Global exponential synchronization of inertial memristive neural networks with time-varying delay via nonlinear controller.

PubMed

Gong, Shuqing; Yang, Shaofu; Guo, Zhenyuan; Huang, Tingwen

2018-06-01

The paper is concerned with the synchronization problem of inertial memristive neural networks with time-varying delay. First, by choosing a proper variable substitution, inertial memristive neural networks described by second-order differential equations can be transformed into first-order differential equations. Then, a novel controller with a linear diffusive term and discontinuous sign term is designed. By using the controller, the sufficient conditions for assuring the global exponential synchronization of the derive and response neural networks are derived based on Lyapunov stability theory and some inequality techniques. Finally, several numerical simulations are provided to substantiate the effectiveness of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

Resting State EEG-based biometrics for individual identification using convolutional neural networks.

PubMed

Lan Ma; Minett, James W; Blu, Thierry; Wang, William S-Y

2015-08-01

Biometrics is a growing field, which permits identification of individuals by means of unique physical features. Electroencephalography (EEG)-based biometrics utilizes the small intra-personal differences and large inter-personal differences between individuals' brainwave patterns. In the past, such methods have used features derived from manually-designed procedures for this purpose. Another possibility is to use convolutional neural networks (CNN) to automatically extract an individual's best and most unique neural features and conduct classification, using EEG data derived from both Resting State with Open Eyes (REO) and Resting State with Closed Eyes (REC). Results indicate that this CNN-based joint-optimized EEG-based Biometric System yields a high degree of accuracy of identification (88%) for 10-class classification. Furthermore, rich inter-personal difference can be found using a very low frequency band (0-2Hz). Additionally, results suggest that the temporal portions over which subjects can be individualized is less than 200 ms.

Pinning synchronization of memristor-based neural networks with time-varying delays.

PubMed

Yang, Zhanyu; Luo, Biao; Liu, Derong; Li, Yueheng

2017-09-01

In this paper, the synchronization of memristor-based neural networks with time-varying delays via pinning control is investigated. A novel pinning method is introduced to synchronize two memristor-based neural networks which denote drive system and response system, respectively. The dynamics are studied by theories of differential inclusions and nonsmooth analysis. In addition, some sufficient conditions are derived to guarantee asymptotic synchronization and exponential synchronization of memristor-based neural networks via the presented pinning control. Furthermore, some improvements about the proposed control method are also discussed in this paper. Finally, the effectiveness of the obtained results is demonstrated by numerical simulations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Finite-time synchronization of fractional-order memristor-based neural networks with time delays.

PubMed

Velmurugan, G; Rakkiyappan, R; Cao, Jinde

2016-01-01

In this paper, we consider the problem of finite-time synchronization of a class of fractional-order memristor-based neural networks (FMNNs) with time delays and investigated it potentially. By using Laplace transform, the generalized Gronwall's inequality, Mittag-Leffler functions and linear feedback control technique, some new sufficient conditions are derived to ensure the finite-time synchronization of addressing FMNNs with fractional order α:1<α<2 and 0<α<1. The results from the theory of fractional-order differential equations with discontinuous right-hand sides are used to investigate the problem under consideration. The derived results are extended to some previous related works on memristor-based neural networks. Finally, three numerical examples are presented to show the effectiveness of our proposed theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

LMI-based approach to stability analysis for fractional-order neural networks with discrete and distributed delays

NASA Astrophysics Data System (ADS)

Zhang, Hai; Ye, Renyu; Liu, Song; Cao, Jinde; Alsaedi, Ahmad; Li, Xiaodi

2018-02-01

This paper is concerned with the asymptotic stability of the Riemann-Liouville fractional-order neural networks with discrete and distributed delays. By constructing a suitable Lyapunov functional, two sufficient conditions are derived to ensure that the addressed neural network is asymptotically stable. The presented stability criteria are described in terms of the linear matrix inequalities. The advantage of the proposed method is that one may avoid calculating the fractional-order derivative of the Lyapunov functional. Finally, a numerical example is given to show the validity and feasibility of the theoretical results.

Human Neural Cell-Based Biosensor

DTIC Science & Technology

2013-05-28

ionotropic receptors in human embryonic stem cell derived neural progenitors. Neuroscience. 2011 Sep 29;192:793-805. Krishnamoorthy M, Gerwe BA, Scharer...coupled receptors . Pharmacol Ther. 2011 Mar;129(3):290-306. Workshop/conference abstracts, presentations, posters, and papers Powe, A, et al

Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.

PubMed

Zhang, Yanjun; Tao, Gang; Chen, Mou

2016-09-01

This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.

Master-slave exponential synchronization of delayed complex-valued memristor-based neural networks via impulsive control.

PubMed

Li, Xiaofan; Fang, Jian-An; Li, Huiyuan

2017-09-01

This paper investigates master-slave exponential synchronization for a class of complex-valued memristor-based neural networks with time-varying delays via discontinuous impulsive control. Firstly, the master and slave complex-valued memristor-based neural networks with time-varying delays are translated to two real-valued memristor-based neural networks. Secondly, an impulsive control law is constructed and utilized to guarantee master-slave exponential synchronization of the neural networks. Thirdly, the master-slave synchronization problems are transformed into the stability problems of the master-slave error system. By employing linear matrix inequality (LMI) technique and constructing an appropriate Lyapunov-Krasovskii functional, some sufficient synchronization criteria are derived. Finally, a numerical simulation is provided to illustrate the effectiveness of the obtained theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Existence and global exponential stability of periodic solution of memristor-based BAM neural networks with time-varying delays.

PubMed

Li, Hongfei; Jiang, Haijun; Hu, Cheng

2016-03-01

In this paper, we investigate a class of memristor-based BAM neural networks with time-varying delays. Under the framework of Filippov solutions, boundedness and ultimate boundedness of solutions of memristor-based BAM neural networks are guaranteed by Chain rule and inequalities technique. Moreover, a new method involving Yoshizawa-like theorem is favorably employed to acquire the existence of periodic solution. By applying the theory of set-valued maps and functional differential inclusions, an available Lyapunov functional and some new testable algebraic criteria are derived for ensuring the uniqueness and global exponential stability of periodic solution of memristor-based BAM neural networks. The obtained results expand and complement some previous work on memristor-based BAM neural networks. Finally, a numerical example is provided to show the applicability and effectiveness of our theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

GABA and Gap Junctions in the Development of Synchronized Activity in Human Pluripotent Stem Cell-Derived Neural Networks

PubMed Central

Mäkinen, Meeri Eeva-Liisa; Ylä-Outinen, Laura; Narkilahti, Susanna

2018-01-01

The electrical activity of the brain arises from single neurons communicating with each other. However, how single neurons interact during early development to give rise to neural network activity remains poorly understood. We studied the emergence of synchronous neural activity in human pluripotent stem cell (hPSC)-derived neural networks simultaneously on a single-neuron level and network level. The contribution of gamma-aminobutyric acid (GABA) and gap junctions to the development of synchronous activity in hPSC-derived neural networks was studied with GABA agonist and antagonist and by blocking gap junctional communication, respectively. We characterized the dynamics of the network-wide synchrony in hPSC-derived neural networks with high spatial resolution (calcium imaging) and temporal resolution microelectrode array (MEA). We found that the emergence of synchrony correlates with a decrease in very strong GABA excitation. However, the synchronous network was found to consist of a heterogeneous mixture of synchronously active cells with variable responses to GABA, GABA agonists and gap junction blockers. Furthermore, we show how single-cell distributions give rise to the network effect of GABA, GABA agonists and gap junction blockers. Finally, based on our observations, we suggest that the earliest form of synchronous neuronal activity depends on gap junctions and a decrease in GABA induced depolarization but not on GABAA mediated signaling. PMID:29559893

Generalised Transfer Functions of Neural Networks

NASA Astrophysics Data System (ADS)

Fung, C. F.; Billings, S. A.; Zhang, H.

1997-11-01

When artificial neural networks are used to model non-linear dynamical systems, the system structure which can be extremely useful for analysis and design, is buried within the network architecture. In this paper, explicit expressions for the frequency response or generalised transfer functions of both feedforward and recurrent neural networks are derived in terms of the network weights. The derivation of the algorithm is established on the basis of the Taylor series expansion of the activation functions used in a particular neural network. This leads to a representation which is equivalent to the non-linear recursive polynomial model and enables the derivation of the transfer functions to be based on the harmonic expansion method. By mapping the neural network into the frequency domain information about the structure of the underlying non-linear system can be recovered. Numerical examples are included to demonstrate the application of the new algorithm. These examples show that the frequency response functions appear to be highly sensitive to the network topology and training, and that the time domain properties fail to reveal deficiencies in the trained network structure.

Assessing neural activity related to decision-making through flexible odds ratio curves and their derivatives.

PubMed

Roca-Pardiñas, Javier; Cadarso-Suárez, Carmen; Pardo-Vazquez, Jose L; Leboran, Victor; Molenberghs, Geert; Faes, Christel; Acuña, Carlos

2011-06-30

It is well established that neural activity is stochastically modulated over time. Therefore, direct comparisons across experimental conditions and determination of change points or maximum firing rates are not straightforward. This study sought to compare temporal firing probability curves that may vary across groups defined by different experimental conditions. Odds-ratio (OR) curves were used as a measure of comparison, and the main goal was to provide a global test to detect significant differences of such curves through the study of their derivatives. An algorithm is proposed that enables ORs based on generalized additive models, including factor-by-curve-type interactions to be flexibly estimated. Bootstrap methods were used to draw inferences from the derivatives curves, and binning techniques were applied to speed up computation in the estimation and testing processes. A simulation study was conducted to assess the validity of these bootstrap-based tests. This methodology was applied to study premotor ventral cortex neural activity associated with decision-making. The proposed statistical procedures proved very useful in revealing the neural activity correlates of decision-making in a visual discrimination task. Copyright © 2011 John Wiley & Sons, Ltd.

Backpropagation and ordered derivatives in the time scales calculus.

PubMed

Seiffertt, John; Wunsch, Donald C

2010-08-01

Backpropagation is the most widely used neural network learning technique. It is based on the mathematical notion of an ordered derivative. In this paper, we present a formulation of ordered derivatives and the backpropagation training algorithm using the important emerging area of mathematics known as the time scales calculus. This calculus, with its potential for application to a wide variety of inter-disciplinary problems, is becoming a key area of mathematics. It is capable of unifying continuous and discrete analysis within one coherent theoretical framework. Using this calculus, we present here a generalization of backpropagation which is appropriate for cases beyond the specifically continuous or discrete. We develop a new multivariate chain rule of this calculus, define ordered derivatives on time scales, prove a key theorem about them, and derive the backpropagation weight update equations for a feedforward multilayer neural network architecture. By drawing together the time scales calculus and the area of neural network learning, we present the first connection of two major fields of research.

Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks.

PubMed

Wang, Leimin; Shen, Yi; Zhang, Guodong

Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.

Analysis and Synthesis of Adaptive Neural Elements and Assembles

DTIC Science & Technology

1992-02-17

effects of neuromodulators on electrically activity. Based on the simulations it appears that there are potentially novel mechanisms with which modulatory...and Byrne, J.H. A learning rule based on empirically-derived activity-dependent neuromodulation supports operant conditioning in a small network...dependent neuromodulation can support operant conditioning in a small oscillatory network". 2. Society for Neuroscience Short Course on Neural

High-order tracking differentiator based adaptive neural control of a flexible air-breathing hypersonic vehicle subject to actuators constraints.

PubMed

Bu, Xiangwei; Wu, Xiaoyan; Tian, Mingyan; Huang, Jiaqi; Zhang, Rui; Ma, Zhen

2015-09-01

In this paper, an adaptive neural controller is exploited for a constrained flexible air-breathing hypersonic vehicle (FAHV) based on high-order tracking differentiator (HTD). By utilizing functional decomposition methodology, the dynamic model is reasonably decomposed into the respective velocity subsystem and altitude subsystem. For the velocity subsystem, a dynamic inversion based neural controller is constructed. By introducing the HTD to adaptively estimate the newly defined states generated in the process of model transformation, a novel neural based altitude controller that is quite simpler than the ones derived from back-stepping is addressed based on the normal output-feedback form instead of the strict-feedback formulation. Based on minimal-learning parameter scheme, only two neural networks with two adaptive parameters are needed for neural approximation. Especially, a novel auxiliary system is explored to deal with the problem of control inputs constraints. Finally, simulation results are presented to test the effectiveness of the proposed control strategy in the presence of system uncertainties and actuators constraints. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Enrichment of skin-derived neural precursor cells from dermal cell populations by altering culture conditions.

PubMed

Bayati, Vahid; Gazor, Rohoullah; Nejatbakhsh, Reza; Negad Dehbashi, Fereshteh

2016-01-01

As stem cells play a critical role in tissue repair, their manipulation for being applied in regenerative medicine is of great importance. Skin-derived precursors (SKPs) may be good candidates for use in cell-based therapy as the only neural stem cells which can be isolated from an accessible tissue, skin. Herein, we presented a simple protocol to enrich neural SKPs by monolayer adherent cultivation to prove the efficacy of this method. To enrich neural SKPs from dermal cell populations, we have found that a monolayer adherent cultivation helps to increase the numbers of neural precursor cells. Indeed, we have cultured dermal cells as monolayer under serum-supplemented (control) and serum-supplemented culture, followed by serum free cultivation (test) and compared. Finally, protein markers of SKPs were assessed and compared in both experimental groups and differentiation potential was evaluated in enriched culture. The cells of enriched culture concurrently expressed fibronectin, vimentin and nestin, an intermediate filament protein expressed in neural and skeletal muscle precursors as compared to control culture. In addition, they possessed a multipotential capacity to differentiate into neurogenic, glial, adipogenic, osteogenic and skeletal myogenic cell lineages. It was concluded that serum-free adherent culture reinforced by growth factors have been shown to be effective on proliferation of skin-derived neural precursor cells (skin-NPCs) and drive their selective and rapid expansion.

Three-dimensional neural differentiation of embryonic stem cells with ACM induction in microfibrous matrices in bioreactors.

PubMed

Liu, Ning; Ouyang, Anli; Li, Yan; Yang, Shang-Tian

2013-01-01

The clinical use of pluripotent stem cell (PSC)-derived neural cells requires an efficient differentiation process for mass production in a bioreactor. Toward this goal, neural differentiation of murine embryonic stem cells (ESCs) in three-dimensional (3D) polyethylene terephthalate microfibrous matrices was investigated in this study. To streamline the process and provide a platform for process integration, the neural differentiation of ESCs was induced with astrocyte-conditioned medium without the formation of embryoid bodies, starting from undifferentiated ESC aggregates expanded in a suspension bioreactor. The 3D neural differentiation was able to generate a complex neural network in the matrices. When compared to 2D differentiation, 3D differentiation in microfibrous matrices resulted in a higher percentage of nestin-positive cells (68% vs. 54%) and upregulated gene expressions of nestin, Nurr1, and tyrosine hydroxylase. High purity of neural differentiation in 3D microfibrous matrix was also demonstrated in a spinner bioreactor with 74% nestin + cells. This study demonstrated the feasibility of a scalable process based on 3D differentiation in microfibrous matrices for the production of ESC-derived neural cells. © 2013 American Institute of Chemical Engineers.

Structural reliability calculation method based on the dual neural network and direct integration method.

PubMed

Li, Haibin; He, Yun; Nie, Xiaobo

2018-01-01

Structural reliability analysis under uncertainty is paid wide attention by engineers and scholars due to reflecting the structural characteristics and the bearing actual situation. The direct integration method, started from the definition of reliability theory, is easy to be understood, but there are still mathematics difficulties in the calculation of multiple integrals. Therefore, a dual neural network method is proposed for calculating multiple integrals in this paper. Dual neural network consists of two neural networks. The neural network A is used to learn the integrand function, and the neural network B is used to simulate the original function. According to the derivative relationships between the network output and the network input, the neural network B is derived from the neural network A. On this basis, the performance function of normalization is employed in the proposed method to overcome the difficulty of multiple integrations and to improve the accuracy for reliability calculations. The comparisons between the proposed method and Monte Carlo simulation method, Hasofer-Lind method, the mean value first-order second moment method have demonstrated that the proposed method is an efficient and accurate reliability method for structural reliability problems.

Biologically-based signal processing system applied to noise removal for signal extraction

DOEpatents

Fu, Chi Yung; Petrich, Loren I.

2004-07-13

The method and system described herein use a biologically-based signal processing system for noise removal for signal extraction. A wavelet transform may be used in conjunction with a neural network to imitate a biological system. The neural network may be trained using ideal data derived from physical principles or noiseless signals to determine to remove noise from the signal.

Flight Test of an Intelligent Flight-Control System

NASA Technical Reports Server (NTRS)

Davidson, Ron; Bosworth, John T.; Jacobson, Steven R.; Thomson, Michael Pl; Jorgensen, Charles C.

2003-01-01

The F-15 Advanced Controls Technology for Integrated Vehicles (ACTIVE) airplane (see figure) was the test bed for a flight test of an intelligent flight control system (IFCS). This IFCS utilizes a neural network to determine critical stability and control derivatives for a control law, the real-time gains of which are computed by an algorithm that solves the Riccati equation. These derivatives are also used to identify the parameters of a dynamic model of the airplane. The model is used in a model-following portion of the control law, in order to provide specific vehicle handling characteristics. The flight test of the IFCS marks the initiation of the Intelligent Flight Control System Advanced Concept Program (IFCS ACP), which is a collaboration between NASA and Boeing Phantom Works. The goals of the IFCS ACP are to (1) develop the concept of a flight-control system that uses neural-network technology to identify aircraft characteristics to provide optimal aircraft performance, (2) develop a self-training neural network to update estimates of aircraft properties in flight, and (3) demonstrate the aforementioned concepts on the F-15 ACTIVE airplane in flight. The activities of the initial IFCS ACP were divided into three Phases, each devoted to the attainment of a different objective. The objective of Phase I was to develop a pre-trained neural network to store and recall the wind-tunnel-based stability and control derivatives of the vehicle. The objective of Phase II was to develop a neural network that can learn how to adjust the stability and control derivatives to account for failures or modeling deficiencies. The objective of Phase III was to develop a flight control system that uses the neural network outputs as a basis for controlling the aircraft. The flight test of the IFCS was performed in stages. In the first stage, the Phase I version of the pre-trained neural network was flown in a passive mode. The neural network software was running using flight data inputs with the outputs provided to instrumentation only. The IFCS was not used to control the airplane. In another stage of the flight test, the Phase I pre-trained neural network was integrated into a Phase III version of the flight control system. The Phase I pretrained neural network provided realtime stability and control derivatives to a Phase III controller that was based on a stochastic optimal feedforward and feedback technique (SOFFT). This combined Phase I/III system was operated together with the research flight-control system (RFCS) of the F-15 ACTIVE during the flight test. The RFCS enables the pilot to switch quickly from the experimental- research flight mode back to the safe conventional mode. These initial IFCS ACP flight tests were completed in April 1999. The Phase I/III flight test milestone was to demonstrate, across a range of subsonic and supersonic flight conditions, that the pre-trained neural network could be used to supply real-time aerodynamic stability and control derivatives to the closed-loop optimal SOFFT flight controller. Additional objectives attained in the flight test included (1) flight qualification of a neural-network-based control system; (2) the use of a combined neural-network/closed-loop optimal flight-control system to obtain level-one handling qualities; and (3) demonstration, through variation of control gains, that different handling qualities can be achieved by setting new target parameters. In addition, data for the Phase-II (on-line-learning) neural network were collected, during the use of stacked-frequency- sweep excitation, for post-flight analysis. Initial analysis of these data showed the potential for future flight tests that will incorporate the real-time identification and on-line learning aspects of the IFCS.

Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks

PubMed Central

Maca, Petr; Pech, Pavel

2016-01-01

The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons. PMID:26880875

Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks.

PubMed

Maca, Petr; Pech, Pavel

2016-01-01

The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948-2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.

Development and function of human cerebral cortex neural networks from pluripotent stem cells in vitro

PubMed Central

Kirwan, Peter; Turner-Bridger, Benita; Peter, Manuel; Momoh, Ayiba; Arambepola, Devika; Robinson, Hugh P. C.; Livesey, Frederick J.

2015-01-01

A key aspect of nervous system development, including that of the cerebral cortex, is the formation of higher-order neural networks. Developing neural networks undergo several phases with distinct activity patterns in vivo, which are thought to prune and fine-tune network connectivity. We report here that human pluripotent stem cell (hPSC)-derived cerebral cortex neurons form large-scale networks that reflect those found in the developing cerebral cortex in vivo. Synchronised oscillatory networks develop in a highly stereotyped pattern over several weeks in culture. An initial phase of increasing frequency of oscillations is followed by a phase of decreasing frequency, before giving rise to non-synchronous, ordered activity patterns. hPSC-derived cortical neural networks are excitatory, driven by activation of AMPA- and NMDA-type glutamate receptors, and can undergo NMDA-receptor-mediated plasticity. Investigating single neuron connectivity within PSC-derived cultures, using rabies-based trans-synaptic tracing, we found two broad classes of neuronal connectivity: most neurons have small numbers (<10) of presynaptic inputs, whereas a small set of hub-like neurons have large numbers of synaptic connections (>40). These data demonstrate that the formation of hPSC-derived cortical networks mimics in vivo cortical network development and function, demonstrating the utility of in vitro systems for mechanistic studies of human forebrain neural network biology. PMID:26395144

Development and function of human cerebral cortex neural networks from pluripotent stem cells in vitro.

PubMed

Kirwan, Peter; Turner-Bridger, Benita; Peter, Manuel; Momoh, Ayiba; Arambepola, Devika; Robinson, Hugh P C; Livesey, Frederick J

2015-09-15

A key aspect of nervous system development, including that of the cerebral cortex, is the formation of higher-order neural networks. Developing neural networks undergo several phases with distinct activity patterns in vivo, which are thought to prune and fine-tune network connectivity. We report here that human pluripotent stem cell (hPSC)-derived cerebral cortex neurons form large-scale networks that reflect those found in the developing cerebral cortex in vivo. Synchronised oscillatory networks develop in a highly stereotyped pattern over several weeks in culture. An initial phase of increasing frequency of oscillations is followed by a phase of decreasing frequency, before giving rise to non-synchronous, ordered activity patterns. hPSC-derived cortical neural networks are excitatory, driven by activation of AMPA- and NMDA-type glutamate receptors, and can undergo NMDA-receptor-mediated plasticity. Investigating single neuron connectivity within PSC-derived cultures, using rabies-based trans-synaptic tracing, we found two broad classes of neuronal connectivity: most neurons have small numbers (<10) of presynaptic inputs, whereas a small set of hub-like neurons have large numbers of synaptic connections (>40). These data demonstrate that the formation of hPSC-derived cortical networks mimics in vivo cortical network development and function, demonstrating the utility of in vitro systems for mechanistic studies of human forebrain neural network biology. © 2015. Published by The Company of Biologists Ltd.

Expansion and differentiation of neural progenitors derived from the human adult enteric nervous system.

PubMed

Metzger, Marco; Bareiss, Petra M; Danker, Timm; Wagner, Silvia; Hennenlotter, Joerg; Guenther, Elke; Obermayr, Florian; Stenzl, Arnulf; Koenigsrainer, Alfred; Skutella, Thomas; Just, Lothar

2009-12-01

Neural stem and progenitor cells from the enteric nervous system have been proposed for use in cell-based therapies against specific neurogastrointestinal disorders. Recently, enteric neural progenitors were generated from human neonatal and early postnatal (until 5 years after birth) gastrointestinal tract tissues. We investigated the proliferation and differentiation of enteric nervous system progenitors isolated from human adult gastrointestinal tract. Human enteric spheroids were generated from adult small and large intestine tissues and then expanded and differentiated, depending on the applied cell culture conditions. For implantation studies, spheres were grafted into fetal slice cultures and embryonic aganglionic hindgut explants from mice. Differentiating enteric neural progenitors were characterized by 5-bromo-2-deoxyuridine labeling, in situ hybridization, immunocytochemistry, quantitative real-time polymerase chain reaction, and electrophysiological studies. The yield of human neurosphere-like bodies was increased by culture in conditional medium derived from fetal mouse enteric progenitors. We were able to generate proliferating enterospheres from adult human small or large intestine tissues; these enterospheres could be subcultured and maintained for several weeks in vitro. Spheroid-derived cells could be differentiated into a variety of neuronal subtypes and glial cells with characteristics of the enteric nervous system. Experiments involving implantation into organotypic intestinal cultures showed the differentiation capacity of neural progenitors in a 3-dimensional environment. It is feasible to isolate and expand enteric progenitor cells from human adult tissue. These findings offer new strategies for enteric stem cell research and future cell-based therapies.

Using neural networks in software repositories

NASA Technical Reports Server (NTRS)

Eichmann, David (Editor); Srinivas, Kankanahalli; Boetticher, G.

1992-01-01

The first topic is an exploration of the use of neural network techniques to improve the effectiveness of retrieval in software repositories. The second topic relates to a series of experiments conducted to evaluate the feasibility of using adaptive neural networks as a means of deriving (or more specifically, learning) measures on software. Taken together, these two efforts illuminate a very promising mechanism supporting software infrastructures - one based upon a flexible and responsive technology.

Some new results on stability and synchronization for delayed inertial neural networks based on non-reduced order method.

PubMed

Li, Xuanying; Li, Xiaotong; Hu, Cheng

2017-12-01

In this paper, without transforming the second order inertial neural networks into the first order differential systems by some variable substitutions, asymptotic stability and synchronization for a class of delayed inertial neural networks are investigated. Firstly, a new Lyapunov functional is constructed to directly propose the asymptotic stability of the inertial neural networks, and some new stability criteria are derived by means of Barbalat Lemma. Additionally, by designing a new feedback control strategy, the asymptotic synchronization of the addressed inertial networks is studied and some effective conditions are obtained. To reduce the control cost, an adaptive control scheme is designed to realize the asymptotic synchronization. It is noted that the dynamical behaviors of inertial neural networks are directly analyzed in this paper by constructing some new Lyapunov functionals, this is totally different from the traditional reduced-order variable substitution method. Finally, some numerical simulations are given to demonstrate the effectiveness of the derived theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting neural network firing pattern from phase resetting curve

NASA Astrophysics Data System (ADS)

Oprisan, Sorinel; Oprisan, Ana

2007-04-01

Autonomous neural networks called central pattern generators (CPG) are composed of endogenously bursting neurons and produce rhythmic activities, such as flying, swimming, walking, chewing, etc. Simplified CPGs for quadrupedal locomotion and swimming are modeled by a ring of neural oscillators such that the output of one oscillator constitutes the input for the subsequent neural oscillator. The phase response curve (PRC) theory discards the detailed conductance-based description of the component neurons of a network and reduces them to ``black boxes'' characterized by a transfer function, which tabulates the transient change in the intrinsic period of a neural oscillator subject to external stimuli. Based on open-loop PRC, we were able to successfully predict the phase-locked period and relative phase between neurons in a half-center network. We derived existence and stability criteria for heterogeneous ring neural networks that are in good agreement with experimental data.

High content screening of defined chemical libraries using normal and glioma-derived neural stem cell lines.

PubMed

Danovi, Davide; Folarin, Amos A; Baranowski, Bart; Pollard, Steven M

2012-01-01

Small molecules with potent biological effects on the fate of normal and cancer-derived stem cells represent both useful research tools and new drug leads for regenerative medicine and oncology. Long-term expansion of mouse and human neural stem cells is possible using adherent monolayer culture. These cultures represent a useful cellular resource to carry out image-based high content screening of small chemical libraries. Improvements in automated microscopy, desktop computational power, and freely available image processing tools, now means that such chemical screens are realistic to undertake in individual academic laboratories. Here we outline a cost effective and versatile time lapse imaging strategy suitable for chemical screening. Protocols are described for the handling and screening of human fetal Neural Stem (NS) cell lines and their malignant counterparts, Glioblastoma-derived neural stem cells (GNS). We focus on identification of cytostatic and cytotoxic "hits" and discuss future possibilities and challenges for extending this approach to assay lineage commitment and differentiation. Copyright © 2012 Elsevier Inc. All rights reserved.

Real-Time Decentralized Neural Control via Backstepping for a Robotic Arm Powered by Industrial Servomotors.

PubMed

Vazquez, Luis A; Jurado, Francisco; Castaneda, Carlos E; Santibanez, Victor

2018-02-01

This paper presents a continuous-time decentralized neural control scheme for trajectory tracking of a two degrees of freedom direct drive vertical robotic arm. A decentralized recurrent high-order neural network (RHONN) structure is proposed to identify online, in a series-parallel configuration and using the filtered error learning law, the dynamics of the plant. Based on the RHONN subsystems, a local neural controller is derived via backstepping approach. The effectiveness of the decentralized neural controller is validated on a robotic arm platform, of our own design and unknown parameters, which uses industrial servomotors to drive the joints.

Incremental evolution of the neural crest, neural crest cells and neural crest-derived skeletal tissues

PubMed Central

Hall, Brian K; Gillis, J Andrew

2013-01-01

Urochordates (ascidians) have recently supplanted cephalochordates (amphioxus) as the extant sister taxon of vertebrates. Given that urochordates possess migratory cells that have been classified as ‘neural crest-like’– and that cephalochordates lack such cells – this phylogenetic hypothesis may have significant implications with respect to the origin of the neural crest and neural crest-derived skeletal tissues in vertebrates. We present an overview of the genes and gene regulatory network associated with specification of the neural crest in vertebrates. We then use these molecular data – alongside cell behaviour, cell fate and embryonic context – to assess putative antecedents (latent homologues) of the neural crest or neural crest cells in ascidians and cephalochordates. Ascidian migratory mesenchymal cells – non-pigment-forming trunk lateral line cells and pigment-forming ‘neural crest-like cells’ (NCLC) – are unlikely latent neural crest cell homologues. Rather, Snail-expressing cells at the neural plate of border of urochordates and cephalochordates likely represent the extent of neural crest elaboration in non-vertebrate chordates. We also review evidence for the evolutionary origin of two neural crest-derived skeletal tissues – cartilage and dentine. Dentine is a bona fide vertebrate novelty, and dentine-secreting odontoblasts represent a cell type that is exclusively derived from the neural crest. Cartilage, on the other hand, likely has a much deeper origin within the Metazoa. The mesodermally derived cellular cartilages of some protostome invertebrates are much more similar to vertebrate cartilage than is the acellular ‘cartilage-like’ tissue in cephalochordate pharyngeal arches. Cartilage, therefore, is not a vertebrate novelty, and a well-developed chondrogenic program was most likely co-opted from mesoderm to the neural crest along the vertebrate stem. We conclude that the neural crest is a vertebrate novelty, but that neural crest cells and their derivatives evolved and diversified in a step-wise fashion – first by elaboration of neural plate border cells, then by the innovation or co-option of new or ancient metazoan cell fates. PMID:22414251

Scalable Expansion of Human Pluripotent Stem Cell-Derived Neural Progenitors in Stirred Suspension Bioreactor Under Xeno-free Condition.

PubMed

Nemati, Shiva; Abbasalizadeh, Saeed; Baharvand, Hossein

2016-01-01

Recent advances in neural differentiation technology have paved the way to generate clinical grade neural progenitor populations from human pluripotent stem cells. These cells are an excellent source for the production of neural cell-based therapeutic products to treat incurable central nervous system disorders such as Parkinson's disease and spinal cord injuries. This progress can be complemented by the development of robust bioprocessing technologies for large scale expansion of clinical grade neural progenitors under GMP conditions for promising clinical use and drug discovery applications. Here, we describe a protocol for a robust, scalable expansion of human neural progenitor cells from pluripotent stem cells as 3D aggregates in a stirred suspension bioreactor. The use of this platform has resulted in easily expansion of neural progenitor cells for several passages with a fold increase of up to 4.2 over a period of 5 days compared to a maximum 1.5-2-fold increase in the adherent static culture over a 1 week period. In the bioreactor culture, these cells maintained self-renewal, karyotype stability, and cloning efficiency capabilities. This approach can be also used for human neural progenitor cells derived from other sources such as the human fetal brain.

Neural cryptography with feedback.

PubMed

Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido

2004-04-01

Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

H∞ state estimation of stochastic memristor-based neural networks with time-varying delays.

PubMed

Bao, Haibo; Cao, Jinde; Kurths, Jürgen; Alsaedi, Ahmed; Ahmad, Bashir

2018-03-01

This paper addresses the problem of H ∞ state estimation for a class of stochastic memristor-based neural networks with time-varying delays. Under the framework of Filippov solution, the stochastic memristor-based neural networks are transformed into systems with interval parameters. The present paper is the first to investigate the H ∞ state estimation problem for continuous-time Itô-type stochastic memristor-based neural networks. By means of Lyapunov functionals and some stochastic technique, sufficient conditions are derived to ensure that the estimation error system is asymptotically stable in the mean square with a prescribed H ∞ performance. An explicit expression of the state estimator gain is given in terms of linear matrix inequalities (LMIs). Compared with other results, our results reduce control gain and control cost effectively. Finally, numerical simulations are provided to demonstrate the efficiency of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

A novel word spotting method based on recurrent neural networks.

PubMed

Frinken, Volkmar; Fischer, Andreas; Manmatha, R; Bunke, Horst

2012-02-01

Keyword spotting refers to the process of retrieving all instances of a given keyword from a document. In the present paper, a novel keyword spotting method for handwritten documents is described. It is derived from a neural network-based system for unconstrained handwriting recognition. As such it performs template-free spotting, i.e., it is not necessary for a keyword to appear in the training set. The keyword spotting is done using a modification of the CTC Token Passing algorithm in conjunction with a recurrent neural network. We demonstrate that the proposed systems outperform not only a classical dynamic time warping-based approach but also a modern keyword spotting system, based on hidden Markov models. Furthermore, we analyze the performance of the underlying neural networks when using them in a recognition task followed by keyword spotting on the produced transcription. We point out the advantages of keyword spotting when compared to classic text line recognition.

WNT/β-catenin signaling mediates human neural crest induction via a pre-neural border intermediate.

PubMed

Leung, Alan W; Murdoch, Barbara; Salem, Ahmed F; Prasad, Maneeshi S; Gomez, Gustavo A; García-Castro, Martín I

2016-02-01

Neural crest (NC) cells arise early in vertebrate development, migrate extensively and contribute to a diverse array of ectodermal and mesenchymal derivatives. Previous models of NC formation suggested derivation from neuralized ectoderm, via meso-ectodermal, or neural-non-neural ectoderm interactions. Recent studies using bird and amphibian embryos suggest an earlier origin of NC, independent of neural and mesodermal tissues. Here, we set out to generate a model in which to decipher signaling and tissue interactions involved in human NC induction. Our novel human embryonic stem cell (ESC)-based model yields high proportions of multipotent NC cells (expressing SOX10, PAX7 and TFAP2A) in 5 days. We demonstrate a crucial role for WNT/β-catenin signaling in launching NC development, while blocking placodal and surface ectoderm fates. We provide evidence of the delicate temporal effects of BMP and FGF signaling, and find that NC development is separable from neural and/or mesodermal contributions. We further substantiate the notion of a neural-independent origin of NC through PAX6 expression and knockdown studies. Finally, we identify a novel pre-neural border state characterized by early WNT/β-catenin signaling targets that displays distinct responses to BMP and FGF signaling from the traditional neural border genes. In summary, our work provides a fast and efficient protocol for human NC differentiation under signaling constraints similar to those identified in vivo in model organisms, and strengthens a framework for neural crest ontogeny that is separable from neural and mesodermal fates. © 2016. Published by The Company of Biologists Ltd.

Physiologically based microenvironment for in vitro neural differentiation of adipose-derived stem cells.

PubMed

Graziano, Adriana Carol Eleonora; Avola, Rosanna; Perciavalle, Vincenzo; Nicoletti, Ferdinando; Cicala, Gianluca; Coco, Marinella; Cardile, Venera

2018-03-26

The limited capacity of nervous system to promote a spontaneous regeneration and the high rate of neurodegenerative diseases appearance are keys factors that stimulate researches both for defining the molecular mechanisms of pathophysiology and for evaluating putative strategies to induce neural tissue regeneration. In this latter aspect, the application of stem cells seems to be a promising approach, even if the control of their differentiation and the maintaining of a safe state of proliferation should be troubled. Here, we focus on adipose tissue-derived stem cells and we seek out the recent advances on the promotion of their neural differentiation, performing a critical integration of the basic biology and physiology of adipose tissue-derived stem cells with the functional modifications that the biophysical, biomechanical and biochemical microenvironment induces to cell phenotype. The pre-clinical studies showed that the neural differentiation by cell stimulation with growth factors benefits from the integration with biomaterials and biophysical interaction like microgravity. All these elements have been reported as furnisher of microenvironments with desirable biological, physical and mechanical properties. A critical review of current knowledge is here proposed, underscoring that a real advance toward a stable, safe and controllable adipose stem cells clinical application will derive from a synergic multidisciplinary approach that involves material engineer, basic cell biology, cell and tissue physiology.

Blur identification by multilayer neural network based on multivalued neurons.

PubMed

Aizenberg, Igor; Paliy, Dmitriy V; Zurada, Jacek M; Astola, Jaakko T

2008-05-01

A multilayer neural network based on multivalued neurons (MLMVN) is a neural network with a traditional feedforward architecture. At the same time, this network has a number of specific different features. Its backpropagation learning algorithm is derivative-free. The functionality of MLMVN is superior to that of the traditional feedforward neural networks and of a variety kernel-based networks. Its higher flexibility and faster adaptation to the target mapping enables to model complex problems using simpler networks. In this paper, the MLMVN is used to identify both type and parameters of the point spread function, whose precise identification is of crucial importance for the image deblurring. The simulation results show the high efficiency of the proposed approach. It is confirmed that the MLMVN is a powerful tool for solving classification problems, especially multiclass ones.

Neural signal processing and closed-loop control algorithm design for an implanted neural recording and stimulation system.

PubMed

Hamilton, Lei; McConley, Marc; Angermueller, Kai; Goldberg, David; Corba, Massimiliano; Kim, Louis; Moran, James; Parks, Philip D; Sang Chin; Widge, Alik S; Dougherty, Darin D; Eskandar, Emad N

2015-08-01

A fully autonomous intracranial device is built to continually record neural activities in different parts of the brain, process these sampled signals, decode features that correlate to behaviors and neuropsychiatric states, and use these features to deliver brain stimulation in a closed-loop fashion. In this paper, we describe the sampling and stimulation aspects of such a device. We first describe the signal processing algorithms of two unsupervised spike sorting methods. Next, we describe the LFP time-frequency analysis and feature derivation from the two spike sorting methods. Spike sorting includes a novel approach to constructing a dictionary learning algorithm in a Compressed Sensing (CS) framework. We present a joint prediction scheme to determine the class of neural spikes in the dictionary learning framework; and, the second approach is a modified OSort algorithm which is implemented in a distributed system optimized for power efficiency. Furthermore, sorted spikes and time-frequency analysis of LFP signals can be used to generate derived features (including cross-frequency coupling, spike-field coupling). We then show how these derived features can be used in the design and development of novel decode and closed-loop control algorithms that are optimized to apply deep brain stimulation based on a patient's neuropsychiatric state. For the control algorithm, we define the state vector as representative of a patient's impulsivity, avoidance, inhibition, etc. Controller parameters are optimized to apply stimulation based on the state vector's current state as well as its historical values. The overall algorithm and software design for our implantable neural recording and stimulation system uses an innovative, adaptable, and reprogrammable architecture that enables advancement of the state-of-the-art in closed-loop neural control while also meeting the challenges of system power constraints and concurrent development with ongoing scientific research designed to define brain network connectivity and neural network dynamics that vary at the individual patient level and vary over time.

Multipotent Caudal Neural Progenitors Derived from Human Pluripotent Stem Cells That Give Rise to Lineages of the Central and Peripheral Nervous System

PubMed Central

Hasegawa, Kouichi; Menheniott, Trevelyan; Rollo, Ben; Zhang, Dongcheng; Hough, Shelley; Alshawaf, Abdullah; Febbraro, Fabia; Ighaniyan, Samiramis; Leung, Jessie; Elliott, David A.; Newgreen, Donald F.; Pera, Martin F.

2015-01-01

Abstract The caudal neural plate is a distinct region of the embryo that gives rise to major progenitor lineages of the developing central and peripheral nervous system, including neural crest and floor plate cells. We show that dual inhibition of the glycogen synthase kinase 3β and activin/nodal pathways by small molecules differentiate human pluripotent stem cells (hPSCs) directly into a preneuroepithelial progenitor population we named “caudal neural progenitors” (CNPs). CNPs coexpress caudal neural plate and mesoderm markers, and, share high similarities to embryonic caudal neural plate cells in their lineage differentiation potential. Exposure of CNPs to BMP2/4, sonic hedgehog, or FGF2 signaling efficiently directs their fate to neural crest/roof plate cells, floor plate cells, and caudally specified neuroepithelial cells, respectively. Neural crest derived from CNPs differentiated to neural crest derivatives and demonstrated extensive migratory properties in vivo. Importantly, we also determined the key extrinsic factors specifying CNPs from human embryonic stem cell include FGF8, canonical WNT, and IGF1. Our studies are the first to identify a multipotent neural progenitor derived from hPSCs, that is the precursor for major neural lineages of the embryonic caudal neural tube. Stem Cells 2015;33:1759–1770 PMID:25753817

The temporal derivative of expected utility: a neural mechanism for dynamic decision-making.

PubMed

Zhang, Xian; Hirsch, Joy

2013-01-15

Real world tasks involving moving targets, such as driving a vehicle, are performed based on continuous decisions thought to depend upon the temporal derivative of the expected utility (∂V/∂t), where the expected utility (V) is the effective value of a future reward. However, the neural mechanisms that underlie dynamic decision-making are not well understood. This study investigates human neural correlates of both V and ∂V/∂t using fMRI and a novel experimental paradigm based on a pursuit-evasion game optimized to isolate components of dynamic decision processes. Our behavioral data show that players of the pursuit-evasion game adopt an exponential discounting function, supporting the expected utility theory. The continuous functions of V and ∂V/∂t were derived from the behavioral data and applied as regressors in fMRI analysis, enabling temporal resolution that exceeded the sampling rate of image acquisition, hyper-temporal resolution, by taking advantage of numerous trials that provide rich and independent manipulation of those variables. V and ∂V/∂t were each associated with distinct neural activity. Specifically, ∂V/∂t was associated with anterior and posterior cingulate cortices, superior parietal lobule, and ventral pallidum, whereas V was primarily associated with supplementary motor, pre and post central gyri, cerebellum, and thalamus. The association between the ∂V/∂t and brain regions previously related to decision-making is consistent with the primary role of the temporal derivative of expected utility in dynamic decision-making. Copyright © 2012 Elsevier Inc. All rights reserved.

The Temporal Derivative of Expected Utility: A Neural Mechanism for Dynamic Decision-making

PubMed Central

Zhang, Xian; Hirsch, Joy

2012-01-01

Real world tasks involving moving targets, such as driving a vehicle, are performed based on continuous decisions thought to depend upon the temporal derivative of the expected utility (∂V/∂t), where the expected utility (V) is the effective value of a future reward. However, those neural mechanisms that underlie dynamic decision-making are not well understood. This study investigates human neural correlates of both V and ∂V/∂t using fMRI and a novel experimental paradigm based on a pursuit-evasion game optimized to isolate components of dynamic decision processes. Our behavioral data show that players of the pursuit-evasion game adopt an exponential discounting function, supporting the expected utility theory. The continuous functions of V and ∂V/∂t were derived from the behavioral data and applied as regressors in fMRI analysis, enabling temporal resolution that exceeded the sampling rate of image acquisition, hyper-temporal resolution, by taking advantage of numerous trials that provide rich and independent manipulation of those variables. V and ∂V/∂t were each associated with distinct neural activity. Specifically, ∂V/∂t was associated with anterior and posterior cingulate cortices, superior parietal lobule, and ventral pallidum, whereas V was primarily associated with supplementary motor, pre and post central gyri, cerebellum, and thalamus. The association between the ∂V/∂t and brain regions previously related to decision-making is consistent with the primary role of the temporal derivative of expected utility in dynamic decision-making. PMID:22963852

An industrial robot singular trajectories planning based on graphs and neural networks

NASA Astrophysics Data System (ADS)

Łęgowski, Adrian; Niezabitowski, Michał

2016-06-01

Singular trajectories are rarely used because of issues during realization. A method of planning trajectories for given set of points in task space with use of graphs and neural networks is presented. In every desired point the inverse kinematics problem is solved in order to derive all possible solutions. A graph of solutions is made. The shortest path is determined to define required nodes in joint space. Neural networks are used to define the path between these nodes.

Novel paths towards neural cellular products for neurological disorders.

PubMed

Daadi, Marcel M

2011-11-01

The prospect of using neural cells derived from stem cells or from reprogrammed adult somatic cells provides a unique opportunity in cell therapy and drug discovery for developing novel strategies for brain repair. Cell-based therapeutic approaches for treating CNS afflictions caused by disease or injury aim to promote structural repair of the injured or diseased neural tissue, an outcome currently not achieved by drug therapy. Preclinical research in animal models of various diseases or injuries report that grafts of neural cells enhance endogenous repair, provide neurotrophic support to neurons undergoing degeneration and replace lost neural cells. In recent years, the sources of neural cells for treating neurological disorders have been rapidly expanding and in addition to offering therapeutic potential, neural cell products hold promise for disease modeling and drug discovery use. Specific neural cell types have been derived from adult or fetal brain, from human embryonic stem cells, from induced pluripotent stem cells and directly transdifferentiated from adult somatic cells, such as skin cells. It is yet to be determined if the latter approach will evolve into a paradigm shift in the fields of stem cell research and regenerative medicine. These multiple sources of neural cells cover a wide spectrum of safety that needs to be balanced with efficacy to determine the viability of the cellular product. In this article, we will review novel sources of neural cells and discuss current obstacles to developing them into viable cellular products for treating neurological disorders.

Unique preservation of neural cells in Hutchinson- Gilford progeria syndrome is due to the expression of the neural-specific miR-9 microRNA.

PubMed

Nissan, Xavier; Blondel, Sophie; Navarro, Claire; Maury, Yves; Denis, Cécile; Girard, Mathilde; Martinat, Cécile; De Sandre-Giovannoli, Annachiara; Levy, Nicolas; Peschanski, Marc

2012-07-26

One puzzling observation in patients affected with Hutchinson-Gilford progeria syndrome (HGPS), who overall exhibit systemic and dramatic premature aging, is the absence of any conspicuous cognitive impairment. Recent studies based on induced pluripotent stem cells derived from HGPS patient cells have revealed a lack of expression in neural derivatives of lamin A, a major isoform of LMNA that is initially produced as a precursor called prelamin A. In HGPS, defective maturation of a mutated prelamin A induces the accumulation of toxic progerin in patient cells. Here, we show that a microRNA, miR-9, negatively controls lamin A and progerin expression in neural cells. This may bear major functional correlates, as alleviation of nuclear blebbing is observed in nonneural cells after miR-9 overexpression. Our results support the hypothesis, recently proposed from analyses in mice, that protection of neural cells from progerin accumulation in HGPS is due to the physiologically restricted expression of miR-9 to that cell lineage. Copyright © 2012 The Authors. Published by Elsevier Inc. All rights reserved.

Design of asymptotic estimators: an approach based on neural networks and nonlinear programming.

PubMed

Alessandri, Angelo; Cervellera, Cristiano; Sanguineti, Marcello

2007-01-01

A methodology to design state estimators for a class of nonlinear continuous-time dynamic systems that is based on neural networks and nonlinear programming is proposed. The estimator has the structure of a Luenberger observer with a linear gain and a parameterized (in general, nonlinear) function, whose argument is an innovation term representing the difference between the current measurement and its prediction. The problem of the estimator design consists in finding the values of the gain and of the parameters that guarantee the asymptotic stability of the estimation error. Toward this end, if a neural network is used to take on this function, the parameters (i.e., the neural weights) are chosen, together with the gain, by constraining the derivative of a quadratic Lyapunov function for the estimation error to be negative definite on a given compact set. It is proved that it is sufficient to impose the negative definiteness of such a derivative only on a suitably dense grid of sampling points. The gain is determined by solving a Lyapunov equation. The neural weights are searched for via nonlinear programming by minimizing a cost penalizing grid-point constraints that are not satisfied. Techniques based on low-discrepancy sequences are applied to deal with a small number of sampling points, and, hence, to reduce the computational burden required to optimize the parameters. Numerical results are reported and comparisons with those obtained by the extended Kalman filter are made.

