneficial to theoretical development of IC and VC as well as a perspective for VC firm managers to choose suitable partners prior to join a VCS

Periodic elastic nanodomains in ultrathin tetrogonal-like BiFeO3 films

We present a synchrotron grazing incidence x-ray diffraction analysis of the domain structure and polar symmetry of highly strained BiFeO3 thin films grown on LaAlO3 substrate. We revealed the existence of periodic elastic nanodomains in the pure tetragonal-like BFO ultrathin films down to a thickness of 6 nm. A unique shear strain accommodation mechanism is disclosed. We further demonstrated that the periodicity of the nanodomains increases with film thickness but deviates from the classical Kittel's square root law in ultrathin thickness regime (6 - 30 nm). Temperature-dependent experiments also reveal the disappearance of periodic modulation above 90C due to a MC-MA structural phase transition.Comment: Accepted in Phys. Rev.

WD‐UNeXt: Weight loss function and dropout U‐Net with ConvNeXt for automatic segmentation of few shot brain gliomas

Abstract Accurate segmentation of brain gliomas (BG) is a crucial and challenging task for effective treatment planning in BG therapy. This study presents the weight loss function and dropout U‐Net with ConvNeXt block (WD‐UNeXt), which precisely segments BG from few shot MRI. The ConvNeXt block, which comprises the main body of the network, is a structure that can extract more detailed features from images. The weight loss function addresses the issue of category imbalance, thereby enhancing the network's ability to achieve more precise segmentation. The training set of BraTS2019 was used to train the network and apply it to test data. Dice similarity coefficient (DSC), sensitivity (Sen), specificity (Spec) and Hausdorff distance (HD) were used to assess the performance of the method. The experimental results demonstrate that the DSC of whole tumour, tumour core and enhancing tumour reached 0.934, 0.911 and 0.851, respectively. Sen of the sub‐regions achieved 0.922, 0.911 and 0.867. Spec and HD reached 1.000, 1.000, 1.000 and 3.224, 2.990, 2.844, respectively. Compared with the performance of state‐of‐the‐art methods, the DSC and HD of WD‐UNeXt were improved to varying degrees. Therefore, this method has considerable potential for the segmentation of BG

High-dimensional neural spike train analysis with generalized count linear dynamical systems

Abstract Latent factor models have been widely used to analyze simultaneous recordings of spike trains from large, heterogeneous neural populations. These models assume the signal of interest in the population is a low-dimensional latent intensity that evolves over time, which is observed in high dimension via noisy point-process observations. These techniques have been well used to capture neural correlations across a population and to provide a smooth, denoised, and concise representation of high-dimensional spiking data. One limitation of many current models is that the observation model is assumed to be Poisson, which lacks the flexibility to capture under-and over-dispersion that is common in recorded neural data, thereby introducing bias into estimates of covariance. Here we develop the generalized count linear dynamical system, which relaxes the Poisson assumption by using a more general exponential family for count data. In addition to containing Poisson, Bernoulli, negative binomial, and other common count distributions as special cases, we show that this model can be tractably learned by extending recent advances in variational inference techniques. We apply our model to data from primate motor cortex and demonstrate performance improvements over state-of-the-art methods, both in capturing the variance structure of the data and in held-out prediction