Niche-dependent development of functional neuronal networks from embryonic stem cell-derived neural populations

Background  

The present work was performed to investigate the ability of two different embryonic stem (ES) cell-derived neural precursor populations to generate functional neuronal networks in vitro. The first ES cell-derived neural precursor population was cultivated as free-floating neural aggregates which are known to form a developmental niche comprising different types of neural cells, including neural precursor cells (NPCs), progenitor cells and even further matured cells. This niche provides by itself a variety of different growth factors and extracellular matrix proteins that influence the proliferation and differentiation of neural precursor and progenitor cells. The second population was cultivated adherently in monolayer cultures to control most stringently the extracellular environment. This population comprises highly homogeneous NPCs which are supposed to represent an attractive way to provide well-defined neuronal progeny. However, the ability of these different ES cell-derived immature neural cell populations to generate functional neuronal networks has not been assessed so far.     

Directed neuronal differentiation of human embryonic stem cells

Background

We have developed a culture system for the efficient and directed differentiation of human embryonic stem cells (HESCs) to neural precursors and neurons.HESC were maintained by manual passaging and were differentiated to a morphologically distinct OCT-4⁺/SSEA-4^- monolayer cell type prior to the derivation of embryoid bodies. Embryoid bodies were grown in suspension in serum free conditions, in the presence of 50% conditioned medium from the human hepatocarcinoma cell line HepG2 (MedII).

Results

A neural precursor population was observed within HESC derived serum free embryoid bodies cultured in MedII conditioned medium, around 7–10 days after derivation. The neural precursors were organized into rosettes comprised of a central cavity surrounded by ring of cells, 4 to 8 cells in width. The central cells within rosettes were proliferating, as indicated by the presence of condensed mitotic chromosomes and by phosphoHistone H3 immunostaining. When plated and maintained in adherent culture, the rosettes of neural precursors were surrounded by large interwoven networks of neurites. Immunostaining demonstrated the expression of nestin in rosettes and associated non-neuronal cell types, and a radial expression of Map-2 in rosettes. Differentiated neurons expressed the markers Map-2 and Neurofilament H, and a subpopulation of the neurons expressed tyrosine hydroxylase, a marker for dopaminergic neurons.

Conclusion

This novel directed differentiation approach led to the efficient derivation of neuronal cultures from HESCs, including the differentiation of tyrosine hydroxylase expressing neurons. HESC were morphologically differentiated to a monolayer OCT-4⁺ cell type, which was used to derive embryoid bodies directly into serum free conditions. Exposure to the MedII conditioned medium enhanced the derivation of neural precursors, the first example of the effect of this conditioned medium on HESC.

     

Differentiation of neurons from neural precursors generated in floating spheres from embryonic stem cells

Background  

Neural differentiation of embryonic stem (ES) cells is usually achieved by induction of ectoderm in embryoid bodies followed by the enrichment of neuronal progenitors using a variety of factors. Obtaining reproducible percentages of neural cells is difficult and the methods are time consuming.     

Culture and evolution on populations of neural networks

Although acquired characteristics are not incorporated into the genotype, some works have pointed to the influence of learning in evolution. We present a dynamic model of neural networks presenting evolutive features, even without modification in genotype, due to the introduction of culture. Our model presents other features that seem to reproduce some aspects of real world populations.     

Statistical characterization of an ensemble of functional neural networks

The Ginzburg-Landau free energy functional with two order parameters has been widely used to describe surfactant adsorption phenomena at the interface between two immiscible fluids such as oil and water. To model surfactant adsorption, additional surfactant related terms are added to the original free energy functional which models an immiscible binary mixture. In this paper, we present a detailed comparison of phase-field models for an immiscible binary mixture with surfactant. In particular, we investigate the effects of mathematical model parameters on equilibrium surfactant profile across the interface between the immiscible binary mixture. Most previous models have severe time-step constraints due to the nonlinear coupling of order parameters. To solve these stability problems, we propose a special case of these models which allows the use of a much larger time-step size. We also apply a type of unconditionally gradient stable scheme and a fast multigrid method to solve the proposed model efficiently and accurately.     

神经网络探索物理问题

基于神经网络的机器学习模型是许多现代技术进步的基础,并被越来越多地用于解决物理问题。     

Analyzing topological characteristics of neuronal functional networks in the rat brain

In this study, we recorded spike trains from brain cortical neurons of several behavioral rats in vivo by using multi-electrode recordings. An NFN was constructed in each trial, obtaining a total of 150 NFNs in this study. The topological characteristics of NFNs were analyzed by using the two most important characteristics of complex networks, namely, small-world structure and community structure. We found that the small-world properties exist in different NFNs constructed in this study. Modular function Q was used to determine the existence of community structure in NFNs, through which we found that community-structure characteristics, which are related to recorded spike train data sets, are more evident in the Y-maze task than in the DM-GM task. Our results can also be used to analyze further the relationship between small-world characteristics and the cognitive behavioral responses of rats.     