DWI-based neural fingerprinting technology: a preliminary study on stroke analysis.

PubMed

Ye, Chenfei; Ma, Heather Ting; Wu, Jun; Yang, Pengfei; Chen, Xuhui; Yang, Zhengyi; Ma, Jingbo

2014-01-01

Stroke is a common neural disorder in neurology clinics. Magnetic resonance imaging (MRI) has become an important tool to assess the neural physiological changes under stroke, such as diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI). Quantitative analysis of MRI images would help medical doctors to localize the stroke area in the diagnosis in terms of structural information and physiological characterization. However, current quantitative approaches can only provide localization of the disorder rather than measure physiological variation of subtypes of ischemic stroke. In the current study, we hypothesize that each kind of neural disorder would have its unique physiological characteristics, which could be reflected by DWI images on different gradients. Based on this hypothesis, a DWI-based neural fingerprinting technology was proposed to classify subtypes of ischemic stroke. The neural fingerprint was constructed by the signal intensity of the region of interest (ROI) on the DWI images under different gradients. The fingerprint derived from the manually drawn ROI could classify the subtypes with accuracy 100%. However, the classification accuracy was worse when using semiautomatic and automatic method in ROI segmentation. The preliminary results showed promising potential of DWI-based neural fingerprinting technology in stroke subtype classification. Further studies will be carried out for enhancing the fingerprinting accuracy and its application in other clinical practices.

3D culture of human pluripotent stem cells in RGD-alginate hydrogel improves retinal tissue development.

PubMed

Hunt, Nicola C; Hallam, Dean; Karimi, Ayesha; Mellough, Carla B; Chen, Jinju; Steel, David H W; Lako, Majlinda

2017-02-01

No treatments exist to effectively treat many retinal diseases. Retinal pigmented epithelium (RPE) and neural retina can be generated from human embryonic stem cells/induced pluripotent stem cells (hESCs/hiPSCs). The efficacy of current protocols is, however, limited. It was hypothesised that generation of laminated neural retina and/or RPE from hiPSCs/hESCs could be enhanced by three dimensional (3D) culture in hydrogels. hiPSC- and hESC-derived embryoid bodies (EBs) were encapsulated in 0.5% RGD-alginate; 1% RGD-alginate; hyaluronic acid (HA) or HA/gelatin hydrogels and maintained until day 45. Compared with controls (no gel), 0.5% RGD-alginate increased: the percentage of EBs with pigmented RPE foci; the percentage EBs with optic vesicles (OVs) and pigmented RPE simultaneously; the area covered by RPE; frequency of RPE cells (CRALBP+); expression of RPE markers (TYR and RPE65) and the retinal ganglion cell marker, MATH5. Furthermore, 0.5% RGD-alginate hydrogel encapsulation did not adversely affect the expression of other neural retina markers (PROX1, CRX, RCVRN, AP2α or VSX2) as determined by qRT-PCR, or the percentage of VSX2 positive cells as determined by flow cytometry. 1% RGD-alginate increased the percentage of EBs with OVs and/or RPE, but did not significantly influence any other measures of retinal differentiation. HA-based hydrogels had no significant effect on retinal tissue development. The results indicated that derivation of retinal tissue from hESCs/hiPSCs can be enhanced by culture in 0.5% RGD-alginate hydrogel. This RGD-alginate scaffold may be useful for derivation, transport and transplantation of neural retina and RPE, and may also enhance formation of other pigmented, neural or epithelial tissue. The burden of retinal disease is ever growing with the increasing age of the world-wide population. Transplantation of retinal tissue derived from human pluripotent stem cells (PSCs) is considered a promising treatment. However, derivation of retinal tissue from PSCs using defined media is a lengthy process and often variable between different cell lines. This study indicated that alginate hydrogels enhanced retinal tissue development from PSCs, whereas hyaluronic acid-based hydrogels did not. This is the first study to show that 3D culture with a biomaterial scaffold can improve retinal tissue derivation from PSCs. These findings indicate potential for the clinical application of alginate hydrogels for the derivation and subsequent transplantation retinal tissue. This work may also have implications for the derivation of other pigmented, neural or epithelial tissue. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.

Deep neural networks for texture classification-A theoretical analysis.

PubMed

Basu, Saikat; Mukhopadhyay, Supratik; Karki, Manohar; DiBiano, Robert; Ganguly, Sangram; Nemani, Ramakrishna; Gayaka, Shreekant

2018-01-01

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Exponential lag function projective synchronization of memristor-based multidirectional associative memory neural networks via hybrid control

NASA Astrophysics Data System (ADS)

Yuan, Manman; Wang, Weiping; Luo, Xiong; Li, Lixiang; Kurths, Jürgen; Wang, Xiao

2018-03-01

This paper is concerned with the exponential lag function projective synchronization of memristive multidirectional associative memory neural networks (MMAMNNs). First, we propose a new model of MMAMNNs with mixed time-varying delays. In the proposed approach, the mixed delays include time-varying discrete delays and distributed time delays. Second, we design two kinds of hybrid controllers. Traditional control methods lack the capability of reflecting variable synaptic weights. In this paper, the controllers are carefully designed to confirm the process of different types of synchronization in the MMAMNNs. Third, sufficient criteria guaranteeing the synchronization of system are derived based on the derive-response concept. Finally, the effectiveness of the proposed mechanism is validated with numerical experiments.

Novel neural control for a class of uncertain pure-feedback systems.

PubMed

Shen, Qikun; Shi, Peng; Zhang, Tianping; Lim, Cheng-Chew

2014-04-01

This paper is concerned with the problem of adaptive neural tracking control for a class of uncertain pure-feedback nonlinear systems. Using the implicit function theorem and backstepping technique, a practical robust adaptive neural control scheme is proposed to guarantee that the tracking error converges to an adjusted neighborhood of the origin by choosing appropriate design parameters. In contrast to conventional Lyapunov-based design techniques, an alternative Lyapunov function is constructed for the development of control law and learning algorithms. Differing from the existing results in the literature, the control scheme does not need to compute the derivatives of virtual control signals at each step in backstepping design procedures. Furthermore, the scheme requires the desired trajectory and its first derivative rather than its first n derivatives. In addition, the useful property of the basis function of the radial basis function, which will be used in control design, is explored. Simulation results illustrate the effectiveness of the proposed techniques.

Examining FKBP5 mRNA expression in human iPSC-derived neural cells

PubMed Central

Lieberman, Richard; Kranzler, Henry R.; Levine, Eric S.; Covault, Jonathan

2016-01-01

In peripheral blood leukocytes, FKBP5 mRNA expression is upregulated following glucocorticoid receptor activation. The single nucleotide polymorphism rs1360780 in FKBP5 is associated with psychiatric illness and has functional molecular effects. However, examination of FKBP5 regulation has largely been limited to peripheral cells, which may not reflect regulation in neural cells. We used 27 human induced pluripotent stem cell lines (iPSCs) derived from 20 subjects to examine FKBP5 mRNA expression following GR activation. Following differentiation into forebrain-lineage neural cultures, cells were exposed to 1μM dexamethasone and mRNA expression of FKBP5 and NR3C1 analyzed. Results from the iPSC-derived neural cells were compared with those from 15 donor matched fibroblast lines. Following dexamethasone treatment, there was a 670% increase in FKBP5 expression in fibroblasts, mimicking findings in peripheral blood-derived cells, but only a 23% increase in iPSC-derived neural cultures. FKBP5 rs1360780 genotype did not affect the induction of FKBP5 mRNA in either fibroblasts or neural cells. These results suggest that iPSC-derived forebrain-lineage neurons may not be an optimal neural cell type in which to examine relationships between GR activation, FKBP5 expression, and genetic variation in human subjects. Further, FKBP5 induction following GR activation may differ between cell types derived from the same individual. PMID:27915167

Engraftment of enteric neural progenitor cells into the injured adult brain.

PubMed

Belkind-Gerson, Jaime; Hotta, Ryo; Whalen, Michael; Nayyar, Naema; Nagy, Nandor; Cheng, Lily; Zuckerman, Aaron; Goldstein, Allan M; Dietrich, Jorg

2016-01-25

A major area of unmet need is the development of strategies to restore neuronal network systems and to recover brain function in patients with neurological disease. The use of cell-based therapies remains an attractive approach, but its application has been challenging due to the lack of suitable cell sources, ethical concerns, and immune-mediated tissue rejection. We propose an innovative approach that utilizes gut-derived neural tissue for cell-based therapies following focal or diffuse central nervous system injury. Enteric neuronal stem and progenitor cells, able to differentiate into neuronal and glial lineages, were isolated from the postnatal enteric nervous system and propagated in vitro. Gut-derived neural progenitors, genetically engineered to express fluorescent proteins, were transplanted into the injured brain of adult mice. Using different models of brain injury in combination with either local or systemic cell delivery, we show that transplanted enteric neuronal progenitor cells survive, proliferate, and differentiate into neuronal and glial lineages in vivo. Moreover, transplanted cells migrate extensively along neuronal pathways and appear to modulate the local microenvironment to stimulate endogenous neurogenesis. Our findings suggest that enteric nervous system derived cells represent a potential source for tissue regeneration in the central nervous system. Further studies are needed to validate these findings and to explore whether autologous gut-derived cell transplantation into the injured brain can result in functional neurologic recovery.

On the utility of the ionosonde Doppler-derived EXB drift during the daytime

NASA Astrophysics Data System (ADS)

Joshi, L. M.; Sripathi, S.

2016-03-01

Vertical EXB drift measured using the ionosonde Doppler sounding during the daytime suffers from an underestimation of the actual EXB drift because the reflection height of the ionosonde signals is also affected by the photochemistry of the ionosphere. Systematic investigations have indicated a fair/good correlation to exist between the C/NOFS and ionosonde Doppler-measured vertical EXB drift during the daytime over magnetic equator. A detailed analysis, however, indicated that the linear relation between the ionosonde Doppler drift and C/NOFS EXB drift varied with seasons. Thus, solar, seasonal, and also geomagnetic variables were included in the Doppler drift correction, using the artificial neural network-based approach. The RMS error in the neural network was found to be smaller than that in the linear regression analysis. Daytime EXB drift was derived using the neural network which was also used to model the ionospheric redistribution in the SAMI2 model. SAMI2 model reproduced strong (weak) equatorial ionization anomaly (EIA) for cases when neural network corrected daytime vertical EXB drift was high (low). Similar features were also observed in GIM TEC maps. Thus, the results indicate that the neural network can be utilized to derive the vertical EXB drift from its proxies, like the ionosonde Doppler drift. These results indicate that the daytime ionosonde measured vertical EXB drift can be relied upon, provided that adequate corrections are applied to it.

Physiologically based microenvironment for in vitro neural differentiation of adipose-derived stem cells

PubMed Central

Graziano, Adriana Carol Eleonora; Avola, Rosanna; Perciavalle, Vincenzo; Nicoletti, Ferdinando; Cicala, Gianluca; Coco, Marinella; Cardile, Venera

2018-01-01

The limited capacity of nervous system to promote a spontaneous regeneration and the high rate of neurodegenerative diseases appearance are keys factors that stimulate researches both for defining the molecular mechanisms of pathophysiology and for evaluating putative strategies to induce neural tissue regeneration. In this latter aspect, the application of stem cells seems to be a promising approach, even if the control of their differentiation and the maintaining of a safe state of proliferation should be troubled. Here, we focus on adipose tissue-derived stem cells and we seek out the recent advances on the promotion of their neural differentiation, performing a critical integration of the basic biology and physiology of adipose tissue-derived stem cells with the functional modifications that the biophysical, biomechanical and biochemical microenvironment induces to cell phenotype. The pre-clinical studies showed that the neural differentiation by cell stimulation with growth factors benefits from the integration with biomaterials and biophysical interaction like microgravity. All these elements have been reported as furnisher of microenvironments with desirable biological, physical and mechanical properties. A critical review of current knowledge is here proposed, underscoring that a real advance toward a stable, safe and controllable adipose stem cells clinical application will derive from a synergic multidisciplinary approach that involves material engineer, basic cell biology, cell and tissue physiology. PMID:29588808

Human embryonic stem cell-derived neurons adopt and regulate the activity of an established neural network

PubMed Central

Weick, Jason P.; Liu, Yan; Zhang, Su-Chun

2011-01-01

Whether hESC-derived neurons can fully integrate with and functionally regulate an existing neural network remains unknown. Here, we demonstrate that hESC-derived neurons receive unitary postsynaptic currents both in vitro and in vivo and adopt the rhythmic firing behavior of mouse cortical networks via synaptic integration. Optical stimulation of hESC-derived neurons expressing Channelrhodopsin-2 elicited both inhibitory and excitatory postsynaptic currents and triggered network bursting in mouse neurons. Furthermore, light stimulation of hESC-derived neurons transplanted to the hippocampus of adult mice triggered postsynaptic currents in host pyramidal neurons in acute slice preparations. Thus, hESC-derived neurons can participate in and modulate neural network activity through functional synaptic integration, suggesting they are capable of contributing to neural network information processing both in vitro and in vivo. PMID:22106298

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns.

PubMed

Akama, Hiroyuki; Miyake, Maki; Jung, Jaeyoung; Murphy, Brian

2015-01-01

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

Comparison of 2D and 3D neural induction methods for the generation of neural progenitor cells from human induced pluripotent stem cells.

PubMed

Chandrasekaran, Abinaya; Avci, Hasan X; Ochalek, Anna; Rösingh, Lone N; Molnár, Kinga; László, Lajos; Bellák, Tamás; Téglási, Annamária; Pesti, Krisztina; Mike, Arpad; Phanthong, Phetcharat; Bíró, Orsolya; Hall, Vanessa; Kitiyanant, Narisorn; Krause, Karl-Heinz; Kobolák, Julianna; Dinnyés, András

2017-12-01

Neural progenitor cells (NPCs) from human induced pluripotent stem cells (hiPSCs) are frequently induced using 3D culture methodologies however, it is unknown whether spheroid-based (3D) neural induction is actually superior to monolayer (2D) neural induction. Our aim was to compare the efficiency of 2D induction with 3D induction method in their ability to generate NPCs, and subsequently neurons and astrocytes. Neural differentiation was analysed at the protein level qualitatively by immunocytochemistry and quantitatively by flow cytometry for NPC (SOX1, PAX6, NESTIN), neuronal (MAP2, TUBB3), cortical layer (TBR1, CUX1) and glial markers (SOX9, GFAP, AQP4). Electron microscopy demonstrated that both methods resulted in morphologically similar neural rosettes. However, quantification of NPCs derived from 3D neural induction exhibited an increase in the number of PAX6/NESTIN double positive cells and the derived neurons exhibited longer neurites. In contrast, 2D neural induction resulted in more SOX1 positive cells. While 2D monolayer induction resulted in slightly less mature neurons, at an early stage of differentiation, the patch clamp analysis failed to reveal any significant differences between the electrophysiological properties between the two induction methods. In conclusion, 3D neural induction increases the yield of PAX6 + /NESTIN + cells and gives rise to neurons with longer neurites, which might be an advantage for the production of forebrain cortical neurons, highlighting the potential of 3D neural induction, independent of iPSCs' genetic background. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Temporal neural networks and transient analysis of complex engineering systems

NASA Astrophysics Data System (ADS)

Uluyol, Onder

A theory is introduced for a multi-layered Local Output Gamma Feedback (LOGF) neural network within the paradigm of Locally-Recurrent Globally-Feedforward neural networks. It is developed for the identification, prediction, and control tasks of spatio-temporal systems and allows for the presentation of different time scales through incorporation of a gamma memory. It is initially applied to the tasks of sunspot and Mackey-Glass series prediction as benchmarks, then it is extended to the task of power level control of a nuclear reactor at different fuel cycle conditions. The developed LOGF neuron model can also be viewed as a Transformed Input and State (TIS) Gamma memory for neural network architectures for temporal processing. The novel LOGF neuron model extends the static neuron model by incorporating into it a short-term memory structure in the form of a digital gamma filter. A feedforward neural network made up of LOGF neurons can thus be used to model dynamic systems. A learning algorithm based upon the Backpropagation-Through-Time (BTT) approach is derived. It is applicable for training a general L-layer LOGF neural network. The spatial and temporal weights and parameters of the network are iteratively optimized for a given problem using the derived learning algorithm.

Distinct Expression of Phenotypic Markers in Placodes- and Neural Crest-Derived Afferent Neurons Innervating the Rat Stomach.

PubMed

Trancikova, Alzbeta; Kovacova, Eva; Ru, Fei; Varga, Kristian; Brozmanova, Mariana; Tatar, Milos; Kollarik, Marian

2018-02-01

Visceral pain is initiated by activation of primary afferent neurons among which the capsaicin-sensitive (TRPV1-positive) neurons play an important role. The stomach is a common source of visceral pain. Similar to other organs, the stomach receives dual spinal and vagal afferent innervation. Developmentally, spinal dorsal root ganglia (DRG) and vagal jugular neurons originate from embryonic neural crest and vagal nodose neurons originate from placodes. In thoracic organs the neural crest- and placodes-derived TRPV1-positive neurons have distinct phenotypes differing in activation profile, neurotrophic regulation and reflex responses. It is unknown to whether such distinction exists in the stomach. We hypothesized that gastric neural crest- and placodes-derived TRPV1-positive neurons express phenotypic markers indicative of placodes and neural crest phenotypes. Gastric DRG and vagal neurons were retrogradely traced by DiI injected into the rat stomach wall. Single-cell RT-PCR was performed on traced gastric neurons. Retrograde tracing demonstrated that vagal gastric neurons locate exclusively into the nodose portion of the rat jugular/petrosal/nodose complex. Gastric DRG TRPV1-positive neurons preferentially expressed markers PPT-A, TrkA and GFRα 3 typical for neural crest-derived TRPV1-positive visceral neurons. In contrast, gastric nodose TRPV1-positive neurons preferentially expressed markers P2X 2 and TrkB typical for placodes-derived TRPV1-positive visceral neurons. Differential expression of neural crest and placodes markers was less pronounced in TRPV1-negative DRG and nodose populations. There are phenotypic distinctions between the neural crest-derived DRG and placodes-derived vagal nodose TRPV1-positive neurons innervating the rat stomach that are similar to those described in thoracic organs.

Integrative analysis of genes and miRNA alterations in human embryonic stem cells-derived neural cells after exposure to silver nanoparticles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oh, Jung-Hwa; Department of human and environmental toxicology, University of Science & Technology, Daejeon 34113; Son, Mi-Young

Given the rapid growth of engineered and customer products made of silver nanoparticles (Ag NPs), understanding their biological and toxicological effects on humans is critically important. The molecular developmental neurotoxic effects associated with exposure to Ag NPs were analyzed at the physiological and molecular levels, using an alternative cell model: human embryonic stem cell (hESC)-derived neural stem/progenitor cells (NPCs). In this study, the cytotoxic effects of Ag NPs (10–200 μg/ml) were examined in these hESC-derived NPCs, which have a capacity for neurogenesis in vitro, at 6 and 24 h. The results showed that Ag NPs evoked significant toxicity in hESC-derivedmore » NPCs at 24 h in a dose-dependent manner. In addition, Ag NPs induced cell cycle arrest and apoptosis following a significant increase in oxidative stress in these cells. To further clarify the molecular mechanisms of the toxicological effects of Ag NPs at the transcriptional and post-transcriptional levels, the global expression profiles of genes and miRNAs were analyzed in hESC-derived NPCs after Ag NP exposure. The results showed that Ag NPs induced oxidative stress and dysfunctional neurogenesis at the molecular level in hESC-derived NPCs. Based on this hESC-derived neural cell model, these findings have increased our understanding of the molecular events underlying developmental neurotoxicity induced by Ag NPs in humans. - Highlights: • This system served as a suitable model for developmental neurotoxicity testing. • Ag NPs induce the apoptosis in human neural cells by ROS generation. • Genes for development of neurons were dysregulated in response to Ag NPs. • Molecular events during early developmental neurotoxicity were proposed.« less

A neutron spectrum unfolding computer code based on artificial neural networks

NASA Astrophysics Data System (ADS)

Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

2014-02-01

The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, the most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding in the HTML format. NSDann unfolding code is freely available, upon request to the authors.

Evolution of the hypoxia-sensitive cells involved in amniote respiratory reflexes

PubMed Central

Hockman, Dorit; Burns, Alan J; Schlosser, Gerhard; Gates, Keith P; Jevans, Benjamin; Mongera, Alessandro; Fisher, Shannon; Unlu, Gokhan; Knapik, Ela W; Kaufman, Charles K; Mosimann, Christian; Zon, Leonard I; Lancman, Joseph J; Dong, P Duc S; Lickert, Heiko; Tucker, Abigail S; Baker, Clare V H

2017-01-01

The evolutionary origins of the hypoxia-sensitive cells that trigger amniote respiratory reflexes – carotid body glomus cells, and ‘pulmonary neuroendocrine cells’ (PNECs) - are obscure. Homology has been proposed between glomus cells, which are neural crest-derived, and the hypoxia-sensitive ‘neuroepithelial cells’ (NECs) of fish gills, whose embryonic origin is unknown. NECs have also been likened to PNECs, which differentiate in situ within lung airway epithelia. Using genetic lineage-tracing and neural crest-deficient mutants in zebrafish, and physical fate-mapping in frog and lamprey, we find that NECs are not neural crest-derived, but endoderm-derived, like PNECs, whose endodermal origin we confirm. We discover neural crest-derived catecholaminergic cells associated with zebrafish pharyngeal arch blood vessels, and propose a new model for amniote hypoxia-sensitive cell evolution: endoderm-derived NECs were retained as PNECs, while the carotid body evolved via the aggregation of neural crest-derived catecholaminergic (chromaffin) cells already associated with blood vessels in anamniote pharyngeal arches. DOI: http://dx.doi.org/10.7554/eLife.21231.001 PMID:28387645

DCS-Neural-Network Program for Aircraft Control and Testing

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C.

2006-01-01

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

Genetic learning in rule-based and neural systems

NASA Technical Reports Server (NTRS)

Smith, Robert E.

1993-01-01

The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells.

PubMed

Kusumoto, Dai; Lachmann, Mark; Kunihiro, Takeshi; Yuasa, Shinsuke; Kishino, Yoshikazu; Kimura, Mai; Katsuki, Toshiomi; Itoh, Shogo; Seki, Tomohisa; Fukuda, Keiichi

2018-06-05

Deep learning technology is rapidly advancing and is now used to solve complex problems. Here, we used deep learning in convolutional neural networks to establish an automated method to identify endothelial cells derived from induced pluripotent stem cells (iPSCs), without the need for immunostaining or lineage tracing. Networks were trained to predict whether phase-contrast images contain endothelial cells based on morphology only. Predictions were validated by comparison to immunofluorescence staining for CD31, a marker of endothelial cells. Method parameters were then automatically and iteratively optimized to increase prediction accuracy. We found that prediction accuracy was correlated with network depth and pixel size of images to be analyzed. Finally, K-fold cross-validation confirmed that optimized convolutional neural networks can identify endothelial cells with high performance, based only on morphology. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Stability analysis for stochastic BAM nonlinear neural network with delays

NASA Astrophysics Data System (ADS)

Lv, Z. W.; Shu, H. S.; Wei, G. L.

2008-02-01

In this paper, stochastic bidirectional associative memory neural networks with constant or time-varying delays is considered. Based on a Lyapunov-Krasovskii functional and the stochastic stability analysis theory, we derive several sufficient conditions in order to guarantee the global asymptotically stable in the mean square. Our investigation shows that the stochastic bidirectional associative memory neural networks are globally asymptotically stable in the mean square if there are solutions to some linear matrix inequalities(LMIs). Hence, the global asymptotic stability of the stochastic bidirectional associative memory neural networks can be easily checked by the Matlab LMI toolbox. A numerical example is given to demonstrate the usefulness of the proposed global asymptotic stability criteria.

Lag Synchronization of Switched Neural Networks via Neural Activation Function and Applications in Image Encryption.

PubMed

Wen, Shiping; Zeng, Zhigang; Huang, Tingwen; Meng, Qinggang; Yao, Wei

2015-07-01

This paper investigates the problem of global exponential lag synchronization of a class of switched neural networks with time-varying delays via neural activation function and applications in image encryption. The controller is dependent on the output of the system in the case of packed circuits, since it is hard to measure the inner state of the circuits. Thus, it is critical to design the controller based on the neuron activation function. Comparing the results, in this paper, with the existing ones shows that we improve and generalize the results derived in the previous literature. Several examples are also given to illustrate the effectiveness and potential applications in image encryption.

Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control.

PubMed

Yang, Shiju; Li, Chuandong; Huang, Tingwen

2016-03-01

The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Global exponential stability for switched memristive neural networks with time-varying delays.

PubMed

Xin, Youming; Li, Yuxia; Cheng, Zunshui; Huang, Xia

2016-08-01

This paper considers the problem of exponential stability for switched memristive neural networks (MNNs) with time-varying delays. Different from most of the existing papers, we model a memristor as a continuous system, and view switched MNNs as switched neural networks with uncertain time-varying parameters. Based on average dwell time technique, mode-dependent average dwell time technique and multiple Lyapunov-Krasovskii functional approach, two conditions are derived to design the switching signal and guarantee the exponential stability of the considered neural networks, which are delay-dependent and formulated by linear matrix inequalities (LMIs). Finally, the effectiveness of the theoretical results is demonstrated by two numerical examples. Copyright © 2016 Elsevier Ltd. All rights reserved.

Constructing general partial differential equations using polynomial and neural networks.

PubMed

Zjavka, Ladislav; Pedrycz, Witold

2016-01-01

Sum fraction terms can approximate multi-variable functions on the basis of discrete observations, replacing a partial differential equation definition with polynomial elementary data relation descriptions. Artificial neural networks commonly transform the weighted sum of inputs to describe overall similarity relationships of trained and new testing input patterns. Differential polynomial neural networks form a new class of neural networks, which construct and solve an unknown general partial differential equation of a function of interest with selected substitution relative terms using non-linear multi-variable composite polynomials. The layers of the network generate simple and composite relative substitution terms whose convergent series combinations can describe partial dependent derivative changes of the input variables. This regression is based on trained generalized partial derivative data relations, decomposed into a multi-layer polynomial network structure. The sigmoidal function, commonly used as a nonlinear activation of artificial neurons, may transform some polynomial items together with the parameters with the aim to improve the polynomial derivative term series ability to approximate complicated periodic functions, as simple low order polynomials are not able to fully make up for the complete cycles. The similarity analysis facilitates substitutions for differential equations or can form dimensional units from data samples to describe real-world problems. Copyright © 2015 Elsevier Ltd. All rights reserved.

Improved cell therapy protocol for Parkinson’s disease based on differentiation efficiency and safety of hESC-, hiPSC and non-human primate iPSC-derived DA neurons

PubMed Central

Maria, Sundberg; Helle, Bogetofte; Tristan, Lawson; Gaynor, Smith; Arnar, Astradsson; Michele, Moore; Teresia, Osborn; Oliver, Cooper; Roger, Spealman; Penelope, Hallett; Ole, Isacson

2013-01-01

The main motor symptoms of Parkinson’s disease are due to the loss of dopaminergic (DA) neurons in the ventral midbrain (VM). For the future treatment of Parkinson’s disease with cell transplantation it is important to develop efficient differentiation methods for production of human iPSCs and hESCs-derived midbrain-type DA neurons. Here we describe an efficient differentiation and sorting strategy for DA-neurons from both human ES/iPS cells and non-human primate iPSCs. The use of non-human primate iPSCs for neuronal differentiation and autologous transplantation is important for pre-clinical evaluation of safety and efficacy of stem cell-derived DA neurons. The aim of this study was to improve the safety of human- and non-human primate-iPSC (PiPSC)-derived DA neurons. According to our results, NCAM+/CD29low sorting enriched VM DA-neurons from pluripotent stem cell-derived neural cell populations. NCAM+/CD29low DA-neurons were positive for FOXA2/TH and EN1/TH and this cell population had increased expression levels of FOXA2, LMX1A, TH, GIRK2, PITX3, EN1, NURR1 mRNA compared to unsorted neural cell populations. PiPSC-derived NCAM+/CD29low DA-neurons were able to restore motor function of 6-OHDA lesioned rats 16 weeks after transplantation. The transplanted sorted cells also integrated in the rodent brain tissue, with robust TH+/hNCAM+ neuritic innervation of the host striatum. One year after autologous transplantation, the primate iPSC-derived neural cells survived in the striatum of one primate without any immunosuppression. These neural cell grafts contained FOXA2/TH-positive neurons in the graft site. This is an important proof of concept for the feasibility and safety of iPSC-derived cell transplantation therapies in the future. PMID:23666606

Containment control of networked autonomous underwater vehicles: A predictor-based neural DSC design.

PubMed

Peng, Zhouhua; Wang, Dan; Wang, Wei; Liu, Lu

2015-11-01

This paper investigates the containment control problem of networked autonomous underwater vehicles in the presence of model uncertainty and unknown ocean disturbances. A predictor-based neural dynamic surface control design method is presented to develop the distributed adaptive containment controllers, under which the trajectories of follower vehicles nearly converge to the dynamic convex hull spanned by multiple reference trajectories over a directed network. Prediction errors, rather than tracking errors, are used to update the neural adaptation laws, which are independent of the tracking error dynamics, resulting in two time-scales to govern the entire system. The stability property of the closed-loop network is established via Lyapunov analysis, and transient property is quantified in terms of L2 norms of the derivatives of neural weights, which are shown to be smaller than the classical neural dynamic surface control approach. Comparative studies are given to show the substantial improvements of the proposed new method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks.

PubMed

Erfanian Saeedi, Nafise; Blamey, Peter J; Burkitt, Anthony N; Grayden, David B

2016-04-01

Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons' action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy.

Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks

PubMed Central

Erfanian Saeedi, Nafise; Blamey, Peter J.; Burkitt, Anthony N.; Grayden, David B.

2016-01-01

Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons’ action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy. PMID:27049657

Integrative analysis of genes and miRNA alterations in human embryonic stem cells-derived neural cells after exposure to silver nanoparticles.

PubMed

Oh, Jung-Hwa; Son, Mi-Young; Choi, Mi-Sun; Kim, Soojin; Choi, A-Young; Lee, Hyang-Ae; Kim, Ki-Suk; Kim, Janghwan; Song, Chang Woo; Yoon, Seokjoo

2016-05-15

Given the rapid growth of engineered and customer products made of silver nanoparticles (Ag NPs), understanding their biological and toxicological effects on humans is critically important. The molecular developmental neurotoxic effects associated with exposure to Ag NPs were analyzed at the physiological and molecular levels, using an alternative cell model: human embryonic stem cell (hESC)-derived neural stem/progenitor cells (NPCs). In this study, the cytotoxic effects of Ag NPs (10-200μg/ml) were examined in these hESC-derived NPCs, which have a capacity for neurogenesis in vitro, at 6 and 24h. The results showed that Ag NPs evoked significant toxicity in hESC-derived NPCs at 24h in a dose-dependent manner. In addition, Ag NPs induced cell cycle arrest and apoptosis following a significant increase in oxidative stress in these cells. To further clarify the molecular mechanisms of the toxicological effects of Ag NPs at the transcriptional and post-transcriptional levels, the global expression profiles of genes and miRNAs were analyzed in hESC-derived NPCs after Ag NP exposure. The results showed that Ag NPs induced oxidative stress and dysfunctional neurogenesis at the molecular level in hESC-derived NPCs. Based on this hESC-derived neural cell model, these findings have increased our understanding of the molecular events underlying developmental neurotoxicity induced by Ag NPs in humans. Copyright © 2015 Elsevier Inc. All rights reserved.

Cell delamination in the mesencephalic neural fold and its implication for the origin of ectomesenchyme

PubMed Central

Lee, Raymond Teck Ho; Nagai, Hiroki; Nakaya, Yukiko; Sheng, Guojun; Trainor, Paul A.; Weston, James A.; Thiery, Jean Paul

2013-01-01

The neural crest is a transient structure unique to vertebrate embryos that gives rise to multiple lineages along the rostrocaudal axis. In cranial regions, neural crest cells are thought to differentiate into chondrocytes, osteocytes, pericytes and stromal cells, which are collectively termed ectomesenchyme derivatives, as well as pigment and neuronal derivatives. There is still no consensus as to whether the neural crest can be classified as a homogenous multipotent population of cells. This unresolved controversy has important implications for the formation of ectomesenchyme and for confirmation of whether the neural fold is compartmentalized into distinct domains, each with a different repertoire of derivatives. Here we report in mouse and chicken that cells in the neural fold delaminate over an extended period from different regions of the cranial neural fold to give rise to cells with distinct fates. Importantly, cells that give rise to ectomesenchyme undergo epithelial-mesenchymal transition from a lateral neural fold domain that does not express definitive neural markers, such as Sox1 and N-cadherin. Additionally, the inference that cells originating from the cranial neural ectoderm have a common origin and cell fate with trunk neural crest cells prompted us to revisit the issue of what defines the neural crest and the origin of the ectomesenchyme. PMID:24198279

Altered proliferation and networks in neural cells derived from idiopathic autistic individuals.

PubMed

Marchetto, Maria C; Belinson, Haim; Tian, Yuan; Freitas, Beatriz C; Fu, Chen; Vadodaria, Krishna; Beltrao-Braga, Patricia; Trujillo, Cleber A; Mendes, Ana P D; Padmanabhan, Krishnan; Nunez, Yanelli; Ou, Jing; Ghosh, Himanish; Wright, Rebecca; Brennand, Kristen; Pierce, Karen; Eichenfield, Lawrence; Pramparo, Tiziano; Eyler, Lisa; Barnes, Cynthia C; Courchesne, Eric; Geschwind, Daniel H; Gage, Fred H; Wynshaw-Boris, Anthony; Muotri, Alysson R

2017-06-01

Autism spectrum disorders (ASD) are common, complex and heterogeneous neurodevelopmental disorders. Cellular and molecular mechanisms responsible for ASD pathogenesis have been proposed based on genetic studies, brain pathology and imaging, but a major impediment to testing ASD hypotheses is the lack of human cell models. Here, we reprogrammed fibroblasts to generate induced pluripotent stem cells, neural progenitor cells (NPCs) and neurons from ASD individuals with early brain overgrowth and non-ASD controls with normal brain size. ASD-derived NPCs display increased cell proliferation because of dysregulation of a β-catenin/BRN2 transcriptional cascade. ASD-derived neurons display abnormal neurogenesis and reduced synaptogenesis leading to functional defects in neuronal networks. Interestingly, defects in neuronal networks could be rescued by insulin growth factor 1 (IGF-1), a drug that is currently in clinical trials for ASD. This work demonstrates that selection of ASD subjects based on endophenotypes unraveled biologically relevant pathway disruption and revealed a potential cellular mechanism for the therapeutic effect of IGF-1.

A comparison between HMLP and HRBF for attitude control.

PubMed

Fortuna, L; Muscato, G; Xibilia, M G

2001-01-01

In this paper the problem of controlling the attitude of a rigid body, such as a Spacecraft, in three-dimensional space is approached by introducing two new control strategies developed in hypercomplex algebra. The proposed approaches are based on two parallel controllers, both derived in quaternion algebra. The first is a feedback controller of the proportional derivative (PD) type, while the second is a feedforward controller, which is implemented either by means of a hypercomplex multilayer perceptron (HMLP) neural network or by means of a hypercomplex radial basis function (HRBF) neural network. Several simulations show the performance of the two approaches. The results are also compared with a classical PD controller and with an adaptive controller, showing the improvements obtained by using neural networks, especially when an external disturbance acts on the rigid body. In particular the HMLP network gave better results when considering trajectories not presented during the learning phase.

On the utility of the ionosonde Doppler derived EXB drift during the daytime

NASA Astrophysics Data System (ADS)

Mohan Joshi, Lalit; Sripathi, Samireddipelle

2016-07-01

Vertical EXB drift measured using the ionosonde Doppler sounding during the daytime suffers from an underestimation of the actual EXB drift. This is due to the photochemistry that determines the height of the F layer during the daytime, in addition to the zonal electric field. Systematic investigations have indicated a fair/good correlation to exist between the C/NOFS and ionosonde Doppler measured vertical EXB drift during the daytime over magnetic equator. A detailed analysis, however, indicated that the linear relation between the ionosonde Doppler drift and C/NOFS EXB drift varied with seasons. Thus, solar, seasonal and also geomagnetic variables were included in the Doppler drift correction, using the artificial neural network based approach. The RMS error in the neural network was found to be lesser than that in the linear regression analysis. Daytime EXB drift was derived using the neural network which was also used to model the ionospheic redistribution in the SAMI2 model. SAMI2 model reproduced strong (/weak) equatorial ionization anomaly (EIA) for cases when neural network corrected daytime vertical EXB drift was high (/low). Similar features were also observed in GIM TEC maps. Thus, the results indicate that the neural network can be utilized to derive the vertical EXB drift from its proxies, like the ionosonde Doppler drift. These results indicate that the daytime ionosonde measured vertical EXB drift can be relied upon, provided adequate corrections are applied to it.

Cre-driver lines used for genetic fate mapping of neural crest cells in the mouse: An overview.

PubMed

Debbache, Julien; Parfejevs, Vadims; Sommer, Lukas

2018-04-19

The neural crest is one of the embryonic structures with the broadest developmental potential in vertebrates. Morphologically, neural crest cells emerge during neurulation in the dorsal folds of the neural tube before undergoing an epithelial-to-mesenchymal transition (EMT), delaminating from the neural tube, and migrating to multiple sites in the growing embryo. Neural crest cells generate cell types as diverse as peripheral neurons and glia, melanocytes, and so-called mesectodermal derivatives that include craniofacial bone and cartilage and smooth muscle cells in cardiovascular structures. In mice, the fate of neural crest cells has been determined mainly by means of transgenesis and genome editing technologies. The most frequently used method relies on the Cre-loxP system, in which expression of Cre-recombinase in neural crest cells or their derivatives genetically enables the expression of a Cre-reporter allele, thus permanently marking neural crest-derived cells. Here, we provide an overview of the Cre-driver lines used in the field and discuss to what extent these lines allow precise neural crest stage and lineage-specific fate mapping. © 2018 The Authors Genesis: The Journal of Genetics and Development Published by Wiley Periodicals, Inc.

Signal recognition efficiencies of artificial neural-network pulse-shape discrimination in HPGe -decay searches

NASA Astrophysics Data System (ADS)

Caldwell, A.; Cossavella, F.; Majorovits, B.; Palioselitis, D.; Volynets, O.

2015-07-01

A pulse-shape discrimination method based on artificial neural networks was applied to pulses simulated for different background, signal and signal-like interactions inside a germanium detector. The simulated pulses were used to investigate variations of efficiencies as a function of used training set. It is verified that neural networks are well-suited to identify background pulses in true-coaxial high-purity germanium detectors. The systematic uncertainty on the signal recognition efficiency derived using signal-like evaluation samples from calibration measurements is estimated to be 5 %. This uncertainty is due to differences between signal and calibration samples.

A Prior for Neural Networks utilizing Enclosing Spheres for Normalization

NASA Astrophysics Data System (ADS)

v. Toussaint, U.; Gori, S.; Dose, V.

2004-11-01

Neural Networks are famous for their advantageous flexibility for problems when there is insufficient knowledge to set up a proper model. On the other hand this flexibility can cause over-fitting and can hamper the generalization properties of neural networks. Many approaches to regularize NN have been suggested but most of them based on ad-hoc arguments. Employing the principle of transformation invariance we derive a general prior in accordance with the Bayesian probability theory for a class of feedforward networks. Optimal networks are determined by Bayesian model comparison verifying the applicability of this approach.

Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface.

PubMed

Lajoie, Guillaume; Krouchev, Nedialko I; Kalaska, John F; Fairhall, Adrienne L; Fetz, Eberhard E

2017-02-01

Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP) rules, suggesting that STDP is key to drive these changes. However, the impact of STDP at the level of circuits, and the mechanisms governing its modification with neural implants remain poorly understood. The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. Our model successfully reproduces key experimental results, both established and new, and offers mechanistic insights into spike-triggered conditioning. Using analytical calculations and numerical simulations, we derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered conditioning in different regimes of cortical activity.

Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface

PubMed Central

Lajoie, Guillaume; Kalaska, John F.; Fairhall, Adrienne L.; Fetz, Eberhard E.

2017-01-01

Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP) rules, suggesting that STDP is key to drive these changes. However, the impact of STDP at the level of circuits, and the mechanisms governing its modification with neural implants remain poorly understood. The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. Our model successfully reproduces key experimental results, both established and new, and offers mechanistic insights into spike-triggered conditioning. Using analytical calculations and numerical simulations, we derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered conditioning in different regimes of cortical activity. PMID:28151957

Based on interval type-2 fuzzy-neural network direct adaptive sliding mode control for SISO nonlinear systems

NASA Astrophysics Data System (ADS)

Lin, Tsung-Chih

2010-12-01

In this paper, a novel direct adaptive interval type-2 fuzzy-neural tracking control equipped with sliding mode and Lyapunov synthesis approach is proposed to handle the training data corrupted by noise or rule uncertainties for nonlinear SISO nonlinear systems involving external disturbances. By employing adaptive fuzzy-neural control theory, the update laws will be derived for approximating the uncertain nonlinear dynamical system. In the meantime, the sliding mode control method and the Lyapunov stability criterion are incorporated into the adaptive fuzzy-neural control scheme such that the derived controller is robust with respect to unmodeled dynamics, external disturbance and approximation errors. In comparison with conventional methods, the advocated approach not only guarantees closed-loop stability but also the output tracking error of the overall system will converge to zero asymptotically without prior knowledge on the upper bound of the lumped uncertainty. Furthermore, chattering effect of the control input will be substantially reduced by the proposed technique. To illustrate the performance of the proposed method, finally simulation example will be given.

DeepMoon: Convolutional neural network trainer to identify moon craters

NASA Astrophysics Data System (ADS)

Silburt, Ari; Zhu, Chenchong; Ali-Dib, Mohamad; Menou, Kristen; Jackson, Alan

2018-05-01

DeepMoon trains a convolutional neural net using data derived from a global digital elevation map (DEM) and catalog of craters to recognize craters on the Moon. The TensorFlow-based pipeline code is divided into three parts. The first generates a set images of the Moon randomly cropped from the DEM, with corresponding crater positions and radii. The second trains a convnet using this data, and the third validates the convnet's predictions.

Neural Crest-Derived Mesenchymal Cells Require Wnt Signaling for Their Development and Drive Invagination of the Telencephalic Midline

PubMed Central

Choe, Youngshik; Zarbalis, Konstantinos S.; Pleasure, Samuel J.

2014-01-01

Embryonic neural crest cells contribute to the development of the craniofacial mesenchyme, forebrain meninges and perivascular cells. In this study, we investigated the function of ß-catenin signaling in neural crest cells abutting the dorsal forebrain during development. In the absence of ß-catenin signaling, neural crest cells failed to expand in the interhemispheric region and produced ectopic smooth muscle cells instead of generating dermal and calvarial mesenchyme. In contrast, constitutive expression of stabilized ß-catenin in neural crest cells increased the number of mesenchymal lineage precursors suggesting that ß-catenin signaling is necessary for the expansion of neural crest-derived mesenchymal cells. Interestingly, the loss of neural crest-derived mesenchymal stem cells (MSCs) leads to failure of telencephalic midline invagination and causes ventricular system defects. This study shows that ß-catenin signaling is required for the switch of neural crest cells to MSCs and mediates the expansion of MSCs to drive the formation of mesenchymal structures of the head. Furthermore, loss of these structures causes striking defects in forebrain morphogenesis. PMID:24516524

The origin and evolution of the neural crest

PubMed Central

Donoghue, Philip C. J.; Graham, Anthony; Kelsh, Robert N.

2009-01-01

Summary Many of the features that distinguish the vertebrates from other chordates are derived from the neural crest, and it has long been argued that the emergence of this multipotent embryonic population was a key innovation underpinning vertebrate evolution. More recently, however, a number of studies have suggested that the evolution of the neural crest was less sudden than previously believed. This has exposed the fact that neural crest, as evidenced by its repertoire of derivative cell types, has evolved through vertebrate evolution. In this light, attempts to derive a typological definition of neural crest, in terms of molecular signatures or networks, are unfounded. We propose a less restrictive, embryological definition of this cell type that facilitates, rather than precludes, investigating the evolution of neural crest. While the evolutionary origin of neural crest has attracted much attention, its subsequent evolution has received almost no attention and yet it is more readily open to experimental investigation and has greater relevance to understanding vertebrate evolution. Finally, we provide a brief outline of how the evolutionary emergence of neural crest potentiality may have proceeded, and how it may be investigated. PMID:18478530

Accelerating glioblastoma drug discovery: Convergence of patient-derived models, genome editing and phenotypic screening.

PubMed

O'Duibhir, Eoghan; Carragher, Neil O; Pollard, Steven M

2017-04-01

Patients diagnosed with glioblastoma (GBM) continue to face a bleak prognosis. It is critical that new effective therapeutic strategies are developed. GBM stem cells have molecular hallmarks of neural stem and progenitor cells and it is possible to propagate both non-transformed normal neural stem cells and GBM stem cells, in defined, feeder-free, adherent culture. These primary stem cell lines provide an experimental model that is ideally suited to cell-based drug discovery or genetic screens in order to identify tumour-specific vulnerabilities. For many solid tumours, including GBM, the genetic disruptions that drive tumour initiation and growth have now been catalogued. CRISPR/Cas-based genome editing technologies have recently emerged, transforming our ability to functionally annotate the human genome. Genome editing opens prospects for engineering precise genetic changes in normal and GBM-derived neural stem cells, which will provide more defined and reliable genetic models, with critical matched pairs of isogenic cell lines. Generation of more complex alleles such as knock in tags or fluorescent reporters is also now possible. These new cellular models can be deployed in cell-based phenotypic drug discovery (PDD). Here we discuss the convergence of these advanced technologies (iPS cells, neural stem cell culture, genome editing and high content phenotypic screening) and how they herald a new era in human cellular genetics that should have a major impact in accelerating glioblastoma drug discovery. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Integrative approaches for modeling regulation and function of the respiratory system.

PubMed

Ben-Tal, Alona; Tawhai, Merryn H

2013-01-01

Mathematical models have been central to understanding the interaction between neural control and breathing. Models of the entire respiratory system-which comprises the lungs and the neural circuitry that controls their ventilation-have been derived using simplifying assumptions to compartmentalize each component of the system and to define the interactions between components. These full system models often rely-through necessity-on empirically derived relationships or parameters, in addition to physiological values. In parallel with the development of whole respiratory system models are mathematical models that focus on furthering a detailed understanding of the neural control network, or of the several functions that contribute to gas exchange within the lung. These models are biophysically based, and rely on physiological parameters. They include single-unit models for a breathing lung or neural circuit, through to spatially distributed models of ventilation and perfusion, or multicircuit models for neural control. The challenge is to bring together these more recent advances in models of neural control with models of lung function, into a full simulation for the respiratory system that builds upon the more detailed models but remains computationally tractable. This requires first understanding the mathematical models that have been developed for the respiratory system at different levels, and which could be used to study how physiological levels of O2 and CO2 in the blood are maintained. Copyright © 2013 Wiley Periodicals, Inc.

Periodicity and stability for variable-time impulsive neural networks.

PubMed

Li, Hongfei; Li, Chuandong; Huang, Tingwen

2017-10-01

The paper considers a general neural networks model with variable-time impulses. It is shown that each solution of the system intersects with every discontinuous surface exactly once via several new well-proposed assumptions. Moreover, based on the comparison principle, this paper shows that neural networks with variable-time impulse can be reduced to the corresponding neural network with fixed-time impulses under well-selected conditions. Meanwhile, the fixed-time impulsive systems can be regarded as the comparison system of the variable-time impulsive neural networks. Furthermore, a series of sufficient criteria are derived to ensure the existence and global exponential stability of periodic solution of variable-time impulsive neural networks, and to illustrate the same stability properties between variable-time impulsive neural networks and the fixed-time ones. The new criteria are established by applying Schaefer's fixed point theorem combined with the use of inequality technique. Finally, a numerical example is presented to show the effectiveness of the proposed results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Development of teeth in chick embryos after mouse neural crest transplantations.

