Observability and Synchronization of Neuron Models

Observability is the property that enables to distinguish two different locations in $n$-dimensional state space from a reduced number of measured variables, usually just one. In high-dimensional systems it is therefore important to make sure that the variable recorded to perform the analysis conveys good observability of the system dynamics. In the case of networks composed of neuron models, the observability of the network depends nontrivially on the observability of the node dynamics and on the topology of the network. The aim of this paper is twofold. First, a study of observability is conducted using four well-known neuron models by computing three different observability coefficients. This not only clarifies observability properties of the models but also shows the limitations of applicability of each type of coefficients in the context of such models. Second, a multivariate singular spectrum analysis (M-SSA) is performed to detect phase synchronization in networks composed by neuron models. This tool, to the best of the authors' knowledge has not been used in the context of networks of neuron models. It is shown that it is possible to detect phase synchronization i)~without having to measure all the state variables, but only one from each node, and ii)~without having to estimate the phase

On the smoothness of nonlinear system identification

We shed new light on the \textit{smoothness} of optimization problems arising in prediction error parameter estimation of linear and nonlinear systems. We show that for regions of the parameter space where the model is not contractive, the Lipschitz constant and $\beta$-smoothness of the objective function might blow up exponentially with the simulation length, making it hard to numerically find minima within those regions or, even, to escape from them. In addition to providing theoretical understanding of this problem, this paper also proposes the use of multiple shooting as a viable solution. The proposed method minimizes the error between a prediction model and the observed values. Rather than running the prediction model over the entire dataset, multiple shooting splits the data into smaller subsets and runs the prediction model over each subset, making the simulation length a design parameter and making it possible to solve problems that would be infeasible using a standard approach. The equivalence to the original problem is obtained by including constraints in the optimization. The new method is illustrated by estimating the parameters of nonlinear systems with chaotic or unstable behavior, as well as neural networks. We also present a comparative analysis of the proposed method with multi-step-ahead prediction error minimization

Impact of Living Donor Liver Transplantation on the Improvement of Hepatocellular Carcinoma Treatment

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths, with increasing incidence. There are different treatment options, but only 30%-40% of HCC cases are diagnosed at an early stage for curative treatment. With the implementation of Milan Criteria for liver transplantation (LT) in HCC cases and its use for organ allocation with successful outcomes, LT has become an optimal treatment. Seeking new criteria for LT and developing updated algorithms for HCC treatment has become a hot topic nowadays. With the experience in living donor liver transplantation (LDLT), especially in Asian countries, LDLT was established and adopted with different criteria for HCC treatment, especially including criteria beyond Milan\u27s size and number of tumors. Living donor grafts are uniquely different than deceased donor grafts as they are not considered a public resource. A living donor graft is rather a private gift intended for a specific recipient. Living donor livers are not limited by organ allocation systems, and this significant advantage of LDLT has opened new frontiers in the treatment of HCC. Improvements in LDLT have had remarkable parallel effects in the successful treatment of HCC as supported by a growing body of literature in the past decade

The shape of jamming arches in two-dimensional deposits of granular materials

We present experimental results on the shape of arches that block the outlet of a two dimensional silo. For a range of outlet sizes, we measure some properties of the arches such as the number of particles involved, the span, the aspect ratio, and the angles between mutually stabilizing particles. These measurements shed light on the role of frictional tangential forces in arching. In addition, we find that arches tend to adopt an aspect ratio (the quotient between height and half the span) close to one, suggesting an isotropic load. The comparison of the experimental results with data from numerical models of the arches formed in the bulk of a granular column reveals the similarities of both, as well as some limitations in the few existing models.Comment: 8 pages; submitted to Physical Review