Brain without mind: Computer simulation of neural networks with modifiable neuronal interactions

Aspects of brain function are examined in terms of a nonlinear dynamical system of highly interconnected neuron-like binary decision elements. The model neurons operate synchronously in discrete time, according to deterministic or probabilistic equations of motion. Plasticity of the nervous system, which underlies such cognitive collective phenomena as adaptive development, learning, and memory, is represented by temporal modification of interneuronal connection strengths depending on momentary or recent neural activity. A formal basis is presented for the construction of local plasticity algorithms, or connection-modification routines, spanning a large class. To build an intuitive understanding of the behavior of discrete-time network models, extensive computer simulations have been carried out (a) for nets with fixed, quasirandom connectivity and (b) for nets with connections that evolve under one or another choice of plasticity algorithm. From the former experiments, insights are gained concerning the spontaneous emergence of order in the form of cyclic modes of neuronal activity. In the course of the latter experiments, a simple plasticity routine (“brainwashing,” or “anti-learning”) was identified which, applied to nets with initially quasirandom connectivity, creates model networks which provide more felicitous starting points for computer experiments on the engramming of content-addressable memories and on learning more generally. The potential relevance of this algorithm to developmental neurobiology and to sleep states is discussed.The model considered is at the same time a synthesis of earlier synchronous neural-network models and an elaboration upon them; accordingly, the present article offers both a focused review of the dynamical properties of such systems and a selection of new findings derived from computer simulation.     

Quantum neural networks: Current status and prospects for development

The idea of quantum artificial neural networks, first formulated in [34], unites the artificial neural network concept with the quantum computation paradigm. Quantum artificial neural networks were first systematically considered in the PhD thesis by T. Menneer (1998). Based on the works of Menneer and Narayanan [42, 43], Kouda, Matsui, and Nishimura [35, 36], Altaisky [2, 68], Zhou [67], and others, quantum-inspired learning algorithms for neural networks were developed, and are now used in various training programs and computer games [29, 30]. The first practically realizable scaled hardware-implemented model of the quantum artificial neural network is obtained by D-Wave Systems, Inc. [33]. It is a quantum Hopfield network implemented on the basis of superconducting quantum interference devices (SQUIDs). In this work we analyze possibilities and underlying principles of an alternative way to implement quantum neural networks on the basis of quantum dots. A possibility of using quantum neural network algorithms in automated control systems, associative memory devices, and in modeling biological and social networks is examined.     

Errors of neural networks

Errors of associative sampling and recognition in neural networks are determined by methods of the general theory of statistical solutions taking into account statistical variation of the input image, the correlation radius of the image, the number of neurons, and the number of images in the memory. The beta-distribution is proposed to be used as the statistics of image variations. The admissibility of the utilization of the distribution is tested by using temporal variations of real scenes as an example. The error in determining the coordinate is found for networks on the basis of optical correlators.     

Free-matrix-based time-dependent discontinuous Lyapunov functional for synchronization of delayed neural networks with sampled-data control

This paper is concerned with the synchronization of delayed neural networks via sampled-data control. A new technique, namely, the free-matrix-based time-dependent discontinuous Lyapunov functional approach, is adopted in constructing the Lyapunov functional, which takes advantage of the sampling characteristic of sawtooth input delay. Based on this discontinuous Lyapunov functional, some less conservative synchronization criteria are established to ensure that the slave system is synchronous with the master system. The desired sampled-data controller can be obtained through the use of the linear matrix inequality(LMI) technique. Finally, two numerical examples are provided to demonstrate the effectiveness and the improvements of the proposed methods.     

Learning and structure of neuronal networks

We study the effect of learning dynamics on network topology. Firstly, a network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity (STDP). This incorporates necessary competition between different edges. The final network we obtain is robust and has a broad degree distribution. Then we study the dynamics of the structure of a formal neural network. For properly chosen input signals, there exists a steady state with a residual network. We compare the motif profile of such a network with that of the real neural network of C. elegans and identify robust qualitative similarities. In particular, our extensive numerical simulations show that this STDP-driven resulting network is robust under variations of model parameters.     

Classification of signal-time curves from dynamic MR mammography by neural networks

The aim of this study was to test the performance of artificial neural networks for the classification of signal-time curves obtained from breast masses by dynamic MRI. Signal-time courses from 105 parenchyma, 162 malignant, and 102 benign tissue regions were examined. The latter two groups were histopathologically verified. Four neural networks corresponding to different temporal resolutions of the signal-time curves were tested. The resolution ranges from 28 measurements with a temporal spacing of 23s to just 3 measurements taken 1.8, 3, and 10 minutes after contrast medium administration. Discrimination between malignant and benign lesions is best if 28 measurement points are used (sensitivity: 84%, specificity: 81%). The use of three measurement points results in 78% sensitivity and 76% specificity. These results correspond to values obtained by human experts who visually evaluated signal-time curves without considering additional morphologic information. All examined networks yielded poor results for the subclassification of the benign lesions into fibroadenomas and benign proliferative changes. Neural networks can computationally fast distinguish between malignant and benign lesions even when only a few post-contrast measurements are made. More precise specification of the type of the benign lesion will require incorporation of additional morphological or pharmacokinetic information.     

A new approach to stability analysis of neural networks with time-varying delay via novel Lyapunov--Krasovskii functional

In this paper,new delay-dependent stability criteria for asymptotic stability of neural networks with time-varying delays are derived.The stability conditions are represented in terms of linear matrix inequalities(LMIs) by constructing new Lyapunov-Krasovskii functional.The proposed functional has an augmented quadratic form with states as well as the nonlinear function to consider the sector and the slope constraints.The less conservativeness of the proposed stability criteria can be guaranteed by using convex properties of the nonlinear function which satisfies the sector and slope bound.Numerical examples are presented to show the effectiveness of the proposed method.     

混沌时滞神经网络系统的反同步

利用状态观测器方法研究了一类带时滞的混沌神经网络系统的反同步问题.与应用于其他混沌系统的反同步方法相比,提出的方法更为简便,并且利用极点配置技术,只要通过调整特征值来实现反同步速率的快慢.最后,给出了数值例子和计算机仿真结果来验证该方案的有效性. 关键词： 混沌神经网络 状态观测器 极点配置技术 反同步