PubMed

Mitsiadis, Thimios A; Chéraud, Yvonnick; Sharpe, Paul; Fontaine-Pérus, Josiane

2003-05-27

Teeth were lost in birds 70-80 million years ago. Current thinking holds that it is the avian cranial neural crest-derived mesenchyme that has lost odontogenic capacity, whereas the oral epithelium retains the signaling properties required to induce odontogenesis. To investigate the odontogenic capacity of ectomesenchyme, we have used neural tube transplantations from mice to chick embryos to replace the chick neural crest cell populations with mouse neural crest cells. The mouse/chick chimeras obtained show evidence of tooth formation showing that avian oral epithelium is able to induce a nonavian developmental program in mouse neural crest-derived mesenchymal cells.

Chitosan derived co-spheroids of neural stem cells and mesenchymal stem cells for neural regeneration.

PubMed

Han, Hao-Wei; Hsu, Shan-Hui

2017-10-01

Chitosan has been considered as candidate biomaterials for neural applications. The effective treatment of neurodegeneration or injury to the central nervous system (CNS) is still in lack nowadays. Adult neural stem cells (NSCs) represents a promising cell source to treat the CNS diseases but they are limited in number. Here, we developed the core-shell spheroids of NSCs (shell) and mesenchymal stem cells (MSCs, core) by co-culturing cells on the chitosan surface. The NSCs in chitosan derived co-spheroids displayed a higher survival rate than those in NSC homo-spheroids. The direct interaction of NSCs with MSCs in the co-spheroids increased the Notch activity and differentiation tendency of NSCs. Meanwhile, the differentiation potential of MSCs in chitosan derived co-spheroids was significantly enhanced toward neural lineages. Furthermore, NSC homo-spheroids and NSC/MSC co-spheroids derived on chitosan were evaluated for their in vivo efficacy by the embryonic and adult zebrafish brain injury models. The locomotion activity of zebrafish receiving chitosan derived NSC homo-spheroids or NSC/MSC co-spheroids was partially rescued in both models. Meanwhile, the higher survival rate was observed in the group of adult zebrafish implanted with chitosan derived NSC/MSC co-spheroids as compared to NSC homo-spheroids. These evidences indicate that chitosan may provide an extracellular matrix-like environment to drive the interaction and the morphological assembly between NSCs and MSCs and promote their neural differentiation capacities, which can be used for neural regeneration. Copyright © 2017 Elsevier B.V. All rights reserved.

Connectivity inference from neural recording data: Challenges, mathematical bases and research directions.

PubMed

Magrans de Abril, Ildefons; Yoshimoto, Junichiro; Doya, Kenji

2018-06-01

This article presents a review of computational methods for connectivity inference from neural activity data derived from multi-electrode recordings or fluorescence imaging. We first identify biophysical and technical challenges in connectivity inference along the data processing pipeline. We then review connectivity inference methods based on two major mathematical foundations, namely, descriptive model-free approaches and generative model-based approaches. We investigate representative studies in both categories and clarify which challenges have been addressed by which method. We further identify critical open issues and possible research directions. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays.

PubMed

Chen, Chuan; Li, Lixiang; Peng, Haipeng; Yang, Yixian

2017-01-01

Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs) with both discrete delay and distributed delay (mixed delays). By means of a simple feedback controller and novel finite time synchronization analysis methods, several new criteria are derived to ensure the finite time synchronization of MCGNNs with mixed delays. The obtained criteria are very concise and easy to verify. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results.

Almost periodic cellular neural networks with neutral-type proportional delays

NASA Astrophysics Data System (ADS)

Xiao, Songlin

2018-03-01

This paper presents a new result on the existence, uniqueness and generalised exponential stability of almost periodic solutions for cellular neural networks with neutral-type proportional delays and D operator. Based on some novel differential inequality techniques, a testable condition is derived to ensure that all the state trajectories of the system converge to an almost periodic solution with a positive exponential convergence rate. The effectiveness of the obtained result is illustrated by a numerical example.

Mouse embryonic stem cell-derived cells reveal niches that support neuronal differentiation in the adult rat brain.

PubMed

Maya-Espinosa, Guadalupe; Collazo-Navarrete, Omar; Millán-Aldaco, Diana; Palomero-Rivero, Marcela; Guerrero-Flores, Gilda; Drucker-Colín, René; Covarrubias, Luis; Guerra-Crespo, Magdalena

2015-02-01

A neurogenic niche can be identified by the proliferation and differentiation of its naturally residing neural stem cells. However, it remains unclear whether "silent" neurogenic niches or regions suitable for neural differentiation, other than the areas of active neurogenesis, exist in the adult brain. Embryoid body (EB) cells derived from embryonic stem cells (ESCs) are endowed with a high potential to respond to specification and neuralization signals of the embryo. Hence, to identify microenvironments in the postnatal and adult rat brain with the capacity to support neuronal differentiation, we transplanted dissociated EB cells to conventional neurogenic and non-neurogenic regions. Our results show a neuronal differentiation pattern of EB cells that was dependent on the host region. Efficient neuronal differentiation of EB cells occurred within an adjacent region to the rostral migratory stream. EB cell differentiation was initially patchy and progressed toward an even distribution along the graft by 15-21 days post-transplantation, giving rise mostly to GABAergic neurons. EB cells in the striatum displayed a lower level of neuronal differentiation and derived into a significant number of astrocytes. Remarkably, when EB cells were transplanted to the striatum of adult rats after a local ischemic stroke, increased number of neuroblasts and neurons were observed. Unexpectedly, we determined that the adult substantia nigra pars compacta, considered a non-neurogenic area, harbors a robust neurogenic environment. Therefore, neurally uncommitted cells derived from ESCs can detect regions that support neuronal differentiation within the adult brain, a fundamental step for the development of stem cell-based replacement therapies. © 2014 AlphaMed Press.

Evolution of vertebrates: a view from the crest

PubMed Central

Bronner, Marianne E.

2016-01-01

The origin of vertebrates was accompanied by the advent of a novel cell type: the neural crest. Emerging from the central nervous system, these cells migrate to diverse locations and differentiate into numerous derivatives. By coupling morphological and gene regulatory information from vertebrates and other chordates, we describe how addition of the neural crest specification program may have enabled cells at the neural plate border to acquire multipotency and migratory ability. Analyzing the topology of the neural crest gene regulatory network can serve as a useful template for understanding vertebrate evolution, including elaboration of neural crest derivatives. PMID:25903629

Global stabilization analysis of inertial memristive recurrent neural networks with discrete and distributed delays.

PubMed

Wang, Leimin; Zeng, Zhigang; Ge, Ming-Feng; Hu, Junhao

2018-05-02

This paper deals with the stabilization problem of memristive recurrent neural networks with inertial items, discrete delays, bounded and unbounded distributed delays. First, for inertial memristive recurrent neural networks (IMRNNs) with second-order derivatives of states, an appropriate variable substitution method is invoked to transfer IMRNNs into a first-order differential form. Then, based on nonsmooth analysis theory, several algebraic criteria are established for the global stabilizability of IMRNNs under proposed feedback control, where the cases with both bounded and unbounded distributed delays are successfully addressed. Finally, the theoretical results are illustrated via the numerical simulations. Copyright © 2018 Elsevier Ltd. All rights reserved.

In vitro generation of three-dimensional substrate-adherent embryonic stem cell-derived neural aggregates for application in animal models of neurological disorders.

PubMed

Hargus, Gunnar; Cui, Yi-Fang; Dihné, Marcel; Bernreuther, Christian; Schachner, Melitta

2012-05-01

In vitro-differentiated embryonic stem (ES) cells comprise a useful source for cell replacement therapy, but the efficiency and safety of a translational approach are highly dependent on optimized protocols for directed differentiation of ES cells into the desired cell types in vitro. Furthermore, the transplantation of three-dimensional ES cell-derived structures instead of a single-cell suspension may improve graft survival and function by providing a beneficial microenvironment for implanted cells. To this end, we have developed a new method to efficiently differentiate mouse ES cells into neural aggregates that consist predominantly (>90%) of postmitotic neurons, neural progenitor cells, and radial glia-like cells. When transplanted into the excitotoxically lesioned striatum of adult mice, these substrate-adherent embryonic stem cell-derived neural aggregates (SENAs) showed significant advantages over transplanted single-cell suspensions of ES cell-derived neural cells, including improved survival of GABAergic neurons, increased cell migration, and significantly decreased risk of teratoma formation. Furthermore, SENAs mediated functional improvement after transplantation into animal models of Parkinson's disease and spinal cord injury. This unit describes in detail how SENAs are efficiently derived from mouse ES cells in vitro and how SENAs are isolated for transplantation. Furthermore, methods are presented for successful implantation of SENAs into animal models of Huntington's disease, Parkinson's disease, and spinal cord injury to study the effects of stem cell-derived neural aggregates in a disease context in vivo.

Neural differentiation of adipose-derived stem cells isolated from GFP transgenic mice

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fujimura, Juri; Department of Pediatrics, Nippon Medical School, Tokyo; E-mail: juri-f@nms.ac.jp

2005-07-22

Taking advantage of homogeneously marked cells from green fluorescent protein (GFP) transgenic mice, we have recently reported that adipose-derived stromal cells (ASCs) could differentiate into mesenchymal lineages in vitro. In this study, we performed neural induction using ASCs from GFP transgenic mice and were able to induce these ASCs into neuronal and glial cell lineages. Most of the neurally induced cells showed bipolar or multipolar appearance morphologically and expressed neuronal markers. Electron microscopy revealed their neuronal morphology. Some cells also showed glial phenotypes, as shown immunocytochemically. The present study clearly shows that ASCs derived from GFP transgenic mice differentiate intomore » neural lineages in vitro, suggesting that these cells might provide an ideal source for further neural stem cell research with possible therapeutic application for neurological disorders.« less

Requirement for Foxd3 in Maintenance of Neural Crest Progenitors

PubMed Central

Teng, Lu; Mundell, Nathan A.; Frist, Audrey Y.; Wang, Qiaohong; Labosky, Patricia A.

2008-01-01

Summary Understanding the molecular mechanisms of stem cell maintenance is critical for the ultimate goal of manipulating stem cells for treatment of disease. Foxd3 is required early in mouse embryogenesis; Foxd3−/− embryos fail around the time of implantation, cells of the inner cell mass cannot be maintained in vitro, and blastocyst-derived stem cell lines cannot be established. Here, we report that Foxd3 is required for maintenance of the multipotent mammalian neural crest. Using tissue specific deletion of Foxd3 in the neural crest, we show that Foxd3flox/−; Wnt1-Cre mice die perinatally with a catastrophic loss of neural crest-derived structures. Cranial neural crest tissues are either missing or severely reduced in size, the peripheral nervous system consists of reduced dorsal root ganglia and cranial nerves, and the entire gastrointestinal tract is devoid of neural crest derivatives. These results demonstrate a global role for this transcriptional repressor in all aspects of neural crest maintenance along the anterior-posterior axis, and establish an unprecedented molecular link between multiple divergent progenitor lineages of the mammalian embryo. PMID:18367558

Requirement for Foxd3 in the maintenance of neural crest progenitors.

PubMed

Teng, Lu; Mundell, Nathan A; Frist, Audrey Y; Wang, Qiaohong; Labosky, Patricia A

2008-05-01

Understanding the molecular mechanisms of stem cell maintenance is crucial for the ultimate goal of manipulating stem cells for the treatment of disease. Foxd3 is required early in mouse embryogenesis; Foxd3(-/-) embryos fail around the time of implantation, cells of the inner cell mass cannot be maintained in vitro, and blastocyst-derived stem cell lines cannot be established. Here, we report that Foxd3 is required for maintenance of the multipotent mammalian neural crest. Using tissue-specific deletion of Foxd3 in the neural crest, we show that Foxd3(flox/-); Wnt1-Cre mice die perinatally with a catastrophic loss of neural crest-derived structures. Cranial neural crest tissues are either missing or severely reduced in size, the peripheral nervous system consists of reduced dorsal root ganglia and cranial nerves, and the entire gastrointestinal tract is devoid of neural crest derivatives. These results demonstrate a global role for this transcriptional repressor in all aspects of neural crest maintenance along the anterior-posterior axis, and establish an unprecedented molecular link between multiple divergent progenitor lineages of the mammalian embryo.

Development of metamodels for predicting aerosol dispersion in ventilated spaces

NASA Astrophysics Data System (ADS)

Hoque, Shamia; Farouk, Bakhtier; Haas, Charles N.

2011-04-01

Artificial neural network (ANN) based metamodels were developed to describe the relationship between the design variables and their effects on the dispersion of aerosols in a ventilated space. A Hammersley sequence sampling (HSS) technique was employed to efficiently explore the multi-parameter design space and to build numerical simulation scenarios. A detailed computational fluid dynamics (CFD) model was applied to simulate these scenarios. The results derived from the CFD simulations were used to train and test the metamodels. Feed forward ANN's were developed to map the relationship between the inputs and the outputs. The predictive ability of the neural network based metamodels was compared to linear and quadratic metamodels also derived from the same CFD simulation results. The ANN based metamodel performed well in predicting the independent data sets including data generated at the boundaries. Sensitivity analysis showed that particle tracking time to residence time and the location of input and output with relation to the height of the room had more impact than the other dimensionless groups on particle behavior.

Machine Vision Within The Framework Of Collective Neural Assemblies

NASA Astrophysics Data System (ADS)

Gupta, Madan M.; Knopf, George K.

1990-03-01

The proposed mechanism for designing a robust machine vision system is based on the dynamic activity generated by the various neural populations embedded in nervous tissue. It is postulated that a hierarchy of anatomically distinct tissue regions are involved in visual sensory information processing. Each region may be represented as a planar sheet of densely interconnected neural circuits. Spatially localized aggregates of these circuits represent collective neural assemblies. Four dynamically coupled neural populations are assumed to exist within each assembly. In this paper we present a state-variable model for a tissue sheet derived from empirical studies of population dynamics. Each population is modelled as a nonlinear second-order system. It is possible to emulate certain observed physiological and psychophysiological phenomena of biological vision by properly programming the interconnective gains . Important early visual phenomena such as temporal and spatial noise insensitivity, contrast sensitivity and edge enhancement will be discussed for a one-dimensional tissue model.

Global asymptotical ω-periodicity of a fractional-order non-autonomous neural networks.

PubMed

Chen, Boshan; Chen, Jiejie

2015-08-01

We study the global asymptotic ω-periodicity for a fractional-order non-autonomous neural networks. Firstly, based on the Caputo fractional-order derivative it is shown that ω-periodic or autonomous fractional-order neural networks cannot generate exactly ω-periodic signals. Next, by using the contraction mapping principle we discuss the existence and uniqueness of S-asymptotically ω-periodic solution for a class of fractional-order non-autonomous neural networks. Then by using a fractional-order differential and integral inequality technique, we study global Mittag-Leffler stability and global asymptotical periodicity of the fractional-order non-autonomous neural networks, which shows that all paths of the networks, starting from arbitrary points and responding to persistent, nonconstant ω-periodic external inputs, asymptotically converge to the same nonconstant ω-periodic function that may be not a solution. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Calculation of soil water erosion modulus based on RUSLE and its assessment under support of artificial neural network].

PubMed

Li, Yuhuan; Wang, Jing; Zhang, Jixian

2006-06-01

With Hengshan County of Shanxi Province in the North Loess Plateau as an example, and by using ETM + and remote sensing data and RUSLE module, this paper quantitatively derived the soil and water loss in loess hilly region based on "3S" technology, and assessed the derivation results under the support of artificial neural network. The results showed that the annual average erosion modulus of Hengshan County was 103.23 t x hm(-2), and the gross erosion loss per year was 4. 38 x 10(7) t. The erosion was increased from northwest to southeast, and varied significantly with topographic position. A slight erosion or no erosion happened in walled basin, flat-headed mountain ridges and sandy area, which always suffered from dropping erosion, while strip erosion often happened on the upslope of mountain ridge and mountaintop flat. Moderate rill erosion always occurred on the middle and down slope of mountain ridge and mountaintop flat, and weighty rushing erosion occurred on the steep ravine and brink. The RUSLE model and artificial neural network technique were feasible and could be propagandized for drainage areas control and preserved practice.

Identification and characterization of secondary neural tube-derived embryonic neural stem cells in vitro.

PubMed

Shaker, Mohammed R; Kim, Joo Yeon; Kim, Hyun; Sun, Woong

2015-05-15

Secondary neurulation is an embryonic progress that gives rise to the secondary neural tube, the precursor of the lower spinal cord region. The secondary neural tube is derived from aggregated Sox2-expressing neural cells at the dorsal region of the tail bud, which eventually forms rosette or tube-like structures to give rise to neural tissues in the tail bud. We addressed whether the embryonic tail contains neural stem cells (NSCs), namely secondary NSCs (sNSCs), with the potential for self-renewal in vitro. Using in vitro neurosphere assays, neurospheres readily formed at the rosette and neural-tube levels, but less frequently at the tail bud tip level. Furthermore, we identified that sNSC-generated neurospheres were significantly smaller in size compared with cortical neurospheres. Interestingly, various cell cycle analyses revealed that this difference was not due to a reduction in the proliferation rate of NSCs, but rather the neuronal commitment of sNSCs, as sNSC-derived neurospheres contain more committed neuronal progenitor cells, even in the presence of epidermal growth factor (EGF) and basic fibroblast growth factor (bFGF). These results suggest that the higher tendency for sNSCs to spontaneously differentiate into progenitor cells may explain the limited expansion of the secondary neural tube during embryonic development.

Genetic disruption of CYP26B1 severely affects development of neural crest derived head structures, but does not compromise hindbrain patterning.

PubMed

Maclean, Glenn; Dollé, Pascal; Petkovich, Martin

2009-03-01

Cyp26b1 encodes a cytochrome-P450 enzyme that catabolizes retinoic acid (RA), a vitamin A derived signaling molecule. We have examined Cyp26b1(-/-) mice and report that mutants exhibit numerous abnormalities in cranial neural crest cell derived tissues. At embryonic day (E) 18.5 Cyp26b1(-/-) animals exhibit a truncated mandible, abnormal tooth buds, reduced ossification of calvaria, and are missing structures of the maxilla and nasal process. Some of these abnormalities may be due to defects in formation of Meckel's cartilage, which is truncated with an unfused distal region at E14.5 in mutant animals. Despite the severe malformations, we did not detect any abnormalities in rhombomere segmentation, or in patterning and migration of anterior hindbrain derived neural crest cells. Abnormal migration of neural crest cells toward the posterior branchial arches was observed, which may underlie defects in larynx and hyoid development. These data suggest different periods of sensitivity of anterior and posterior hindbrain neural crest derivatives to elevated levels of RA in the absence of CYP26B1. (c) 2009 Wiley-Liss, Inc.

A Markovian event-based framework for stochastic spiking neural networks.

PubMed

Touboul, Jonathan D; Faugeras, Olivier D

2011-11-01

In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.

Generalized Predictive and Neural Generalized Predictive Control of Aerospace Systems

NASA Technical Reports Server (NTRS)

Kelkar, Atul G.

2000-01-01

The research work presented in this thesis addresses the problem of robust control of uncertain linear and nonlinear systems using Neural network-based Generalized Predictive Control (NGPC) methodology. A brief overview of predictive control and its comparison with Linear Quadratic (LQ) control is given to emphasize advantages and drawbacks of predictive control methods. It is shown that the Generalized Predictive Control (GPC) methodology overcomes the drawbacks associated with traditional LQ control as well as conventional predictive control methods. It is shown that in spite of the model-based nature of GPC it has good robustness properties being special case of receding horizon control. The conditions for choosing tuning parameters for GPC to ensure closed-loop stability are derived. A neural network-based GPC architecture is proposed for the control of linear and nonlinear uncertain systems. A methodology to account for parametric uncertainty in the system is proposed using on-line training capability of multi-layer neural network. Several simulation examples and results from real-time experiments are given to demonstrate the effectiveness of the proposed methodology.

Approximately adaptive neural cooperative control for nonlinear multiagent systems with performance guarantee

NASA Astrophysics Data System (ADS)

Wang, Jing; Yang, Tianyu; Staskevich, Gennady; Abbe, Brian

2017-04-01

This paper studies the cooperative control problem for a class of multiagent dynamical systems with partially unknown nonlinear system dynamics. In particular, the control objective is to solve the state consensus problem for multiagent systems based on the minimisation of certain cost functions for individual agents. Under the assumption that there exist admissible cooperative controls for such class of multiagent systems, the formulated problem is solved through finding the optimal cooperative control using the approximate dynamic programming and reinforcement learning approach. With the aid of neural network parameterisation and online adaptive learning, our method renders a practically implementable approximately adaptive neural cooperative control for multiagent systems. Specifically, based on the Bellman's principle of optimality, the Hamilton-Jacobi-Bellman (HJB) equation for multiagent systems is first derived. We then propose an approximately adaptive policy iteration algorithm for multiagent cooperative control based on neural network approximation of the value functions. The convergence of the proposed algorithm is rigorously proved using the contraction mapping method. The simulation results are included to validate the effectiveness of the proposed algorithm.

A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2001-01-01

In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

Neural crest contributions to the lamprey head

NASA Technical Reports Server (NTRS)

McCauley, David W.; Bronner-Fraser, Marianne

2003-01-01

The neural crest is a vertebrate-specific cell population that contributes to the facial skeleton and other derivatives. We have performed focal DiI injection into the cranial neural tube of the developing lamprey in order to follow the migratory pathways of discrete groups of cells from origin to destination and to compare neural crest migratory pathways in a basal vertebrate to those of gnathostomes. The results show that the general pathways of cranial neural crest migration are conserved throughout the vertebrates, with cells migrating in streams analogous to the mandibular and hyoid streams. Caudal branchial neural crest cells migrate ventrally as a sheet of cells from the hindbrain and super-pharyngeal region of the neural tube and form a cylinder surrounding a core of mesoderm in each pharyngeal arch, similar to that seen in zebrafish and axolotl. In addition to these similarities, we also uncovered important differences. Migration into the presumptive caudal branchial arches of the lamprey involves both rostral and caudal movements of neural crest cells that have not been described in gnathostomes, suggesting that barriers that constrain rostrocaudal movement of cranial neural crest cells may have arisen after the agnathan/gnathostome split. Accordingly, neural crest cells from a single axial level contributed to multiple arches and there was extensive mixing between populations. There was no apparent filling of neural crest derivatives in a ventral-to-dorsal order, as has been observed in higher vertebrates, nor did we find evidence of a neural crest contribution to cranial sensory ganglia. These results suggest that migratory constraints and additional neural crest derivatives arose later in gnathostome evolution.

Embryonic origin and Hox status determine progenitor cell fate during adult bone regeneration.

PubMed

Leucht, Philipp; Kim, Jae-Beom; Amasha, Raimy; James, Aaron W; Girod, Sabine; Helms, Jill A

2008-09-01

The fetal skeleton arises from neural crest and from mesoderm. Here, we provide evidence that each lineage contributes a unique stem cell population to the regeneration of injured adult bones. Using Wnt1Cre::Z/EG mice we found that the neural crest-derived mandible heals with neural crest-derived skeletal stem cells, whereas the mesoderm-derived tibia heals with mesoderm-derived stem cells. We tested whether skeletal stem cells from each lineage were functionally interchangeable by grafting mesoderm-derived cells into mandibular defects, and vice versa. All of the grafting scenarios, except one, healed through the direct differentiation of skeletal stem cells into osteoblasts; when mesoderm-derived cells were transplanted into tibial defects they differentiated into osteoblasts but when transplanted into mandibular defects they differentiated into chondrocytes. A mismatch between the Hox gene expression status of the host and donor cells might be responsible for this aberration in bone repair. We found that initially, mandibular skeletal progenitor cells are Hox-negative but that they adopt a Hoxa11-positive profile when transplanted into a tibial defect. Conversely, tibial skeletal progenitor cells are Hox-positive and maintain this Hox status even when transplanted into a Hox-negative mandibular defect. Skeletal progenitor cells from the two lineages also show differences in osteogenic potential and proliferation, which translate into more robust in vivo bone regeneration by neural crest-derived cells. Thus, embryonic origin and Hox gene expression status distinguish neural crest-derived from mesoderm-derived skeletal progenitor cells, and both characteristics influence the process of adult bone regeneration.

Development and Tissue Origins of the Mammalian Cranial Base

PubMed Central

Iseki, S.; Bamforth, S. D.; Olsen, B. R.; Morriss-Kay, G. M.

2008-01-01

The vertebrate cranial base is a complex structure composed of bone, cartilage and other connective tissues underlying the brain; it is intimately connected with development of the face and cranial vault. Despite its central importance in craniofacial development, morphogenesis and tissue origins of the cranial base have not been studied in detail in the mouse, an important model organism. We describe here the location and time of appearance of the cartilages of the chondrocranium. We also examine the tissue origins of the mouse cranial base using a neural crest cell lineage cell marker, Wnt1-Cre/R26R, and a mesoderm lineage cell marker, Mesp1-Cre/R26R. The chondrocranium develops between E11 and E16 in the mouse, beginning with development of the caudal (occipital) chondrocranium, followed by chondrogenesis rostrally to form the nasal capsule, and finally fusion of these two parts via the midline central stem and the lateral struts of the vault cartilages. X-Gal staining of transgenic mice from E8.0 to 10 days post-natal showed that neural crest cells contribute to all of the cartilages that form the ethmoid, presphenoid, and basisphenoid bones with the exception of the hypochiasmatic cartilages. The basioccipital bone and non-squamous parts of the temporal bones are mesoderm derived. Therefore the prechordal head is mostly composed of neural crest-derived tissues, as predicted by the New Head Hypothesis. However, the anterior location of the mesoderm-derived hypochiasmatic cartilages, which are closely linked with the extra-ocular muscles, suggests that some tissues associated with the visual apparatus may have evolved independently of the rest of the “New Head”. PMID:18680740

A regularization approach to continuous learning with an application to financial derivatives pricing.

PubMed

Ormoneit, D

1999-12-01

We consider the training of neural networks in cases where the nonlinear relationship of interest gradually changes over time. One possibility to deal with this problem is by regularization where a variation penalty is added to the usual mean squared error criterion. To learn the regularized network weights we suggest the Iterative Extended Kalman Filter (IEKF) as a learning rule, which may be derived from a Bayesian perspective on the regularization problem. A primary application of our algorithm is in financial derivatives pricing, where neural networks may be used to model the dependency of the derivatives' price on one or several underlying assets. After giving a brief introduction to the problem of derivatives pricing we present experiments with German stock index options data showing that a regularized neural network trained with the IEKF outperforms several benchmark models and alternative learning procedures. In particular, the performance may be greatly improved using a newly designed neural network architecture that accounts for no-arbitrage pricing restrictions.

Training Data Requirement for a Neural Network to Predict Aerodynamic Coefficients

NASA Technical Reports Server (NTRS)

Korsmeyer, David (Technical Monitor); Rajkumar, T.; Bardina, Jorge

2003-01-01

Basic aerodynamic coefficients are modeled as functions of angle of attack, speed brake deflection angle, Mach number, and side slip angle. Most of the aerodynamic parameters can be well-fitted using polynomial functions. We previously demonstrated that a neural network is a fast, reliable way of predicting aerodynamic coefficients. We encountered few under fitted and/or over fitted results during prediction. The training data for the neural network are derived from wind tunnel test measurements and numerical simulations. The basic questions that arise are: how many training data points are required to produce an efficient neural network prediction, and which type of transfer functions should be used between the input-hidden layer and hidden-output layer. In this paper, a comparative study of the efficiency of neural network prediction based on different transfer functions and training dataset sizes is presented. The results of the neural network prediction reflect the sensitivity of the architecture, transfer functions, and training dataset size.

Predicting protein complex geometries with a neural network.

PubMed

Chae, Myong-Ho; Krull, Florian; Lorenzen, Stephan; Knapp, Ernst-Walter

2010-03-01

A major challenge of the protein docking problem is to define scoring functions that can distinguish near-native protein complex geometries from a large number of non-native geometries (decoys) generated with noncomplexed protein structures (unbound docking). In this study, we have constructed a neural network that employs the information from atom-pair distance distributions of a large number of decoys to predict protein complex geometries. We found that docking prediction can be significantly improved using two different types of polar hydrogen atoms. To train the neural network, 2000 near-native decoys of even distance distribution were used for each of the 185 considered protein complexes. The neural network normalizes the information from different protein complexes using an additional protein complex identity input neuron for each complex. The parameters of the neural network were determined such that they mimic a scoring funnel in the neighborhood of the native complex structure. The neural network approach avoids the reference state problem, which occurs in deriving knowledge-based energy functions for scoring. We show that a distance-dependent atom pair potential performs much better than a simple atom-pair contact potential. We have compared the performance of our scoring function with other empirical and knowledge-based scoring functions such as ZDOCK 3.0, ZRANK, ITScore-PP, EMPIRE, and RosettaDock. In spite of the simplicity of the method and its functional form, our neural network-based scoring function achieves a reasonable performance in rigid-body unbound docking of proteins. Proteins 2010. (c) 2009 Wiley-Liss, Inc.

Application of neural networks with orthogonal activation functions in control of dynamical systems

NASA Astrophysics Data System (ADS)

Nikolić, Saša S.; Antić, Dragan S.; Milojković, Marko T.; Milovanović, Miroslav B.; Perić, Staniša Lj.; Mitić, Darko B.

2016-04-01

In this article, we present a new method for the synthesis of almost and quasi-orthogonal polynomials of arbitrary order. Filters designed on the bases of these functions are generators of generalised quasi-orthogonal signals for which we derived and presented necessary mathematical background. Based on theoretical results, we designed and practically implemented generalised first-order (k = 1) quasi-orthogonal filter and proved its quasi-orthogonality via performed experiments. Designed filters can be applied in many scientific areas. In this article, generated functions were successfully implemented in Nonlinear Auto Regressive eXogenous (NARX) neural network as activation functions. One practical application of the designed orthogonal neural network is demonstrated through the example of control of the complex technical non-linear system - laboratory magnetic levitation system. Obtained results were compared with neural networks with standard activation functions and orthogonal functions of trigonometric shape. The proposed network demonstrated superiority over existing solutions in the sense of system performances.

Online Recorded Data-Based Composite Neural Control of Strict-Feedback Systems With Application to Hypersonic Flight Dynamics.

PubMed

Xu, Bin; Yang, Daipeng; Shi, Zhongke; Pan, Yongping; Chen, Badong; Sun, Fuchun

2017-09-25

This paper investigates the online recorded data-based composite neural control of uncertain strict-feedback systems using the backstepping framework. In each step of the virtual control design, neural network (NN) is employed for uncertainty approximation. In previous works, most designs are directly toward system stability ignoring the fact how the NN is working as an approximator. In this paper, to enhance the learning ability, a novel prediction error signal is constructed to provide additional correction information for NN weight update using online recorded data. In this way, the neural approximation precision is highly improved, and the convergence speed can be faster. Furthermore, the sliding mode differentiator is employed to approximate the derivative of the virtual control signal, and thus, the complex analysis of the backstepping design can be avoided. The closed-loop stability is rigorously established, and the boundedness of the tracking error can be guaranteed. Through simulation of hypersonic flight dynamics, the proposed approach exhibits better tracking performance.

The synchronous neural interactions test as a functional neuromarker for post-traumatic stress disorder (PTSD): a robust classification method based on the bootstrap

NASA Astrophysics Data System (ADS)

Georgopoulos, A. P.; Tan, H.-R. M.; Lewis, S. M.; Leuthold, A. C.; Winskowski, A. M.; Lynch, J. K.; Engdahl, B.

2010-02-01

Traumatic experiences can produce post-traumatic stress disorder (PTSD) which is a debilitating condition and for which no biomarker currently exists (Institute of Medicine (US) 2006 Posttraumatic Stress Disorder: Diagnosis and Assessment (Washington, DC: National Academies)). Here we show that the synchronous neural interactions (SNI) test which assesses the functional interactions among neural populations derived from magnetoencephalographic (MEG) recordings (Georgopoulos A P et al 2007 J. Neural Eng. 4 349-55) can successfully differentiate PTSD patients from healthy control subjects. Externally cross-validated, bootstrap-based analyses yielded >90% overall accuracy of classification. In addition, all but one of 18 patients who were not receiving medications for their disease were correctly classified. Altogether, these findings document robust differences in brain function between the PTSD and control groups that can be used for differential diagnosis and which possess the potential for assessing and monitoring disease progression and effects of therapy.

Adaptively combined FIR and functional link artificial neural network equalizer for nonlinear communication channel.

PubMed

Zhao, Haiquan; Zhang, Jiashu

2009-04-01

This paper proposes a novel computational efficient adaptive nonlinear equalizer based on combination of finite impulse response (FIR) filter and functional link artificial neural network (CFFLANN) to compensate linear and nonlinear distortions in nonlinear communication channel. This convex nonlinear combination results in improving the speed while retaining the lower steady-state error. In addition, since the CFFLANN needs not the hidden layers, which exist in conventional neural-network-based equalizers, it exhibits a simpler structure than the traditional neural networks (NNs) and can require less computational burden during the training mode. Moreover, appropriate adaptation algorithm for the proposed equalizer is derived by the modified least mean square (MLMS). Results obtained from the simulations clearly show that the proposed equalizer using the MLMS algorithm can availably eliminate various intensity linear and nonlinear distortions, and be provided with better anti-jamming performance. Furthermore, comparisons of the mean squared error (MSE), the bit error rate (BER), and the effect of eigenvalue ratio (EVR) of input correlation matrix are presented.

Generation of Oligodendrogenic Spinal Neural Progenitor Cells From Human Induced Pluripotent Stem Cells.

PubMed

Khazaei, Mohamad; Ahuja, Christopher S; Fehlings, Michael G

2017-08-14

This unit describes protocols for the efficient generation of oligodendrogenic neural progenitor cells (o-NPCs) from human induced pluripotent stem cells (hiPSCs). Specifically, detailed methods are provided for the maintenance and differentiation of hiPSCs, human induced pluripotent stem cell-derived neural progenitor cells (hiPS-NPCs), and human induced pluripotent stem cell-oligodendrogenic neural progenitor cells (hiPSC-o-NPCs) with the final products being suitable for in vitro experimentation or in vivo transplantation. Throughout, cell exposure to growth factors and patterning morphogens has been optimized for both concentration and timing, based on the literature and empirical experience, resulting in a robust and highly efficient protocol. Using this derivation procedure, it is possible to obtain millions of oligodendrogenic-NPCs within 40 days of initial cell plating which is substantially shorter than other protocols for similar cell types. This protocol has also been optimized to use translationally relevant human iPSCs as the parent cell line. The resultant cells have been extensively characterized both in vitro and in vivo and express key markers of an oligodendrogenic lineage. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley and Sons, Inc.

The Neural-fuzzy Thermal Error Compensation Controller on CNC Machining Center

NASA Astrophysics Data System (ADS)

Tseng, Pai-Chung; Chen, Shen-Len

The geometric errors and structural thermal deformation are factors that influence the machining accuracy of Computer Numerical Control (CNC) machining center. Therefore, researchers pay attention to thermal error compensation technologies on CNC machine tools. Some real-time error compensation techniques have been successfully demonstrated in both laboratories and industrial sites. The compensation results still need to be enhanced. In this research, the neural-fuzzy theory has been conducted to derive a thermal prediction model. An IC-type thermometer has been used to detect the heat sources temperature variation. The thermal drifts are online measured by a touch-triggered probe with a standard bar. A thermal prediction model is then derived by neural-fuzzy theory based on the temperature variation and the thermal drifts. A Graphic User Interface (GUI) system is also built to conduct the user friendly operation interface with Insprise C++ Builder. The experimental results show that the thermal prediction model developed by neural-fuzzy theory methodology can improve machining accuracy from 80µm to 3µm. Comparison with the multi-variable linear regression analysis the compensation accuracy is increased from ±10µm to ±3µm.

A neural network to retrieve the mesoscale instantaneous latent heat flux over oceans from SSM/I observations

NASA Technical Reports Server (NTRS)

Bourras, D.; Eymard, L.; Liu, W. T.

2000-01-01

The turbulent latent and sensible heat fluxes are necessary to study heat budget of the upper ocean or initialize ocean general circulation models. In order to retrieve the latent heat flux from satellite observations authors mostly use a bulk approximation of the flux whose parameters are derived from different instrument. In this paper, an approach based on artificial neural networks is proposed and compared to the bulk method on a global data set and 3 local data sets.

p75 neurotrophin receptor positive dental pulp stem cells: new hope for patients with neurodegenerative disease and neural injury.

PubMed

Dai, Jie-wen; Yuan, Hao; Shen, Shun-yao; Lu, Jing-ting; Zhu, Xiao-fang; Yang, Tong; Zhang, Jiang-fei; Shen, Guo-fang

2013-08-01

Neurodegenerative diseases and neural injury are 2 of the most feared disorders that afflict humankind by leading to permanent paralysis and loss of sensation. Cell based treatment for these diseases had gained special interest in recent years. Previous studies showed that dental pulp stem cells (DPSCs) could differentiate toward functionally active neurons both in vitro and in vivo, and could promote neuranagenesis through both cell-autonomous and paracrine neuroregenerative activities. Some of these neuroregenerative activities were unique to tooth-derived stem cells and superior to bone marrow stromal cells. However, DPSCs used in most of these studies were mixed and unfractionated dental pulp cells that contain several types of cells, and most were fibroblast cells while just contain a small portion of DPSCs. Thus, there might be weaker ability of neuranagenesis and more side effects from the fibroblast cells that cannot differentiate into neural cells. p75 neurotrophin receptor (p75NTR) positive DPSCs subpopulation was derived from migrating cranial neural crest cells and had been isolated from DPSCs, which had capacity of differentiation into neurons and repairing neural system. In this article, we hypothesize that p75NTR positive DPSCs simultaneously have greater propensity for neuronal differentiation and fewer side effects from fibroblast, and in vivo transptantation of autologous p75NTR positive DPSCs is a novel method for neuranagenesis. This will bring great hope to patients with neurodegenerative disease and neural injury.

Analysis of Neural Stem Cells from Human Cortical Brain Structures In Vitro.

PubMed

Aleksandrova, M A; Poltavtseva, R A; Marei, M V; Sukhikh, G T

2016-05-01

Comparative immunohistochemical analysis of the neocortex from human fetuses showed that neural stem and progenitor cells are present in the brain throughout the gestation period, at least from week 8 through 26. At the same time, neural stem cells from the first and second trimester fetuses differed by the distribution, morphology, growth, and quantity. Immunocytochemical analysis of neural stem cells derived from fetuses at different gestation terms and cultured under different conditions showed their differentiation capacity. Detailed analysis of neural stem cell populations derived from fetuses on gestation weeks 8-9, 18-20, and 26 expressing Lex/SSEA1 was performed.

Network-centric decision architecture for financial or 1/f data models

NASA Astrophysics Data System (ADS)

Jaenisch, Holger M.; Handley, James W.; Massey, Stoney; Case, Carl T.; Songy, Claude G.

2002-12-01

This paper presents a decision architecture algorithm for training neural equation based networks to make autonomous multi-goal oriented, multi-class decisions. These architectures make decisions based on their individual goals and draw from the same network centric feature set. Traditionally, these architectures are comprised of neural networks that offer marginal performance due to lack of convergence of the training set. We present an approach for autonomously extracting sample points as I/O exemplars for generation of multi-branch, multi-node decision architectures populated by adaptively derived neural equations. To test the robustness of this architecture, open source data sets in the form of financial time series were used, requiring a three-class decision space analogous to the lethal, non-lethal, and clutter discrimination problem. This algorithm and the results of its application are presented here.

Efficient and rapid derivation of primitive neural stem cells and generation of brain subtype neurons from human pluripotent stem cells.

PubMed

Yan, Yiping; Shin, Soojung; Jha, Balendu Shekhar; Liu, Qiuyue; Sheng, Jianting; Li, Fuhai; Zhan, Ming; Davis, Janine; Bharti, Kapil; Zeng, Xianmin; Rao, Mahendra; Malik, Nasir; Vemuri, Mohan C

2013-11-01

Human pluripotent stem cells (hPSCs), including human embryonic stem cells and human induced pluripotent stem cells, are unique cell sources for disease modeling, drug discovery screens, and cell therapy applications. The first step in producing neural lineages from hPSCs is the generation of neural stem cells (NSCs). Current methods of NSC derivation involve the time-consuming, labor-intensive steps of an embryoid body generation or coculture with stromal cell lines that result in low-efficiency derivation of NSCs. In this study, we report a highly efficient serum-free pluripotent stem cell neural induction medium that can induce hPSCs into primitive NSCs (pNSCs) in 7 days, obviating the need for time-consuming, laborious embryoid body generation or rosette picking. The pNSCs expressed the neural stem cell markers Pax6, Sox1, Sox2, and Nestin; were negative for Oct4; could be expanded for multiple passages; and could be differentiated into neurons, astrocytes, and oligodendrocytes, in addition to the brain region-specific neuronal subtypes GABAergic, dopaminergic, and motor neurons. Global gene expression of the transcripts of pNSCs was comparable to that of rosette-derived and human fetal-derived NSCs. This work demonstrates an efficient method to generate expandable pNSCs, which can be further differentiated into central nervous system neurons and glia with temporal, spatial, and positional cues of brain regional heterogeneity. This method of pNSC derivation sets the stage for the scalable production of clinically relevant neural cells for cell therapy applications in good manufacturing practice conditions.

Functional electrical stimulation controlled by artificial neural networks: pilot experiments with simple movements are promising for rehabilitation applications.

PubMed

Ferrante, Simona; Pedrocchi, Alessandra; Iannò, Marco; De Momi, Elena; Ferrarin, Maurizio; Ferrigno, Giancarlo

2004-01-01

This study falls within the ambit of research on functional electrical stimulation for the design of rehabilitation training for spinal cord injured patients. In this context, a crucial issue is the control of the stimulation parameters in order to optimize the patterns of muscle activation and to increase the duration of the exercises. An adaptive control system (NEURADAPT) based on artificial neural networks (ANNs) was developed to control the knee joint in accordance with desired trajectories by stimulating quadriceps muscles. This strategy includes an inverse neural model of the stimulated limb in the feedforward line and a neural network trained on-line in the feedback loop. NEURADAPT was compared with a linear closed-loop proportional integrative derivative (PID) controller and with a model-based neural controller (NEUROPID). Experiments on two subjects (one healthy and one paraplegic) show the good performance of NEURADAPT, which is able to reduce the time lag introduced by the PID controller. In addition, control systems based on ANN techniques do not require complicated calibration procedures at the beginning of each experimental session. After the initial learning phase, the ANN, thanks to its generalization capacity, is able to cope with a certain range of variability of skeletal muscle properties.

Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.

PubMed

Nitta, Tohru

2017-10-01

We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).

Relationship between ketamine-induced developmental neurotoxicity and NMDA receptor-mediated calcium influx in neural stem cell-derived neurons.

PubMed

Wang, Cheng; Liu, Fang; Patterson, Tucker A; Paule, Merle G; Slikker, William

2017-05-01

Ketamine, a noncompetitive NMDA receptor antagonist, is used as a general anesthetic and recent data suggest that general anesthetics can cause neuronal damage when exposure occurs during early brain development. To elucidate the underlying mechanisms associated with ketamine-induced neurotoxicity, stem cell-derived models, such as rodent neural stem cells harvested from rat fetuses and/or neural stem cells derived from human induced pluripotent stem cells (iPSC) can be utilized. Prolonged exposure of rodent neural stem cells to clinically-relevant concentrations of ketamine resulted in elevated NMDA receptor levels as indicated by NR1subunit over-expression in neurons. This was associated with enhanced damage in neurons. In contrast, the viability and proliferation rate of undifferentiated neural stem cells were not significantly affected after ketamine exposure. Calcium imaging data indicated that 50μM NMDA did not cause a significant influx of calcium in typical undifferentiated neural stem cells; however, it did produce an immediate elevation of intracellular free Ca 2+ [Ca 2+ ] i in differentiated neurons derived from the same neural stem cells. This paper reviews the literature on this subject and previous findings suggest that prolonged exposure of developing neurons to ketamine produces an increase in NMDA receptor expression (compensatory up-regulation) which allows for a higher/toxic influx of calcium into neurons once ketamine is removed from the system, leading to neuronal cell death likely due to elevated reactive oxygen species generation. The absence of functional NMDA receptors in cultured neural stem cells likely explains why clinically-relevant concentrations of ketamine did not affect undifferentiated neural stem cell viability. Published by Elsevier B.V.

Reference ability neural networks and behavioral performance across the adult life span.

PubMed

Habeck, Christian; Eich, Teal; Razlighi, Ray; Gazes, Yunglin; Stern, Yaakov

2018-05-15

To better understand the impact of aging, along with other demographic and brain health variables, on the neural networks that support different aspects of cognitive performance, we applied a brute-force search technique based on Principal Components Analysis to derive 4 corresponding spatial covariance patterns (termed Reference Ability Neural Networks -RANNs) from a large sample of participants across the age range. 255 clinically healthy, community-dwelling adults, aged 20-77, underwent fMRI while performing 12 tasks, 3 tasks for each of the following cognitive reference abilities: Episodic Memory, Reasoning, Perceptual Speed, and Vocabulary. The derived RANNs (1) showed selective activation to their specific cognitive domain and (2) correlated with behavioral performance. Quasi out-of-sample replication with Monte-Carlo 5-fold cross validation was built into our approach, and all patterns indicated their corresponding reference ability and predicted performance in held-out data to a degree significantly greater than chance level. RANN-pattern expression for Episodic Memory, Reasoning and Vocabulary were associated selectively with age, while the pattern for Perceptual Speed showed no such age-related influences. For each participant we also looked at residual activity unaccounted for by the RANN-pattern derived for the cognitive reference ability. Higher residual activity was associated with poorer brain-structural health and older age, but -apart from Vocabulary-not with cognitive performance, indicating that older participants with worse brain-structural health might recruit alternative neural resources to maintain performance levels. Copyright © 2018 Elsevier Inc. All rights reserved.

Centralized and decentralized global outer-synchronization of asymmetric recurrent time-varying neural network by data-sampling.

PubMed

Lu, Wenlian; Zheng, Ren; Chen, Tianping

2016-03-01

In this paper, we discuss outer-synchronization of the asymmetrically connected recurrent time-varying neural networks. By using both centralized and decentralized discretization data sampling principles, we derive several sufficient conditions based on three vector norms to guarantee that the difference of any two trajectories starting from different initial values of the neural network converges to zero. The lower bounds of the common time intervals between data samples in centralized and decentralized principles are proved to be positive, which guarantees exclusion of Zeno behavior. A numerical example is provided to illustrate the efficiency of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Quantitative Live Imaging of Human Embryonic Stem Cell Derived Neural Rosettes Reveals Structure-Function Dynamics Coupled to Cortical Development.

PubMed

Ziv, Omer; Zaritsky, Assaf; Yaffe, Yakey; Mutukula, Naresh; Edri, Reuven; Elkabetz, Yechiel

2015-10-01

Neural stem cells (NSCs) are progenitor cells for brain development, where cellular spatial composition (cytoarchitecture) and dynamics are hypothesized to be linked to critical NSC capabilities. However, understanding cytoarchitectural dynamics of this process has been limited by the difficulty to quantitatively image brain development in vivo. Here, we study NSC dynamics within Neural Rosettes--highly organized multicellular structures derived from human pluripotent stem cells. Neural rosettes contain NSCs with strong epithelial polarity and are expected to perform apical-basal interkinetic nuclear migration (INM)--a hallmark of cortical radial glial cell development. We developed a quantitative live imaging framework to characterize INM dynamics within rosettes. We first show that the tendency of cells to follow the INM orientation--a phenomenon we referred to as radial organization, is associated with rosette size, presumably via mechanical constraints of the confining structure. Second, early forming rosettes, which are abundant with founder NSCs and correspond to the early proliferative developing cortex, show fast motions and enhanced radial organization. In contrast, later derived rosettes, which are characterized by reduced NSC capacity and elevated numbers of differentiated neurons, and thus correspond to neurogenesis mode in the developing cortex, exhibit slower motions and decreased radial organization. Third, later derived rosettes are characterized by temporal instability in INM measures, in agreement with progressive loss in rosette integrity at later developmental stages. Finally, molecular perturbations of INM by inhibition of actin or non-muscle myosin-II (NMII) reduced INM measures. Our framework enables quantification of cytoarchitecture NSC dynamics and may have implications in functional molecular studies, drug screening, and iPS cell-based platforms for disease modeling.

HD iPSC-derived neural progenitors accumulate in culture and are susceptible to BDNF withdrawal due to glutamate toxicity

PubMed Central

Mattis, Virginia B.; Tom, Colton; Akimov, Sergey; Saeedian, Jasmine; Østergaard, Michael E.; Southwell, Amber L.; Doty, Crystal N.; Ornelas, Loren; Sahabian, Anais; Lenaeus, Lindsay; Mandefro, Berhan; Sareen, Dhruv; Arjomand, Jamshid; Hayden, Michael R.; Ross, Christopher A.; Svendsen, Clive N.

2015-01-01

Huntington's disease (HD) is a fatal neurodegenerative disease, caused by expansion of polyglutamine repeats in the Huntingtin gene, with longer expansions leading to earlier ages of onset. The HD iPSC Consortium has recently reported a new in vitro model of HD based on the generation of induced pluripotent stem cells (iPSCs) from HD patients and controls. The current study has furthered the disease in a dish model of HD by generating new non-integrating HD and control iPSC lines. Both HD and control iPSC lines can be efficiently differentiated into neurons/glia; however, the HD-derived cells maintained a significantly greater number of nestin-expressing neural progenitor cells compared with control cells. This cell population showed enhanced vulnerability to brain-derived neurotrophic factor (BDNF) withdrawal in the juvenile-onset HD (JHD) lines, which appeared to be CAG repeat-dependent and mediated by the loss of signaling from the TrkB receptor. It was postulated that this increased death following BDNF withdrawal may be due to glutamate toxicity, as the N-methyl-d-aspartate (NMDA) receptor subunit NR2B was up-regulated in the cultures. Indeed, blocking glutamate signaling, not just through the NMDA but also mGlu and AMPA/Kainate receptors, completely reversed the cell death phenotype. This study suggests that the pathogenesis of JHD may involve in part a population of ‘persistent’ neural progenitors that are selectively vulnerable to BDNF withdrawal. Similar results were seen in adult hippocampal-derived neural progenitors isolated from the BACHD model mouse. Together, these results provide important insight into HD mechanisms at early developmental time points, which may suggest novel approaches to HD therapeutics. PMID:25740845

The Artificial Neural Networks Based on Scalarization Method for a Class of Bilevel Biobjective Programming Problem

PubMed Central

Chen, Zhong; Liu, June; Li, Xiong

2017-01-01

A two-stage artificial neural network (ANN) based on scalarization method is proposed for bilevel biobjective programming problem (BLBOP). The induced set of the BLBOP is firstly expressed as the set of minimal solutions of a biobjective optimization problem by using scalar approach, and then the whole efficient set of the BLBOP is derived by the proposed two-stage ANN for exploring the induced set. In order to illustrate the proposed method, seven numerical examples are tested and compared with results in the classical literature. Finally, a practical problem is solved by the proposed algorithm. PMID:29312446

Investigating the performance of neural network backpropagation algorithms for TEC estimations using South African GPS data

NASA Astrophysics Data System (ADS)

Habarulema, J. B.; McKinnell, L.-A.

2012-05-01

In this work, results obtained by investigating the application of different neural network backpropagation training algorithms are presented. This was done to assess the performance accuracy of each training algorithm in total electron content (TEC) estimations using identical datasets in models development and verification processes. Investigated training algorithms are standard backpropagation (SBP), backpropagation with weight delay (BPWD), backpropagation with momentum (BPM) term, backpropagation with chunkwise weight update (BPC) and backpropagation for batch (BPB) training. These five algorithms are inbuilt functions within the Stuttgart Neural Network Simulator (SNNS) and the main objective was to find out the training algorithm that generates the minimum error between the TEC derived from Global Positioning System (GPS) observations and the modelled TEC data. Another investigated algorithm is the MatLab based Levenberg-Marquardt backpropagation (L-MBP), which achieves convergence after the least number of iterations during training. In this paper, neural network (NN) models were developed using hourly TEC data (for 8 years: 2000-2007) derived from GPS observations over a receiver station located at Sutherland (SUTH) (32.38° S, 20.81° E), South Africa. Verification of the NN models for all algorithms considered was performed on both "seen" and "unseen" data. Hourly TEC values over SUTH for 2003 formed the "seen" dataset. The "unseen" dataset consisted of hourly TEC data for 2002 and 2008 over Cape Town (CPTN) (33.95° S, 18.47° E) and SUTH, respectively. The models' verification showed that all algorithms investigated provide comparable results statistically, but differ significantly in terms of time required to achieve convergence during input-output data training/learning. This paper therefore provides a guide to neural network users for choosing appropriate algorithms based on the availability of computation capabilities used for research.

Wnt/Yes-Associated Protein Interactions During Neural Tissue Patterning of Human Induced Pluripotent Stem Cells.

PubMed

Bejoy, Julie; Song, Liqing; Zhou, Yi; Li, Yan

2018-04-01

Human induced pluripotent stem cells (hiPSCs) have special ability to self-assemble into neural spheroids or mini-brain-like structures. During the self-assembly process, Wnt signaling plays an important role in regional patterning and establishing positional identity of hiPSC-derived neural progenitors. Recently, the role of Wnt signaling in regulating Yes-associated protein (YAP) expression (nuclear or cytoplasmic), the pivotal regulator during organ growth and tissue generation, has attracted increasing interests. However, the interactions between Wnt and YAP expression for neural lineage commitment of hiPSCs remain poorly explored. The objective of this study is to investigate the effects of Wnt signaling and YAP expression on the cellular population in three-dimensional (3D) neural spheroids derived from hiPSCs. In this study, Wnt signaling was activated using CHIR99021 for 3D neural spheroids derived from human iPSK3 cells through embryoid body formation. Our results indicate that Wnt activation induces nuclear localization of YAP and upregulates the expression of HOXB4, the marker for hindbrain/spinal cord. By contrast, the cells exhibit more rostral forebrain neural identity (expression of TBR1) without Wnt activation. Cytochalasin D was then used to induce cytoplasmic YAP and the results showed the decreased HOXB4 expression. In addition, the incorporation of microparticles in the neural spheroids was investigated for the perturbation of neural patterning. This study may indicate the bidirectional interactions of Wnt signaling and YAP expression during neural tissue patterning, which have the significance in neurological disease modeling, drug screening, and neural tissue regeneration.

Engineering three dimensional micro nerve tissue using postnatal stem cells from human dental apical papilla.

PubMed

Kim, Byung-Chul; Jun, Sung-Min; Kim, So Yeon; Kwon, Yong-Dae; Choe, Sung Chul; Kim, Eun-Chul; Lee, Jae-Hyung; Kim, Jinseok; Suh, Jun-Kyo Francis; Hwang, Yu-Shik

2017-04-01

The in vitro generation of cell-based three dimensional (3D) nerve tissue is an attractive subject to improve graft survival and integration into host tissue for neural tissue regeneration or to model biological events in stem cell differentiation. Although 3D organotypic culture strategies are well established for 3D nerve tissue formation of pluripotent stem cells to study underlying biology in nerve development, cell-based nerve tissues have not been developed using human postnatal stem cells with therapeutic potential. Here, we established a culture strategy for the generation of in vitro cell-based 3D nerve tissue from postnatal stem cells from apical papilla (SCAPs) of teeth, which originate from neural crest-derived ectomesenchyme cells. A stem cell population capable of differentiating into neural cell lineages was generated during the ex vivo expansion of SCAPs in the presence of EGF and bFGF, and SCAPs differentiated into neural cells, showing neural cell lineage-related molecular and gene expression profiles, morphological changes and electrophysical property under neural-inductive culture conditions. Moreover, we showed the first evidence that 3D cell-based nerve-like tissue with axons and myelin structures could be generated from SCAPs via 3D organotypic culture using an integrated bioprocess composed of polyethylene glycol (PEG) microwell-mediated cell spheroid formation and subsequent dynamic culture in a high aspect ratio vessel (HARV) bioreactor. In conclusion, the culture strategy in our study provides a novel approach to develop in vitro engineered nerve tissue using SCAPs and a foundation to study biological events in the neural differentiation of postnatal stem cells. Biotechnol. Bioeng. 2017;114: 903-914. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload.

PubMed

Sharma, Richa; Kumar, Vikas; Gaur, Prerna; Mittal, A P

2016-05-01

Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Injectable polypeptide hydrogels via methionine modification for neural stem cell delivery.

PubMed

Wollenberg, A L; O'Shea, T M; Kim, J H; Czechanski, A; Reinholdt, L G; Sofroniew, M V; Deming, T J

2018-04-05

Injectable hydrogels with tunable physiochemical and biological properties are potential tools for improving neural stem/progenitor cell (NSPC) transplantation to treat central nervous system (CNS) injury and disease. Here, we developed injectable diblock copolypeptide hydrogels (DCH) for NSPC transplantation that contain hydrophilic segments of modified l-methionine (Met). Multiple Met-based DCH were fabricated by post-polymerization modification of Met to various functional derivatives, and incorporation of different amino acid comonomers into hydrophilic segments. Met-based DCH assembled into self-healing hydrogels with concentration and composition dependent mechanical properties. Mechanical properties of non-ionic Met-sulfoxide formulations (DCH MO ) were stable across diverse aqueous media while cationic formulations showed salt ion dependent stiffness reduction. Murine NSPC survival in DCH MO was equivalent to that of standard culture conditions, and sulfoxide functionality imparted cell non-fouling character. Within serum rich environments in vitro, DCH MO was superior at preserving NSPC stemness and multipotency compared to cell adhesive materials. NSPC in DCH MO injected into uninjured forebrain remained local and, after 4 weeks, exhibited an immature astroglial phenotype that integrated with host neural tissue and acted as cellular substrates that supported growth of host-derived axons. These findings demonstrate that Met-based DCH are suitable vehicles for further study of NSPC transplantation in CNS injury and disease models. Copyright © 2018 Elsevier Ltd. All rights reserved.

Transplantation of neurons derived from human iPS cells cultured on collagen matrix into guinea-pig cochleae.

PubMed

Ishikawa, Masaaki; Ohnishi, Hiroe; Skerleva, Desislava; Sakamoto, Tatsunori; Yamamoto, Norio; Hotta, Akitsu; Ito, Juichi; Nakagawa, Takayuki

2017-06-01

The present study examined the efficacy of a neural induction method for human induced pluripotent stem (iPS) cells to eliminate undifferentiated cells and to determine the feasibility of transplanting neurally induced cells into guinea-pig cochleae for replacement of spiral ganglion neurons (SGNs). A stepwise method for differentiation of human iPS cells into neurons was used. First, a neural induction method was established on Matrigel-coated plates; characteristics of cell populations at each differentiation step were assessed. Second, neural stem cells were differentiated into neurons on a three-dimensional (3D) collagen matrix, using the same protocol of culture on Matrigel-coated plates; neuron subtypes in differentiated cells on a 3D collagen matrix were examined. Then, human iPS cell-derived neurons cultured on a 3D collagen matrix were transplanted into intact guinea-pig cochleae, followed by histological analysis. In vitro analyses revealed successful induction of neural stem cells from human iPS cells, with no retention of undifferentiated cells expressing OCT3/4. After the neural differentiation of neural stem cells, approximately 70% of cells expressed a neuronal marker, 90% of which were positive for vesicular glutamate transporter 1 (VGLUT1). The expression pattern of neuron subtypes in differentiated cells on a 3D collagen matrix was identical to that of the differentiated cells on Matrigel-coated plates. In addition, the survival of transplant-derived neurons was achieved when inflammatory responses were appropriately controlled. Our preparation method for human iPS cell-derived neurons efficiently eliminated undifferentiated cells and contributed to the settlement of transplant-derived neurons expressing VGLUT1 in guinea-pig cochleae. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Maximally Informative Stimuli and Tuning Curves for Sigmoidal Rate-Coding Neurons and Populations

NASA Astrophysics Data System (ADS)

McDonnell, Mark D.; Stocks, Nigel G.

2008-08-01

A general method for deriving maximally informative sigmoidal tuning curves for neural systems with small normalized variability is presented. The optimal tuning curve is a nonlinear function of the cumulative distribution function of the stimulus and depends on the mean-variance relationship of the neural system. The derivation is based on a known relationship between Shannon’s mutual information and Fisher information, and the optimality of Jeffrey’s prior. It relies on the existence of closed-form solutions to the converse problem of optimizing the stimulus distribution for a given tuning curve. It is shown that maximum mutual information corresponds to constant Fisher information only if the stimulus is uniformly distributed. As an example, the case of sub-Poisson binomial firing statistics is analyzed in detail.

Mapping and monitoring changes in vegetation communities of Jasper Ridge, CA, using spectral fractions derived from AVIRIS images

NASA Technical Reports Server (NTRS)

Sabol, Donald E., Jr.; Roberts, Dar A.; Adams, John B.; Smith, Milton O.

1993-01-01

An important application of remote sensing is to map and monitor changes over large areas of the land surface. This is particularly significant with the current interest in monitoring vegetation communities. Most of traditional methods for mapping different types of plant communities are based upon statistical classification techniques (i.e., parallel piped, nearest-neighbor, etc.) applied to uncalibrated multispectral data. Classes from these techniques are typically difficult to interpret (particularly to a field ecologist/botanist). Also, classes derived for one image can be very different from those derived from another image of the same area, making interpretation of observed temporal changes nearly impossible. More recently, neural networks have been applied to classification. Neural network classification, based upon spectral matching, is weak in dealing with spectral mixtures (a condition prevalent in images of natural surfaces). Another approach to mapping vegetation communities is based on spectral mixture analysis, which can provide a consistent framework for image interpretation. Roberts et al. (1990) mapped vegetation using the band residuals from a simple mixing model (the same spectral endmembers applied to all image pixels). Sabol et al. (1992b) and Roberts et al. (1992) used different methods to apply the most appropriate spectral endmembers to each image pixel, thereby allowing mapping of vegetation based upon the the different endmember spectra. In this paper, we describe a new approach to classification of vegetation communities based upon the spectra fractions derived from spectral mixture analysis. This approach was applied to three 1992 AVIRIS images of Jasper Ridge, California to observe seasonal changes in surface composition.

Finite-time and fixed-time synchronization analysis of inertial memristive neural networks with time-varying delays.

PubMed

Wei, Ruoyu; Cao, Jinde; Alsaedi, Ahmed

2018-02-01

This paper investigates the finite-time synchronization and fixed-time synchronization problems of inertial memristive neural networks with time-varying delays. By utilizing the Filippov discontinuous theory and Lyapunov stability theory, several sufficient conditions are derived to ensure finite-time synchronization of inertial memristive neural networks. Then, for the purpose of making the setting time independent of initial condition, we consider the fixed-time synchronization. A novel criterion guaranteeing the fixed-time synchronization of inertial memristive neural networks is derived. Finally, three examples are provided to demonstrate the effectiveness of our main results.

Targeted Deletion of Sox10 by Wnt1-cre Defects Neuronal Migration and Projection in the Mouse Inner Ear

PubMed Central

Mao, YanYan; Reiprich, Simone; Wegner, Michael; Fritzsch, Bernd

2014-01-01

Sensory nerves of the brainstem are mostly composed of placode-derived neurons, neural crest-derived neurons and neural crest-derived Schwann cells. This mixed origin of cells has made it difficult to dissect interdependence for fiber guidance. Inner ear-derived neurons are known to connect to the brain after delayed loss of Schwann cells in ErbB2 mutants. However, the ErbB2 mutant related alterations in the ear and the brain compound interpretation of the data. We present here a new model to evaluate exclusively the effect of Schwann cell loss on inner ear innervation. Conditional deletion of the neural crest specific transcription factor, Sox10, using the rhombic lip/neural crest specific Wnt1-cre driver spares Sox10 expression in the ear. We confirm that neural crest-derived cells provide a stop signal for migrating spiral ganglion neurons. In the absence of Schwann cells, spiral ganglion neurons migrate into the center of the cochlea and even out of the ear toward the brain. Spiral ganglion neuron afferent processes reach the organ of Corti, but many afferent fibers bypass the organ of Corti to enter the lateral wall of the cochlea. In contrast to this peripheral disorganization, the central projection to cochlear nuclei is normal. Compared to ErbB2 mutants, conditional Sox10 mutants have limited cell death in spiral ganglion neurons, indicating that the absence of Schwann cells alone contributes little to the embryonic survival of neurons. These data suggest that neural crest-derived cells are dispensable for all central and some peripheral targeting of inner ear neurons. However, Schwann cells provide a stop signal for migratory spiral ganglion neurons and facilitate proper targeting of the organ of Corti by spiral ganglion afferents. PMID:24718611

A simple method to derive bounds on the size and to train multilayer neural networks

NASA Technical Reports Server (NTRS)

Sartori, Michael A.; Antsaklis, Panos J.

1991-01-01

A new derivation is presented for the bounds on the size of a multilayer neural network to exactly implement an arbitrary training set; namely, the training set can be implemented with zero error with two layers and with the number of the hidden-layer neurons equal to no.1 is greater than p - 1. The derivation does not require the separation of the input space by particular hyperplanes, as in previous derivations. The weights for the hidden layer can be chosen almost arbitrarily, and the weights for the output layer can be found by solving no.1 + 1 linear equations. The method presented exactly solves (M), the multilayer neural network training problem, for any arbitrary training set.

Synchronization of generalized reaction-diffusion neural networks with time-varying delays based on general integral inequalities and sampled-data control approach.

PubMed

Dharani, S; Rakkiyappan, R; Cao, Jinde; Alsaedi, Ahmed

2017-08-01

This paper explores the problem of synchronization of a class of generalized reaction-diffusion neural networks with mixed time-varying delays. The mixed time-varying delays under consideration comprise of both discrete and distributed delays. Due to the development and merits of digital controllers, sampled-data control is a natural choice to establish synchronization in continuous-time systems. Using a newly introduced integral inequality, less conservative synchronization criteria that assure the global asymptotic synchronization of the considered generalized reaction-diffusion neural network and mixed delays are established in terms of linear matrix inequalities (LMIs). The obtained easy-to-test LMI-based synchronization criteria depends on the delay bounds in addition to the reaction-diffusion terms, which is more practicable. Upon solving these LMIs by using Matlab LMI control toolbox, a desired sampled-data controller gain can be acuqired without any difficulty. Finally, numerical examples are exploited to express the validity of the derived LMI-based synchronization criteria.

Large-scale generation of human iPSC-derived neural stem cells/early neural progenitor cells and their neuronal differentiation.

PubMed

D'Aiuto, Leonardo; Zhi, Yun; Kumar Das, Dhanjit; Wilcox, Madeleine R; Johnson, Jon W; McClain, Lora; MacDonald, Matthew L; Di Maio, Roberto; Schurdak, Mark E; Piazza, Paolo; Viggiano, Luigi; Sweet, Robert; Kinchington, Paul R; Bhattacharjee, Ayantika G; Yolken, Robert; Nimgaonka, Vishwajit L; Nimgaonkar, Vishwajit L

2014-01-01

Induced pluripotent stem cell (iPSC)-based technologies offer an unprecedented opportunity to perform high-throughput screening of novel drugs for neurological and neurodegenerative diseases. Such screenings require a robust and scalable method for generating large numbers of mature, differentiated neuronal cells. Currently available methods based on differentiation of embryoid bodies (EBs) or directed differentiation of adherent culture systems are either expensive or are not scalable. We developed a protocol for large-scale generation of neuronal stem cells (NSCs)/early neural progenitor cells (eNPCs) and their differentiation into neurons. Our scalable protocol allows robust and cost-effective generation of NSCs/eNPCs from iPSCs. Following culture in neurobasal medium supplemented with B27 and BDNF, NSCs/eNPCs differentiate predominantly into vesicular glutamate transporter 1 (VGLUT1) positive neurons. Targeted mass spectrometry analysis demonstrates that iPSC-derived neurons express ligand-gated channels and other synaptic proteins and whole-cell patch-clamp experiments indicate that these channels are functional. The robust and cost-effective differentiation protocol described here for large-scale generation of NSCs/eNPCs and their differentiation into neurons paves the way for automated high-throughput screening of drugs for neurological and neurodegenerative diseases.

Strategies for memory-based decision making: Modeling behavioral and neural signatures within a cognitive architecture.

PubMed

Fechner, Hanna B; Pachur, Thorsten; Schooler, Lael J; Mehlhorn, Katja; Battal, Ceren; Volz, Kirsten G; Borst, Jelmer P

2016-12-01

How do people use memories to make inferences about real-world objects? We tested three strategies based on predicted patterns of response times and blood-oxygen-level-dependent (BOLD) responses: one strategy that relies solely on recognition memory, a second that retrieves additional knowledge, and a third, lexicographic (i.e., sequential) strategy, that considers knowledge conditionally on the evidence obtained from recognition memory. We implemented the strategies as computational models within the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture, which allowed us to derive behavioral and neural predictions that we then compared to the results of a functional magnetic resonance imaging (fMRI) study in which participants inferred which of two cities is larger. Overall, versions of the lexicographic strategy, according to which knowledge about many but not all alternatives is searched, provided the best account of the joint patterns of response times and BOLD responses. These results provide insights into the interplay between recognition and additional knowledge in memory, hinting at an adaptive use of these two sources of information in decision making. The results highlight the usefulness of implementing models of decision making within a cognitive architecture to derive predictions on the behavioral and neural level. Copyright © 2016 Elsevier B.V. All rights reserved.

Brain structure and function correlates of cognitive subtypes in schizophrenia.

PubMed

Geisler, Daniel; Walton, Esther; Naylor, Melissa; Roessner, Veit; Lim, Kelvin O; Charles Schulz, S; Gollub, Randy L; Calhoun, Vince D; Sponheim, Scott R; Ehrlich, Stefan

2015-10-30

Stable neuropsychological deficits may provide a reliable basis for identifying etiological subtypes of schizophrenia. The aim of this study was to identify clusters of individuals with schizophrenia based on dimensions of neuropsychological performance, and to characterize their neural correlates. We acquired neuropsychological data as well as structural and functional magnetic resonance imaging from 129 patients with schizophrenia and 165 healthy controls. We derived eight cognitive dimensions and subsequently applied a cluster analysis to identify possible schizophrenia subtypes. Analyses suggested the following four cognitive clusters of schizophrenia: (1) Diminished Verbal Fluency, (2) Diminished Verbal Memory and Poor Motor Control, (3) Diminished Face Memory and Slowed Processing, and (4) Diminished Intellectual Function. The clusters were characterized by a specific pattern of structural brain changes in areas such as Wernicke's area, lingual gyrus and occipital face area, and hippocampus as well as differences in working memory-elicited neural activity in several fronto-parietal brain regions. Separable measures of cognitive function appear to provide a method for deriving cognitive subtypes meaningfully related to brain structure and function. Because the present study identified brain-based neural correlates of the cognitive clusters, the proposed groups of individuals with schizophrenia have some external validity. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Neural attractor network for application in visual field data classification.

PubMed

Fink, Wolfgang

2004-07-07

The purpose was to introduce a novel method for computer-based classification of visual field data derived from perimetric examination, that may act as a 'counsellor', providing an independent 'second opinion' to the diagnosing physician. The classification system consists of a Hopfield-type neural attractor network that obtains its input data from perimetric examination results. An iterative relaxation process determines the states of the neurons dynamically. Therefore, even 'noisy' perimetric output, e.g., early stages of a disease, may eventually be classified correctly according to the predefined idealized visual field defect (scotoma) patterns, stored as attractors of the network, that are found with diseases of the eye, optic nerve and the central nervous system. Preliminary tests of the classification system on real visual field data derived from perimetric examinations have shown a classification success of over 80%. Some of the main advantages of the Hopfield-attractor-network-based approach over feed-forward type neural networks are: (1) network architecture is defined by the classification problem; (2) no training is required to determine the neural coupling strengths; (3) assignment of an auto-diagnosis confidence level is possible by means of an overlap parameter and the Hamming distance. In conclusion, the novel method for computer-based classification of visual field data, presented here, furnishes a valuable first overview and an independent 'second opinion' in judging perimetric examination results, pointing towards a final diagnosis by a physician. It should not be considered a substitute for the diagnosing physician. Thanks to the worldwide accessibility of the Internet, the classification system offers a promising perspective towards modern computer-assisted diagnosis in both medicine and tele-medicine, for example and in particular, with respect to non-ophthalmic clinics or in communities where perimetric expertise is not readily available.

Neural network disturbance observer-based distributed finite-time formation tracking control for multiple unmanned helicopters.

PubMed

Wang, Dandan; Zong, Qun; Tian, Bailing; Shao, Shikai; Zhang, Xiuyun; Zhao, Xinyi

2018-02-01

The distributed finite-time formation tracking control problem for multiple unmanned helicopters is investigated in this paper. The control object is to maintain the positions of follower helicopters in formation with external interferences. The helicopter model is divided into a second order outer-loop subsystem and a second order inner-loop subsystem based on multiple-time scale features. Using radial basis function neural network (RBFNN) technique, we first propose a novel finite-time multivariable neural network disturbance observer (FMNNDO) to estimate the external disturbance and model uncertainty, where the neural network (NN) approximation errors can be dynamically compensated by adaptive law. Next, based on FMNNDO, a distributed finite-time formation tracking controller and a finite-time attitude tracking controller are designed using the nonsingular fast terminal sliding mode (NFTSM) method. In order to estimate the second derivative of the virtual desired attitude signal, a novel finite-time sliding mode integral filter is designed. Finally, Lyapunov analysis and multiple-time scale principle ensure the realization of control goal in finite-time. The effectiveness of the proposed FMNNDO and controllers are then verified by numerical simulations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Local synchronization of chaotic neural networks with sampled-data and saturating actuators.

PubMed

Wu, Zheng-Guang; Shi, Peng; Su, Hongye; Chu, Jian

2014-12-01

This paper investigates the problem of local synchronization of chaotic neural networks with sampled-data and actuator saturation. A new time-dependent Lyapunov functional is proposed for the synchronization error systems. The advantage of the constructed Lyapunov functional lies in the fact that it is positive definite at sampling times but not necessarily between sampling times, and makes full use of the available information about the actual sampling pattern. A local stability condition of the synchronization error systems is derived, based on which a sampled-data controller with respect to the actuator saturation is designed to ensure that the master neural networks and slave neural networks are locally asymptotically synchronous. Two optimization problems are provided to compute the desired sampled-data controller with the aim of enlarging the set of admissible initial conditions or the admissible sampling upper bound ensuring the local synchronization of the considered chaotic neural networks. A numerical example is used to demonstrate the effectiveness of the proposed design technique.

New application of intelligent agents in sporadic amyotrophic lateral sclerosis identifies unexpected specific genetic background.

PubMed

Penco, Silvana; Buscema, Massimo; Patrosso, Maria Cristina; Marocchi, Alessandro; Grossi, Enzo

2008-05-30

Few genetic factors predisposing to the sporadic form of amyotrophic lateral sclerosis (ALS) have been identified, but the pathology itself seems to be a true multifactorial disease in which complex interactions between environmental and genetic susceptibility factors take place. The purpose of this study was to approach genetic data with an innovative statistical method such as artificial neural networks to identify a possible genetic background predisposing to the disease. A DNA multiarray panel was applied to genotype more than 60 polymorphisms within 35 genes selected from pathways of lipid and homocysteine metabolism, regulation of blood pressure, coagulation, inflammation, cellular adhesion and matrix integrity, in 54 sporadic ALS patients and 208 controls. Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis. An unexpected discovery of a strong genetic background in sporadic ALS using a DNA multiarray panel and analytical processing of the data with advanced artificial neural networks was found. The predictive accuracy obtained with Linear Discriminant Analysis and Standard Artificial Neural Networks ranged from 70% to 79% (average 75.31%) and from 69.1 to 86.2% (average 76.6%) respectively. The corresponding value obtained with Advanced Intelligent Systems reached an average of 96.0% (range 94.4 to 97.6%). This latter approach allowed the identification of seven genetic variants essential to differentiate cases from controls: apolipoprotein E arg158cys; hepatic lipase -480 C/T; endothelial nitric oxide synthase 690 C/T and glu298asp; vitamin K-dependent coagulation factor seven arg353glu, glycoprotein Ia/IIa 873 G/A and E-selectin ser128arg. This study provides an alternative and reliable method to approach complex diseases. Indeed, the application of a novel artificial intelligence-based method offers a new insight into genetic markers of sporadic ALS pointing out the existence of a strong genetic background.

Viability and neural differentiation of mesenchymal stem cells derived from the umbilical cord following perinatal asphyxia.

PubMed

Aly, H; Mohsen, L; Badrawi, N; Gabr, H; Ali, Z; Akmal, D

2012-09-01

Hypoxia-ischemia is the leading cause of neurological handicaps in newborns worldwide. Mesenchymal stem cells (MSCs) collected from fresh cord blood of asphyxiated newborns have the potential to regenerate damaged neural tissues. The aim of this study was to examine the capacity for MSCs to differentiate into neural tissue that could subsequently be used for autologous transplantation. We collected cord blood samples from full-term newborns with perinatal hypoxemia (n=27), healthy newborns (n=14) and non-hypoxic premature neonates (n=14). Mononuclear cells were separated, counted, and then analyzed by flow cytometry to assess various stem cell populations. MSCs were isolated by plastic adherence and characterized by morphology. Cells underwent immunophenotyping and trilineage differentiation potential. They were then cultured in conditions favoring neural differentiation. Neural lineage commitment was detected using immunohistochemical staining for glial fibrillary acidic protein, tubulin III and oligodendrocyte marker O4 antibodies. Mononuclear cell count and viability did not differ among the three groups of infants. Neural differentiation was best demonstrated in the cells derived from hypoxia-ischemia term neonates, of which 69% had complete and 31% had partial neural differentiation. Cells derived from preterm neonates had the least amount of neural differentiation, whereas partial differentiation was observed in only 12%. These findings support the potential utilization of umbilical cord stem cells as a source for autologous transplant in asphyxiated neonates.

Hepatocyte growth factor/scatter factor-MET signaling in neural crest-derived melanocyte development.

PubMed

Kos, L; Aronzon, A; Takayama, H; Maina, F; Ponzetto, C; Merlino, G; Pavan, W

1999-02-01

The mechanisms governing development of neural crest-derived melanocytes, and how alterations in these pathways lead to hypopigmentation disorders, are not completely understood. Hepatocyte growth factor/scatter factor (HGF/SF) signaling through the tyrosine-kinase receptor, MET, is capable of promoting the proliferation, increasing the motility, and maintaining high tyrosinase activity and melanin synthesis of melanocytes in vitro. In addition, transgenic mice that ubiquitously overexpress HGF/SF demonstrate hyperpigmentation in the skin and leptomenigenes and develop melanomas. To investigate whether HGF/ SF-MET signaling is involved in the development of neural crest-derived melanocytes, transgenic embryos, ubiquitously overexpressing HGF/SF, were analyzed. In HGF/SF transgenic embryos, the distribution of melanoblasts along the characteristic migratory pathway was not affected. However, additional ectopically localized melanoblasts were also observed in the dorsal root ganglia and neural tube, as early as 11.5 days post coitus (p.c.). We utilized an in vitro neural crest culture assay to further explore the role of HGF/SF-MET signaling in neural crest development. HGF/SF added to neural crest cultures increased melanoblast number, permitted differentiation into pigmented melanocytes, promoted melanoblast survival, and could replace mast-cell growth factor/Steel factor (MGF) in explant cultures. To examine whether HGF/SF-MET signaling is required for the proper development of melanocytes, embryos with a targeted Met null mutation (Met-/-) were analysed. In Met-/- embryos, melanoblast number and location were not overtly affected up to 14 days p.c. These results demonstrate that HGF/SF-MET signaling influences, but is not required for, the initial development of neural crest-derived melanocytes in vivo and in vitro.

Gut vagal sensory signaling regulates hippocampus function through multi-order pathways.

PubMed

Suarez, Andrea N; Hsu, Ted M; Liu, Clarissa M; Noble, Emily E; Cortella, Alyssa M; Nakamoto, Emily M; Hahn, Joel D; de Lartigue, Guillaume; Kanoski, Scott E

2018-06-05

The vagus nerve is the primary means of neural communication between the gastrointestinal (GI) tract and the brain. Vagally mediated GI signals activate the hippocampus (HPC), a brain region classically linked with memory function. However, the endogenous relevance of GI-derived vagal HPC communication is unknown. Here we utilize a saporin (SAP)-based lesioning procedure to reveal that selective GI vagal sensory/afferent ablation in rats impairs HPC-dependent episodic and spatial memory, effects associated with reduced HPC neurotrophic and neurogenesis markers. To determine the neural pathways connecting the gut to the HPC, we utilize monosynaptic and multisynaptic virus-based tracing methods to identify the medial septum as a relay connecting the medial nucleus tractus solitarius (where GI vagal afferents synapse) to dorsal HPC glutamatergic neurons. We conclude that endogenous GI-derived vagal sensory signaling promotes HPC-dependent memory function via a multi-order brainstem-septal pathway, thereby identifying a previously unknown role for the gut-brain axis in memory control.

Deduction of reservoir operating rules for application in global hydrological models

NASA Astrophysics Data System (ADS)

Coerver, Hubertus M.; Rutten, Martine M.; van de Giesen, Nick C.

2018-01-01

A big challenge in constructing global hydrological models is the inclusion of anthropogenic impacts on the water cycle, such as caused by dams. Dam operators make decisions based on experience and often uncertain information. In this study information generally available to dam operators, like inflow into the reservoir and storage levels, was used to derive fuzzy rules describing the way a reservoir is operated. Using an artificial neural network capable of mimicking fuzzy logic, called the ANFIS adaptive-network-based fuzzy inference system, fuzzy rules linking inflow and storage with reservoir release were determined for 11 reservoirs in central Asia, the US and Vietnam. By varying the input variables of the neural network, different configurations of fuzzy rules were created and tested. It was found that the release from relatively large reservoirs was significantly dependent on information concerning recent storage levels, while release from smaller reservoirs was more dependent on reservoir inflows. Subsequently, the derived rules were used to simulate reservoir release with an average Nash-Sutcliffe coefficient of 0.81.

Induced Pluripotent Stem Cells Generated from P0-Cre;Z/EG Transgenic Mice

PubMed Central

Ogawa, Yasuhiro; Eto, Akira; Miyake, Chisato; Tsuchida, Nana; Miyake, Haruka; Takaku, Yasuhiro; Hagiwara, Hiroaki; Oishi, Kazuhiko

2015-01-01

Neural crest (NC) cells are a migratory, multipotent cell population that arises at the neural plate border, and migrate from the dorsal neural tube to their target tissues, where they differentiate into various cell types. Abnormal development of NC cells can result in severe congenital birth defects. Because only a limited number of cells can be obtained from an embryo, mechanistic studies are difficult to perform with directly isolated NC cells. Protein zero (P0) is expressed by migrating NC cells during the early embryonic period. In the P0-Cre;Z/EG transgenic mouse, transient activation of the P0 promoter induces Cre-mediated recombination, indelibly tagging NC-derived cells with enhanced green fluorescent protein (EGFP). Induced pluripotent stem cell (iPSC) technology offers new opportunities for both mechanistic studies and development of stem cell-based therapies. Here, we report the generation of iPSCs from the P0-Cre;Z/EG mouse. P0-Cre;Z/EG mouse-derived iPSCs (P/G-iPSCs) exhibited pluripotent stem cell properties. In lineage-directed differentiation studies, P/G-iPSCs were efficiently differentiated along the neural lineage while expressing EGFP. These results suggest that P/G-iPSCs are useful to study NC development and NC-associated diseases. PMID:26382630

Human embryonic stem cell-derived neural crest cells capable of expressing markers of osteochondral or meningeal-choroid plexus differentiation.

PubMed

Sternberg, Hal; Jiang, Jianjie; Sim, Pamela; Kidd, Jennifer; Janus, Jeffrey; Rinon, Ariel; Edgar, Ron; Shitrit, Alina; Larocca, David; Chapman, Karen B; Binette, Francois; West, Michael D

2014-01-01

The transcriptome and fate potential of three diverse human embryonic stem cell-derived clonal embryonic progenitor cell lines with markers of cephalic neural crest are compared when differentiated in the presence of combinations of TGFβ3, BMP4, SCF and HyStem-C matrices. The cell lines E69 and T42 were compared with MEL2, using gene expression microarrays, immunocytochemistry and ELISA. In the undifferentiated progenitor state, each line displayed unique markers of cranial neural crest including TFAP2A and CD24; however, none expressed distal HOX genes including HOXA2 or HOXB2, or the mesenchymal stem cell marker CD74. The lines also showed diverse responses when differentiated in the presence of exogenous BMP4, BMP4 and TGFβ3, SCF, and SCF and TGFβ3. The clones E69 and T42 showed a profound capacity for expression of endochondral ossification markers when differentiated in the presence of BMP4 and TGFβ3, choroid plexus markers in the presence of BMP4 alone, and leptomeningeal markers when differentiated in SCF without TGFβ3. The clones E69 and T42 may represent a scalable source of primitive cranial neural crest cells useful in the study of cranial embryology, and potentially cell-based therapy.

Induced Pluripotent Stem Cells Generated from P0-Cre;Z/EG Transgenic Mice.

PubMed

Ogawa, Yasuhiro; Eto, Akira; Miyake, Chisato; Tsuchida, Nana; Miyake, Haruka; Takaku, Yasuhiro; Hagiwara, Hiroaki; Oishi, Kazuhiko

2015-01-01

Neural crest (NC) cells are a migratory, multipotent cell population that arises at the neural plate border, and migrate from the dorsal neural tube to their target tissues, where they differentiate into various cell types. Abnormal development of NC cells can result in severe congenital birth defects. Because only a limited number of cells can be obtained from an embryo, mechanistic studies are difficult to perform with directly isolated NC cells. Protein zero (P0) is expressed by migrating NC cells during the early embryonic period. In the P0-Cre;Z/EG transgenic mouse, transient activation of the P0 promoter induces Cre-mediated recombination, indelibly tagging NC-derived cells with enhanced green fluorescent protein (EGFP). Induced pluripotent stem cell (iPSC) technology offers new opportunities for both mechanistic studies and development of stem cell-based therapies. Here, we report the generation of iPSCs from the P0-Cre;Z/EG mouse. P0-Cre;Z/EG mouse-derived iPSCs (P/G-iPSCs) exhibited pluripotent stem cell properties. In lineage-directed differentiation studies, P/G-iPSCs were efficiently differentiated along the neural lineage while expressing EGFP. These results suggest that P/G-iPSCs are useful to study NC development and NC-associated diseases.

Diprosopia revisited in light of the recognized role of neural crest cells in facial development.

PubMed

Carles, D; Weichhold, W; Alberti, E M; Léger, F; Pigeau, F; Horovitz, J

1995-01-01

The aim of this study is to compare the theory of embryogenesis of the face with human diprosopia. This peculiar form of conjoined twinning is of great interest because 1) only the facial structures are duplicated and 2) almost all cases have a rather monomorphic pattern. The hypothesis is that an initial duplication of the notochord leads to two neural plates and subsequently duplicated neural crests. In those conditions, derivatives of the neural crests will be partially or totally duplicated; therefore, in diprosopia, the duplicated facial structures would be considered to be neural crest derivatives. If these structures are identical to those that are experimentally demonstrated to be neural crest derivatives in animals, these findings are an argument to apply this theory of facial embryogenesis in man. Serial horizontal sections of the face of two diprosopic fetuses (11 and 21 weeks gestation) were studied macro- and microscopically to determine the external and internal structures that are duplicated. Complete postmortem examination was performed in search for additional malformations. The face of both fetuses showed a very similar morphologic pattern with duplication of ocular, nasal, and buccal structures. The nasal fossae and the anterior part of the tongue were also duplicated, albeit the posterior part and the pharyngolaryngeal structures were unique. Additional facial clefts were present in both fetuses. Extrafacial anomalies were represented by a craniorachischisis, two fused vertebral columns and, in the older fetus, by a complex cardiac malformation morphologically identical to malformations induced by removal or grafting of additional cardiac neural crest cells in animals. These pathological findings could identify the facial structures that are neural crest derivatives in man. They are similar to those experimentally demonstrated to be neural crest derivatives in animals. In this respect, diprosopia could be considered as the end of a spectrum, whereas the other end is agnathia-holoprosencephaly complex. This assumption has to be discussed, but we want to draw attention to the fact that diprosopia must not be considered as a curious form of conjoined twinning, but as a major means of bringing us a better knowledge of the facial embryogenesis in man.

Neural Differentiation of Human Pluripotent Stem Cells for Nontherapeutic Applications: Toxicology, Pharmacology, and In Vitro Disease Modeling.

PubMed

Yap, May Shin; Nathan, Kavitha R; Yeo, Yin; Lim, Lee Wei; Poh, Chit Laa; Richards, Mark; Lim, Wei Ling; Othman, Iekhsan; Heng, Boon Chin

2015-01-01

Human pluripotent stem cells (hPSCs) derived from either blastocyst stage embryos (hESCs) or reprogrammed somatic cells (iPSCs) can provide an abundant source of human neuronal lineages that were previously sourced from human cadavers, abortuses, and discarded surgical waste. In addition to the well-known potential therapeutic application of these cells in regenerative medicine, these are also various promising nontherapeutic applications in toxicological and pharmacological screening of neuroactive compounds, as well as for in vitro modeling of neurodegenerative and neurodevelopmental disorders. Compared to alternative research models based on laboratory animals and immortalized cancer-derived human neural cell lines, neuronal cells differentiated from hPSCs possess the advantages of species specificity together with genetic and physiological normality, which could more closely recapitulate in vivo conditions within the human central nervous system. This review critically examines the various potential nontherapeutic applications of hPSC-derived neuronal lineages and gives a brief overview of differentiation protocols utilized to generate these cells from hESCs and iPSCs.

Internal representations for face detection: an application of noise-based image classification to BOLD responses.

PubMed

Nestor, Adrian; Vettel, Jean M; Tarr, Michael J

2013-11-01

What basic visual structures underlie human face detection and how can we extract such structures directly from the amplitude of neural responses elicited by face processing? Here, we address these issues by investigating an extension of noise-based image classification to BOLD responses recorded in high-level visual areas. First, we assess the applicability of this classification method to such data and, second, we explore its results in connection with the neural processing of faces. To this end, we construct luminance templates from white noise fields based on the response of face-selective areas in the human ventral cortex. Using behaviorally and neurally-derived classification images, our results reveal a family of simple but robust image structures subserving face representation and detection. Thus, we confirm the role played by classical face selective regions in face detection and we help clarify the representational basis of this perceptual function. From a theory standpoint, our findings support the idea of simple but highly diagnostic neurally-coded features for face detection. At the same time, from a methodological perspective, our work demonstrates the ability of noise-based image classification in conjunction with fMRI to help uncover the structure of high-level perceptual representations. Copyright © 2012 Wiley Periodicals, Inc.

QSRR using evolved artificial neural network for 52 common pharmaceuticals and drugs of abuse in hair from UPLC-TOF-MS.

PubMed

Noorizadeh, Hadi; Farmany, Abbas; Narimani, Hojat; Noorizadeh, Mehrab

2013-05-01

A quantitative structure-retention relationship (QSRR) study based on an artificial neural network (ANN) was carried out for the prediction of the ultra-performance liquid chromatography-Time-of-Flight mass spectrometry (UPLC-TOF-MS) retention time (RT) of a set of 52 pharmaceuticals and drugs of abuse in hair. The genetic algorithm was used as a variable selection tool. A partial least squares (PLS) method was used to select the best descriptors which were used as input neurons in neural network model. For choosing the best predictive model from among comparable models, square correlation coefficient R(2) for the whole set calculated based on leave-group-out predicted values of the training set and model-derived predicted values for the test set compounds is suggested to be a good criterion. Finally, to improve the results, structure-retention relationships were followed by a non-linear approach using artificial neural networks and consequently better results were obtained. This also demonstrates the advantages of ANN. Copyright © 2011 John Wiley & Sons, Ltd.

Comparative evaluation of Chitosan, Cellulose Acetate, and Polyethersulfone Nanofiber Scaffolds for Neural Differentiation

PubMed Central

Du, Jian; Tan, Elaine; Kim, Hyo Jun; Zhang, Allen; Bhattacharya, Rahul; Yarema, Kevin J

2013-01-01

Based on accumulating evidence that the 3D topography and the chemical features of a growth surface influence neuronal differentiation, we combined these two features by evaluating the cytotoxicity, proliferation, and differentiation of the rat PC12 line and human neural stem cells (hNSCs) on chitosan (CS), cellulose acetate (CA), and polyethersulfone (PES)-derived electrospun nanofibers that had similar diameters, centered in the 200 to 500 nm range. None of the nanofibrous materials were cytotoxic compared to 2D (e.g., flat surface) controls; however, proliferation generally was inhibited on the nanofibrous scaffolds although to a lesser extent on the polysaccharide-derived materials compared to PES. In an exception to the trend towards slower growth on the 3D substrates, hNSCs differentiated on the CS nanofibers proliferated faster than the 2D controls and both cell types showed enhanced indication of neuronal differentiation on the CS scaffolds. Together, these results demonstrate beneficial attributes of CS for neural tissue engineering when this polysaccharide is used in the context of the defined 3D topography found in electrospun nanofibers. PMID:24274534

Stability analysis for uncertain switched neural networks with time-varying delay.

PubMed

Shen, Wenwen; Zeng, Zhigang; Wang, Leimin

2016-11-01

In this paper, stability for a class of uncertain switched neural networks with time-varying delay is investigated. By exploring the mode-dependent properties of each subsystem, all the subsystems are categorized into stable and unstable ones. Based on Lyapunov-like function method and average dwell time technique, some delay-dependent sufficient conditions are derived to guarantee the exponential stability of considered uncertain switched neural networks. Compared with general results, our proposed approach distinguishes the stable and unstable subsystems rather than viewing all subsystems as being stable, thus getting less conservative criteria. Finally, two numerical examples are provided to show the validity and the advantages of the obtained results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Impulsive Effects on Quasi-Synchronization of Neural Networks With Parameter Mismatches and Time-Varying Delay.

PubMed

Tang, Ze; Park, Ju H; Feng, Jianwen

2018-04-01

This paper is concerned with the exponential synchronization issue of nonidentically coupled neural networks with time-varying delay. Due to the parameter mismatch phenomena existed in neural networks, the problem of quasi-synchronization is thus discussed by applying some impulsive control strategies. Based on the definition of average impulsive interval and the extended comparison principle for impulsive systems, some criteria for achieving the quasi-synchronization of neural networks are derived. More extensive ranges of impulsive effects are discussed so that impulse could either play an effective role or play an adverse role in the final network synchronization. In addition, according to the extended formula for the variation of parameters with time-varying delay, precisely exponential convergence rates and quasi-synchronization errors are obtained, respectively, in view of different types impulsive effects. Finally, some numerical simulations with different types of impulsive effects are presented to illustrate the effectiveness of theoretical analysis.

Dissipativity and stability analysis of fractional-order complex-valued neural networks with time delay.

PubMed

Velmurugan, G; Rakkiyappan, R; Vembarasan, V; Cao, Jinde; Alsaedi, Ahmed

2017-02-01

As we know, the notion of dissipativity is an important dynamical property of neural networks. Thus, the analysis of dissipativity of neural networks with time delay is becoming more and more important in the research field. In this paper, the authors establish a class of fractional-order complex-valued neural networks (FCVNNs) with time delay, and intensively study the problem of dissipativity, as well as global asymptotic stability of the considered FCVNNs with time delay. Based on the fractional Halanay inequality and suitable Lyapunov functions, some new sufficient conditions are obtained that guarantee the dissipativity of FCVNNs with time delay. Moreover, some sufficient conditions are derived in order to ensure the global asymptotic stability of the addressed FCVNNs with time delay. Finally, two numerical simulations are posed to ensure that the attention of our main results are valuable. Copyright © 2016 Elsevier Ltd. All rights reserved.

IGF-1 Signaling Plays an Important Role in the Formation of Three-Dimensional Laminated Neural Retina and Other Ocular Structures From Human Embryonic Stem Cells.

PubMed

Mellough, Carla B; Collin, Joseph; Khazim, Mahmoud; White, Kathryn; Sernagor, Evelyne; Steel, David H W; Lako, Majlinda

2015-08-01

We and others have previously demonstrated that retinal cells can be derived from human embryonic stem cells (hESCs) and induced pluripotent stem cells under defined culture conditions. While both cell types can give rise to retinal derivatives in the absence of inductive cues, this requires extended culture periods and gives lower overall yield. Further understanding of this innate differentiation ability, the identification of key factors that drive the differentiation process, and the development of clinically compatible culture conditions to reproducibly generate functional neural retina is an important goal for clinical cell based therapies. We now report that insulin-like growth factor 1 (IGF-1) can orchestrate the formation of three-dimensional ocular-like structures from hESCs which, in addition to retinal pigmented epithelium and neural retina, also contain primitive lens and corneal-like structures. Inhibition of IGF-1 receptor signaling significantly reduces the formation of optic vesicle and optic cups, while exogenous IGF-1 treatment enhances the formation of correctly laminated retinal tissue composed of multiple retinal phenotypes that is reminiscent of the developing vertebrate retina. Most importantly, hESC-derived photoreceptors exhibit advanced maturation features such as the presence of primitive rod- and cone-like photoreceptor inner and outer segments and phototransduction-related functional responses as early as 6.5 weeks of differentiation, making these derivatives promising candidates for cell replacement studies and in vitro disease modeling. © 2015 AlphaMed Press.

Construction of a pulse-coupled dipole network capable of fear-like and relief-like responses

NASA Astrophysics Data System (ADS)

Lungsi Sharma, B.

2016-07-01

The challenge for neuroscience as an interdisciplinary programme is the integration of ideas among the disciplines to achieve a common goal. This paper deals with the problem of deriving a pulse-coupled neural network that is capable of demonstrating behavioural responses (fear-like and relief-like). Current pulse-coupled neural networks are designed mostly for engineering applications, particularly image processing. The discovered neural network was constructed using the method of minimal anatomies approach. The behavioural response of a level-coded activity-based model was used as a reference. Although the spiking-based model and the activity-based model are of different scales, the use of model-reference principle means that the characteristics that is referenced is its functional properties. It is demonstrated that this strategy of dissection and systematic construction is effective in the functional design of pulse-coupled neural network system with nonlinear signalling. The differential equations for the elastic weights in the reference model are replicated in the pulse-coupled network geometrically. The network reflects a possible solution to the problem of punishment and avoidance. The network developed in this work is a new network topology for pulse-coupled neural networks. Therefore, the model-reference principle is a powerful tool in connecting neuroscience disciplines. The continuity of concepts and phenomena is further maintained by systematic construction using methods like the method of minimal anatomies.

Single-Factor SOX2 Mediates Direct Neural Reprogramming of Human Mesenchymal Stem Cells via Transfection of In Vitro Transcribed mRNA.

PubMed

Kim, Bo-Eun; Choi, Soon Won; Shin, Ji-Hee; Kim, Jae-Jun; Kang, Insung; Lee, Byung-Chul; Lee, Jin Young; Kook, Myoung Geun; Kang, Kyung-Sun

2018-01-01

Neural stem cells (NSCs) are a prominent cell source for understanding neural pathogenesis and for developing therapeutic applications to treat neurodegenerative disease because of their regenerative capacity and multipotency. Recently, a variety of cellular reprogramming technologies have been developed to facilitate in vitro generation of NSCs, called induced NSCs (iNSCs). However, the genetic safety aspects of established virus-based reprogramming methods have been considered, and non-integrating reprogramming methods have been developed. Reprogramming with in vitro transcribed (IVT) mRNA is one of the genetically safe reprogramming methods because exogenous mRNA temporally exists in the cell and is not integrated into the chromosome. Here, we successfully generated expandable iNSCs from human umbilical cord blood-derived mesenchymal stem cells (UCB-MSCs) via transfection with IVT mRNA encoding SOX2 (SOX2 mRNA) with properly optimized conditions. We confirmed that generated human UCB-MSC-derived iNSCs (UM-iNSCs) possess characteristics of NSCs, including multipotency and self-renewal capacity. Additionally, we transfected human dermal fibroblasts (HDFs) with SOX2 mRNA. Compared with human embryonic stem cell-derived NSCs, HDFs transfected with SOX2 mRNA exhibited neural reprogramming with similar morphologies and NSC-enriched mRNA levels, but they showed limited proliferation ability. Our results demonstrated that human UCB-MSCs can be used for direct reprogramming into NSCs through transfection with IVT mRNA encoding a single factor, which provides an integration-free reprogramming tool for future therapeutic application.

Recurrent genomic instability of chromosome 1q in neural derivatives of human embryonic stem cells

PubMed Central

Varela, Christine; Denis, Jérôme Alexandre; Polentes, Jérôme; Feyeux, Maxime; Aubert, Sophie; Champon, Benoite; Piétu, Geneviève; Peschanski, Marc; Lefort, Nathalie

2012-01-01

Human pluripotent stem cells offer a limitless source of cells for regenerative medicine. Neural derivatives of human embryonic stem cells (hESCs) are currently being used for cell therapy in 3 clinical trials. However, hESCs are prone to genomic instability, which could limit their clinical utility. Here, we report that neural differentiation of hESCs systematically produced a neural stem cell population that could be propagated for more than 50 passages without entering senescence; this was true for all 6 hESC lines tested. The apparent spontaneous loss of evolution toward normal senescence of somatic cells was associated with a jumping translocation of chromosome 1q. This chromosomal defect has previously been associated with hematologic malignancies and pediatric brain tumors with poor clinical outcome. Neural stem cells carrying the 1q defect implanted into the brains of rats failed to integrate and expand, whereas normal cells engrafted. Our results call for additional quality controls to be implemented to ensure genomic integrity not only of undifferentiated pluripotent stem cells, but also of hESC derivatives that form cell therapy end products, particularly neural lines. PMID:22269325

Hematopoietic Stem Cells in Neural-crest Derived Bone Marrow.

PubMed

Jiang, Nan; Chen, Mo; Yang, Guodong; Xiang, Lusai; He, Ling; Hei, Thomas K; Chotkowski, Gregory; Tarnow, Dennis P; Finkel, Myron; Ding, Lei; Zhou, Yanheng; Mao, Jeremy J

2016-12-21

Hematopoietic stem cells (HSCs) in the endosteum of mesoderm-derived appendicular bones have been extensively studied. Neural crest-derived bones differ from appendicular bones in developmental origin, mode of bone formation and pathological bone resorption. Whether neural crest-derived bones harbor HSCs is elusive. Here, we discovered HSC-like cells in postnatal murine mandible, and benchmarked them with donor-matched, mesoderm-derived femur/tibia HSCs, including clonogenic assay and long-term culture. Mandibular CD34 negative, LSK cells proliferated similarly to appendicular HSCs, and differentiated into all hematopoietic lineages. Mandibular HSCs showed a consistent deficiency in lymphoid differentiation, including significantly fewer CD229 + fractions, PreProB, ProB, PreB and B220 + slgM cells. Remarkably, mandibular HSCs reconstituted irradiated hematopoietic bone marrow in vivo, just as appendicular HSCs. Genomic profiling of osteoblasts from mandibular and femur/tibia bone marrow revealed deficiencies in several HSC niche regulators among mandibular osteoblasts including Cxcl12. Neural crest derived bone harbors HSCs that function similarly to appendicular HSCs but are deficient in the lymphoid lineage. Thus, lymphoid deficiency of mandibular HSCs may be accounted by putative niche regulating genes. HSCs in craniofacial bones have functional implications in homeostasis, osteoclastogenesis, immune functions, tumor metastasis and infections such as osteonecrosis of the jaw.

Generation of diverse neural cell types through direct conversion

PubMed Central

Petersen, Gayle F; Strappe, Padraig M

2016-01-01

A characteristic of neurological disorders is the loss of critical populations of cells that the body is unable to replace, thus there has been much interest in identifying methods of generating clinically relevant numbers of cells to replace those that have been damaged or lost. The process of neural direct conversion, in which cells of one lineage are converted into cells of a neural lineage without first inducing pluripotency, shows great potential, with evidence of the generation of a range of functional neural cell types both in vitro and in vivo, through viral and non-viral delivery of exogenous factors, as well as chemical induction methods. Induced neural cells have been proposed as an attractive alternative to neural cells derived from embryonic or induced pluripotent stem cells, with prospective roles in the investigation of neurological disorders, including neurodegenerative disease modelling, drug screening, and cellular replacement for regenerative medicine applications, however further investigations into improving the efficacy and safety of these methods need to be performed before neural direct conversion becomes a clinically viable option. In this review, we describe the generation of diverse neural cell types via direct conversion of somatic cells, with comparison against stem cell-based approaches, as well as discussion of their potential research and clinical applications. PMID:26981169

Stability and synchronization analysis of inertial memristive neural networks with time delays.

PubMed

Rakkiyappan, R; Premalatha, S; Chandrasekar, A; Cao, Jinde

2016-10-01

This paper is concerned with the problem of stability and pinning synchronization of a class of inertial memristive neural networks with time delay. In contrast to general inertial neural networks, inertial memristive neural networks is applied to exhibit the synchronization and stability behaviors due to the physical properties of memristors and the differential inclusion theory. By choosing an appropriate variable transmission, the original system can be transformed into first order differential equations. Then, several sufficient conditions for the stability of inertial memristive neural networks by using matrix measure and Halanay inequality are derived. These obtained criteria are capable of reducing computational burden in the theoretical part. In addition, the evaluation is done on pinning synchronization for an array of linearly coupled inertial memristive neural networks, to derive the condition using matrix measure strategy. Finally, the two numerical simulations are presented to show the effectiveness of acquired theoretical results.

Synchronization of Switched Neural Networks With Communication Delays via the Event-Triggered Control.

PubMed

Wen, Shiping; Zeng, Zhigang; Chen, Michael Z Q; Huang, Tingwen

2017-10-01

This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results.

Artificial Neural Networks: A Novel Approach to Analysing the Nutritional Ecology of a Blowfly Species, Chrysomya megacephala

PubMed Central

Bianconi, André; Zuben, Cláudio J. Von; Serapião, Adriane B. de S.; Govone, José S.

2010-01-01

Bionomic features of blowflies may be clarified and detailed by the deployment of appropriate modelling techniques such as artificial neural networks, which are mathematical tools widely applied to the resolution of complex biological problems. The principal aim of this work was to use three well-known neural networks, namely Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Adaptive Neural Network-Based Fuzzy Inference System (ANFIS), to ascertain whether these tools would be able to outperform a classical statistical method (multiple linear regression) in the prediction of the number of resultant adults (survivors) of experimental populations of Chrysomya megacephala (F.) (Diptera: Calliphoridae), based on initial larval density (number of larvae), amount of available food, and duration of immature stages. The coefficient of determination (R2) derived from the RBF was the lowest in the testing subset in relation to the other neural networks, even though its R2 in the training subset exhibited virtually a maximum value. The ANFIS model permitted the achievement of the best testing performance. Hence this model was deemed to be more effective in relation to MLP and RBF for predicting the number of survivors. All three networks outperformed the multiple linear regression, indicating that neural models could be taken as feasible techniques for predicting bionomic variables concerning the nutritional dynamics of blowflies. PMID:20569135

Notochord-derived Shh concentrates in close association with the apically positioned basal body in neural target cells and forms a dynamic gradient during neural patterning.

PubMed

Chamberlain, Chester E; Jeong, Juhee; Guo, Chaoshe; Allen, Benjamin L; McMahon, Andrew P

2008-03-01

Sonic hedgehog (Shh) ligand secreted by the notochord induces distinct ventral cell identities in the adjacent neural tube by a concentration-dependent mechanism. To study this process, we genetically engineered mice that produce bioactive, fluorescently labeled Shh from the endogenous locus. We show that Shh ligand concentrates in close association with the apically positioned basal body of neural target cells, forming a dynamic, punctate gradient in the ventral neural tube. Both ligand lipidation and target field response influence the gradient profile, but not the ability of Shh to concentrate around the basal body. Further, subcellular analysis suggests that Shh from the notochord might traffic into the neural target field by means of an apical-to-basal-oriented microtubule scaffold. This study, in which we directly observe, measure, localize and modify notochord-derived Shh ligand in the context of neural patterning, provides several new insights into mechanisms of Shh morphogen action.

Human adenovirus serotypes 4p and 11p are efficiently expressed in cell lines of neural tumour origin.

PubMed

Skog, Johan; Mei, Ya-Fang; Wadell, Göran

2002-06-01

Most currently used adenovirus vectors are based upon adenovirus serotypes 2 and 5 (Ad2 and Ad5), which have limited efficiencies for gene transfer to human neural cells. Both serotypes bind to the known adenovirus receptor, CAR (coxsackievirus and adenovirus receptor), and have restricted cell tropism. The purpose of this study was to find vector candidates that are superior to Ad5 in infecting human neural tumours. Using flow cytometry, the vector candidates Ad4p, Ad11p and Ad17p were compared to the commonly used adenovirus vector Ad5v for their binding capacity to neural cell lines derived from glioblastoma, medulloblastoma and neuroblastoma cell lines. The production of viral structural proteins and the CAR-binding properties of the different serotypes were also assessed in these cells. Computer-based models of the fibre knobs of Ad4p and Ad17 were created based upon the crystallized fibre knob structure of adenoviruses and analysed for putative receptor-interacting regions that differed from the fibre knob of Ad5. The non CAR-binding vector candidate Ad11p showed clearly the best binding capacity to all of the neural cell lines, binding more than 90% of cells of all of the neural cell lines tested, in contrast to 20% or less for the commonly used vector Ad5v. Ad4p and Ad11p were also internalized and produced viral proteins more successfully than Ad5. Ad4p showed a low binding ability but a very efficient capacity for infection in cell culture. Ad17p virions neither bound or efficiently infected any of the neural cell lines studied.

Neurodynamics With Spatial Self-Organization

NASA Technical Reports Server (NTRS)

Zak, Michail A.

1993-01-01

Report presents theoretical study of dynamics of neural network organizing own response in both phase space and in position space. Postulates several mathematical models of dynamics including spatial derivatives representing local interconnections among neurons. Shows how neural responses propagate via these interconnections and how spatial pattern of neural responses formed in homogeneous biological neural network.

Pdgfrα functions in endothelial-derived cells to regulate neural crest cells and the development of the great arteries.

PubMed

Aghajanian, Haig; Cho, Young Kuk; Rizer, Nicholas W; Wang, Qiaohong; Li, Li; Degenhardt, Karl; Jain, Rajan

2017-09-01

Originating as a single vessel emerging from the embryonic heart, the truncus arteriosus must septate and remodel into the aorta and pulmonary artery to support postnatal life. Defective remodeling or septation leads to abnormalities collectively known as conotruncal defects, which are associated with significant mortality and morbidity. Multiple populations of cells must interact to coordinate outflow tract remodeling, and the cardiac neural crest has emerged as particularly important during this process. Abnormalities in the cardiac neural crest have been implicated in the pathogenesis of multiple conotruncal defects, including persistent truncus arteriosus, double outlet right ventricle and tetralogy of Fallot. However, the role of the neural crest in the pathogenesis of another conotruncal abnormality, transposition of the great arteries, is less well understood. In this report, we demonstrate an unexpected role of Pdgfra in endothelial cells and their derivatives during outflow tract development. Loss of Pdgfra in endothelium and endothelial-derived cells results in double outlet right ventricle and transposition of the great arteries. Our data suggest that loss of Pdgfra in endothelial-derived mesenchyme in the outflow tract endocardial cushions leads to a secondary defect in neural crest migration during development. © 2017. Published by The Company of Biologists Ltd.

A chemically defined substrate for the expansion and neuronal differentiation of human pluripotent stem cell-derived neural progenitor cells.

PubMed

Tsai, Yihuan; Cutts, Josh; Kimura, Azuma; Varun, Divya; Brafman, David A

2015-07-01

Due to the limitation of current pharmacological therapeutic strategies, stem cell therapies have emerged as a viable option for treating many incurable neurological disorders. Specifically, human pluripotent stem cell (hPSC)-derived neural progenitor cells (hNPCs), a multipotent cell population that is capable of near indefinite expansion and subsequent differentiation into the various cell types that comprise the central nervous system (CNS), could provide an unlimited source of cells for such cell-based therapies. However the clinical application of these cells will require (i) defined, xeno-free conditions for their expansion and neuronal differentiation and (ii) scalable culture systems that enable their expansion and neuronal differentiation in numbers sufficient for regenerative medicine and drug screening purposes. Current extracellular matrix protein (ECMP)-based substrates for the culture of hNPCs are expensive, difficult to isolate, subject to batch-to-batch variations, and, therefore, unsuitable for clinical application of hNPCs. Using a high-throughput array-based screening approach, we identified a synthetic polymer, poly(4-vinyl phenol) (P4VP), that supported the long-term proliferation and self-renewal of hNPCs. The hNPCs cultured on P4VP maintained their characteristic morphology, expressed high levels of markers of multipotency, and retained their ability to differentiate into neurons. Such chemically defined substrates will eliminate critical roadblocks for the utilization of hNPCs for human neural regenerative repair, disease modeling, and drug discovery. Copyright © 2015. Published by Elsevier B.V.

Macroscopic neural mass model constructed from a current-based network model of spiking neurons.

PubMed

Umehara, Hiroaki; Okada, Masato; Teramae, Jun-Nosuke; Naruse, Yasushi

2017-02-01

Neural mass models (NMMs) are efficient frameworks for describing macroscopic cortical dynamics including electroencephalogram and magnetoencephalogram signals. Originally, these models were formulated on an empirical basis of synaptic dynamics with relatively long time constants. By clarifying the relations between NMMs and the dynamics of microscopic structures such as neurons and synapses, we can better understand cortical and neural mechanisms from a multi-scale perspective. In a previous study, the NMMs were analytically derived by averaging the equations of synaptic dynamics over the neurons in the population and further averaging the equations of the membrane-potential dynamics. However, the averaging of synaptic current assumes that the neuron membrane potentials are nearly time invariant and that they remain at sub-threshold levels to retain the conductance-based model. This approximation limits the NMM to the non-firing state. In the present study, we newly propose a derivation of a NMM by alternatively approximating the synaptic current which is assumed to be independent of the membrane potential, thus adopting a current-based model. Our proposed model releases the constraint of the nearly constant membrane potential. We confirm that the obtained model is reducible to the previous model in the non-firing situation and that it reproduces the temporal mean values and relative power spectrum densities of the average membrane potentials for the spiking neurons. It is further ensured that the existing NMM properly models the averaged dynamics over individual neurons even if they are spiking in the populations.

Cell Therapy From Bench to Bedside Translation in CNS Neurorestoratology Era

PubMed Central

Huang, Hongyun; Chen, Lin; Sanberg, Paul

2010-01-01

Recent advances in cell biology, neural injury and repair, and the progress towards development of neurorestorative interventions are the basis for increased optimism. Based on the complexity of the processes of demyelination and remyelination, degeneration and regeneration, damage and repair, functional loss and recovery, it would be expected that effective therapeutic approaches will require a combination of strategies encompassing neuroplasticity, immunomodulation, neuroprotection, neurorepair, neuroreplacement, and neuromodulation. Cell-based restorative treatment has become a new trend, and increasing data worldwide have strongly proven that it has a pivotal therapeutic value in CNS disease. Moreover, functional neurorestoration has been achieved to a certain extent in the CNS clinically. Up to now, the cells successfully used in preclinical experiments and/or clinical trial/treatment include fetal/embryonic brain and spinal cord tissue, stem cells (embryonic stem cells, neural stem/progenitor cells, hematopoietic stem cells, adipose-derived adult stem/precursor cells, skin-derived precursor, induced pluripotent stem cells), glial cells (Schwann cells, oligodendrocyte, olfactory ensheathing cells, astrocytes, microglia, tanycytes), neuronal cells (various phenotypic neurons and Purkinje cells), mesenchymal stromal cells originating from bone marrow, umbilical cord, and umbilical cord blood, epithelial cells derived from the layer of retina and amnion, menstrual blood-derived stem cells, Sertoli cells, and active macrophages, etc. Proof-of-concept indicates that we have now entered a new era in neurorestoratology. PMID:21359168

Fixed-time synchronization of memristor-based BAM neural networks with time-varying discrete delay.

PubMed

Chen, Chuan; Li, Lixiang; Peng, Haipeng; Yang, Yixian

2017-12-01

This paper is devoted to studying the fixed-time synchronization of memristor-based BAM neural networks (MBAMNNs) with discrete delay. Fixed-time synchronization means that synchronization can be achieved in a fixed time for any initial values of the considered systems. In the light of the double-layer structure of MBAMNNs, we design two similar feedback controllers. Based on Lyapunov stability theories, several criteria are established to guarantee that the drive and response MBAMNNs can realize synchronization in a fixed time. In particular, by changing the parameters of controllers, this fixed time can be adjusted to some desired value in advance, irrespective of the initial values of MBAMNNs. Numerical simulations are included to validate the derived results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Direct adaptive control of wind energy conversion systems using Gaussian networks.

PubMed

Mayosky, M A; Cancelo, I E

1999-01-01

Grid connected wind energy conversion systems (WECS) present interesting control demands, due to the intrinsic nonlinear characteristics of windmills and electric generators. In this paper a direct adaptive control strategy for WECS control is proposed. It is based on the combination of two control actions: a radial basis zfunction network-based adaptive controller, which drives the tracking error to zero with user specified dynamics, and a supervisory controller, based on crude bounds of the system's nonlinearities. The supervisory controller fires when the finite neural-network approximation properties cannot be guaranteed. The form of the supervisor control and the adaptation law for the neural controller are derived from a Lyapunov analysis of stability. The results are applied to a typical turbine/generator pair, showing the feasibility of the proposed solution.

Application of olfactory tissue and its neural progenitors to schizophrenia and psychiatric research

PubMed Central

Lavoie, Joëlle; Sawa, Akira; Ishizuka, Koko

2017-01-01

Purpose of review The goal of this review article is to introduce olfactory epithelium (OE)-derived cell/tissue models as a promising surrogate system to study the molecular mechanisms implicated in schizophrenia (SZ) and other neuropsychiatric disorders. Here we particularly focus the utility of their neural progenitors. Recent findings Recent investigations of the pathophysiology of SZ using OE-derived tissue/cell models have provided insights about SZ-associated alterations in neurodevelopment, stress response, and gene/protein expression regulatory pathways. Summary The OE retains the capacity for lifelong neurogenesis and regeneration, because of the presence of neural stem cells and progenitors. Thus, both mature neurons and neural progenitors can be obtained from the OE without the need for genetic reprogramming and related confounds. Furthermore, the OE is highly scalable resource in translational settings. Here we also demonstrate recent findings from research using OE-derived tissue/cell models in SZ and other brain disorders. In summary, we propose that the OE as a promising resource to study neural molecular and cellular signatures relevant to the pathology of SZ and other mental disorders. PMID:28333692

Id expression in amphioxus and lamprey highlights the role of gene cooption during neural crest evolution

NASA Technical Reports Server (NTRS)

Meulemans, Daniel; McCauley, David; Bronner-Fraser, Marianne

2003-01-01

Neural crest cells are unique to vertebrates and generate many of the adult structures that differentiate them from their closest invertebrate relatives, the cephalochordates. Id genes are robust markers of neural crest cells at all stages of development. We compared Id gene expression in amphioxus and lamprey to ask if cephalochordates deploy Id genes at the neural plate border and dorsal neural tube in a manner similar to vertebrates. Furthermore, we examined whether Id expression in these cells is a basal vertebrate trait or a derived feature of gnathostomes. We found that while expression of Id genes in the mesoderm and endoderm is conserved between amphioxus and vertebrates, expression in the lateral neural plate border and dorsal neural tube is a vertebrate novelty. Furthermore, expression of lamprey Id implies that recruitment of Id genes to these cells occurred very early in the vertebrate lineage. Based on expression in amphioxus we postulate that Id cooption conferred sensory cell progenitor-like properties upon the lateral neurectoderm, and pharyngeal mesoderm-like properties upon cranial neural crest. Amphioxus Id expression is also consistent with homology between the anterior neurectoderm of amphioxus and the presumptive placodal ectoderm of vertebrates. These observations support the idea that neural crest evolution was driven in large part by cooption of multipurpose transcriptional regulators from other tissues and cell types.

LVQ and backpropagation neural networks applied to NASA SSME data

NASA Technical Reports Server (NTRS)

Doniere, Timothy F.; Dhawan, Atam P.

1993-01-01

Feedfoward neural networks with backpropagation learning have been used as function approximators for modeling the space shuttle main engine (SSME) sensor signals. The modeling of these sensor signals is aimed at the development of a sensor fault detection system that can be used during ground test firings. The generalization capability of a neural network based function approximator depends on the training vectors which in this application may be derived from a number of SSME ground test-firings. This yields a large number of training vectors. Large training sets can cause the time required to train the network to be very large. Also, the network may not be able to generalize for large training sets. To reduce the size of the training sets, the SSME test-firing data is reduced using the learning vector quantization (LVQ) based technique. Different compression ratios were used to obtain compressed data in training the neural network model. The performance of the neural model trained using reduced sets of training patterns is presented and compared with the performance of the model trained using complete data. The LVQ can also be used as a function approximator. The performance of the LVQ as a function approximator using reduced training sets is presented and compared with the performance of the backpropagation network.

A Mouse Ependymoma Model Provides Molecular Insights into Tumor Formation.

PubMed

Pajtler, Kristian W; Pfister, Stefan M

2018-06-26

Ozawa et al. present a murine tumor model resembling the most frequent molecular group of human supratentorial ependymoma, ST-EPN-RELA. Their model shows RELA-fusion-based de novo ependymoma tumorigenesis in the forebrain derived from neural stem cells. Copyright © 2018. Published by Elsevier Inc.

Computational neural networks in chemistry: Model free mapping devices for predicting chemical reactivity from molecular structure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elrod, D.W.

1992-01-01

Computational neural networks (CNNs) are a computational paradigm inspired by the brain's massively parallel network of highly interconnected neurons. The power of computational neural networks derives not so much from their ability to model the brain as from their ability to learn by example and to map highly complex, nonlinear functions, without the need to explicitly specify the functional relationship. Two central questions about CNNs were investigated in the context of predicting chemical reactions: (1) the mapping properties of neural networks and (2) the representation of chemical information for use in CNNs. Chemical reactivity is here considered an example ofmore » a complex, nonlinear function of molecular structure. CNN's were trained using modifications of the back propagation learning rule to map a three dimensional response surface similar to those typically observed in quantitative structure-activity and structure-property relationships. The computational neural network's mapping of the response surface was found to be robust to the effects of training sample size, noisy data and intercorrelated input variables. The investigation of chemical structure representation led to the development of a molecular structure-based connection-table representation suitable for neural network training. An extension of this work led to a BE-matrix structure representation that was found to be general for several classes of reactions. The CNN prediction of chemical reactivity and regiochemistry was investigated for electrophilic aromatic substitution reactions, Markovnikov addition to alkenes, Saytzeff elimination from haloalkanes, Diels-Alder cycloaddition, and retro Diels-Alder ring opening reactions using these connectivity-matrix derived representations. The reaction predictions made by the CNNs were more accurate than those of an expert system and were comparable to predictions made by chemists.« less

Enteric nervous system specific deletion of Foxd3 disrupts glial cell differentiation and activates compensatory enteric progenitors.

PubMed

Mundell, Nathan A; Plank, Jennifer L; LeGrone, Alison W; Frist, Audrey Y; Zhu, Lei; Shin, Myung K; Southard-Smith, E Michelle; Labosky, Patricia A

2012-03-15

The enteric nervous system (ENS) arises from the coordinated migration, expansion and differentiation of vagal and sacral neural crest progenitor cells. During development, vagal neural crest cells enter the foregut and migrate in a rostro-to-caudal direction, colonizing the entire gastrointestinal tract and generating the majority of the ENS. Sacral neural crest contributes to a subset of enteric ganglia in the hindgut, colonizing the colon in a caudal-to-rostral wave. During this process, enteric neural crest-derived progenitors (ENPs) self-renew and begin expressing markers of neural and glial lineages as they populate the intestine. Our earlier work demonstrated that the transcription factor Foxd3 is required early in neural crest-derived progenitors for self-renewal, multipotency and establishment of multiple neural crest-derived cells and structures including the ENS. Here, we describe Foxd3 expression within the fetal and postnatal intestine: Foxd3 was strongly expressed in ENPs as they colonize the gastrointestinal tract and was progressively restricted to enteric glial cells. Using a novel Ednrb-iCre transgene to delete Foxd3 after vagal neural crest cells migrate into the midgut, we demonstrated a late temporal requirement for Foxd3 during ENS development. Lineage labeling of Ednrb-iCre expressing cells in Foxd3 mutant embryos revealed a reduction of ENPs throughout the gut and loss of Ednrb-iCre lineage cells in the distal colon. Although mutant mice were viable, defects in patterning and distribution of ENPs were associated with reduced proliferation and severe reduction of glial cells derived from the Ednrb-iCre lineage. Analyses of ENS-lineage and differentiation in mutant embryos suggested activation of a compensatory population of Foxd3-positive ENPs that did not express the Ednrb-iCre transgene. Our findings highlight the crucial roles played by Foxd3 during ENS development including progenitor proliferation, neural patterning, and glial differentiation and may help delineate distinct molecular programs controlling vagal versus sacral neural crest development. Copyright © 2012 Elsevier Inc. All rights reserved.

GDNF facilitates differentiation of the adult dentate gyrus-derived neural precursor cells into astrocytes via STAT3

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boku, Shuken, E-mail: shuboku@med.hokudai.ac.jp; Nakagawa, Shin; Takamura, Naoki

2013-05-17

Highlights: •GDNF has no effect on ADP proliferation and apoptosis. •GDNF increases ADP differentiation into astrocyte. •A specific inhibitor of STAT3 decreases the astrogliogenic effect of GDNF. •STAT3 knockdown by lentiviral shRNA vector also decreases the astrogliogenic effect of GDNF. •GDNF increases the phosphorylation of STAT3. -- Abstract: While the pro-neurogenic actions of antidepressants in the adult hippocampal dentate gyrus (DG) are thought to be one of the mechanisms through which antidepressants exert their therapeutic actions, antidepressants do not increase proliferation of neural precursor cells derived from the adult DG. Because previous studies showed that antidepressants increase the expression andmore » secretion of glial cell line-derived neurotrophic factor (GDNF) in C6 glioma cells derived from rat astrocytes and GDNF increases neurogenesis in adult DG in vivo, we investigated the effects of GDNF on the proliferation, differentiation and apoptosis of cultured neural precursor cells derived from the adult DG. Data showed that GDNF facilitated the differentiation of neural precursor cells into astrocytes but had no effect on their proliferation or apoptosis. Moreover, GDNF increased the phosphorylation of STAT3, and both a specific inhibitor of STAT3 and lentiviral shRNA for STAT3 decreased their differentiation into astrocytes. Taken together, our findings suggest that GDNF facilitates astrogliogenesis from neural precursor cells in adult DG through activating STAT3 and that this action might indirectly affect neurogenesis.« less

Preclinical Analysis of Fetal Human Mesencephalic Neural Progenitor Cell Lines: Characterization and Safety In Vitro and In Vivo

PubMed Central

Moon, Jisook; Schwarz, Sigrid C.; Lee, Hyun‐Seob; Kang, Jun Mo; Lee, Young‐Eun; Kim, Bona; Sung, Mi‐Young; Höglinger, Günter; Wegner, Florian; Kim, Jin Su; Chung, Hyung‐Min; Chang, Sung Woon; Cha, Kwang Yul; Kim, Kwang‐Soo

2016-01-01

Abstract We have developed a good manufacturing practice for long‐term cultivation of fetal human midbrain‐derived neural progenitor cells. The generation of human dopaminergic neurons may serve as a tool of either restorative cell therapies or cellular models, particularly as a reference for phenotyping region‐specific human neural stem cell lines such as human embryonic stem cells and human inducible pluripotent stem cells. We cultivated 3 different midbrain neural progenitor lines at 10, 12, and 14 weeks of gestation for more than a year and characterized them in great detail, as well as in comparison with Lund mesencephalic cells. The whole cultivation process of tissue preparation, cultivation, and cryopreservation was developed using strict serum‐free conditions and standardized operating protocols under clean‐room conditions. Long‐term‐cultivated midbrain‐derived neural progenitor cells retained stemness, midbrain fate specificity, and floorplate markers. The potential to differentiate into authentic A9‐specific dopaminergic neurons was markedly elevated after prolonged expansion, resulting in large quantities of functional dopaminergic neurons without genetic modification. In restorative cell therapeutic approaches, midbrain‐derived neural progenitor cells reversed impaired motor function in rodents, survived well, and did not exhibit tumor formation in immunodeficient nude mice in the short or long term (8 and 30 weeks, respectively). We conclude that midbrain‐derived neural progenitor cells are a promising source for human dopaminergic neurons and suitable for long‐term expansion under good manufacturing practice, thus opening the avenue for restorative clinical applications or robust cellular models such as high‐content or high‐throughput screening. Stem Cells Translational Medicine 2017;6:576–588 PMID:28191758

Arrested neural and advanced mesenchymal differentiation of glioblastoma cells-comparative study with neural progenitors

PubMed Central

2009-01-01

Background Although features of variable differentiation in glioblastoma cell cultures have been reported, a comparative analysis of differentiation properties of normal neural GFAP positive progenitors, and those shown by glioblastoma cells, has not been performed. Methods Following methods were used to compare glioblastoma cells and GFAP+NNP (NHA): exposure to neural differentiation medium, exposure to adipogenic and osteogenic medium, western blot analysis, immunocytochemistry, single cell assay, BrdU incorporation assay. To characterize glioblastoma cells EGFR amplification analysis, LOH/MSI analysis, and P53 nucleotide sequence analysis were performed. Results In vitro differentiation of cancer cells derived from eight glioblastomas was compared with GFAP-positive normal neural progenitors (GFAP+NNP). Prior to exposure to differentiation medium, both types of cells showed similar multilineage phenotype (CD44+/MAP2+/GFAP+/Vimentin+/Beta III-tubulin+/Fibronectin+) and were positive for SOX-2 and Nestin. In contrast to GFAP+NNP, an efficient differentiation arrest was observed in all cell lines isolated from glioblastomas. Nevertheless, a subpopulation of cells isolated from four glioblastomas differentiated after serum-starvation with varying efficiency into derivatives indistinguishable from the neural derivatives of GFAP+NNP. Moreover, the cells derived from a majority of glioblastomas (7 out of 8), as well as GFAP+NNP, showed features of mesenchymal differentiation when exposed to medium with serum. Conclusion Our results showed that stable co-expression of multilineage markers by glioblastoma cells resulted from differentiation arrest. According to our data up to 95% of glioblastoma cells can present in vitro multilineage phenotype. The mesenchymal differentiation of glioblastoma cells is advanced and similar to mesenchymal differentiation of normal neural progenitors GFAP+NNP. PMID:19216795

IMNN: Information Maximizing Neural Networks

NASA Astrophysics Data System (ADS)

Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.

2018-04-01

This software trains artificial neural networks to find non-linear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). As compressing large data sets vastly simplifies both frequentist and Bayesian inference, important information may be inadvertently missed. Likelihood-free inference based on automatically derived IMNN summaries produces summaries that are good approximations to sufficient statistics. IMNNs are robustly capable of automatically finding optimal, non-linear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima.

Neural network robust tracking control with adaptive critic framework for uncertain nonlinear systems.

PubMed

Wang, Ding; Liu, Derong; Zhang, Yun; Li, Hongyi

2018-01-01

In this paper, we aim to tackle the neural robust tracking control problem for a class of nonlinear systems using the adaptive critic technique. The main contribution is that a neural-network-based robust tracking control scheme is established for nonlinear systems involving matched uncertainties. The augmented system considering the tracking error and the reference trajectory is formulated and then addressed under adaptive critic optimal control formulation, where the initial stabilizing controller is not needed. The approximate control law is derived via solving the Hamilton-Jacobi-Bellman equation related to the nominal augmented system, followed by closed-loop stability analysis. The robust tracking control performance is guaranteed theoretically via Lyapunov approach and also verified through simulation illustration. Copyright © 2017 Elsevier Ltd. All rights reserved.

Chronic behavior evaluation of a micro-machined neural implant with optimized design based on an experimentally derived model.

PubMed

Andrei, Alexandru; Welkenhuysen, Marleen; Ameye, Lieveke; Nuttin, Bart; Eberle, Wolfgang

2011-01-01

Understanding the mechanical interactions between implants and the surrounding tissue is known to have an important role for improving the bio-compatibility of such devices. Using a recently developed model, a particular micro-machined neural implant design aiming the reduction of insertion forces dependence on the insertion speed was optimized. Implantations with 10 and 100 μm/s insertion speeds showed excellent agreement with the predicted behavior. Lesion size, gliosis (GFAP), inflammation (ED1) and neuronal cells density (NeuN) was evaluated after 6 week of chronic implantation showing no insertion speed dependence.

Beta Hebbian Learning as a New Method for Exploratory Projection Pursuit.

PubMed

Quintián, Héctor; Corchado, Emilio

2017-09-01

In this research, a novel family of learning rules called Beta Hebbian Learning (BHL) is thoroughly investigated to extract information from high-dimensional datasets by projecting the data onto low-dimensional (typically two dimensional) subspaces, improving the existing exploratory methods by providing a clear representation of data's internal structure. BHL applies a family of learning rules derived from the Probability Density Function (PDF) of the residual based on the beta distribution. This family of rules may be called Hebbian in that all use a simple multiplication of the output of the neural network with some function of the residuals after feedback. The derived learning rules can be linked to an adaptive form of Exploratory Projection Pursuit and with artificial distributions, the networks perform as the theory suggests they should: the use of different learning rules derived from different PDFs allows the identification of "interesting" dimensions (as far from the Gaussian distribution as possible) in high-dimensional datasets. This novel algorithm, BHL, has been tested over seven artificial datasets to study the behavior of BHL parameters, and was later applied successfully over four real datasets, comparing its results, in terms of performance, with other well-known Exploratory and projection models such as Maximum Likelihood Hebbian Learning (MLHL), Locally-Linear Embedding (LLE), Curvilinear Component Analysis (CCA), Isomap and Neural Principal Component Analysis (Neural PCA).

Automatic physical inference with information maximizing neural networks

NASA Astrophysics Data System (ADS)

Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.

2018-04-01

Compressing large data sets to a manageable number of summaries that are informative about the underlying parameters vastly simplifies both frequentist and Bayesian inference. When only simulations are available, these summaries are typically chosen heuristically, so they may inadvertently miss important information. We introduce a simulation-based machine learning technique that trains artificial neural networks to find nonlinear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). In test cases where the posterior can be derived exactly, likelihood-free inference based on automatically derived IMNN summaries produces nearly exact posteriors, showing that these summaries are good approximations to sufficient statistics. In a series of numerical examples of increasing complexity and astrophysical relevance we show that IMNNs are robustly capable of automatically finding optimal, nonlinear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima. We anticipate that the automatic physical inference method described in this paper will be essential to obtain both accurate and precise cosmological parameter estimates from complex and large astronomical data sets, including those from LSST and Euclid.

Neural network representation and learning of mappings and their derivatives

NASA Technical Reports Server (NTRS)

White, Halbert; Hornik, Kurt; Stinchcombe, Maxwell; Gallant, A. Ronald

1991-01-01

Discussed here are recent theorems proving that artificial neural networks are capable of approximating an arbitrary mapping and its derivatives as accurately as desired. This fact forms the basis for further results establishing the learnability of the desired approximations, using results from non-parametric statistics. These results have potential applications in robotics, chaotic dynamics, control, and sensitivity analysis. An example involving learning the transfer function and its derivatives for a chaotic map is discussed.

Human Fetal Keratocytes Have Multipotent Characteristics in the Developing Avian Embryo

PubMed Central

Chao, Jennifer R.; Bronner, Marianne E.

2013-01-01

The human cornea contains stem cells that can be induced to express markers consistent with multipotency in cell culture; however, there have been no studies demonstrating that human corneal keratocytes are multipotent. The objective of this study is to examine the potential of human fetal keratocytes (HFKs) to differentiate into neural crest-derived tissues when challenged in an embryonic environment. HFKs were injected bilaterally into the cranial mesenchyme adjacent to the neural tube and the periocular mesenchyme in chick embryos at embryonic days 1.5 and 3, respectively. The injected keratocytes were detected by immunofluorescence using the human cell-specific marker, HuNu. HuNu-positive keratocytes injected along the neural crest pathway were localized adjacent to HNK-1-positive migratory host neural crest cells and in the cardiac cushion mesenchyme. The HuNu-positive cells transformed into neural crest derivatives such as smooth muscle in cranial blood vessels, stromal keratocytes, and corneal endothelium. However, they failed to form neurons despite their presence in the condensing trigeminal ganglion. These results show that HFKs retain the ability to differentiate into some neural crest-derived tissues. Their ability to respond to embryonic cues and generate corneal endothelium and stromal keratocytes provides a basis for understanding the feasibility of creating specialized cells for possible use in regenerative medicine. PMID:23461574

High-Throughput Screening to Identify Compounds That Increase Fragile X Mental Retardation Protein Expression in Neural Stem Cells Differentiated From Fragile X Syndrome Patient-Derived Induced Pluripotent Stem Cells.

PubMed

Kumari, Daman; Swaroop, Manju; Southall, Noel; Huang, Wenwei; Zheng, Wei; Usdin, Karen

2015-07-01

: Fragile X syndrome (FXS), the most common form of inherited cognitive disability, is caused by a deficiency of the fragile X mental retardation protein (FMRP). In most patients, the absence of FMRP is due to an aberrant transcriptional silencing of the fragile X mental retardation 1 (FMR1) gene. FXS has no cure, and the available treatments only provide symptomatic relief. Given that FMR1 gene silencing in FXS patient cells can be partially reversed by treatment with compounds that target repressive epigenetic marks, restoring FMRP expression could be one approach for the treatment of FXS. We describe a homogeneous and highly sensitive time-resolved fluorescence resonance energy transfer assay for FMRP detection in a 1,536-well plate format. Using neural stem cells differentiated from an FXS patient-derived induced pluripotent stem cell (iPSC) line that does not express any FMRP, we screened a collection of approximately 5,000 known tool compounds and approved drugs using this FMRP assay and identified 6 compounds that modestly increase FMR1 gene expression in FXS patient cells. Although none of these compounds resulted in clinically relevant levels of FMR1 mRNA, our data provide proof of principle that this assay combined with FXS patient-derived neural stem cells can be used in a high-throughput format to identify better lead compounds for FXS drug development. In this study, a specific and sensitive fluorescence resonance energy transfer-based assay for fragile X mental retardation protein detection was developed and optimized for high-throughput screening (HTS) of compound libraries using fragile X syndrome (FXS) patient-derived neural stem cells. The data suggest that this HTS format will be useful for the identification of better lead compounds for developing new therapeutics for FXS. This assay can also be adapted for FMRP detection in clinical and research settings. ©AlphaMed Press.

Generation of highly purified neural stem cells from human adipose-derived mesenchymal stem cells by Sox1 activation.

PubMed

Feng, Nianhua; Han, Qin; Li, Jing; Wang, Shihua; Li, Hongling; Yao, Xinglei; Zhao, Robert Chunhua

2014-03-01

Neural stem cells (NSCs) are ideal candidates in stem cell-based therapy for neurodegenerative diseases. However, it is unfeasible to get enough quantity of NSCs for clinical application. Generation of NSCs from human adipose-derived mesenchymal stem cells (hAD-MSCs) will provide a solution to this problem. Currently, the differentiation of hAD-MSCs into highly purified NSCs with biological functions is rarely reported. In our study, we established a three-step NSC-inducing protocol, in which hAD-MSCs were induced to generate NSCs with high purity after sequentially cultured in the pre-inducing medium (Step1), the N2B27 medium (Step2), and the N2B27 medium supplement with basic fibroblast growth factor and epidermal growth factor (Step3). These hAD-MSC-derived NSCs (adNSCs) can form neurospheres and highly express Sox1, Pax6, Nestin, and Vimentin; the proportion was 96.1% ± 1.3%, 96.8% ± 1.7%, 96.2% ± 1.3%, and 97.2% ± 2.5%, respectively, as detected by flow cytometry. These adNSCs can further differentiate into astrocytes, oligodendrocytes, and functional neurons, which were able to generate tetrodotoxin-sensitive sodium current. Additionally, we found that the neural differentiation of hAD-MSCs were significantly suppressed by Sox1 interference, and what's more, Step1 was a key step for the following induction, probably because it was associated with the initiation and nuclear translocation of Sox1, an important transcriptional factor for neural development. Finally, we observed that bone morphogenetic protein signal was inhibited, and Wnt/β-catenin signal was activated during inducing process, and both signals were related with Sox1 expression. In conclusion, we successfully established a three-step inducing protocol to derive NSCs from hAD-MSCs with high purity by Sox1 activation. These findings might enable to acquire enough autologous transplantable NSCs for the therapy of neurodegenerative diseases in clinic.

HD iPSC-derived neural progenitors accumulate in culture and are susceptible to BDNF withdrawal due to glutamate toxicity.

PubMed

Mattis, Virginia B; Tom, Colton; Akimov, Sergey; Saeedian, Jasmine; Østergaard, Michael E; Southwell, Amber L; Doty, Crystal N; Ornelas, Loren; Sahabian, Anais; Lenaeus, Lindsay; Mandefro, Berhan; Sareen, Dhruv; Arjomand, Jamshid; Hayden, Michael R; Ross, Christopher A; Svendsen, Clive N

2015-06-01

Huntington's disease (HD) is a fatal neurodegenerative disease, caused by expansion of polyglutamine repeats in the Huntingtin gene, with longer expansions leading to earlier ages of onset. The HD iPSC Consortium has recently reported a new in vitro model of HD based on the generation of induced pluripotent stem cells (iPSCs) from HD patients and controls. The current study has furthered the disease in a dish model of HD by generating new non-integrating HD and control iPSC lines. Both HD and control iPSC lines can be efficiently differentiated into neurons/glia; however, the HD-derived cells maintained a significantly greater number of nestin-expressing neural progenitor cells compared with control cells. This cell population showed enhanced vulnerability to brain-derived neurotrophic factor (BDNF) withdrawal in the juvenile-onset HD (JHD) lines, which appeared to be CAG repeat-dependent and mediated by the loss of signaling from the TrkB receptor. It was postulated that this increased death following BDNF withdrawal may be due to glutamate toxicity, as the N-methyl-d-aspartate (NMDA) receptor subunit NR2B was up-regulated in the cultures. Indeed, blocking glutamate signaling, not just through the NMDA but also mGlu and AMPA/Kainate receptors, completely reversed the cell death phenotype. This study suggests that the pathogenesis of JHD may involve in part a population of 'persistent' neural progenitors that are selectively vulnerable to BDNF withdrawal. Similar results were seen in adult hippocampal-derived neural progenitors isolated from the BACHD model mouse. Together, these results provide important insight into HD mechanisms at early developmental time points, which may suggest novel approaches to HD therapeutics. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

New genes in the evolution of the neural crest differentiation program

PubMed Central

2007-01-01

Background Development of the vertebrate head depends on the multipotency and migratory behavior of neural crest derivatives. This cell population is considered a vertebrate innovation and, accordingly, chordate ancestors lacked neural crest counterparts. The identification of neural crest specification genes expressed in the neural plate of basal chordates, in addition to the discovery of pigmented migratory cells in ascidians, has challenged this hypothesis. These new findings revive the debate on what is new and what is ancient in the genetic program that controls neural crest formation. Results To determine the origin of neural crest genes, we analyzed Phenotype Ontology annotations to select genes that control the development of this tissue. Using a sequential blast pipeline, we phylogenetically classified these genes, as well as those associated with other tissues, in order to define tissue-specific profiles of gene emergence. Of neural crest genes, 9% are vertebrate innovations. Our comparative analyses show that, among different tissues, the neural crest exhibits a particularly high rate of gene emergence during vertebrate evolution. A remarkable proportion of the new neural crest genes encode soluble ligands that control neural crest precursor specification into each cell lineage, including pigmented, neural, glial, and skeletal derivatives. Conclusion We propose that the evolution of the neural crest is linked not only to the recruitment of ancestral regulatory genes but also to the emergence of signaling peptides that control the increasingly complex lineage diversification of this plastic cell population. PMID:17352807

Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe

PubMed Central

Shlizerman, Eli; Riffell, Jeffrey A.; Kutz, J. Nathan

2014-01-01

The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units), and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (1) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (2) characterize scent recognition, i.e., decision-making based on olfactory signals and (3) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns. PMID:25165442

Classification of 2-dimensional array patterns: assembling many small neural networks is better than using a large one.

PubMed

Chen, Liang; Xue, Wei; Tokuda, Naoyuki

2010-08-01

In many pattern classification/recognition applications of artificial neural networks, an object to be classified is represented by a fixed sized 2-dimensional array of uniform type, which corresponds to the cells of a 2-dimensional grid of the same size. A general neural network structure, called an undistricted neural network, which takes all the elements in the array as inputs could be used for problems such as these. However, a districted neural network can be used to reduce the training complexity. A districted neural network usually consists of two levels of sub-neural networks. Each of the lower level neural networks, called a regional sub-neural network, takes the elements in a region of the array as its inputs and is expected to output a temporary class label, called an individual opinion, based on the partial information of the entire array. The higher level neural network, called an assembling sub-neural network, uses the outputs (opinions) of regional sub-neural networks as inputs, and by consensus derives the label decision for the object. Each of the sub-neural networks can be trained separately and thus the training is less expensive. The regional sub-neural networks can be trained and performed in parallel and independently, therefore a high speed can be achieved. We prove theoretically in this paper, using a simple model, that a districted neural network is actually more stable than an undistricted neural network in noisy environments. We conjecture that the result is valid for all neural networks. This theory is verified by experiments involving gender classification and human face recognition. We conclude that a districted neural network is highly recommended for neural network applications in recognition or classification of 2-dimensional array patterns in highly noisy environments. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Purification of human induced pluripotent stem cell-derived neural precursors using magnetic activated cell sorting.

PubMed

Rodrigues, Gonçalo M C; Fernandes, Tiago G; Rodrigues, Carlos A V; Cabral, Joaquim M S; Diogo, Maria Margarida

2015-01-01

Neural precursor (NP) cells derived from human induced pluripotent stem cells (hiPSCs), and their neuronal progeny, will play an important role in disease modeling, drug screening tests, central nervous system development studies, and may even become valuable for regenerative medicine treatments. Nonetheless, it is challenging to obtain homogeneous and synchronously differentiated NP populations from hiPSCs, and after neural commitment many pluripotent stem cells remain in the differentiated cultures. Here, we describe an efficient and simple protocol to differentiate hiPSC-derived NPs in 12 days, and we include a final purification stage where Tra-1-60+ pluripotent stem cells (PSCs) are removed using magnetic activated cell sorting (MACS), leaving the NP population nearly free of PSCs.

IDH1R132H in Neural Stem Cells: Differentiation Impaired by Increased Apoptosis

PubMed Central

Rosiak, Kamila; Smolarz, Maciej; Stec, Wojciech J.; Peciak, Joanna; Grzela, Dawid; Winiecka-Klimek, Marta; Stoczynska-Fidelus, Ewelina; Krynska, Barbara; Piaskowski, Sylwester; Rieske, Piotr

2016-01-01

Background The high frequency of mutations in the isocitrate dehydrogenase 1 (IDH1) gene in diffuse gliomas indicates its importance in the process of gliomagenesis. These mutations result in loss of the normal function and acquisition of the neomorphic activity converting α-ketoglutarate to 2-hydroxyglutarate. This potential oncometabolite may induce the epigenetic changes, resulting in the deregulated expression of numerous genes, including those related to the differentiation process or cell survivability. Methods Neural stem cells were derived from human induced pluripotent stem cells following embryoid body formation. Neural stem cells transduced with mutant IDH1R132H, empty vector, non-transduced and overexpressing IDH1WT controls were differentiated into astrocytes and neurons in culture. The neuronal and astrocytic differentiation was determined by morphology and expression of lineage specific markers (MAP2, Synapsin I and GFAP) as determined by real-time PCR and immunocytochemical staining. Apoptosis was evaluated by real-time observation of Caspase-3 activation and measurement of PARP cleavage by Western Blot. Results Compared with control groups, cells expressing IDH1R132H retained an undifferentiated state and lacked morphological changes following stimulated differentiation. The significant inhibitory effect of IDH1R132H on neuronal and astrocytic differentiation was confirmed by immunocytochemical staining for markers of neural stem cells. Additionally, real-time PCR indicated suppressed expression of lineage markers. High percentage of apoptotic cells was detected within IDH1R132H-positive neural stem cells population and their derivatives, if compared to normal neural stem cells and their derivatives. The analysis of PARP and Caspase-3 activity confirmed apoptosis sensitivity in mutant protein-expressing neural cells. Conclusions Our study demonstrates that expression of IDH1R132H increases apoptosis susceptibility of neural stem cells and their derivatives. Robust apoptosis causes differentiation deficiency of IDH1R132H-expressing cells. PMID:27145078

Intervertebral disc-derived stem cells: implications for regenerative medicine and neural repair.

PubMed

Erwin, W Mark; Islam, Diana; Eftekarpour, Eftekhar; Inman, Robert D; Karim, Muhammad Zia; Fehlings, Michael G

2013-02-01

An in vitro and in vivo evaluation of intervertebral disc (IVD)-derived stem/progenitor cells. To determine the chondrogenic, adipogenic, osteogenic, and neurogenic differentiation capacity of disc-derived stem/progenitor cells in vitro and neurogenic differentiation in vivo. Tissue repair strategies require a source of appropriate cells that could be used to replace dead or damaged cells and tissues such as stem cells. Here we examined the potential use of IVD-derived stem cells in regenerative medicine approaches and neural repair. Nonchondrodystrophic canine IVD nucleus pulposus (NP) cells were used to generate stem/progenitor cells (NP progenitor cells [NPPCs]) and the NPPCs were differentiated in vitro into chondrogenic, adipogenic, and neurogenic lineages and in vivo into the neurogenic lineage. NPPCs were compared with bone marrow-derived mesenchymal (stromal) stem cells in terms of the expression of stemness genes. The expression of the neural crest marker protein 0 and the Brachyury gene were evaluated in NP cells and NPPCs. NPPCs contain stem/progenitor cells and express "stemness" genes such as Sox2, Oct3/4, Nanog, CD133, Nestin, and neural cell adhesion molecule but differ from mesenchymal (stromal) stem cells in the higher expression of the Nanog gene by NPPCs. NPPCs do not express protein 0 or the Brachyury gene both of which are expressed by the totality of IVD NP cells. The percentage of NPPCs within the IVD is 1% of the total as derived by colony-forming assay. NPPCs are capable of differentiating along chondrogenic, adipogenic, and neurogenic lineages in vitro and into oligodendrocyte, neuron, and astroglial specific precursor cells in vivo within the compact myelin-deficient shiverer mouse. We propose that the IVD NP represents a regenerative niche suggesting that the IVD could represent a readily accessible source of precursor cells for neural repair and regeneration.

Novel adaptive neural control design for a constrained flexible air-breathing hypersonic vehicle based on actuator compensation

NASA Astrophysics Data System (ADS)

Bu, Xiangwei; Wu, Xiaoyan; He, Guangjun; Huang, Jiaqi

2016-03-01

This paper investigates the design of a novel adaptive neural controller for the longitudinal dynamics of a flexible air-breathing hypersonic vehicle with control input constraints. To reduce the complexity of controller design, the vehicle dynamics is decomposed into the velocity subsystem and the altitude subsystem, respectively. For each subsystem, only one neural network is utilized to approach the lumped unknown function. By employing a minimal-learning parameter method to estimate the norm of ideal weight vectors rather than their elements, there are only two adaptive parameters required for neural approximation. Thus, the computational burden is lower than the ones derived from neural back-stepping schemes. Specially, to deal with the control input constraints, additional systems are exploited to compensate the actuators. Lyapunov synthesis proves that all the closed-loop signals involved are uniformly ultimately bounded. Finally, simulation results show that the adopted compensation scheme can tackle actuator constraint effectively and moreover velocity and altitude can stably track their reference trajectories even when the physical limitations on control inputs are in effect.

Anomalous neural circuit function in schizophrenia during a virtual Morris water task.

PubMed

Folley, Bradley S; Astur, Robert; Jagannathan, Kanchana; Calhoun, Vince D; Pearlson, Godfrey D

2010-02-15

Previous studies have reported learning and navigation impairments in schizophrenia patients during virtual reality allocentric learning tasks. The neural bases of these deficits have not been explored using functional MRI despite well-explored anatomic characterization of these paradigms in non-human animals. Our objective was to characterize the differential distributed neural circuits involved in virtual Morris water task performance using independent component analysis (ICA) in schizophrenia patients and controls. Additionally, we present behavioral data in order to derive relationships between brain function and performance, and we have included a general linear model-based analysis in order to exemplify the incremental and differential results afforded by ICA. Thirty-four individuals with schizophrenia and twenty-eight healthy controls underwent fMRI scanning during a block design virtual Morris water task using hidden and visible platform conditions. Independent components analysis was used to deconstruct neural contributions to hidden and visible platform conditions for patients and controls. We also examined performance variables, voxel-based morphometry and hippocampal subparcellation, and regional BOLD signal variation. Independent component analysis identified five neural circuits. Mesial temporal lobe regions, including the hippocampus, were consistently task-related across conditions and groups. Frontal, striatal, and parietal circuits were recruited preferentially during the visible condition for patients, while frontal and temporal lobe regions were more saliently recruited by controls during the hidden platform condition. Gray matter concentrations and BOLD signal in hippocampal subregions were associated with task performance in controls but not patients. Patients exhibited impaired performance on the hidden and visible conditions of the task, related to negative symptom severity. While controls showed coupling between neural circuits, regional neuroanatomy, and behavior, patients activated different task-related neural circuits, not associated with appropriate regional neuroanatomy. GLM analysis elucidated several comparable regions, with the exception of the hippocampus. Inefficient allocentric learning and memory in patients may be related to an inability to recruit appropriate task-dependent neural circuits. Copyright 2009 Elsevier Inc. All rights reserved.

Discovery of Benzofuran Derivatives that Collaborate with Insulin-Like Growth Factor 1 (IGF-1) to Promote Neuroprotection.

PubMed

Wakabayashi, Takeshi; Tokunaga, Norihito; Tokumaru, Kazuyuki; Ohra, Taiichi; Koyama, Nobuyuki; Hayashi, Satoru; Yamada, Ryuji; Shirasaki, Mikio; Inui, Yoshitaka; Tsukamoto, Tetsuya

2016-05-26

A series of benzofuran derivatives with neuroprotective activity in collaboration with IGF-1 was discovered using a newly developed cell-based assay involving primary neural cells prepared from rat hippocampal and cerebral cortical tissues. A structure-activity relationship study identified compound 8 as exhibiting potent activity and brain penetrability. An in vitro pharmacological study demonstrated that although IGF-1 and 8 individually exhibited the neuroprotective effect, the latter acted in collaboration with IGF-1 to enhance neuroprotective activity.

Neural Stem Cells Derived from Human Parthenogenetic Stem Cells Engraft and Promote Recovery in a Nonhuman Primate Model of Parkinson's Disease.

PubMed

Gonzalez, Rodolfo; Garitaonandia, Ibon; Poustovoitov, Maxim; Abramihina, Tatiana; McEntire, Caleb; Culp, Ben; Attwood, Jordan; Noskov, Alexander; Christiansen-Weber, Trudy; Khater, Marwa; Mora-Castilla, Sergio; To, Cuong; Crain, Andrew; Sherman, Glenn; Semechkin, Andrey; Laurent, Louise C; Elsworth, John D; Sladek, John; Snyder, Evan Y; Redmond, D Eugene; Kern, Russell A

2016-11-01

Cell therapy has attracted considerable interest as a promising therapeutic alternative for patients with Parkinson's disease (PD). Clinical studies have shown that grafted fetal neural tissue can achieve considerable biochemical and clinical improvements in PD. However, the source of fetal tissue grafts is limited and ethically controversial. Human parthenogenetic stem cells offer a good alternative because they are derived from unfertilized oocytes without destroying potentially viable human embryos and can be used to generate an unlimited supply of neural cells for transplantation. We have previously reported that human parthenogenetic stem cell-derived neural stem cells (hpNSCs) successfully engraft, survive long term, and increase brain dopamine (DA) levels in rodent and nonhuman primate models of PD. Here we report the results of a 12-month transplantation study of hpNSCs in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-lesioned African green monkeys with moderate to severe clinical parkinsonian symptoms. The hpNSCs manufactured under current good manufacturing practice (cGMP) conditions were injected bilaterally into the striatum and substantia nigra of immunosuppressed monkeys. Transplantation of hpNSCs was safe and well tolerated by the animals with no dyskinesia, tumors, ectopic tissue formation, or other test article-related serious adverse events. We observed that hpNSCs promoted behavioral recovery; increased striatal DA concentration, fiber innervation, and number of dopaminergic neurons; and induced the expression of genes and pathways downregulated in PD compared to vehicle control animals. These results provide further evidence for the clinical translation of hpNSCs and support the approval of the world's first pluripotent stem cell-based phase I/IIa study for the treatment of PD (Clinical Trial Identifier NCT02452723).

On-line Tool Wear Detection on DCMT070204 Carbide Tool Tip Based on Noise Cutting Audio Signal using Artificial Neural Network

NASA Astrophysics Data System (ADS)

Prasetyo, T.; Amar, S.; Arendra, A.; Zam Zami, M. K.

2018-01-01

This study develops an on-line detection system to predict the wear of DCMT070204 tool tip during the cutting process of the workpiece. The machine used in this research is CNC ProTurn 9000 to cut ST42 steel cylinder. The audio signal has been captured using the microphone placed in the tool post and recorded in Matlab. The signal is recorded at the sampling rate of 44.1 kHz, and the sampling size of 1024. The recorded signal is 110 data derived from the audio signal while cutting using a normal chisel and a worn chisel. And then perform signal feature extraction in the frequency domain using Fast Fourier Transform. Feature selection is done based on correlation analysis. And tool wear classification was performed using artificial neural networks with 33 input features selected. This artificial neural network is trained with back propagation method. Classification performance testing yields an accuracy of 74%.

Neural-net-based image matching

NASA Astrophysics Data System (ADS)

Jerebko, Anna K.; Barabanov, Nikita E.; Luciv, Vadim R.; Allinson, Nigel M.

2000-04-01

The paper describes a neural-based method for matching spatially distorted image sets. The matching of partially overlapping images is important in many applications-- integrating information from images formed from different spectral ranges, detecting changes in a scene and identifying objects of differing orientations and sizes. Our approach consists of extracting contour features from both images, describing the contour curves as sets of line segments, comparing these sets, determining the corresponding curves and their common reference points, calculating the image-to-image co-ordinate transformation parameters on the basis of the most successful variant of the derived curve relationships. The main steps are performed by custom neural networks. The algorithms describe in this paper have been successfully tested on a large set of images of the same terrain taken in different spectral ranges, at different seasons and rotated by various angles. In general, this experimental verification indicates that the proposed method for image fusion allows the robust detection of similar objects in noisy, distorted scenes where traditional approaches often fail.

Three-dimensional scaffolding to investigate neuronal derivatives of human embryonic stem cells.

PubMed

Soman, Pranav; Tobe, Brian T D; Lee, Jin Woo; Winquist, Alicia M; Singec, Ilyas; Vecchio, Kenneth S; Snyder, Evan Y; Chen, Shaochen

2012-10-01

Access to unlimited numbers of live human neurons derived from stem cells offers unique opportunities for in vitro modeling of neural development, disease-related cellular phenotypes, and drug testing and discovery. However, to develop informative cellular in vitro assays, it is important to consider the relevant in vivo environment of neural tissues. Biomimetic 3D scaffolds are tools to culture human neurons under defined mechanical and physico-chemical properties providing an interconnected porous structure that may potentially enable a higher or more complex organization than traditional two-dimensional monolayer conditions. It is known that even minor variations in the internal geometry and mechanical properties of 3D scaffolds can impact cell behavior including survival, growth, and cell fate choice. In this report, we describe the design and engineering of 3D synthetic polyethylene glycol (PEG)-based and biodegradable gelatin-based scaffolds generated by a free form fabrication technique with precise internal geometry and elastic stiffnesses. We show that human neurons, derived from human embryonic stem (hESC) cells, are able to adhere to these scaffolds and form organoid structures that extend in three dimensions as demonstrated by confocal and electron microscopy. Future refinements of scaffold structure, size and surface chemistries may facilitate long term experiments and designing clinically applicable bioassays.

The Effects of Low-Dose Bisphenol A and Bisphenol F on Neural Differentiation of a Fetal Brain-Derived Neural Progenitor Cell Line.

PubMed

Fujiwara, Yuki; Miyazaki, Wataru; Koibuchi, Noriyuki; Katoh, Takahiko

2018-01-01

Environmental chemicals are known to disrupt the endocrine system in humans and to have adverse effects on several organs including the developing brain. Recent studies indicate that exposure to environmental chemicals during gestation can interfere with neuronal differentiation, subsequently affecting normal brain development in newborns. Xenoestrogen, bisphenol A (BPA), which is widely used in plastic products, is one such chemical. Adverse effects of exposure to BPA during pre- and postnatal periods include the disruption of brain function. However, the effect of BPA on neural differentiation remains unclear. In this study, we explored the effects of BPA or bisphenol F (BPF), an alternative compound for BPA, on neural differentiation using ReNcell, a human fetus-derived neural progenitor cell line. Maintenance in growth factor-free medium initiated the differentiation of ReNcell to neuronal cells including neurons, astrocytes, and oligodendrocytes. We exposed the cells to BPA or BPF for 3 days from the period of initiation and performed real-time PCR for neural markers such as β III-tubulin and glial fibrillary acidic protein (GFAP), and Olig2. The β III-tubulin mRNA level decreased in response to BPA, but not BPF, exposure. We also observed that the number of β III-tubulin-positive cells in the BPA-exposed group was less than that of the control group. On the other hand, there were no changes in the MAP2 mRNA level. These results indicate that BPA disrupts neural differentiation in human-derived neural progenitor cells, potentially disrupting brain development.

Human induced pluripotent stem cell-derived glial cells and neural progenitors display divergent responses to Zika and dengue infections.

PubMed

Muffat, Julien; Li, Yun; Omer, Attya; Durbin, Ann; Bosch, Irene; Bakiasi, Grisilda; Richards, Edward; Meyer, Aaron; Gehrke, Lee; Jaenisch, Rudolf

2018-06-18

Maternal Zika virus (ZIKV) infection during pregnancy is recognized as the cause of an epidemic of microcephaly and other neurological anomalies in human fetuses. It remains unclear how ZIKV accesses the highly vulnerable population of neural progenitors of the fetal central nervous system (CNS), and which cell types of the CNS may be viral reservoirs. In contrast, the related dengue virus (DENV) does not elicit teratogenicity. To model viral interaction with cells of the fetal CNS in vitro, we investigated the tropism of ZIKV and DENV for different induced pluripotent stem cell-derived human cells, with a particular focus on microglia-like cells. We show that ZIKV infected isogenic neural progenitors, astrocytes, and microglia-like cells (pMGLs), but was only cytotoxic to neural progenitors. Infected glial cells propagated ZIKV and maintained ZIKV load over time, leading to viral spread to susceptible cells. DENV triggered stronger immune responses and could be cleared by neural and glial cells more efficiently. pMGLs, when cocultured with neural spheroids, invaded the tissue and, when infected with ZIKV, initiated neural infection. Since microglia derive from primitive macrophages originating in proximity to the maternal vasculature, they may act as a viral reservoir for ZIKV and establish infection of the fetal brain. Infection of immature neural stem cells by invading microglia may occur in the early stages of pregnancy, before angiogenesis in the brain rudiments. Our data are also consistent with ZIKV and DENV affecting the integrity of the blood-brain barrier, thus allowing infection of the brain later in life.

Neural Network Computing and Natural Language Processing.

ERIC Educational Resources Information Center

Borchardt, Frank

1988-01-01

Considers the application of neural network concepts to traditional natural language processing and demonstrates that neural network computing architecture can: (1) learn from actual spoken language; (2) observe rules of pronunciation; and (3) reproduce sounds from the patterns derived by its own processes. (Author/CB)

Comparative transcriptome analysis in induced neural stem cells reveals defined neural cell identities in vitro and after transplantation into the adult rodent brain.

PubMed

Hallmann, Anna-Lena; Araúzo-Bravo, Marcos J; Zerfass, Christina; Senner, Volker; Ehrlich, Marc; Psathaki, Olympia E; Han, Dong Wook; Tapia, Natalia; Zaehres, Holm; Schöler, Hans R; Kuhlmann, Tanja; Hargus, Gunnar

2016-05-01

Reprogramming technology enables the production of neural progenitor cells (NPCs) from somatic cells by direct transdifferentiation. However, little is known on how neural programs in these induced neural stem cells (iNSCs) differ from those of alternative stem cell populations in vitro and in vivo. Here, we performed transcriptome analyses on murine iNSCs in comparison to brain-derived neural stem cells (NSCs) and pluripotent stem cell-derived NPCs, which revealed distinct global, neural, metabolic and cell cycle-associated marks in these populations. iNSCs carried a hindbrain/posterior cell identity, which could be shifted towards caudal, partially to rostral but not towards ventral fates in vitro. iNSCs survived after transplantation into the rodent brain and exhibited in vivo-characteristics, neural and metabolic programs similar to transplanted NSCs. However, iNSCs vastly retained caudal identities demonstrating cell-autonomy of regional programs in vivo. These data could have significant implications for a variety of in vitro- and in vivo-applications using iNSCs. Copyright © 2016 Roslin Cells Ltd. Published by Elsevier B.V. All rights reserved.

HNK-1 immunoreactivity during early morphogenesis of the head region in a nonmodel vertebrate, crocodile embryo

NASA Astrophysics Data System (ADS)

Kundrát, Martin

2008-11-01

The present study examines HNK-1 immunoidentification of a population of the neural crest (NC) during early head morphogenesis in the nonmodel vertebrate, the crocodile ( Crocodylus niloticus) embryos. Although HNK-1 is not an exclusive NC marker among vertebrates, temporospatial immunoreactive patterns found in the crocodile are almost consistent with NC patterns derived from gene expression studies known in birds (the closest living relatives of crocodiles) and mammals. In contrast to birds, the HNK-1 epitope is immunoreactive in NC cells at the neural fold level in crocodile embryos and therefore provides sufficient base to assess early migratory events of the cephalic NC. I found that crocodile NC forms three classic migratory pathways in the head: mandibular, hyoid, and branchial. Further, I demonstrate that, besides this classic phenotype, there is also a forebrain-derived migratory population, which consolidates into a premandibular stream in the crocodile. In contrast to the closely related chick model, crocodilian premandibular and mandibular NC cells arise from the open neural tube suggesting that species-specific heterochronic behavior of NC may be involved in the formation of different vertebrate facial phenotypes.

Deriving neural network controllers from neuro-biological data: implementation of a single-leg stick insect controller.

PubMed

von Twickel, Arndt; Büschges, Ansgar; Pasemann, Frank

2011-02-01

This article presents modular recurrent neural network controllers for single legs of a biomimetic six-legged robot equipped with standard DC motors. Following arguments of Ekeberg et al. (Arthropod Struct Dev 33:287-300, 2004), completely decentralized and sensori-driven neuro-controllers were derived from neuro-biological data of stick-insects. Parameters of the controllers were either hand-tuned or optimized by an evolutionary algorithm. Employing identical controller structures, qualitatively similar behaviors were achieved for robot and for stick insect simulations. For a wide range of perturbing conditions, as for instance changing ground height or up- and downhill walking, swing as well as stance control were shown to be robust. Behavioral adaptations, like varying locomotion speeds, could be achieved by changes in neural parameters as well as by a mechanical coupling to the environment. To a large extent the simulated walking behavior matched biological data. For example, this was the case for body support force profiles and swing trajectories under varying ground heights. The results suggest that the single-leg controllers are suitable as modules for hexapod controllers, and they might therefore bridge morphological- and behavioral-based approaches to stick insect locomotion control.

Surface modification of polydimethylsiloxane (PDMS) induced proliferation and neural-like cells differentiation of umbilical cord blood-derived mesenchymal stem cells.

PubMed

Kim, Sun-Jung; Lee, Jae Kyoo; Kim, Jin Won; Jung, Ji-Won; Seo, Kwangwon; Park, Sang-Bum; Roh, Kyung-Hwan; Lee, Sae-Rom; Hong, Yun Hwa; Kim, Sang Jeong; Lee, Yong-Soon; Kim, Sung June; Kang, Kyung-Sun

2008-08-01

Stem cell-based therapy has recently emerged for use in novel therapeutics for incurable diseases. For successful recovery from neurologic diseases, the most pivotal factor is differentiation and directed neuronal cell growth. In this study, we fabricated three different widths of a micro-pattern on polydimethylsiloxane (PDMS; 1, 2, and 4 microm). Surface modification of the PDMS was investigated for its capacity to manage proliferation and differentiation of neural-like cells from umbilical cord blood-derived mesenchymal stem cells (UCB-MSCs). Among the micro-patterned PDMS fabrications, the 1 microm-patterned PDMS significantly increased cell proliferation and most of the cells differentiated into neuronal cells. In addition, the 1 microm-patterned PDMS induced an increase in cytosolic calcium, while the differentiated cells on the flat and 4 microm-patterned PDMS had no response. PDMS with a 1 microm pattern was also aligned to direct orientation within 10 degrees angles. Taken together, micro-patterned PDMS supported UCB-MSC proliferation and induced neural like-cell differentiation. Our data suggest that micro-patterned PDMS might be a guiding method for stem cell therapy that would improve its therapeutic action in neurological diseases.

Feature extraction using extrema sampling of discrete derivatives for spike sorting in implantable upper-limb neural prostheses.

PubMed

Zamani, Majid; Demosthenous, Andreas

2014-07-01

Next generation neural interfaces for upper-limb (and other) prostheses aim to develop implantable interfaces for one or more nerves, each interface having many neural signal channels that work reliably in the stump without harming the nerves. To achieve real-time multi-channel processing it is important to integrate spike sorting on-chip to overcome limitations in transmission bandwidth. This requires computationally efficient algorithms for feature extraction and clustering suitable for low-power hardware implementation. This paper describes a new feature extraction method for real-time spike sorting based on extrema analysis (namely positive peaks and negative peaks) of spike shapes and their discrete derivatives at different frequency bands. Employing simulation across different datasets, the accuracy and computational complexity of the proposed method are assessed and compared with other methods. The average classification accuracy of the proposed method in conjunction with online sorting (O-Sort) is 91.6%, outperforming all the other methods tested with the O-Sort clustering algorithm. The proposed method offers a better tradeoff between classification error and computational complexity, making it a particularly strong choice for on-chip spike sorting.

Modality Switching Cost during Property Verification by 7 Years of Age

ERIC Educational Resources Information Center

Ambrosi, Solene; Kalenine, Solene; Blaye, Agnes; Bonthoux, Francoise

2011-01-01

Recent studies in neuroimagery and cognitive psychology support the view of sensory-motor based knowledge: when processing an object concept, neural systems would re-enact previous experiences with this object. In this experiment, a conceptual switching cost paradigm derived from Pecher, Zeelenberg, and Barsalou (2003, 2004) was used to…

Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation.

PubMed

Jimeno Yepes, Antonio

2017-09-01

Word sense disambiguation helps identifying the proper sense of ambiguous words in text. With large terminologies such as the UMLS Metathesaurus ambiguities appear and highly effective disambiguation methods are required. Supervised learning algorithm methods are used as one of the approaches to perform disambiguation. Features extracted from the context of an ambiguous word are used to identify the proper sense of such a word. The type of features have an impact on machine learning methods, thus affect disambiguation performance. In this work, we have evaluated several types of features derived from the context of the ambiguous word and we have explored as well more global features derived from MEDLINE using word embeddings. Results show that word embeddings improve the performance of more traditional features and allow as well using recurrent neural network classifiers based on Long-Short Term Memory (LSTM) nodes. The combination of unigrams and word embeddings with an SVM sets a new state of the art performance with a macro accuracy of 95.97 in the MSH WSD data set. Copyright © 2017 Elsevier Inc. All rights reserved.

Neural Differentiation of Human Pluripotent Stem Cells for Nontherapeutic Applications: Toxicology, Pharmacology, and In Vitro Disease Modeling

PubMed Central

Yap, May Shin; Nathan, Kavitha R.; Yeo, Yin; Poh, Chit Laa; Richards, Mark; Lim, Wei Ling; Othman, Iekhsan; Heng, Boon Chin

2015-01-01

Human pluripotent stem cells (hPSCs) derived from either blastocyst stage embryos (hESCs) or reprogrammed somatic cells (iPSCs) can provide an abundant source of human neuronal lineages that were previously sourced from human cadavers, abortuses, and discarded surgical waste. In addition to the well-known potential therapeutic application of these cells in regenerative medicine, these are also various promising nontherapeutic applications in toxicological and pharmacological screening of neuroactive compounds, as well as for in vitro modeling of neurodegenerative and neurodevelopmental disorders. Compared to alternative research models based on laboratory animals and immortalized cancer-derived human neural cell lines, neuronal cells differentiated from hPSCs possess the advantages of species specificity together with genetic and physiological normality, which could more closely recapitulate in vivo conditions within the human central nervous system. This review critically examines the various potential nontherapeutic applications of hPSC-derived neuronal lineages and gives a brief overview of differentiation protocols utilized to generate these cells from hESCs and iPSCs. PMID:26089911

Revisiting tests for neglected nonlinearity using artificial neural networks.

PubMed

Cho, Jin Seo; Ishida, Isao; White, Halbert

2011-05-01

Tests for regression neglected nonlinearity based on artificial neural networks (ANNs) have so far been studied by separately analyzing the two ways in which the null of regression linearity can hold. This implies that the asymptotic behavior of general ANN-based tests for neglected nonlinearity is still an open question. Here we analyze a convenient ANN-based quasi-likelihood ratio statistic for testing neglected nonlinearity, paying careful attention to both components of the null. We derive the asymptotic null distribution under each component separately and analyze their interaction. Somewhat remarkably, it turns out that the previously known asymptotic null distribution for the type 1 case still applies, but under somewhat stronger conditions than previously recognized. We present Monte Carlo experiments corroborating our theoretical results and showing that standard methods can yield misleading inference when our new, stronger regularity conditions are violated.

Finite-time stability and synchronization of memristor-based fractional-order fuzzy cellular neural networks

NASA Astrophysics Data System (ADS)

Zheng, Mingwen; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian; Zhang, Yanping; Zhao, Hui

2018-06-01

This paper mainly studies the finite-time stability and synchronization problems of memristor-based fractional-order fuzzy cellular neural network (MFFCNN). Firstly, we discuss the existence and uniqueness of the Filippov solution of the MFFCNN according to the Banach fixed point theorem and give a sufficient condition for the existence and uniqueness of the solution. Secondly, a sufficient condition to ensure the finite-time stability of the MFFCNN is obtained based on the definition of finite-time stability of the MFFCNN and Gronwall-Bellman inequality. Thirdly, by designing a simple linear feedback controller, the finite-time synchronization criterion for drive-response MFFCNN systems is derived according to the definition of finite-time synchronization. These sufficient conditions are easy to verify. Finally, two examples are given to show the effectiveness of the proposed results.

Neural networks for feedback feedforward nonlinear control systems.

PubMed

Parisini, T; Zoppoli, R

1994-01-01

This paper deals with the problem of designing feedback feedforward control strategies to drive the state of a dynamic system (in general, nonlinear) so as to track any desired trajectory joining the points of given compact sets, while minimizing a certain cost function (in general, nonquadratic). Due to the generality of the problem, conventional methods are difficult to apply. Thus, an approximate solution is sought by constraining control strategies to take on the structure of multilayer feedforward neural networks. After discussing the approximation properties of neural control strategies, a particular neural architecture is presented, which is based on what has been called the "linear-structure preserving principle". The original functional problem is then reduced to a nonlinear programming one, and backpropagation is applied to derive the optimal values of the synaptic weights. Recursive equations to compute the gradient components are presented, which generalize the classical adjoint system equations of N-stage optimal control theory. Simulation results related to nonlinear nonquadratic problems show the effectiveness of the proposed method.

Manifold decoding for neural representations of face viewpoint and gaze direction using magnetoencephalographic data.

PubMed

Kuo, Po-Chih; Chen, Yong-Sheng; Chen, Li-Fen

2018-05-01

The main challenge in decoding neural representations lies in linking neural activity to representational content or abstract concepts. The transformation from a neural-based to a low-dimensional representation may hold the key to encoding perceptual processes in the human brain. In this study, we developed a novel model by which to represent two changeable features of faces: face viewpoint and gaze direction. These features are embedded in spatiotemporal brain activity derived from magnetoencephalographic data. Our decoding results demonstrate that face viewpoint and gaze direction can be represented by manifold structures constructed from brain responses in the bilateral occipital face area and right superior temporal sulcus, respectively. Our results also show that the superposition of brain activity in the manifold space reveals the viewpoints of faces as well as directions of gazes as perceived by the subject. The proposed manifold representation model provides a novel opportunity to gain further insight into the processing of information in the human brain. © 2018 Wiley Periodicals, Inc.

FGF8 signaling sustains progenitor status and multipotency of cranial neural crest-derived mesenchymal cells in vivo and in vitro

PubMed Central

Shao, Meiying; Liu, Chao; Song, Yingnan; Ye, Wenduo; He, Wei; Yuan, Guohua; Gu, Shuping; Lin, Congxin; Ma, Liang; Zhang, Yanding; Tian, Weidong; Hu, Tao; Chen, YiPing

2015-01-01

The cranial neural crest (CNC) cells play a vital role in craniofacial development and regeneration. They are multi-potent progenitors, being able to differentiate into various types of tissues. Both pre-migratory and post-migratory CNC cells are plastic, taking on diverse fates by responding to different inductive signals. However, what sustains the multipotency of CNC cells and derivatives remains largely unknown. In this study, we present evidence that FGF8 signaling is able to sustain progenitor status and multipotency of CNC-derived mesenchymal cells both in vivo and in vitro. We show that augmented FGF8 signaling in pre-migratory CNC cells prevents cell differentiation and organogenesis in the craniofacial region by maintaining their progenitor status. CNC-derived mesenchymal cells with Fgf8 overexpression or control cells in the presence of exogenous FGF8 exhibit prolonged survival, proliferation, and multi-potent differentiation capability in cell cultures. Remarkably, exogenous FGF8 also sustains the capability of CNC-derived mesenchymal cells to participate in organogenesis such as odontogenesis. Furthermore, FGF8-mediated signaling strongly promotes adipogenesis but inhibits osteogenesis of CNC-derived mesenchymal cells in vitro. Our results reveal a specific role for FGF8 in the maintenance of progenitor status and in fate determination of CNC cells, implicating a potential application in expansion and fate manipulation of CNC-derived cells in stem cell-based craniofacial regeneration. PMID:26243590

Neural transcription factors bias cleavage stage blastomeres to give rise to neural ectoderm

PubMed Central

Gaur, Shailly; Mandelbaum, Max; Herold, Mona; Majumdar, Himani Datta; Neilson, Karen M.; Maynard, Thomas M.; Mood, Kathy; Daar, Ira O.; Moody, Sally A.

2016-01-01

The decision by embryonic ectoderm to give rise to epidermal versus neural derivatives is the result of signaling events during blastula and gastrula stages. However, there also is evidence in Xenopus that cleavage stage blastomeres contain maternally derived molecules that bias them toward a neural fate. We used a blastomere explant culture assay to test whether maternally deposited transcription factors bias 16-cell blastomere precursors of epidermal or neural ectoderm to express early zygotic neural genes in the absence of gastrulation interactions or exogenously supplied signaling factors. We found that Foxd4l1, Zic2, Gmnn and Sox11 each induced explants made from ventral, epidermis-producing blastomeres to express early neural genes, and that at least some of the Foxd4l1 and Zic2 activity is required at cleavage stages. Similarly, providing extra Foxd4l1 or Zic2 to explants made from dorsal, neural plate-producing blastomeres significantly increased expression of early neural genes, whereas knocking down either significantly reduced them. These results show that maternally delivered transcription factors bias cleavage stage blastomeres to a neural fate. We demonstrate that mouse and human homologues of Foxd4l1 have similar functional domains compared to the frog protein, as well as conserved transcriptional activities when expressed in Xenopus embryos and blastomere explants. PMID:27092474

Nonsmooth Finite-Time Synchronization of Switched Coupled Neural Networks.

PubMed

Liu, Xiaoyang; Cao, Jinde; Yu, Wenwu; Song, Qiang

2016-10-01

This paper is concerned with the finite-time synchronization (FTS) issue of switched coupled neural networks with discontinuous or continuous activations. Based on the framework of nonsmooth analysis, some discontinuous or continuous controllers are designed to force the coupled networks to synchronize to an isolated neural network. Some sufficient conditions are derived to ensure the FTS by utilizing the well-known finite-time stability theorem for nonlinear systems. Compared with the previous literatures, such synchronization objective will be realized when the activations and the controllers are both discontinuous. The obtained results in this paper include and extend the earlier works on the synchronization issue of coupled networks with Lipschitz continuous conditions. Moreover, an upper bound of the settling time for synchronization is estimated. Finally, numerical simulations are given to demonstrate the effectiveness of the theoretical results.

Influence and timing of arrival of murine neural crest on pancreatic beta cell development and maturation.

PubMed

Plank, Jennifer L; Mundell, Nathan A; Frist, Audrey Y; LeGrone, Alison W; Kim, Thomas; Musser, Melissa A; Walter, Teagan J; Labosky, Patricia A

2011-01-15

Interactions between cells from the ectoderm and mesoderm influence development of the endodermally-derived pancreas. While much is known about how mesoderm regulates pancreatic development, relatively little is understood about how and when the ectodermally-derived neural crest regulates pancreatic development and specifically, beta cell maturation. A previous study demonstrated that signals from the neural crest regulate beta cell proliferation and ultimately, beta cell mass. Here, we expand on that work to describe timing of neural crest arrival at the developing pancreatic bud and extend our knowledge of the non-cell autonomous role for neural crest derivatives in the process of beta cell maturation. We demonstrated that murine neural crest entered the pancreatic mesenchyme between the 26 and 27 somite stages (approximately 10.0 dpc) and became intermingled with pancreatic progenitors as the epithelium branched into the surrounding mesenchyme. Using a neural crest-specific deletion of the Forkhead transcription factor Foxd3, we ablated neural crest cells that migrate to the pancreatic primordium. Consistent with previous data, in the absence of Foxd3, and therefore the absence of neural crest cells, proliferation of insulin-expressing cells and insulin-positive area are increased. Analysis of endocrine cell gene expression in the absence of neural crest demonstrated that, although the number of insulin-expressing cells was increased, beta cell maturation was significantly impaired. Decreased MafA and Pdx1 expression illustrated the defect in beta cell maturation; we discovered that without neural crest, there was a reduction in the percentage of insulin-positive cells that co-expressed Glut2 and Pdx1 compared to controls. In addition, transmission electron microscopy analyses revealed decreased numbers of characteristic insulin granules and the presence of abnormal granules in insulin-expressing cells from mutant embryos. Together, these data demonstrate that the neural crest is a critical regulator of beta cell development on two levels: by negatively regulating beta cell proliferation and by promoting beta cell maturation. Copyright © 2010 Elsevier Inc. All rights reserved.

Recursive least-squares learning algorithms for neural networks

NASA Astrophysics Data System (ADS)

Lewis, Paul S.; Hwang, Jenq N.

1990-11-01

This paper presents the development of a pair of recursive least squares (ItLS) algorithms for online training of multilayer perceptrons which are a class of feedforward artificial neural networks. These algorithms incorporate second order information about the training error surface in order to achieve faster learning rates than are possible using first order gradient descent algorithms such as the generalized delta rule. A least squares formulation is derived from a linearization of the training error function. Individual training pattern errors are linearized about the network parameters that were in effect when the pattern was presented. This permits the recursive solution of the least squares approximation either via conventional RLS recursions or by recursive QR decomposition-based techniques. The computational complexity of the update is 0(N2) where N is the number of network parameters. This is due to the estimation of the N x N inverse Hessian matrix. Less computationally intensive approximations of the ilLS algorithms can be easily derived by using only block diagonal elements of this matrix thereby partitioning the learning into independent sets. A simulation example is presented in which a neural network is trained to approximate a two dimensional Gaussian bump. In this example RLS training required an order of magnitude fewer iterations on average (527) than did training with the generalized delta rule (6 1 BACKGROUND Artificial neural networks (ANNs) offer an interesting and potentially useful paradigm for signal processing and pattern recognition. The majority of ANN applications employ the feed-forward multilayer perceptron (MLP) network architecture in which network parameters are " trained" by a supervised learning algorithm employing the generalized delta rule (GDIt) [1 2]. The GDR algorithm approximates a fixed step steepest descent algorithm using derivatives computed by error backpropagatiori. The GDII algorithm is sometimes referred to as the backpropagation algorithm. However in this paper we will use the term backpropagation to refer only to the process of computing error derivatives. While multilayer perceptrons provide a very powerful nonlinear modeling capability GDR training can be very slow and inefficient. In linear adaptive filtering the analog of the GDR algorithm is the leastmean- squares (LMS) algorithm. Steepest descent-based algorithms such as GDR or LMS are first order because they use only first derivative or gradient information about the training error to be minimized. To speed up the training process second order algorithms may be employed that take advantage of second derivative or Hessian matrix information. Second order information can be incorporated into MLP training in different ways. In many applications especially in the area of pattern recognition the training set is finite. In these cases block learning can be applied using standard nonlinear optimization techniques [3 4 5].

Learning polynomial feedforward neural networks by genetic programming and backpropagation.

PubMed

Nikolaev, N Y; Iba, H

2003-01-01

This paper presents an approach to learning polynomial feedforward neural networks (PFNNs). The approach suggests, first, finding the polynomial network structure by means of a population-based search technique relying on the genetic programming paradigm, and second, further adjustment of the best discovered network weights by an especially derived backpropagation algorithm for higher order networks with polynomial activation functions. These two stages of the PFNN learning process enable us to identify networks with good training as well as generalization performance. Empirical results show that this approach finds PFNN which outperform considerably some previous constructive polynomial network algorithms on processing benchmark time series.

Pluripotent Human embryonic stem cell derived neural lineages for in vitro modelling of enterovirus 71 infection and therapy.

PubMed

Yap, May Shin; Tang, Yin Quan; Yeo, Yin; Lim, Wei Ling; Lim, Lee Wei; Tan, Kuan Onn; Richards, Mark; Othman, Iekhsan; Poh, Chit Laa; Heng, Boon Chin

2016-01-06

The incidence of neurological complications and fatalities associated with Hand, Foot & Mouth disease has increased over recent years, due to emergence of newly-evolved strains of Enterovirus 71 (EV71). In the search for new antiviral therapeutics against EV71, accurate and sensitive in vitro cellular models for preliminary studies of EV71 pathogenesis is an essential prerequisite, before progressing to expensive and time-consuming live animal studies and clinical trials. This study thus investigated whether neural lineages derived from pluripotent human embryonic stem cells (hESC) can fulfil this purpose. EV71 infection of hESC-derived neural stem cells (NSC) and mature neurons (MN) was carried out in vitro, in comparison with RD and SH-SY5Y cell lines. Upon assessment of post-infection survivability and EV71 production by the various types, it was observed that NSC were significantly more susceptible to EV71 infection compared to MN, RD (rhabdomyosarcoma) and SH-SY5Y cells, which was consistent with previous studies on mice. The SP81 peptide had significantly greater inhibitory effect on EV71 production by NSC and MN compared to the cancer-derived RD and SH-SY5Y cell lines. Hence, this study demonstrates that hESC-derived neural lineages can be utilized as in vitro models for studying EV71 pathogenesis and for screening of antiviral therapeutics.

Application of Two-Dimensional AWE Algorithm in Training Multi-Dimensional Neural Network Model

DTIC Science & Technology

2003-07-01

hybrid scheme . the general neural network method (Table 3.1). The training process of the software- ACKNOWLEDGMENT "Neuralmodeler" is shown in Fig. 3.2...engineering. Artificial neural networks (ANNs) have emerged Training a neural network model is the key of as a powerful technique for modeling general neural...coefficients am, the derivatives method of moments (MoM). The variables in the of matrix I have to be generated . A closed form model are frequency

Leukemia inhibitory factor (LIF) enhances MAP2 + and HUC/D + neurons and influences neurite extension during differentiation of neural progenitors derived from human embryonic stem cells.

EPA Science Inventory

Leukemia Inhibitory Factor (L1F), a member of the Interleukin 6 cytokine family, has a role in differentiation of Human Neural Progenitor (hNP) cells in vitro. hNP cells, derived from Human Embryonic Stem (hES) cells, have an unlimited capacity for self-renewal in monolayer cultu...

A new switching control for finite-time synchronization of memristor-based recurrent neural networks.

PubMed

Gao, Jie; Zhu, Peiyong; Alsaedi, Ahmed; Alsaadi, Fuad E; Hayat, Tasawar

2017-02-01

In this paper, finite-time synchronization (FTS) of memristor-based recurrent neural networks (MNNs) with time-varying delays is investigated by designing a new switching controller. First, by using the differential inclusions theory and set-valued maps, sufficient conditions to ensure FTS of MNNs are obtained under the two cases of 0<α<1 and α=0, and it is derived that α=0 is the critical value of 0<α<1. Next, it is discussed deeply on the relation between the parameter α and the synchronization time. Then, a new controller with a switching parameter α is designed which can shorten the synchronization time. Finally, some numerical simulation examples are provided to illustrate the effectiveness of the proposed results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Prognostic value of electroencephalography (EEG) for brain injury after cardiopulmonary resuscitation.

PubMed

Feng, Guibo; Jiang, Guohui; Li, Zhiwei; Wang, Xuefeng

2016-06-01

Cardiac arrest (CA) patients can experience neurological sequelae or even death after successful cardiopulmonary resuscitation (CPR) due to cerebral hypoxia- and ischemia-reperfusion-mediated brain injury. Thus, it is important to perform early prognostic evaluations in CA patients. Electroencephalography (EEG) is an important tool for determining the prognosis of hypoxic-ischemic encephalopathy due to its real-time measurement of brain function. Based on EEG, burst suppression, a burst suppression ratio >0.239, periodic discharges, status epilepticus, stimulus-induced rhythmic, periodic or ictal discharges, non-reactive EEG, and the BIS value based on quantitative EEG may be associated with the prognosis of CA after successful CPR. As measures of neural network integrity, the values of small-world characteristics of the neural network derived from EEG patterns have potential applications.

A novel memristive multilayer feedforward small-world neural network with its applications in PID control.

PubMed

Dong, Zhekang; Duan, Shukai; Hu, Xiaofang; Wang, Lidan; Li, Hai

2014-01-01

In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN) inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme.

A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control

PubMed Central

Dong, Zhekang; Duan, Shukai; Hu, Xiaofang; Wang, Lidan

2014-01-01

In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN) inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme. PMID:25202723

Krox20 defines a subpopulation of cardiac neural crest cells contributing to arterial valves and bicuspid aortic valve.

PubMed

Odelin, Gaëlle; Faure, Emilie; Coulpier, Fanny; Di Bonito, Maria; Bajolle, Fanny; Studer, Michèle; Avierinos, Jean-François; Charnay, Patrick; Topilko, Piotr; Zaffran, Stéphane

2018-01-03

Although cardiac neural crest cells are required at early stages of arterial valve development, their contribution during valvular leaflet maturation remains poorly understood. Here, we show in mouse that neural crest cells from pre-otic and post-otic regions make distinct contributions to the arterial valve leaflets. Genetic fate-mapping analysis of Krox20-expressing neural crest cells shows a large contribution to the borders and the interleaflet triangles of the arterial valves. Loss of Krox20 function results in hyperplastic aortic valve and partially penetrant bicuspid aortic valve formation. Similar defects are observed in neural crest Krox20 -deficient embryos. Genetic lineage tracing in Krox20 -/- mutant mice shows that endothelial-derived cells are normal, whereas neural crest-derived cells are abnormally increased in number and misplaced in the valve leaflets. In contrast, genetic ablation of Krox20 -expressing cells is not sufficient to cause an aortic valve defect, suggesting that adjacent cells can compensate this depletion. Our findings demonstrate a crucial role for Krox20 in arterial valve development and reveal that an excess of neural crest cells may be associated with bicuspid aortic valve. © 2018. Published by The Company of Biologists Ltd.

A neutron spectrum unfolding code based on generalized regression artificial neural networks.

PubMed

Del Rosario Martinez-Blanco, Ma; Ornelas-Vargas, Gerardo; Castañeda-Miranda, Celina Lizeth; Solís-Sánchez, Luis Octavio; Castañeda-Miranada, Rodrigo; Vega-Carrillo, Héctor René; Celaya-Padilla, Jose M; Garza-Veloz, Idalia; Martínez-Fierro, Margarita; Ortiz-Rodríguez, José Manuel

2016-11-01

The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, i.e. the optimum selection of the network topology and the long training time. Compared to BPNN, it's usually much faster to train a generalized regression neural network (GRNN). That's mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum, provided that the optimal values of spread has been determined and that the dataset adequately represents the problem space. In addition, GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest in the neutron spectrometry domain. This work presents a computational tool based on GRNN capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages using a k-fold cross validation of 3 folds, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a 6 LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Rejuvenation of MPTP-induced human neural precursor cell senescence by activating autophagy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Liang; Dong, Chuanming; Department of Anatomy and Neurobiology, The Jiangsu Key Laboratory of Neuroregeneration, Nantong University, Nantong

Aging of neural stem cell, which can affect brain homeostasis, may be caused by many cellular mechanisms. Autophagy dysfunction was found in aged and neurodegenerative brains. However, little is known about the relationship between autophagy and human neural stem cell (hNSC) aging. The present study used 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP) to treat neural precursor cells (NPCs) derived from human embryonic stem cell (hESC) line H9 and investigate related molecular mechanisms involved in this process. MPTP-treated NPCs were found to undergo premature senescence [determined by increased senescence-associated-β-galactosidase (SA-β-gal) activity, elevated intracellular reactive oxygen species level, and decreased proliferation] and weremore » associated with impaired autophagy. Additionally, the cellular senescence phenotypes were manifested at the molecular level by a significant increase in p21 and p53 expression, a decrease in SOD2 expression, and a decrease in expression of some key autophagy-related genes such as Atg5, Atg7, Atg12, and Beclin 1. Furthermore, we found that the senescence-like phenotype of MPTP-treated hNPCs was rejuvenated through treatment with a well-known autophagy enhancer rapamycin, which was blocked by suppression of essential autophagy gene Beclin 1. Taken together, these findings reveal the critical role of autophagy in the process of hNSC aging, and this process can be reversed by activating autophagy. - Highlights: • We successfully establish hESC-derived neural precursor cells. • MPTP treatment induced senescence-like state in hESC-derived NPCs. • MPTP treatment induced impaired autophagy of hESC-derived NPCs. • MPTP-induced hESC-derived NPC senescence was rejuvenated by activating autophagy.« less

Prediction of stock market characteristics using neural networks

NASA Astrophysics Data System (ADS)

Pandya, Abhijit S.; Kondo, Tadashi; Shah, Trupti U.; Gandhi, Viraf R.

1999-03-01

International stocks trading, currency and derivative contracts play an increasingly important role for many investors. Neural network is playing a dominant role in predicting the trends in stock markets and in currency speculation. In most economic applications, the success rate using neural networks is limited to 70 - 80%. By means of the new approach of GMDH (Group Method of Data Handling) neural network predictions can be improved further by 10 - 15%. It was observed in our study, that using GMDH for short, noisy or inaccurate data sample resulted in the best-simplified model. In the GMDH model accuracy of prediction is higher and the structure is simpler than that of the usual full physical model. As an example, prediction of the activity on the stock exchange in New York was considered. On the basis of observations in the period of Jan '95 to July '98, several variables of the stock market (S&P 500, Small Cap, Dow Jones, etc.) were predicted. A model portfolio using various stocks (Amgen, Merck, Office Depot, etc.) was built and its performance was evaluated based on neural network forecasting of the closing prices. Comparison of results was made with various neural network models such as Multilayer Perceptrons with Back Propagation, and the GMDH neural network. Variations of GMDH were studied and analysis of their performance is reported in the paper.

Losing the music: aging affects the perception and subcortical neural representation of musical harmony.

PubMed

Bones, Oliver; Plack, Christopher J

2015-03-04

When two musical notes with simple frequency ratios are played simultaneously, the resulting musical chord is pleasing and evokes a sense of resolution or "consonance". Complex frequency ratios, on the other hand, evoke feelings of tension or "dissonance". Consonance and dissonance form the basis of harmony, a central component of Western music. In earlier work, we provided evidence that consonance perception is based on neural temporal coding in the brainstem (Bones et al., 2014). Here, we show that for listeners with clinically normal hearing, aging is associated with a decline in both the perceptual distinction and the distinctiveness of the neural representations of different categories of two-note chords. Compared with younger listeners, older listeners rated consonant chords as less pleasant and dissonant chords as more pleasant. Older listeners also had less distinct neural representations of consonant and dissonant chords as measured using a Neural Consonance Index derived from the electrophysiological "frequency-following response." The results withstood a control for the effect of age on general affect, suggesting that different mechanisms are responsible for the perceived pleasantness of musical chords and affective voices and that, for listeners with clinically normal hearing, age-related differences in consonance perception are likely to be related to differences in neural temporal coding. Copyright © 2015 Bones and Plack.

Neural adaptive control for vibration suppression in composite fin-tip of aircraft.

PubMed

Suresh, S; Kannan, N; Sundararajan, N; Saratchandran, P

2008-06-01

In this paper, we present a neural adaptive control scheme for active vibration suppression of a composite aircraft fin tip. The mathematical model of a composite aircraft fin tip is derived using the finite element approach. The finite element model is updated experimentally to reflect the natural frequencies and mode shapes very accurately. Piezo-electric actuators and sensors are placed at optimal locations such that the vibration suppression is a maximum. Model-reference direct adaptive neural network control scheme is proposed to force the vibration level within the minimum acceptable limit. In this scheme, Gaussian neural network with linear filters is used to approximate the inverse dynamics of the system and the parameters of the neural controller are estimated using Lyapunov based update law. In order to reduce the computational burden, which is critical for real-time applications, the number of hidden neurons is also estimated in the proposed scheme. The global asymptotic stability of the overall system is ensured using the principles of Lyapunov approach. Simulation studies are carried-out using sinusoidal force functions of varying frequency. Experimental results show that the proposed neural adaptive control scheme is capable of providing significant vibration suppression in the multiple bending modes of interest. The performance of the proposed scheme is better than the H(infinity) control scheme.

Losing the Music: Aging Affects the Perception and Subcortical Neural Representation of Musical Harmony

PubMed Central

Plack, Christopher J.

2015-01-01

When two musical notes with simple frequency ratios are played simultaneously, the resulting musical chord is pleasing and evokes a sense of resolution or “consonance”. Complex frequency ratios, on the other hand, evoke feelings of tension or “dissonance”. Consonance and dissonance form the basis of harmony, a central component of Western music. In earlier work, we provided evidence that consonance perception is based on neural temporal coding in the brainstem (Bones et al., 2014). Here, we show that for listeners with clinically normal hearing, aging is associated with a decline in both the perceptual distinction and the distinctiveness of the neural representations of different categories of two-note chords. Compared with younger listeners, older listeners rated consonant chords as less pleasant and dissonant chords as more pleasant. Older listeners also had less distinct neural representations of consonant and dissonant chords as measured using a Neural Consonance Index derived from the electrophysiological “frequency-following response.” The results withstood a control for the effect of age on general affect, suggesting that different mechanisms are responsible for the perceived pleasantness of musical chords and affective voices and that, for listeners with clinically normal hearing, age-related differences in consonance perception are likely to be related to differences in neural temporal coding. PMID:25740534

Wise promotes coalescence of cells of neural crest and placode origins in the trigeminal region during head development.

PubMed

Shigetani, Yasuyo; Howard, Sara; Guidato, Sonia; Furushima, Kenryo; Abe, Takaya; Itasaki, Nobue

2008-07-15

While most cranial ganglia contain neurons of either neural crest or placodal origin, neurons of the trigeminal ganglion derive from both populations. The Wnt signaling pathway is known to be required for the development of neural crest cells and for trigeminal ganglion formation, however, migrating neural crest cells do not express any known Wnt ligands. Here we demonstrate that Wise, a Wnt modulator expressed in the surface ectoderm overlying the trigeminal ganglion, play a role in promoting the assembly of placodal and neural crest cells. When overexpressed in chick, Wise causes delamination of ectodermal cells and attracts migrating neural crest cells. Overexpression of Wise is thus sufficient to ectopically induce ganglion-like structures consisting of both origins. The function of Wise is likely synergized with Wnt6, expressed in an overlapping manner with Wise in the surface ectoderm. Electroporation of morpholino antisense oligonucleotides against Wise and Wnt6 causes decrease in the contact of neural crest cells with the delaminated placode-derived cells. In addition, targeted deletion of Wise in mouse causes phenotypes that can be explained by a decrease in the contribution of neural crest cells to the ophthalmic lobe of the trigeminal ganglion. These data suggest that Wise is able to function cell non-autonomously on neural crest cells and promote trigeminal ganglion formation.

Neural Network and Regression Approximations in High Speed Civil Transport Aircraft Design Optimization

NASA Technical Reports Server (NTRS)

Patniak, Surya N.; Guptill, James D.; Hopkins, Dale A.; Lavelle, Thomas M.

1998-01-01

Nonlinear mathematical-programming-based design optimization can be an elegant method. However, the calculations required to generate the merit function, constraints, and their gradients, which are frequently required, can make the process computational intensive. The computational burden can be greatly reduced by using approximating analyzers derived from an original analyzer utilizing neural networks and linear regression methods. The experience gained from using both of these approximation methods in the design optimization of a high speed civil transport aircraft is the subject of this paper. The Langley Research Center's Flight Optimization System was selected for the aircraft analysis. This software was exercised to generate a set of training data with which a neural network and a regression method were trained, thereby producing the two approximating analyzers. The derived analyzers were coupled to the Lewis Research Center's CometBoards test bed to provide the optimization capability. With the combined software, both approximation methods were examined for use in aircraft design optimization, and both performed satisfactorily. The CPU time for solution of the problem, which had been measured in hours, was reduced to minutes with the neural network approximation and to seconds with the regression method. Instability encountered in the aircraft analysis software at certain design points was also eliminated. On the other hand, there were costs and difficulties associated with training the approximating analyzers. The CPU time required to generate the input-output pairs and to train the approximating analyzers was seven times that required for solution of the problem.

Identifying apple surface defects using principal components analysis and artifical neural networks

USDA-ARS?s Scientific Manuscript database

Artificial neural networks and principal components were used to detect surface defects on apples in near-infrared images. Neural networks were trained and tested on sets of principal components derived from columns of pixels from images of apples acquired at two wavelengths (740 nm and 950 nm). I...

Cyclin-dependent kinase 4 signaling acts as a molecular switch between syngenic differentiation and neural transdifferentiation in human mesenchymal stem cells

PubMed Central

Lee, Janet; Baek, Jeong-Hwa; Choi, Kyu-Sil; Kim, Hyun-Soo; Park, Hye-Young; Ha, Geun-Hyoung; Park, Ho; Lee, Kyo-Won; Lee, Chang Geun; Yang, Dong-Yun; Moon, Hyo Eun; Paek, Sun Ha; Lee, Chang-Woo

2013-01-01

Multipotent mesenchymal stem/stromal cells (MSCs) are capable of differentiating into a variety of cell types from different germ layers. However, the molecular and biochemical mechanisms underlying the transdifferentiation of MSCs into specific cell types still need to be elucidated. In this study, we unexpectedly found that treatment of human adipose- and bone marrow-derived MSCs with cyclin-dependent kinase (CDK) inhibitor, in particular CDK4 inhibitor, selectively led to transdifferentiation into neural cells with a high frequency. Specifically, targeted inhibition of CDK4 expression using recombinant adenovial shRNA induced the neural transdifferentiation of human MSCs. However, the inhibition of CDK4 activity attenuated the syngenic differentiation of human adipose-derived MSCs. Importantly, the forced regulation of CDK4 activity showed reciprocal reversibility between neural differentiation and dedifferentiation of human MSCs. Together, these results provide novel molecular evidence underlying the neural transdifferentiation of human MSCs; in addition, CDK4 signaling appears to act as a molecular switch from syngenic differentiation to neural transdifferentiation of human MSCs. PMID:23324348

Neural crest stem cell multipotency requires Foxd3 to maintain neural potential and repress mesenchymal fates.

PubMed

Mundell, Nathan A; Labosky, Patricia A

2011-02-01

Neural crest (NC) progenitors generate a wide array of cell types, yet molecules controlling NC multipotency and self-renewal and factors mediating cell-intrinsic distinctions between multipotent versus fate-restricted progenitors are poorly understood. Our earlier work demonstrated that Foxd3 is required for maintenance of NC progenitors in the embryo. Here, we show that Foxd3 mediates a fate restriction choice for multipotent NC progenitors with loss of Foxd3 biasing NC toward a mesenchymal fate. Neural derivatives of NC were lost in Foxd3 mutant mouse embryos, whereas abnormally fated NC-derived vascular smooth muscle cells were ectopically located in the aorta. Cranial NC defects were associated with precocious differentiation towards osteoblast and chondrocyte cell fates, and individual mutant NC from different anteroposterior regions underwent fate changes, losing neural and increasing myofibroblast potential. Our results demonstrate that neural potential can be separated from NC multipotency by the action of a single gene, and establish novel parallels between NC and other progenitor populations that depend on this functionally conserved stem cell protein to regulate self-renewal and multipotency.

Human iPSC-Derived Neural Progenitors Are an Effective Drug Discovery Model for Neurological mtDNA Disorders.

PubMed

Lorenz, Carmen; Lesimple, Pierre; Bukowiecki, Raul; Zink, Annika; Inak, Gizem; Mlody, Barbara; Singh, Manvendra; Semtner, Marcus; Mah, Nancy; Auré, Karine; Leong, Megan; Zabiegalov, Oleksandr; Lyras, Ekaterini-Maria; Pfiffer, Vanessa; Fauler, Beatrix; Eichhorst, Jenny; Wiesner, Burkhard; Huebner, Norbert; Priller, Josef; Mielke, Thorsten; Meierhofer, David; Izsvák, Zsuzsanna; Meier, Jochen C; Bouillaud, Frédéric; Adjaye, James; Schuelke, Markus; Wanker, Erich E; Lombès, Anne; Prigione, Alessandro

2017-05-04

Mitochondrial DNA (mtDNA) mutations frequently cause neurological diseases. Modeling of these defects has been difficult because of the challenges associated with engineering mtDNA. We show here that neural progenitor cells (NPCs) derived from human induced pluripotent stem cells (iPSCs) retain the parental mtDNA profile and exhibit a metabolic switch toward oxidative phosphorylation. NPCs derived in this way from patients carrying a deleterious homoplasmic mutation in the mitochondrial gene MT-ATP6 (m.9185T>C) showed defective ATP production and abnormally high mitochondrial membrane potential (MMP), plus altered calcium homeostasis, which represents a potential cause of neural impairment. High-content screening of FDA-approved drugs using the MMP phenotype highlighted avanafil, which we found was able to partially rescue the calcium defect in patient NPCs and differentiated neurons. Overall, our results show that iPSC-derived NPCs provide an effective model for drug screening to target mtDNA disorders that affect the nervous system. Copyright © 2016 Elsevier Inc. All rights reserved.

Enhanced expression of FNDC5 in human embryonic stem cell-derived neural cells along with relevant embryonic neural tissues.

PubMed

Ghahrizjani, Fatemeh Ahmadi; Ghaedi, Kamran; Salamian, Ahmad; Tanhaei, Somayeh; Nejati, Alireza Shoaraye; Salehi, Hossein; Nabiuni, Mohammad; Baharvand, Hossein; Nasr-Esfahani, Mohammad Hossein

2015-02-25

Availability of human embryonic stem cells (hESCs) has enhanced the capability of basic and clinical research in the context of human neural differentiation. Derivation of neural progenitor (NP) cells from hESCs facilitates the process of human embryonic development through the generation of neuronal subtypes. We have recently indicated that fibronectin type III domain containing 5 protein (FNDC5) expression is required for appropriate neural differentiation of mouse embryonic stem cells (mESCs). Bioinformatics analyses have shown the presence of three isoforms for human FNDC5 mRNA. To differentiate which isoform of FNDC5 is involved in the process of human neural differentiation, we have used hESCs as an in vitro model for neural differentiation by retinoic acid (RA) induction. The hESC line, Royan H5, was differentiated into a neural lineage in defined adherent culture treated by RA and basic fibroblast growth factor (bFGF). We collected all cell types that included hESCs, rosette structures, and neural cells in an attempt to assess the expression of FNDC5 isoforms. There was a contiguous increase in all three FNDC5 isoforms during the neural differentiation process. Furthermore, the highest level of expression of the isoforms was significantly observed in neural cells compared to hESCs and the rosette structures known as neural precursor cells (NPCs). High expression levels of FNDC5 in human fetal brain and spinal cord tissues have suggested the involvement of this gene in neural tube development. Additional research is necessary to determine the major function of FDNC5 in this process. Copyright © 2014 Elsevier B.V. All rights reserved.

The role of Foxi family transcription factors in otic placode and neural crest cell development

PubMed Central

Edlund, Renée K.; Birol, Onur; Groves, Andrew K.

2015-01-01

The mammalian outer, middle and inner ears have different embryonic origins and evolved at different times in the vertebrate lineage. The outer ear is derived from first and second branchial arch ectoderm and mesoderm, the middle ear ossicles are derived from neural crest mesenchymal cells that invade the first and second branchial arches, whereas the inner ear and its associated vestibule-acoustic (VIIIth) ganglion are derived from the otic placode. In this review, we discuss recent findings in the development of these structures and describe the contributions of members of a Forkhead transcription factor family, the Foxi family to their formation. Foxi transcription factors are critical for formation of the otic placode, survival of the branchial arch neural crest, and developmental remodeling of the branchial arch ectoderm. PMID:25662269

Artificial Neural Network with Hardware Training and Hardware Refresh

NASA Technical Reports Server (NTRS)

Duong, Tuan A. (Inventor)

2003-01-01

A neural network circuit is provided having a plurality of circuits capable of charge storage. Also provided is a plurality of circuits each coupled to at least one of the plurality of charge storage circuits and constructed to generate an output in accordance with a neuron transfer function. Each of a plurality of circuits is coupled to one of the plurality of neuron transfer function circuits and constructed to generate a derivative of the output. A weight update circuit updates the charge storage circuits based upon output from the plurality of transfer function circuits and output from the plurality of derivative circuits. In preferred embodiments, separate training and validation networks share the same set of charge storage circuits and may operate concurrently. The validation network has a separate transfer function circuits each being coupled to the charge storage circuits so as to replicate the training network s coupling of the plurality of charge storage to the plurality of transfer function circuits. The plurality of transfer function circuits may be constructed each having a transconductance amplifier providing differential currents combined to provide an output in accordance with a transfer function. The derivative circuits may have a circuit constructed to generate a biased differential currents combined so as to provide the derivative of the transfer function.

Regenerative therapy for vestibular disorders using human induced pluripotent stem cells (iPSCs): neural differentiation of human iPSC-derived neural stem cells after in vitro transplantation into mouse vestibular epithelia.

PubMed

Taura, Akiko; Nakashima, Noriyuki; Ohnishi, Hiroe; Nakagawa, Takayuki; Funabiki, Kazuo; Ito, Juichi; Omori, Koichi

2016-10-01

Vestibular ganglion cells, which convey sense of motion from vestibular hair cells to the brainstem, are known to degenerate with aging and after vestibular neuritis. Thus, regeneration of vestibular ganglion cells is important to aid in the recovery of balance for associated disorders. The present study derived hNSCs from induced pluripotent stem cells (iPSCs) and transplanted these cells into mouse utricle tissues. After a 7-day co-culture period, histological and electrophysiological examinations of transplanted hNSCs were performed. Injected hNSC-derived cells produced elongated axon-like structures within the utricle tissue that made contact with vestibular hair cells. A proportion of hNSC-derived cells showed spontaneous firing activities, similar to those observed in cultured mouse vestibular ganglion cells. However, hNSC-derived cells around the mouse utricle persisted as immature neurons or occasionally differentiated into putative astrocytes. Moreover, electrophysiological examination showed hNSC-derived cells around utricles did not exhibit any obvious spontaneous firing activities. Injected human neural stem cells (hNSCs) showed signs of morphological maturation including reconnection to denervated hair cells and partial physiological maturation, suggesting hNSC-derived cells possibly differentiated into neurons.

RIP140/PGC-1α axis involved in vitamin A-induced neural differentiation by increasing mitochondrial function.

PubMed

Mu, Qing; Yu, Weidong; Zheng, Shuying; Shi, Hongxia; Li, Mei; Sun, Jie; Wang, Di; Hou, Xiaoli; Liu, Ling; Wang, Xinjuan; Zhao, Zhuran; Liang, Rong; Zhang, Xue; Dong, Wei; Zeng, Chaomei; Guo, Jingzhu

2018-03-07

Vitamin A deficiency and mitochondrial dysfunction are both associated with neural differentiation-related disorders, such as Alzheimer's disease (AD) and Down syndrome (DS). The mechanism of vitamin A-induced neural differentiation and the notion that vitamin A can regulate the morphology and function of mitochondria in its induction of neural differentiation through the RIP140/PGC-1α axis are unclear. The aim of this study was to investigate the roles and underlying mechanisms of RIP140/PGC-1α axis in vitamin A-induced neural differentiation. Human neuroblastoma cells (SH-SY5Y) were used as a model of neural stem cells, which were incubated with DMSO, 9-cis-retinoic acid (9-cis-RA), 13-cis-retinoic acid (13-cis-RA) and all-trans-retinoic acid (at-RA). Neural differentiation of SH-SY5Y was evaluated by Sandquist calculation, combined with immunofluorescence and real-time polymerase chain reaction (PCR) of neural markers. Mitochondrial function was estimated by ultrastructure assay using transmission electron microscopy (TEM) combined with the expression of PGC-1α and NEMGs using real-time PCR. The participation of the RA signaling pathway was demonstrated by adding RA receptor antagonists. Vitamin A derivatives are able to regulate mitochondrial morphology and function, and furthermore to induce neural differentiation through the RA signaling pathway. The RIP140/PGC-1α axis is involved in the regulation of mitochondrial function in vitamin A derivative-induced neural differentiation.

Alcohol-Induced Molecular Dysregulation in Human Embryonic Stem Cell-Derived Neural Precursor Cells

PubMed Central

Kim, Yi Young; Roubal, Ivan; Lee, Youn Soo; Kim, Jin Seok; Hoang, Michael; Mathiyakom, Nathan; Kim, Yong

2016-01-01

Adverse effect of alcohol on neural function has been well documented. Especially, the teratogenic effect of alcohol on neurodevelopment during embryogenesis has been demonstrated in various models, which could be a pathologic basis for fetal alcohol spectrum disorders (FASDs). While the developmental defects from alcohol abuse during gestation have been described, the specific mechanisms by which alcohol mediates these injuries have yet to be determined. Recent studies have shown that alcohol has significant effect on molecular and cellular regulatory mechanisms in embryonic stem cell (ESC) differentiation including genes involved in neural development. To test our hypothesis that alcohol induces molecular alterations during neural differentiation we have derived neural precursor cells from pluripotent human ESCs in the presence or absence of ethanol treatment. Genome-wide transcriptomic profiling identified molecular alterations induced by ethanol exposure during neural differentiation of hESCs into neural rosettes and neural precursor cell populations. The Database for Annotation, Visualization and Integrated Discovery (DAVID) functional analysis on significantly altered genes showed potential ethanol’s effect on JAK-STAT signaling pathway, neuroactive ligand-receptor interaction, Toll-like receptor (TLR) signaling pathway, cytokine-cytokine receptor interaction and regulation of autophagy. We have further quantitatively verified ethanol-induced alterations of selected candidate genes. Among verified genes we further examined the expression of P2RX3, which is associated with nociception, a peripheral pain response. We found ethanol significantly reduced the level of P2RX3 in undifferentiated hESCs, but induced the level of P2RX3 mRNA and protein in hESC-derived NPCs. Our result suggests ethanol-induced dysregulation of P2RX3 along with alterations in molecules involved in neural activity such as neuroactive ligand-receptor interaction may be a molecular event associated with alcohol-related peripheral neuropathy of an enhanced nociceptive response. PMID:27682028

Mapping auditory nerve firing density using high-level compound action potentials and high-pass noise masking a

PubMed Central

Earl, Brian R.; Chertoff, Mark E.

2012-01-01

Future implementation of regenerative treatments for sensorineural hearing loss may be hindered by the lack of diagnostic tools that specify the target(s) within the cochlea and auditory nerve for delivery of therapeutic agents. Recent research has indicated that the amplitude of high-level compound action potentials (CAPs) is a good predictor of overall auditory nerve survival, but does not pinpoint the location of neural damage. A location-specific estimate of nerve pathology may be possible by using a masking paradigm and high-level CAPs to map auditory nerve firing density throughout the cochlea. This initial study in gerbil utilized a high-pass masking paradigm to determine normative ranges for CAP-derived neural firing density functions using broadband chirp stimuli and low-frequency tonebursts, and to determine if cochlear outer hair cell (OHC) pathology alters the distribution of neural firing in the cochlea. Neural firing distributions for moderate-intensity (60 dB pSPL) chirps were affected by OHC pathology whereas those derived with high-level (90 dB pSPL) chirps were not. These results suggest that CAP-derived neural firing distributions for high-level chirps may provide an estimate of auditory nerve survival that is independent of OHC pathology. PMID:22280596

Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

NASA Technical Reports Server (NTRS)

Hageman, Jacob J.; Smith, Mark S.; Stachowiak, Susan

2003-01-01

An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

On the complexity of neural network classifiers: a comparison between shallow and deep architectures.

PubMed

Bianchini, Monica; Scarselli, Franco

2014-08-01

Recently, researchers in the artificial neural network field have focused their attention on connectionist models composed by several hidden layers. In fact, experimental results and heuristic considerations suggest that deep architectures are more suitable than shallow ones for modern applications, facing very complex problems, e.g., vision and human language understanding. However, the actual theoretical results supporting such a claim are still few and incomplete. In this paper, we propose a new approach to study how the depth of feedforward neural networks impacts on their ability in implementing high complexity functions. First, a new measure based on topological concepts is introduced, aimed at evaluating the complexity of the function implemented by a neural network, used for classification purposes. Then, deep and shallow neural architectures with common sigmoidal activation functions are compared, by deriving upper and lower bounds on their complexity, and studying how the complexity depends on the number of hidden units and the used activation function. The obtained results seem to support the idea that deep networks actually implements functions of higher complexity, so that they are able, with the same number of resources, to address more difficult problems.

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

PubMed

Zenke, Friedemann; Ganguli, Surya

2018-06-01

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

A novel nonlinear adaptive filter using a pipelined second-order Volterra recurrent neural network.

PubMed

Zhao, Haiquan; Zhang, Jiashu

2009-12-01

To enhance the performance and overcome the heavy computational complexity of recurrent neural networks (RNN), a novel nonlinear adaptive filter based on a pipelined second-order Volterra recurrent neural network (PSOVRNN) is proposed in this paper. A modified real-time recurrent learning (RTRL) algorithm of the proposed filter is derived in much more detail. The PSOVRNN comprises of a number of simple small-scale second-order Volterra recurrent neural network (SOVRNN) modules. In contrast to the standard RNN, these modules of a PSOVRNN can be performed simultaneously in a pipelined parallelism fashion, which can lead to a significant improvement in its total computational efficiency. Moreover, since each module of the PSOVRNN is a SOVRNN in which nonlinearity is introduced by the recursive second-order Volterra (RSOV) expansion, its performance can be further improved. Computer simulations have demonstrated that the PSOVRNN performs better than the pipelined recurrent neural network (PRNN) and RNN for nonlinear colored signals prediction and nonlinear channel equalization. However, the superiority of the PSOVRNN over the PRNN is at the cost of increasing computational complexity due to the introduced nonlinear expansion of each module.

Generation of Human Induced Pluripotent Stem Cell‐Derived Bona Fide Neural Stem Cells for Ex Vivo Gene Therapy of Metachromatic Leukodystrophy

PubMed Central

Meneghini, Vasco; Sala, Davide; De Cicco, Silvia; Luciani, Marco; Cavazzin, Chiara; Paulis, Marianna; Mentzen, Wieslawa; Morena, Francesco; Giannelli, Serena; Sanvito, Francesca; Villa, Anna; Bulfone, Alessandro; Broccoli, Vania; Martino, Sabata

2016-01-01

Abstract Allogeneic fetal‐derived human neural stem cells (hfNSCs) that are under clinical evaluation for several neurodegenerative diseases display a favorable safety profile, but require immunosuppression upon transplantation in patients. Neural progenitors derived from patient‐specific induced pluripotent stem cells (iPSCs) may be relevant for autologous ex vivo gene‐therapy applications to treat genetic diseases with unmet medical need. In this scenario, obtaining iPSC‐derived neural stem cells (NSCs) showing a reliable “NSC signature” is mandatory. Here, we generated human iPSC (hiPSC) clones via reprogramming of skin fibroblasts derived from normal donors and patients affected by metachromatic leukodystrophy (MLD), a fatal neurodegenerative lysosomal storage disease caused by genetic defects of the arylsulfatase A (ARSA) enzyme. We differentiated hiPSCs into NSCs (hiPS‐NSCs) sharing molecular, phenotypic, and functional identity with hfNSCs, which we used as a “gold standard” in a side‐by‐side comparison when validating the phenotype of hiPS‐NSCs and predicting their performance after intracerebral transplantation. Using lentiviral vectors, we efficiently transduced MLD hiPSCs, achieving supraphysiological ARSA activity that further increased upon neural differentiation. Intracerebral transplantation of hiPS‐NSCs into neonatal and adult immunodeficient MLD mice stably restored ARSA activity in the whole central nervous system. Importantly, we observed a significant decrease of sulfatide storage when ARSA‐overexpressing cells were used, with a clear advantage in those mice receiving neonatal as compared with adult intervention. Thus, we generated a renewable source of ARSA‐overexpressing iPSC‐derived bona fide hNSCs with improved features compared with clinically approved hfNSCs. Patient‐specific ARSA‐overexpressing hiPS‐NSCs may be used in autologous ex vivo gene therapy protocols to provide long‐lasting enzymatic supply in MLD‐affected brains. Stem Cells Translational Medicine 2017;6:352–368 PMID:28191778

Classification of breast cancer cytological specimen using convolutional neural network

NASA Astrophysics Data System (ADS)

Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman

2017-01-01

The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

Teaching learning based optimization-functional link artificial neural network filter for mixed noise reduction from magnetic resonance image.

PubMed

Kumar, M; Mishra, S K

2017-01-01

The clinical magnetic resonance imaging (MRI) images may get corrupted due to the presence of the mixture of different types of noises such as Rician, Gaussian, impulse, etc. Most of the available filtering algorithms are noise specific, linear, and non-adaptive. There is a need to develop a nonlinear adaptive filter that adapts itself according to the requirement and effectively applied for suppression of mixed noise from different MRI images. In view of this, a novel nonlinear neural network based adaptive filter i.e. functional link artificial neural network (FLANN) whose weights are trained by a recently developed derivative free meta-heuristic technique i.e. teaching learning based optimization (TLBO) is proposed and implemented. The performance of the proposed filter is compared with five other adaptive filters and analyzed by considering quantitative metrics and evaluating the nonparametric statistical test. The convergence curve and computational time are also included for investigating the efficiency of the proposed as well as competitive filters. The simulation outcomes of proposed filter outperform the other adaptive filters. The proposed filter can be hybridized with other evolutionary technique and utilized for removing different noise and artifacts from others medical images more competently.

PSA-NCAM-Negative Neural Crest Cells Emerging during Neural Induction of Pluripotent Stem Cells Cause Mesodermal Tumors and Unwanted Grafts

PubMed Central

Lee, Dongjin R.; Yoo, Jeong-Eun; Lee, Jae Souk; Park, Sanghyun; Lee, Junwon; Park, Chul-Yong; Ji, Eunhyun; Kim, Han-Soo; Hwang, Dong-Youn; Kim, Dae-Sung; Kim, Dong-Wook

2015-01-01

Summary Tumorigenic potential of human pluripotent stem cells (hPSCs) is an important issue in clinical applications. Despite many efforts, PSC-derived neural precursor cells (NPCs) have repeatedly induced tumors in animal models even though pluripotent cells were not detected. We found that polysialic acid-neural cell adhesion molecule (PSA-NCAM)− cells among the early NPCs caused tumors, whereas PSA-NCAM+ cells were nontumorigenic. Molecular profiling, global gene analysis, and multilineage differentiation of PSA-NCAM− cells confirm that they are multipotent neural crest stem cells (NCSCs) that could differentiate into both ectodermal and mesodermal lineages. Transplantation of PSA-NCAM− cells in a gradient manner mixed with PSA-NCAM+ cells proportionally increased mesodermal tumor formation and unwanted grafts such as PERIPHERIN+ cells or pigmented cells in the rat brain. Therefore, we suggest that NCSCs are a critical target for tumor prevention in hPSC-derived NPCs, and removal of PSA-NCAM− cells eliminates the tumorigenic potential originating from NCSCs after transplantation. PMID:25937368

Imitation, empathy, and mirror neurons.

PubMed

Iacoboni, Marco

2009-01-01

There is a convergence between cognitive models of imitation, constructs derived from social psychology studies on mimicry and empathy, and recent empirical findings from the neurosciences. The ideomotor framework of human actions assumes a common representational format for action and perception that facilitates imitation. Furthermore, the associative sequence learning model of imitation proposes that experience-based Hebbian learning forms links between sensory processing of the actions of others and motor plans. Social psychology studies have demonstrated that imitation and mimicry are pervasive, automatic, and facilitate empathy. Neuroscience investigations have demonstrated physiological mechanisms of mirroring at single-cell and neural-system levels that support the cognitive and social psychology constructs. Why were these neural mechanisms selected, and what is their adaptive advantage? Neural mirroring solves the "problem of other minds" (how we can access and understand the minds of others) and makes intersubjectivity possible, thus facilitating social behavior.

Improving Odometric Accuracy for an Autonomous Electric Cart.

PubMed

Toledo, Jonay; Piñeiro, Jose D; Arnay, Rafael; Acosta, Daniel; Acosta, Leopoldo

2018-01-12

In this paper, a study of the odometric system for the autonomous cart Verdino, which is an electric vehicle based on a golf cart, is presented. A mathematical model of the odometric system is derived from cart movement equations, and is used to compute the vehicle position and orientation. The inputs of the system are the odometry encoders, and the model uses the wheels diameter and distance between wheels as parameters. With this model, a least square minimization is made in order to get the nominal best parameters. This model is updated, including a real time wheel diameter measurement improving the accuracy of the results. A neural network model is used in order to learn the odometric model from data. Tests are made using this neural network in several configurations and the results are compared to the mathematical model, showing that the neural network can outperform the first proposed model.

Neural network L1 adaptive control of MIMO systems with nonlinear uncertainty.

PubMed

Zhen, Hong-tao; Qi, Xiao-hui; Li, Jie; Tian, Qing-min

2014-01-01

An indirect adaptive controller is developed for a class of multiple-input multiple-output (MIMO) nonlinear systems with unknown uncertainties. This control system is comprised of an L 1 adaptive controller and an auxiliary neural network (NN) compensation controller. The L 1 adaptive controller has guaranteed transient response in addition to stable tracking. In this architecture, a low-pass filter is adopted to guarantee fast adaptive rate without generating high-frequency oscillations in control signals. The auxiliary compensation controller is designed to approximate the unknown nonlinear functions by MIMO RBF neural networks to suppress the influence of uncertainties. NN weights are tuned on-line with no prior training and the project operator ensures the weights bounded. The global stability of the closed-system is derived based on the Lyapunov function. Numerical simulations of an MIMO system coupled with nonlinear uncertainties are used to illustrate the practical potential of our theoretical results.

Decoupling control of vehicle chassis system based on neural network inverse system

NASA Astrophysics Data System (ADS)

Wang, Chunyan; Zhao, Wanzhong; Luan, Zhongkai; Gao, Qi; Deng, Ke

2018-06-01

Steering and suspension are two important subsystems affecting the handling stability and riding comfort of the chassis system. In order to avoid the interference and coupling of the control channels between active front steering (AFS) and active suspension subsystems (ASS), this paper presents a composite decoupling control method, which consists of a neural network inverse system and a robust controller. The neural network inverse system is composed of a static neural network with several integrators and state feedback of the original chassis system to approach the inverse system of the nonlinear systems. The existence of the inverse system for the chassis system is proved by the reversibility derivation of Interactor algorithm. The robust controller is based on the internal model control (IMC), which is designed to improve the robustness and anti-interference of the decoupled system by adding a pre-compensation controller to the pseudo linear system. The results of the simulation and vehicle test show that the proposed decoupling controller has excellent decoupling performance, which can transform the multivariable system into a number of single input and single output systems, and eliminate the mutual influence and interference. Furthermore, it has satisfactory tracking capability and robust performance, which can improve the comprehensive performance of the chassis system.

Screening of bioactive peptides using an embryonic stem cell-based neurodifferentiation assay.

PubMed

Xu, Ruodan; Feyeux, Maxime; Julien, Stéphanie; Nemes, Csilla; Albrechtsen, Morten; Dinnyés, Andras; Krause, Karl-Heinz

2014-05-01

Differentiation of pluripotent stem cells, PSCs, towards neural lineages has attracted significant attention, given the potential use of such cells for in vitro studies and for regenerative medicine. The present experiments were designed to identify bioactive peptides which direct PSC differentiation towards neural cells. Fifteen peptides were designed based on NCAM, FGFR, and growth factors sequences. The effect of peptides was screened using a mouse embryonic stem cell line expressing luciferase dual reporter construct driven by promoters for neural tubulin and for elongation factor 1. Cell number was estimated by measuring total cellular DNA. We identified five peptides which enhanced activities of both promoters without relevant changes in cell number. We selected the two most potent peptides for further analysis: the NCAM-derived mimetic FGLL and the synthetic NCAM ligand, Plannexin. Both compounds induced phenotypic neuronal differentiation, as evidenced by increased neurite outgrowth. In summary, we used a simple, but sensitive screening approach to identify the neurogenic peptides. These peptides will not only provide new clues concerning pathways of neurogenesis, but they may also be interesting biotechnology tools for in vitro generation of neurons.

Global cluster synchronization in nonlinearly coupled community networks with heterogeneous coupling delays.

PubMed

Tseng, Jui-Pin

2017-02-01

This investigation establishes the global cluster synchronization of complex networks with a community structure based on an iterative approach. The units comprising the network are described by differential equations, and can be non-autonomous and involve time delays. In addition, units in the different communities can be governed by different equations. The coupling configuration of the network is rather general. The coupling terms can be non-diffusive, nonlinear, asymmetric, and with heterogeneous coupling delays. Based on this approach, both delay-dependent and delay-independent criteria for global cluster synchronization are derived. We implement the present approach for a nonlinearly coupled neural network with heterogeneous coupling delays. Two numerical examples are given to show that neural networks can behave in a variety of new collective ways under the synchronization criteria. These examples also demonstrate that neural networks remain synchronized in spite of coupling delays between neurons across different communities; however, they may lose synchrony if the coupling delays between the neurons within the same community are too large, such that the synchronization criteria are violated. Copyright © 2016 Elsevier Ltd. All rights reserved.

Prediction of proprotein convertase cleavage sites.

PubMed

Duckert, Peter; Brunak, Søren; Blom, Nikolaj

2004-01-01

Many secretory proteins and peptides are synthesized as inactive precursors that in addition to signal peptide cleavage undergo post-translational processing to become biologically active polypeptides. Precursors are usually cleaved at sites composed of single or paired basic amino acid residues by members of the subtilisin/kexin-like proprotein convertase (PC) family. In mammals, seven members have been identified, with furin being the one first discovered and best characterized. Recently, the involvement of furin in diseases ranging from Alzheimer's disease and cancer to anthrax and Ebola fever has created additional focus on proprotein processing. We have developed a method for prediction of cleavage sites for PCs based on artificial neural networks. Two different types of neural networks have been constructed: a furin-specific network based on experimental results derived from the literature, and a general PC-specific network trained on data from the Swiss-Prot protein database. The method predicts cleavage sites in independent sequences with a sensitivity of 95% for the furin neural network and 62% for the general PC network. The ProP method is made publicly available at http://www.cbs.dtu.dk/services/ProP.

Neural stem cell-based treatment for neurodegenerative diseases.

PubMed

Kim, Seung U; Lee, Hong J; Kim, Yun B

2013-10-01

Human neurodegenerative diseases such as Parkinson's disease (PD), Huntington's disease (HD), amyotrophic lateral sclerosis (ALS) and Alzheimer's disease (AD) are caused by a loss of neurons and glia in the brain or spinal cord. Neurons and glial cells have successfully been generated from stem cells such as embryonic stem cells (ESCs), mesenchymal stem cells (MSCs) and neural stem cells (NSCs), and stem cell-based cell therapies for neurodegenerative diseases have been developed. A recent advance in generation of a new class of pluripotent stem cells, induced pluripotent stem cells (iPSCs), derived from patients' own skin fibroblasts, opens doors for a totally new field of personalized medicine. Transplantation of NSCs, neurons or glia generated from stem cells in animal models of neurodegenerative diseases, including PD, HD, ALS and AD, demonstrates clinical improvement and also life extension of these animals. Additional therapeutic benefits in these animals can be provided by stem cell-mediated gene transfer of therapeutic genes such as neurotrophic factors and enzymes. Although further research is still needed, cell and gene therapy based on stem cells, particularly using neurons and glia derived from iPSCs, ESCs or NSCs, will become a routine treatment for patients suffering from neurodegenerative diseases and also stroke and spinal cord injury. © 2013 Japanese Society of Neuropathology.

Mean Field Analysis of Large-Scale Interacting Populations of Stochastic Conductance-Based Spiking Neurons Using the Klimontovich Method

NASA Astrophysics Data System (ADS)

Gandolfo, Daniel; Rodriguez, Roger; Tuckwell, Henry C.

2017-03-01

We investigate the dynamics of large-scale interacting neural populations, composed of conductance based, spiking model neurons with modifiable synaptic connection strengths, which are possibly also subjected to external noisy currents. The network dynamics is controlled by a set of neural population probability distributions (PPD) which are constructed along the same lines as in the Klimontovich approach to the kinetic theory of plasmas. An exact non-closed, nonlinear, system of integro-partial differential equations is derived for the PPDs. As is customary, a closing procedure leads to a mean field limit. The equations we have obtained are of the same type as those which have been recently derived using rigorous techniques of probability theory. The numerical solutions of these so called McKean-Vlasov-Fokker-Planck equations, which are only valid in the limit of infinite size networks, actually shows that the statistical measures as obtained from PPDs are in good agreement with those obtained through direct integration of the stochastic dynamical system for large but finite size networks. Although numerical solutions have been obtained for networks of Fitzhugh-Nagumo model neurons, which are often used to approximate Hodgkin-Huxley model neurons, the theory can be readily applied to networks of general conductance-based model neurons of arbitrary dimension.

Combined Use of Tissue Morphology, Neural Network Analysis of Chromatin Texture and Clinical Variables to Predict Prostate Cancer Agressiveness from Biopsy Water

DTIC Science & Technology

2000-10-01

Purpose: To combine clinical, serum, pathologic and computer derived information into an artificial neural network to develop/validate a model to...Development of an artificial neural network (year 02). Prospective validation of this model (projected year 03). All models will be tested and

Combined Use of Tissue Morphology, Neural Network Analysis of Chromatin Texture & Clinical Variables to Predict Prostate Cancer Agressiveness from Biopsy Material

DTIC Science & Technology

1999-10-01

THE PURPOSE OF THIS REPORT IS TO COMBINE CLINICAL, SERUM, PATHOLOGICAL AND COMPUTER DERIVED INFORMATION INTO AN ARTIFICIAL NEURAL NETWORK TO DEVELOP...01). Development of a artificial neural network model (year 02). Prospective validation of this model (projected year 03). All models will be tested

Short-Term Grafting of Human Neural Stem Cells: Electrophysiological Properties and Motor Behavioral Amelioration in Experimental Parkinsons Disease.

PubMed

Martnez-Serrano, Alberto; Pereira, Marta P; Avaliani, Natalia; Nelke, Anna; Kokaia, Merab; Ramos-Moreno, Tania

2016-12-13

Cell replacement therapy in Parkinsons disease (PD) still lacks a study addressing the acquisition of electrophysiological properties of human grafted neural stem cells and their relation with the emergence of behavioral recovery after transplantation in the short term. Here we study the electrophysiological and biochemical profiles of two ventral mesencephalic human neural stem cell (NSC) clonal lines (C30-Bcl-XL and C32-Bcl-XL) that express high levels of Bcl-XL to enhance their neurogenic capacity, after grafting in an in vitro parkinsonian model. Electrophysiological recordings show that the majority of the cells derived from the transplants are not mature at 6 weeks after grafting, but 6.7% of the studied cells showed mature electrophysiological profiles. Nevertheless, parallel in vivo behavioral studies showed a significant motor improvement at 7 weeks postgrafting in the animals receiving C30-Bcl-XL, the cell line producing the highest amount of TH+ cells. Present results show that, at this postgrafting time point, behavioral amelioration highly correlates with the spatial dispersion of the TH+ grafted cells in the caudate putamen. The spatial dispersion, along with a high number of dopaminergic-derived cells, is crucial for behavioral improvements. Our findings have implications for long-term standardization of stem cell-based approaches in Parkinsons disease.

Model risk for European-style stock index options.

PubMed

Gençay, Ramazan; Gibson, Rajna

2007-01-01

In empirical modeling, there have been two strands for pricing in the options literature, namely the parametric and nonparametric models. Often, the support for the nonparametric methods is based on a benchmark such as the Black-Scholes (BS) model with constant volatility. In this paper, we study the stochastic volatility (SV) and stochastic volatility random jump (SVJ) models as parametric benchmarks against feedforward neural network (FNN) models, a class of neural network models. Our choice for FNN models is due to their well-studied universal approximation properties of an unknown function and its partial derivatives. Since the partial derivatives of an option pricing formula are risk pricing tools, an accurate estimation of the unknown option pricing function is essential for pricing and hedging. Our findings indicate that FNN models offer themselves as robust option pricing tools, over their sophisticated parametric counterparts in predictive settings. There are two routes to explain the superiority of FNN models over the parametric models in forecast settings. These are nonnormality of return distributions and adaptive learning.

Residual Shuffling Convolutional Neural Networks for Deep Semantic Image Segmentation Using Multi-Modal Data

NASA Astrophysics Data System (ADS)

Chen, K.; Weinmann, M.; Gao, X.; Yan, M.; Hinz, S.; Jutzi, B.; Weinmann, M.

2018-05-01

In this paper, we address the deep semantic segmentation of aerial imagery based on multi-modal data. Given multi-modal data composed of true orthophotos and the corresponding Digital Surface Models (DSMs), we extract a variety of hand-crafted radiometric and geometric features which are provided separately and in different combinations as input to a modern deep learning framework. The latter is represented by a Residual Shuffling Convolutional Neural Network (RSCNN) combining the characteristics of a Residual Network with the advantages of atrous convolution and a shuffling operator to achieve a dense semantic labeling. Via performance evaluation on a benchmark dataset, we analyze the value of different feature sets for the semantic segmentation task. The derived results reveal that the use of radiometric features yields better classification results than the use of geometric features for the considered dataset. Furthermore, the consideration of data on both modalities leads to an improvement of the classification results. However, the derived results also indicate that the use of all defined features is less favorable than the use of selected features. Consequently, data representations derived via feature extraction and feature selection techniques still provide a gain if used as the basis for deep semantic segmentation.

Fibulin-1 is required for morphogenesis of neural crest-derived structures

PubMed Central

Cooley, Marion A.; Kern, Christine B.; Fresco, Victor M.; Wessels, Andy; Thompson, Robert P.; McQuinn, Tim C.; Twal, Waleed O.; Mjaatvedt, Corey H.; Drake, Christopher J.; Argraves, W. Scott

2008-01-01

Here we report that mouse embryos homozygous for a gene trap insertion in the fibulin-1 (Fbln1) gene are deficient in Fbln1 and exhibit cardiac ventricular wall thinning and ventricular septal defects with double outlet right ventricle or overriding aorta. Fbln1 nulls also display anomalies of aortic arch arteries, hypoplasia of the thymus and thyroid, underdeveloped skull bones, malformations of cranial nerves and hemorrhagic blood vessels in the head and neck. The spectrum of malformations is consistent with Fbln1 influencing neural crest cell (NCC)-dependent development of these tissues. This is supported by evidence that Fbln1 expression is associated with streams of cranial NCCs migrating adjacent to rhombomeres 2–7 and that Fbln1-deficient embryos display patterning anomalies of NCCs forming cranial nerves IX and X, which derive from rhombomeres 6 and 7. Additionally, Fbln1-deficient embryos show increased apoptosis in areas populated by NCCs derived from rhombomeres 4, 6 and 7. Based on these findings, it is concluded that Fbln1 is required for the directed migration and survival of cranial NCCs contributing to the development of pharyngeal glands, craniofacial skeleton, cranial nerves, aortic arch arteries, cardiac outflow tract and cephalic blood vessels. PMID:18538758

Alternative generation of CNS neural stem cells and PNS derivatives from neural crest-derived peripheral stem cells.

PubMed

Weber, Marlen; Apostolova, Galina; Widera, Darius; Mittelbronn, Michel; Dechant, Georg; Kaltschmidt, Barbara; Rohrer, Hermann

2015-02-01

Neural crest-derived stem cells (NCSCs) from the embryonic peripheral nervous system (PNS) can be reprogrammed in neurosphere (NS) culture to rNCSCs that produce central nervous system (CNS) progeny, including myelinating oligodendrocytes. Using global gene expression analysis we now demonstrate that rNCSCs completely lose their previous PNS characteristics and acquire the identity of neural stem cells derived from embryonic spinal cord. Reprogramming proceeds rapidly and results in a homogenous population of Olig2-, Sox3-, and Lex-positive CNS stem cells. Low-level expression of pluripotency inducing genes Oct4, Nanog, and Klf4 argues against a transient pluripotent state during reprogramming. The acquisition of CNS properties is prevented in the presence of BMP4 (BMP NCSCs) as shown by marker gene expression and the potential to produce PNS neurons and glia. In addition, genes characteristic for mesenchymal and perivascular progenitors are expressed, which suggests that BMP NCSCs are directed toward a pericyte progenitor/mesenchymal stem cell (MSC) fate. Adult NCSCs from mouse palate, an easily accessible source of adult NCSCs, display strikingly similar properties. They do not generate cells with CNS characteristics but lose the neural crest markers Sox10 and p75 and produce MSC-like cells. These findings show that embryonic NCSCs acquire a full CNS identity in NS culture. In contrast, MSC-like cells are generated from BMP NCSCs and pNCSCs, which reveals that postmigratory NCSCs are a source for MSC-like cells up to the adult stage. © 2014 AlphaMed Press.

Cell Motility and Invasiveness of Neurofibromin-Deficient Neural Crest Cells and Malignant Triton Tumor Lines

DTIC Science & Technology

2005-06-01

derived cells, we isolated first branchial arch mesenchymal populations, as well as trigeminal ganglion non-neuronal cells, from mouse embryos and measured...demonstrate that loss of neurofibromin affects the invasiveness of neural crest-derived (trigeminal ganglion) and cranial mesenchymal ( branchial arch) cell...trigeminal and branchial arch cells between El0 and El 2 indicates that the roles of neurofibromin in controlling motility may become increasingly

Chicken HOXA3 Gene: Its Expression Pattern and Role in Branchial Nerve Precursor Cell Migration

PubMed Central

Watari-Goshima, Natsuko; Chisaka, Osamu

2011-01-01

In vertebrates, the proximal and distal sensory ganglia of the branchial nerves are derived from neural crest cells (NCCs) and placodes, respectively. We previously reported that in Hoxa3 knockout mouse embryos, NCCs and placode-derived cells of the glossopharyngeal nerve were defective in their migration. In this report, to determine the cell-type origin for this Hoxa3 knockout phenotype, we blocked the expression of the gene with antisense morpholino oligonucleotides (MO) specifically in either NCCs/neural tube or placodal cells of chicken embryos. Our results showed that HOXA3 function was required for the migration of the epibranchial placode-derived cells and that HOXA3 regulated this cell migration in both NCCs/neural tube and placodal cells. We also report that the expression pattern of chicken HOXA3 was slightly different from that of mouse Hoxa3. PMID:21278919

The active principle region of Buyang Huanwu decoction induced differentiation of bone marrow-derived mesenchymal stem cells into neural-like cells

PubMed Central

Zheng, Jinghui; Wan, Yi; Chi, Jianhuai; Shen, Dekai; Wu, Tingting; Li, Weimin; Du, Pengcheng

2012-01-01

The present study induced in vitro-cultured passage 4 bone marrow-derived mesenchymal stem cells to differentiate into neural-like cells with a mixture of alkaloid, polysaccharide, aglycone, glycoside, essential oils, and effective components of Buyang Huanwu decoction (active principle region of decoction for invigorating yang for recuperation). After 28 days, nestin and neuron-specific enolase were expressed in the cytoplasm. Reverse transcription-PCR and western blot analyses showed that nestin and neuron-specific enolase mRNA and protein expression was greater in the active principle region group compared with the original formula group. Results demonstrated that the active principle region of Buyang Huanwu decoction induced greater differentiation of rat bone marrow-derived mesenchymal stem cells into neural-like cells in vitro than the original Buyang Huanwu decoction formula. PMID:25806066

GABRA2 Alcohol Dependence Risk Allele is Associated with Reduced Expression of Chromosome 4p12 GABAA Subunit Genes in Human Neural Cultures.

PubMed

Lieberman, Richard; Kranzler, Henry R; Joshi, Pujan; Shin, Dong-Guk; Covault, Jonathan

2015-09-01

Genetic variation in a region of chromosome 4p12 that includes the GABAA subunit gene GABRA2 has been reproducibly associated with alcohol dependence (AD). However, the molecular mechanisms underlying the association are unknown. This study examined correlates of in vitro gene expression of the AD-associated GABRA2 rs279858*C-allele in human neural cells using an induced pluripotent stem cell (iPSC) model system. We examined mRNA expression of chromosome 4p12 GABAA subunit genes (GABRG1, GABRA2, GABRA4, and GABRB1) in 36 human neural cell lines differentiated from iPSCs using quantitative polymerase chain reaction and next-generation RNA sequencing. mRNA expression in adult human brain was examined using the BrainCloud and BRAINEAC data sets. We found significantly lower levels of GABRA2 mRNA in neural cell cultures derived from rs279858*C-allele carriers. Levels of GABRA2 RNA were correlated with those of the other 3 chromosome 4p12 GABAA genes, but not other neural genes. Cluster analysis based on the relative RNA levels of the 4 chromosome 4p12 GABAA genes identified 2 distinct clusters of cell lines, a low-expression cluster associated with rs279858*C-allele carriers and a high-expression cluster enriched for the rs279858*T/T genotype. In contrast, there was no association of genotype with chromosome 4p12 GABAA gene expression in postmortem adult cortex in either the BrainCloud or BRAINEAC data sets. AD-associated variation in GABRA2 is associated with differential expression of the entire cluster of GABAA subunit genes on chromosome 4p12 in human iPSC-derived neural cell cultures. The absence of a parallel effect in postmortem human adult brain samples suggests that AD-associated genotype effects on GABAA expression, although not present in mature cortex, could have effects on regulation of the chromosome 4p12 GABAA cluster during neural development. Copyright © 2015 by the Research Society on Alcoholism.

Weaving and neural complexity in symmetric quantum states

NASA Astrophysics Data System (ADS)

Susa, Cristian E.; Girolami, Davide

2018-04-01

We study the behaviour of two different measures of the complexity of multipartite correlation patterns, weaving and neural complexity, for symmetric quantum states. Weaving is the weighted sum of genuine multipartite correlations of any order, where the weights are proportional to the correlation order. The neural complexity, originally introduced to characterize correlation patterns in classical neural networks, is here extended to the quantum scenario. We derive closed formulas of the two quantities for GHZ states mixed with white noise.

A Collaborative Neurodynamic Approach to Multiple-Objective Distributed Optimization.

PubMed

Yang, Shaofu; Liu, Qingshan; Wang, Jun

2018-04-01

This paper is concerned with multiple-objective distributed optimization. Based on objective weighting and decision space decomposition, a collaborative neurodynamic approach to multiobjective distributed optimization is presented. In the approach, a system of collaborative neural networks is developed to search for Pareto optimal solutions, where each neural network is associated with one objective function and given constraints. Sufficient conditions are derived for ascertaining the convergence to a Pareto optimal solution of the collaborative neurodynamic system. In addition, it is proved that each connected subsystem can generate a Pareto optimal solution when the communication topology is disconnected. Then, a switching-topology-based method is proposed to compute multiple Pareto optimal solutions for discretized approximation of Pareto front. Finally, simulation results are discussed to substantiate the performance of the collaborative neurodynamic approach. A portfolio selection application is also given.

New results on anti-synchronization of switched neural networks with time-varying delays and lag signals.

PubMed

Cao, Yuting; Wen, Shiping; Chen, Michael Z Q; Huang, Tingwen; Zeng, Zhigang

2016-09-01

This paper investigates the problem of global exponential anti-synchronization of a class of switched neural networks with time-varying delays and lag signals. Considering the packed circuits, the controller is dependent on the output of the system as the inner states are very hard to measure. Therefore, it is necessary to investigate the controller based on the output of the neuron cell. Through theoretical analysis, it is obvious that the obtained ones improve and generalize the results derived in the previous literature. To illustrate the effectiveness, a simulation example with applications in image encryptions is also presented in the paper. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mittag-Leffler stability of fractional-order neural networks in the presence of generalized piecewise constant arguments.

PubMed

Wu, Ailong; Liu, Ling; Huang, Tingwen; Zeng, Zhigang

2017-01-01

Neurodynamic system is an emerging research field. To understand the essential motivational representations of neural activity, neurodynamics is an important question in cognitive system research. This paper is to investigate Mittag-Leffler stability of a class of fractional-order neural networks in the presence of generalized piecewise constant arguments. To identify neural types of computational principles in mathematical and computational analysis, the existence and uniqueness of the solution of neurodynamic system is the first prerequisite. We prove that the existence and uniqueness of the solution of the network holds when some conditions are satisfied. In addition, self-active neurodynamic system demands stable internal dynamical states (equilibria). The main emphasis will be then on several sufficient conditions to guarantee a unique equilibrium point. Furthermore, to provide deeper explanations of neurodynamic process, Mittag-Leffler stability is studied in detail. The established results are based on the theories of fractional differential equation and differential equation with generalized piecewise constant arguments. The derived criteria improve and extend the existing related results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Toward an account of clinical anxiety predicated on basic, neurally mapped mechanisms of Pavlovian fear-learning: the case for conditioned overgeneralization.

PubMed

Lissek, Shmuel

2012-04-01

The past two decades have brought dramatic progress in the neuroscience of anxiety due, in no small part, to animal findings specifying the neurobiology of Pavlovian fear-conditioning. Fortuitously, this neurally mapped process of fear learning is widely expressed in humans, and has been centrally implicated in the etiology of clinical anxiety. Fear-conditioning experiments in anxiety patients thus represent a unique opportunity to bring recent advances in animal neuroscience to bear on working, brain-based models of clinical anxiety. The current presentation details the neural basis and clinical relevance of fear conditioning, and highlights generalization of conditioned fear to stimuli resembling the conditioned danger cue as one of the more robust conditioning markers of clinical anxiety. Studies testing such generalization across a variety of anxiety disorders (panic, generalized anxiety disorder, and social anxiety disorder) with systematic methods developed in animals will next be presented. Finally, neural accounts of overgeneralization deriving from animal and human data will be described with emphasis given to implications for the neurobiology and treatment of clinical anxiety. © 2012 Wiley Periodicals, Inc.

Carbon nanotube multilayered nanocomposites as multifunctional substrates for actuating neuronal differentiation and functions of neural stem cells.

PubMed

Shao, Han; Li, Tingting; Zhu, Rong; Xu, Xiaoting; Yu, Jiandong; Chen, Shengfeng; Song, Li; Ramakrishna, Seeram; Lei, Zhigang; Ruan, Yiwen; He, Liumin

2018-08-01

Carbon nanotubes (CNTs) have shown potential applications in neuroscience as growth substrates owing to their numerous unique properties. However, a key concern in the fabrication of homogeneous composites is the serious aggregation of CNTs during incorporation into the biomaterial matrix. Moreover, the regulation mechanism of CNT-based substrates on neural differentiation remains unclear. Here, a novel strategy was introduced for the construction of CNT nanocomposites via layer-by-layer assembly of negatively charged multi-walled CNTs and positively charged poly(dimethyldiallylammonium chloride). Results demonstrated that the CNT-multilayered nanocomposites provided a potent regulatory signal over neural stem cells (NSCs), including cell adhesion, viability, differentiation, neurite outgrowth, and electrophysiological maturation of NSC-derived neurons. Importantly, the dynamic molecular mechanisms in the NSC differentiation involved the integrin-mediated interactions between NSCs and CNT multilayers, thereby activating focal adhesion kinase, subsequently triggering downstream signaling events to regulate neuronal differentiation and synapse formation. This study provided insights for future applications of CNT-multilayered nanomaterials in neural fields as potent modulators of stem cell behavior. Copyright © 2018 Elsevier Ltd. All rights reserved.

Prolonged Expansion Induces Spontaneous Neural Progenitor Differentiation from Human Gingiva-Derived Mesenchymal Stem Cells.

PubMed

Rajan, Thangavelu Soundara; Scionti, Domenico; Diomede, Francesca; Piattelli, Adriano; Bramanti, Placido; Mazzon, Emanuela; Trubiani, Oriana

2017-12-01

Neural crest-derived mesenchymal stem cells (MSCs) obtained from dental tissues received considerable interest in regenerative medicine, particularly in nerve regeneration owing to their embryonic origin and ease of harvest. Proliferation efficacy and differentiation capacity into diverse cell lineages propose dental MSCs as an in vitro tool for disease modeling. In this study, we investigated the spontaneous differentiation efficiency of dental MSCs obtained from human gingiva tissue (hGMSCs) into neural progenitor cells after extended passaging. At passage 41, the morphology of hGMSCs changed from typical fibroblast-like shape into sphere-shaped cells with extending processes. Next-generation transcriptomics sequencing showed increased expression of neural progenitor markers such as NES, MEIS2, and MEST. In addition, de novo expression of neural precursor genes, such as NRN1, PHOX2B, VANGL2, and NTRK3, was noticed in passage 41. Immunocytochemistry results showed suppression of neurogenesis repressors TP53 and p21, whereas Western blot results revealed the expression of neurotrophic factors BDNF and NT3 at passage 41. Our results showed the spontaneous efficacy of hGMSCs to differentiate into neural precursor cells over prolonged passages and that these cells may assist in producing novel in vitro disease models that are associated with neural development.

Anosmin-1 is essential for neural crest and cranial placodes formation in Xenopus.

PubMed

Bae, Chang-Joon; Hong, Chang-Soo; Saint-Jeannet, Jean-Pierre

2018-01-15

During embryogenesis vertebrates develop a complex craniofacial skeleton associated with sensory organs. These structures are primarily derived from two embryonic cell populations the neural crest and cranial placodes, respectively. Neural crest cells and cranial placodes are specified through the integrated action of several families of signaling molecules, and the subsequent activation of a complex network of transcription factors. Here we describe the expression and function of Anosmin-1 (Anos1), an extracellular matrix protein, during neural crest and cranial placodes development in Xenopus laevis. Anos1 was identified as a target of Pax3 and Zic1, two transcription factors necessary and sufficient to generate neural crest and cranial placodes. Anos1 is expressed in cranial neural crest progenitors at early neurula stage and in cranial placode derivatives later in development. We show that Anos1 function is required for neural crest and sensory organs development in Xenopus, consistent with the defects observed in Kallmann syndrome patients carrying a mutation in ANOS1. These findings indicate that anos1 has a conserved function in the development of craniofacial structures, and indicate that anos1-depleted Xenopus embryos represent a useful model to analyze the pathogenesis of Kallmann syndrome. Copyright © 2017. Published by Elsevier Inc.

Globally fixed-time synchronization of coupled neutral-type neural network with mixed time-varying delays.

PubMed

Zheng, Mingwen; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian; Zhang, Yanping; Zhao, Hui

2018-01-01

This paper mainly studies the globally fixed-time synchronization of a class of coupled neutral-type neural networks with mixed time-varying delays via discontinuous feedback controllers. Compared with the traditional neutral-type neural network model, the model in this paper is more general. A class of general discontinuous feedback controllers are designed. With the help of the definition of fixed-time synchronization, the upper right-hand derivative and a defined simple Lyapunov function, some easily verifiable and extensible synchronization criteria are derived to guarantee the fixed-time synchronization between the drive and response systems. Finally, two numerical simulations are given to verify the correctness of the results.

Globally fixed-time synchronization of coupled neutral-type neural network with mixed time-varying delays

PubMed Central

2018-01-01

This paper mainly studies the globally fixed-time synchronization of a class of coupled neutral-type neural networks with mixed time-varying delays via discontinuous feedback controllers. Compared with the traditional neutral-type neural network model, the model in this paper is more general. A class of general discontinuous feedback controllers are designed. With the help of the definition of fixed-time synchronization, the upper right-hand derivative and a defined simple Lyapunov function, some easily verifiable and extensible synchronization criteria are derived to guarantee the fixed-time synchronization between the drive and response systems. Finally, two numerical simulations are given to verify the correctness of the results. PMID:29370248

A Neural Network Model for K(λ) Retrieval and Application to Global Kpar Monitoring.

PubMed

Chen, Jun; Zhu, Yuanli; Wu, Yongsheng; Cui, Tingwei; Ishizaka, Joji; Ju, Yongtao

2015-01-01

Accurate estimation of diffuse attenuation coefficients in the visible wavelengths Kd(λ) from remotely sensed data is particularly challenging in global oceanic and coastal waters. The objectives of the present study are to evaluate the applicability of a semi-analytical Kd(λ) retrieval model (SAKM) and Jamet's neural network model (JNNM), and then develop a new neural network Kd(λ) retrieval model (NNKM). Based on the comparison of Kd(λ) predicted by these models with in situ measurements taken from the global oceanic and coastal waters, all of the NNKM, SAKM, and JNNM models work well in Kd(λ) retrievals, but the NNKM model works more stable and accurate than both SAKM and JNNM models. The near-infrared band-based and shortwave infrared band-based combined model is used to remove the atmospheric effects on MODIS data. The Kd(λ) data was determined from the atmospheric corrected MODIS data using the NNKM, JNNM, and SAKM models. The results show that the NNKM model produces <30% uncertainty in deriving Kd(λ) from global oceanic and coastal waters, which is 4.88-17.18% more accurate than SAKM and JNNM models. Furthermore, we employ an empirical approach to calculate Kpar from the NNKM model-derived diffuse attenuation coefficient at visible bands (443, 488, 555, and 667 nm). The results show that our model presents a satisfactory performance in deriving Kpar from the global oceanic and coastal waters with 20.2% uncertainty. The Kpar are quantified from MODIS data atmospheric correction using our model. Comparing with field measurements, our model produces ~31.0% uncertainty in deriving Kpar from Bohai Sea. Finally, the applicability of our model for general oceanographic studies is briefly illuminated by applying it to climatological monthly mean remote sensing reflectance for time ranging from July, 2002- July 2014 at the global scale. The results indicate that the high Kd(λ) and Kpar values are usually found around the coastal zones in the high latitude regions, while low Kd(λ) and Kpar values are usually found in the open oceans around the low-latitude regions. These results could improve our knowledge about the light field under waters at either the global or basin scales, and be potentially used into general circulation models to estimate the heat flux between atmosphere and ocean.

In Vitro Evaluation of Biocompatibility of Uncoated Thermally Reduced Graphene and Carbon Nanotube-Loaded PVDF Membranes with Adult Neural Stem Cell-Derived Neurons and Glia

PubMed Central

Defteralı, Çağla; Verdejo, Raquel; Majeed, Shahid; Boschetti-de-Fierro, Adriana; Méndez-Gómez, Héctor R.; Díaz-Guerra, Eva; Fierro, Daniel; Buhr, Kristian; Abetz, Clarissa; Martínez-Murillo, Ricardo; Vuluga, Daniela; Alexandre, Michaël; Thomassin, Jean-Michel; Detrembleur, Christophe; Jérôme, Christine; Abetz, Volker; López-Manchado, Miguel Ángel; Vicario-Abejón, Carlos

2016-01-01

Graphene, graphene-based nanomaterials (GBNs), and carbon nanotubes (CNTs) are being investigated as potential substrates for the growth of neural cells. However, in most in vitro studies, the cells were seeded on these materials coated with various proteins implying that the observed effects on the cells could not solely be attributed to the GBN and CNT properties. Here, we studied the biocompatibility of uncoated thermally reduced graphene (TRG) and poly(vinylidene fluoride) (PVDF) membranes loaded with multi-walled CNTs (MWCNTs) using neural stem cells isolated from the adult mouse olfactory bulb (termed aOBSCs). When aOBSCs were induced to differentiate on coverslips treated with TRG or control materials (polyethyleneimine-PEI and polyornithine plus fibronectin-PLO/F) in a serum-free medium, neurons, astrocytes, and oligodendrocytes were generated in all conditions, indicating that TRG permits the multi-lineage differentiation of aOBSCs. However, the total number of cells was reduced on both PEI and TRG. In a serum-containing medium, aOBSC-derived neurons and oligodendrocytes grown on TRG were more numerous than in controls; the neurons developed synaptic boutons and oligodendrocytes were more branched. In contrast, neurons growing on PVDF membranes had reduced neurite branching, and on MWCNTs-loaded membranes oligodendrocytes were lower in numbers than in controls. Overall, these findings indicate that uncoated TRG may be biocompatible with the generation, differentiation, and maturation of aOBSC-derived neurons and glial cells, implying a potential use for TRG to study functional neuronal networks. PMID:27999773

A simple, xeno-free method for oligodendrocyte generation from human neural stem cells derived from umbilical cord: engagement of gelatinases in cell commitment and differentiation.

PubMed

Sypecka, Joanna; Ziemka-Nalecz, Małgorzata; Dragun-Szymczak, Patrycja; Zalewska, Teresa

2017-05-01

Oligodendrocyte progenitors (OPCs) are ranked among the most likely candidates for cell-based strategies aimed at treating neurodegenerative diseases accompanied by dys/demyelination of the central nervous system (CNS). In this regard, different sources of stem cells are being tested to elaborate xeno-free protocols for efficient generation of OPCs for clinical applications. In the present study, neural stem cells of human umbilical cord blood (HUCB-NSCs) have been used to derive OPCs and subsequently to differentiate them into mature, GalC-expressing oligodendrocytes. Applied components of the extracellular matrix (ECM) and the analogues of physiological substances known to increase glial commitment of neural stem cells have been shown to significantly increase the yield of the resulting OPC fraction. The efficiency of ECM components in promoting oligodendrocyte commitment and differentiation prompted us to investigate the potential role of gelatinases in those processes. Subsequently, endogenous and ECM metalloproteinases (MMPs) activity has been compared with that detected in primary cultures of rat oligodendrocytes in vitro, as well as in rat brains in vivo. The data indicate that gelatinases are engaged in gliogenesis both in vitro and in vivo, although differently, which presumably results from distinct extracellular conditions. In conclusion, the study presents an efficient xeno-free method of deriving oligodendrocyte from HUCB-NSCs and analyses the engagement of MMP-2/MMP-9 in the processes of cell commitment and maturation. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Microparticle Shedding from Neural Progenitor Cells and Vascular Compartment Cells Is Increased in Ischemic Stroke.

PubMed

Chiva-Blanch, Gemma; Suades, Rosa; Crespo, Javier; Peña, Esther; Padró, Teresa; Jiménez-Xarrié, Elena; Martí-Fàbregas, Joan; Badimon, Lina

2016-01-01

Ischemic stroke has shown to induce platelet and endothelial microparticle shedding, but whether stroke induces microparticle shedding from additional blood and vascular compartment cells is unclear. Neural precursor cells have been shown to replace dying neurons at sites of brain injury; however, if neural precursor cell activation is associated to microparticle shedding, and whether this activation is maintained at long term and associates to stroke type and severity remains unknown. We analyzed neural precursor cells and blood and vascular compartment cells microparticle shedding after an acute ischemic stroke. Forty-four patients were included in the study within the first 48h after the onset of stroke. The cerebral lesion size was evaluated at 3-7 days of the stroke. Circulating microparticles from neural precursor cells and blood and vascular compartment cells (platelets, endothelial cells, erythrocytes, leukocytes, lymphocytes, monocytes and smooth muscle cells) were analyzed by flow cytometry at the onset of stroke and at 7 and 90 days. Forty-four age-matched high cardiovascular risk subjects without documented vascular disease were used as controls. Compared to high cardiovascular risk controls, patients showed higher number of neural precursor cell- and all blood and vascular compartment cell-derived microparticles at the onset of stroke, and after 7 and 90 days. At 90 days, neural precursor cell-derived microparticles decreased and smooth muscle cell-derived microparticles increased compared to levels at the onset of stroke, but only in those patients with the highest stroke-induced cerebral lesions. Stroke increases blood and vascular compartment cell and neural precursor cell microparticle shedding, an effect that is chronically maintained up to 90 days after the ischemic event. These results show that stroke induces a generalized blood and vascular cell activation and the initiation of neuronal cell repair process after stroke. Larger cerebral lesions associate with deeper vessel injury affecting vascular smooth muscle cells.

Microparticle Shedding from Neural Progenitor Cells and Vascular Compartment Cells Is Increased in Ischemic Stroke

PubMed Central

Chiva-Blanch, Gemma; Suades, Rosa; Crespo, Javier; Peña, Esther; Padró, Teresa; Jiménez-Xarrié, Elena; Martí-Fàbregas, Joan; Badimon, Lina

2016-01-01

Purpose Ischemic stroke has shown to induce platelet and endothelial microparticle shedding, but whether stroke induces microparticle shedding from additional blood and vascular compartment cells is unclear. Neural precursor cells have been shown to replace dying neurons at sites of brain injury; however, if neural precursor cell activation is associated to microparticle shedding, and whether this activation is maintained at long term and associates to stroke type and severity remains unknown. We analyzed neural precursor cells and blood and vascular compartment cells microparticle shedding after an acute ischemic stroke. Methods Forty-four patients were included in the study within the first 48h after the onset of stroke. The cerebral lesion size was evaluated at 3–7 days of the stroke. Circulating microparticles from neural precursor cells and blood and vascular compartment cells (platelets, endothelial cells, erythrocytes, leukocytes, lymphocytes, monocytes and smooth muscle cells) were analyzed by flow cytometry at the onset of stroke and at 7 and 90 days. Forty-four age-matched high cardiovascular risk subjects without documented vascular disease were used as controls. Results Compared to high cardiovascular risk controls, patients showed higher number of neural precursor cell- and all blood and vascular compartment cell-derived microparticles at the onset of stroke, and after 7 and 90 days. At 90 days, neural precursor cell-derived microparticles decreased and smooth muscle cell-derived microparticles increased compared to levels at the onset of stroke, but only in those patients with the highest stroke-induced cerebral lesions. Conclusions Stroke increases blood and vascular compartment cell and neural precursor cell microparticle shedding, an effect that is chronically maintained up to 90 days after the ischemic event. These results show that stroke induces a generalized blood and vascular cell activation and the initiation of neuronal cell repair process after stroke. Larger cerebral lesions associate with deeper vessel injury affecting vascular smooth muscle cells. PMID:26815842

Neural tissue engineering: Bioresponsive nanoscaffolds using engineered self-assembling peptides.

PubMed

Koss, K M; Unsworth, L D

2016-10-15

Rescuing or repairing neural tissues is of utmost importance to the patient's quality of life after an injury. To remedy this, many novel biomaterials are being developed that are, ideally, non-invasive and directly facilitate neural wound healing. As such, this review surveys the recent approaches and applications of self-assembling peptides and peptide amphiphiles, for building multi-faceted nanoscaffolds for direct application to neural injury. Specifically, methods enabling cellular interactions with the nanoscaffold and controlling the release of bioactive molecules from the nanoscaffold for the express purpose of directing endogenous cells in damaged or diseased neural tissues is presented. An extensive overview of recently derived self-assembling peptide-based materials and their use as neural nanoscaffolds is presented. In addition, an overview of potential bioactive peptides and ligands that could be used to direct behaviour of endogenous cells are categorized with their biological effects. Finally, a number of neurotrophic and anti-inflammatory drugs are described and discussed. Smaller therapeutic molecules are emphasized, as they are thought to be able to have less potential effect on the overall peptide self-assembly mechanism. Options for potential nanoscaffolds and drug delivery systems are suggested. Self-assembling nanoscaffolds have many inherent properties making them amenable to tissue engineering applications: ease of synthesis, ease of customization with bioactive moieties, and amenable for in situ nanoscaffold formation. The combination of the existing knowledge on bioactive motifs for neural engineering and the self-assembling propensity of peptides is discussed in specific reference to neural tissue engineering. Copyright © 2016. Published by Elsevier Ltd.

Modeling of UH-60A Hub Accelerations with Neural Networks

NASA Technical Reports Server (NTRS)

Kottapalli, Sesi

2002-01-01

Neural network relationships between the full-scale, flight test hub accelerations and the corresponding three N/rev pilot floor vibration components (vertical, lateral, and longitudinal) are studied. The present quantitative effort on the UH-60A Black Hawk hub accelerations considers the lateral and longitudinal vibrations. An earlier study had considered the vertical vibration. The NASA/Army UH-60A Airloads Program flight test database is used. A physics based "maneuver-effect-factor (MEF)", derived using the roll-angle and the pitch-rate, is used. Fundamentally, the lateral vibration data show high vibration levels (up to 0.3 g's) at low airspeeds (for example, during landing flares) and at high airspeeds (for example, during turns). The results show that the advance ratio and the gross weight together can predict the vertical and the longitudinal vibration. However, the advance ratio and the gross weight together cannot predict the lateral vibration. The hub accelerations and the advance ratio can be used to satisfactorily predict the vertical, lateral, and longitudinal vibration. The present study shows that neural network based representations of all three UH-60A pilot floor vibration components (vertical, lateral, and longitudinal) can be obtained using the hub accelerations along with the gross weight and the advance ratio. The hub accelerations are clearly a factor in determining the pilot vibration. The present conclusions potentially allow for the identification of neural network relationships between the experimental hub accelerations obtained from wind tunnel testing and the experimental pilot vibration data obtained from flight testing. A successful establishment of the above neural network based link between the wind tunnel hub accelerations and the flight test vibration data can increase the value of wind tunnel testing.

The Neural Border: Induction, Specification and Maturation of the territory that generates Neural Crest cells.

PubMed

Pla, Patrick; Monsoro-Burq, Anne H

2018-05-28

The neural crest is induced at the edge between the neural plate and the nonneural ectoderm, in an area called the neural (plate) border, during gastrulation and neurulation. In recent years, many studies have explored how this domain is patterned, and how the neural crest is induced within this territory, that also participates to the prospective dorsal neural tube, the dorsalmost nonneural ectoderm, as well as placode derivatives in the anterior area. This review highlights the tissue interactions, the cell-cell signaling and the molecular mechanisms involved in this dynamic spatiotemporal patterning, resulting in the induction of the premigratory neural crest. Collectively, these studies allow building a complex neural border and early neural crest gene regulatory network, mostly composed by transcriptional regulations but also, more recently, including novel signaling interactions. Copyright © 2018. Published by Elsevier Inc.

Region stability analysis and tracking control of memristive recurrent neural network.

PubMed

Bao, Gang; Zeng, Zhigang; Shen, Yanjun

2018-02-01

Memristor is firstly postulated by Leon Chua and realized by Hewlett-Packard (HP) laboratory. Research results show that memristor can be used to simulate the synapses of neurons. This paper presents a class of recurrent neural network with HP memristors. Firstly, it shows that memristive recurrent neural network has more compound dynamics than the traditional recurrent neural network by simulations. Then it derives that n dimensional memristive recurrent neural network is composed of [Formula: see text] sub neural networks which do not have a common equilibrium point. By designing the tracking controller, it can make memristive neural network being convergent to the desired sub neural network. At last, two numerical examples are given to verify the validity of our result. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effects of the Post-Spinal Cord Injury Microenvironment on the Differentiation Capacity of Human Neural Stem Cells Derived from Induced Pluripotent Stem Cells.

PubMed

López-Serrano, Clara; Torres-Espín, Abel; Hernández, Joaquim; Alvarez-Palomo, Ana B; Requena, Jordi; Gasull, Xavier; Edel, Michael J; Navarro, Xavier

2016-10-01

Spinal cord injury (SCI) causes loss of neural functions below the level of the lesion due to interruption of spinal pathways and secondary neurodegenerative processes. The transplant of neural stem cells (NSCs) is a promising approach for the repair of SCI. Reprogramming of adult somatic cells into induced pluripotent stem cells (iPSCs) is expected to provide an autologous source of iPSC-derived NSCs, avoiding the immune response as well as ethical issues. However, there is still limited information on the behavior and differentiation pattern of transplanted iPSC-derived NSCs within the damaged spinal cord. We transplanted iPSC-derived NSCs, obtained from adult human somatic cells, into rats at 0 or 7 days after SCI, and evaluated motor-evoked potentials and locomotion of the animals. We histologically analyzed engraftment, proliferation, and differentiation of the iPSC-derived NSCs and the spared tissue in the spinal cords at 7, 21, and 63 days posttransplant. Both transplanted groups showed a late decline in functional recovery compared to vehicle-injected groups. Histological analysis showed proliferation of transplanted cells within the tissue and that cells formed a mass. At the final time point, most grafted cells differentiated to neural and astroglial lineages, but not into oligodendrocytes, while some grafted cells remained undifferentiated and proliferative. The proinflammatory tissue microenviroment of the injured spinal cord induced proliferation of the grafted cells and, therefore, there are possible risks associated with iPSC-derived NSC transplantation. New approaches are needed to promote and guide cell differentiation, as well as reduce their tumorigenicity once the cells are transplanted at the lesion site.

Weaving and neural complexity in symmetric quantum states

DOE PAGES

Susa, Cristian E.; Girolami, Davide

2017-12-27

Here, we study the behaviour of two different measures of the complexity of multipartite correlation patterns, weaving and neural complexity, for symmetric quantum states. Weaving is the weighted sum of genuine multipartite correlations of any order, where the weights are proportional to the correlation order. The neural complexity, originally introduced to characterize correlation patterns in classical neural networks, is here extended to the quantum scenario. We derive closed formulas of the two quantities for GHZ states mixed with white noise.

Weaving and neural complexity in symmetric quantum states

DOE Office of Scientific and Technical Information (OSTI.GOV)

Susa, Cristian E.; Girolami, Davide

Here, we study the behaviour of two different measures of the complexity of multipartite correlation patterns, weaving and neural complexity, for symmetric quantum states. Weaving is the weighted sum of genuine multipartite correlations of any order, where the weights are proportional to the correlation order. The neural complexity, originally introduced to characterize correlation patterns in classical neural networks, is here extended to the quantum scenario. We derive closed formulas of the two quantities for GHZ states mixed with white noise.

Extracellular Matrix Hydrogel Derived from Human Umbilical Cord as a Scaffold for Neural Tissue Repair and Its Comparison with Extracellular Matrix from Porcine Tissues.

PubMed

Kočí, Zuzana; Výborný, Karel; Dubišová, Jana; Vacková, Irena; Jäger, Aleš; Lunov, Oleg; Jiráková, Klára; Kubinová, Šárka

2017-06-01

Extracellular matrix (ECM) hydrogels prepared by tissue decellularization have been reported as natural injectable materials suitable for neural tissue repair. In this study, we prepared ECM hydrogel derived from human umbilical cord (UC) and evaluated its composition and mechanical and biological properties in comparison with the previously described ECM hydrogels derived from porcine urinary bladder (UB), brain, and spinal cord. The ECM hydrogels did not differ from each other in the concentration of collagen, while the highest content of glycosaminoglycans as well as the shortest gelation time was found for UC-ECM. The elastic modulus was then found to be the highest for UB-ECM. In spite of a different origin, topography, and composition, all ECM hydrogels similarly promoted the migration of human mesenchymal stem cells (MSCs) and differentiation of neural stem cells, as well as axonal outgrowth in vitro. However, only UC-ECM significantly improved proliferation of tissue-specific UC-derived MSCs when compared with the other ECMs. Injection of UC-ECM hydrogels into a photothrombotic cortical ischemic lesion in rats proved its in vivo gelation and infiltration with host macrophages. In summary, this study proposes UC-ECM hydrogel as an easily accessible biomaterial of human origin, which has the potential for neural as well as other soft tissue reconstruction.

Cryopreservation of embryonic stem cell-derived multicellular neural aggregates labeled with micron-sized particles of iron oxide for magnetic resonance imaging.

PubMed

Yan, Yuanwei; Sart, Sébastien; Calixto Bejarano, Fabian; Muroski, Megan E; Strouse, Geoffrey F; Grant, Samuel C; Li, Yan

2015-01-01

Magnetic resonance imaging (MRI) provides an effective approach to track labeled pluripotent stem cell (PSC)-derived neural progenitor cells (NPCs) for neurological disorder treatments after cell labeling with a contrast agent, such as an iron oxide derivative. Cryopreservation of pre-labeled neural cells, especially in three-dimensional (3D) structure, can provide a uniform cell population and preserve the stem cell niche for the subsequent applications. In this study, the effects of cryopreservation on PSC-derived multicellular NPC aggregates labeled with micron-sized particles of iron oxide (MPIO) were investigated. These NPC aggregates were labeled prior to cryopreservation because labeling thawed cells can be limited by inefficient intracellular uptake, variations in labeling efficiency, and increased culture time before use, minimizing their translation to clinical settings. The results indicated that intracellular MPIO incorporation was retained after cryopreservation (70-80% labeling efficiency), and MPIO labeling had little adverse effects on cell recovery, proliferation, cytotoxicity and neural lineage commitment post-cryopreservation. MRI analysis showed comparable detectability for the MPIO-labeled cells before and after cryopreservation indicated by T2 and T2* relaxation rates. Cryopreserving MPIO-labeled 3D multicellular NPC aggregates can be applied in in vivo cell tracking studies and lead to more rapid translation from preservation to clinical implementation. © 2015 American Institute of Chemical Engineers.

Perivascular Mesenchymal Stem Cells From the Adult Human Brain Harbor No Instrinsic Neuroectodermal but High Mesodermal Differentiation Potential.

PubMed

Lojewski, Xenia; Srimasorn, Sumitra; Rauh, Juliane; Francke, Silvan; Wobus, Manja; Taylor, Verdon; Araúzo-Bravo, Marcos J; Hallmeyer-Elgner, Susanne; Kirsch, Matthias; Schwarz, Sigrid; Schwarz, Johannes; Storch, Alexander; Hermann, Andreas

2015-10-01

Brain perivascular cells have recently been identified as a novel mesodermal cell type in the human brain. These cells reside in the perivascular niche and were shown to have mesodermal and, to a lesser extent, tissue-specific differentiation potential. Mesenchymal stem cells (MSCs) are widely proposed for use in cell therapy in many neurological disorders; therefore, it is of importance to better understand the "intrinsic" MSC population of the human brain. We systematically characterized adult human brain-derived pericytes during in vitro expansion and differentiation and compared these cells with fetal and adult human brain-derived neural stem cells (NSCs) and adult human bone marrow-derived MSCs. We found that adult human brain pericytes, which can be isolated from the hippocampus and from subcortical white matter, are-in contrast to adult human NSCs-easily expandable in monolayer cultures and show many similarities to human bone marrow-derived MSCs both regarding both surface marker expression and after whole transcriptome profile. Human brain pericytes showed a negligible propensity for neuroectodermal differentiation under various differentiation conditions but efficiently generated mesodermal progeny. Consequently, human brain pericytes resemble bone marrow-derived MSCs and might be very interesting for possible autologous and endogenous stem cell-based treatment strategies and cell therapeutic approaches for treating neurological diseases. Perivascular mesenchymal stem cells (MSCs) recently gained significant interest because of their appearance in many tissues including the human brain. MSCs were often reported as being beneficial after transplantation in the central nervous system in different neurological diseases; therefore, adult brain perivascular cells derived from human neural tissue were systematically characterized concerning neural stem cell and MSC marker expression, transcriptomics, and mesodermal and inherent neuroectodermal differentiation potential in vitro and in vivo after in utero transplantation. This study showed the lack of an innate neuronal but high mesodermal differentiation potential. Because of their relationship to mesenchymal stem cells, these adult brain perivascular mesodermal cells are of great interest for possible autologous therapeutic use. ©AlphaMed Press.

Neurovascular Cell Sheet Transplantation in a Canine Model of Intracranial Hemorrhage

PubMed Central

Lee, Woo-Jin; Lee, Jong Young; Jung, Keun-Hwa; Lee, Soon-Tae; Kim, Hyo Yeol; Park, Dong-Kyu; Yu, Jung-Suk; Kim, So-Yun; Jeon, Daejong; Kim, Manho; Lee, Sang Kun; Roh, Jae-Kyu; Chu, Kon

2017-01-01

Cell-based therapy for intracerebral hemorrhage (ICH) has a great therapeutic potential. However, methods to effectively induce direct regeneration of the damaged neural tissue after cell transplantation have not been established, which, if done, would improve the efficacy of cell-based therapy. In this study, we aimed to develop a cell sheet with neurovasculogenic potential and evaluate its usefulness in a canine ICH model. We designed a composite cell sheet made of neural progenitors derived from human olfactory neuroepithelium and vascular progenitors from human adipose tissue-derived stromal cells. We also generated a physiologic canine ICH model by manually injecting and then infusing autologous blood under arterial pressure. We transplanted the sheet cells (cell sheet group) or saline (control group) at the cortex over the hematoma at subacute stages (2 weeks from ICH induction). At 4 weeks from the cell transplantation, cell survival, migration, and differentiation were evaluated. Hemispheric atrophy and neurobehavioral recovery were also compared between the groups. As a result, the cell sheet was rich in extracellular matrices and expressed neurotrophic factors as well as the markers for neuronal development. After transplantation, the cells successfully survived for 4 weeks, and a large portion of those migrated to the perihematomal site and differentiated into neurons and pericytes (20% and 30% of migrated stem cells, respectively). Transplantation of cell sheets alleviated hemorrhage-related hemispheric atrophy (p = 0.042) and showed tendency for improving functional recovery (p = 0.062). Therefore, we concluded that the cell sheet transplantation technique might induce direct regeneration of neural tissue and might improve outcomes of intracerebral hemorrhage. PMID:28713638

Astrocyte glycogen as an emergency fuel under conditions of glucose deprivation or intense neural activity.

PubMed

Brown, Angus M; Ransom, Bruce R

2015-02-01

Energy metabolism in the brain is a complex process that is incompletely understood. Although glucose is agreed as the main energy support of the brain, the role of glucose is not clear, which has led to controversies that can be summarized as follows: the fate of glucose, once it enters the brain is unclear. It is not known the form in which glucose enters the cells (neurons and glia) within the brain, nor the degree of metabolic shuttling of glucose derived metabolites between cells, with a key limitation in our knowledge being the extent of oxidative metabolism, and how increased tissue activity alters this. Glycogen is present within the brain and is derived from glucose. Glycogen is stored in astrocytes and acts to provide short-term delivery of substrates to neural elements, although it may also contribute an important component to astrocyte metabolism. The roles played by glycogen awaits further study, but to date its most important role is in supporting neural elements during increased firing activity, where signaling molecules, proposed to be elevated interstitial K(+), indicative of elevated neural firing rates, activate glycogen phosphorylase leading to increased production of glycogen derived substrate.

Improving and accelerating the differentiation and functional maturation of human stem cell-derived neurons: role of extracellular calcium and GABA.

PubMed

Kemp, Paul J; Rushton, David J; Yarova, Polina L; Schnell, Christian; Geater, Charlene; Hancock, Jane M; Wieland, Annalena; Hughes, Alis; Badder, Luned; Cope, Emma; Riccardi, Daniela; Randall, Andrew D; Brown, Jonathan T; Allen, Nicholas D; Telezhkin, Vsevolod

2016-11-15

Neurons differentiated from pluripotent stem cells using established neural culture conditions often exhibit functional deficits. Recently, we have developed enhanced media which both synchronize the neurogenesis of pluripotent stem cell-derived neural progenitors and accelerate their functional maturation; together these media are termed SynaptoJuice. This pair of media are pro-synaptogenic and generate authentic, mature synaptic networks of connected forebrain neurons from a variety of induced pluripotent and embryonic stem cell lines. Such enhanced rate and extent of synchronized maturation of pluripotent stem cell-derived neural progenitor cells generates neurons which are characterized by a relatively hyperpolarized resting membrane potential, higher spontaneous and induced action potential activity, enhanced synaptic activity, more complete development of a mature inhibitory GABA A receptor phenotype and faster production of electrical network activity when compared to standard differentiation media. This entire process - from pre-patterned neural progenitor to active neuron - takes 3 weeks or less, making it an ideal platform for drug discovery and disease modelling in the fields of human neurodegenerative and neuropsychiatric disorders, such as Huntington's disease, Parkinson's disease, Alzheimer's disease and Schizophrenia. © 2016 The Authors. The Journal of Physiology © 2016 The Physiological Society.

Design of a universal two-layered neural network derived from the PLI theory

NASA Astrophysics Data System (ADS)

Hu, Chia-Lun J.

2004-05-01

The if-and-only-if (IFF) condition that a set of M analog-to-digital vector-mapping relations can be learned by a one-layered-feed-forward neural network (OLNN) is that all the input analog vectors dichotomized by the i-th output bit must be positively, linearly independent, or PLI. If they are not PLI, then the OLNN just cannot learn no matter what learning rules is employed because the solution of the connection matrix does not exist mathematically. However, in this case, one can still design a parallel-cascaded, two-layered, perceptron (PCTLP) to acheive this general mapping goal. The design principle of this "universal" neural network is derived from the major mathematical properties of the PLI theory - changing the output bits of the dependent relations existing among the dichotomized input vectors to make the PLD relations PLI. Then with a vector concatenation technique, the required mapping can still be learned by this PCTLP system with very high efficiency. This paper will report in detail the mathematical derivation of the general design principle and the design procedures of the PCTLP neural network system. It then will be verified in general by a practical numerical example.

Predicting Motivation: Computational Models of PFC Can Explain Neural Coding of Motivation and Effort-based Decision-making in Health and Disease.

PubMed

Vassena, Eliana; Deraeve, James; Alexander, William H

2017-10-01

Human behavior is strongly driven by the pursuit of rewards. In daily life, however, benefits mostly come at a cost, often requiring that effort be exerted to obtain potential benefits. Medial PFC (MPFC) and dorsolateral PFC (DLPFC) are frequently implicated in the expectation of effortful control, showing increased activity as a function of predicted task difficulty. Such activity partially overlaps with expectation of reward and has been observed both during decision-making and during task preparation. Recently, novel computational frameworks have been developed to explain activity in these regions during cognitive control, based on the principle of prediction and prediction error (predicted response-outcome [PRO] model [Alexander, W. H., & Brown, J. W. Medial prefrontal cortex as an action-outcome predictor. Nature Neuroscience, 14, 1338-1344, 2011], hierarchical error representation [HER] model [Alexander, W. H., & Brown, J. W. Hierarchical error representation: A computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Computation, 27, 2354-2410, 2015]). Despite the broad explanatory power of these models, it is not clear whether they can also accommodate effects related to the expectation of effort observed in MPFC and DLPFC. Here, we propose a translation of these computational frameworks to the domain of effort-based behavior. First, we discuss how the PRO model, based on prediction error, can explain effort-related activity in MPFC, by reframing effort-based behavior in a predictive context. We propose that MPFC activity reflects monitoring of motivationally relevant variables (such as effort and reward), by coding expectations and discrepancies from such expectations. Moreover, we derive behavioral and neural model-based predictions for healthy controls and clinical populations with impairments of motivation. Second, we illustrate the possible translation to effort-based behavior of the HER model, an extended version of PRO model based on hierarchical error prediction, developed to explain MPFC-DLPFC interactions. We derive behavioral predictions that describe how effort and reward information is coded in PFC and how changing the configuration of such environmental information might affect decision-making and task performance involving motivation.

Multistability of neural networks with discontinuous non-monotonic piecewise linear activation functions and time-varying delays.

PubMed

Nie, Xiaobing; Zheng, Wei Xing

2015-05-01

This paper is concerned with the problem of coexistence and dynamical behaviors of multiple equilibrium points for neural networks with discontinuous non-monotonic piecewise linear activation functions and time-varying delays. The fixed point theorem and other analytical tools are used to develop certain sufficient conditions that ensure that the n-dimensional discontinuous neural networks with time-varying delays can have at least 5(n) equilibrium points, 3(n) of which are locally stable and the others are unstable. The importance of the derived results is that it reveals that the discontinuous neural networks can have greater storage capacity than the continuous ones. Moreover, different from the existing results on multistability of neural networks with discontinuous activation functions, the 3(n) locally stable equilibrium points obtained in this paper are located in not only saturated regions, but also unsaturated regions, due to the non-monotonic structure of discontinuous activation functions. A numerical simulation study is conducted to illustrate and support the derived theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Dual embryonic origin and patterning of the pharyngeal skeleton in the axolotl (Ambystoma mexicanum).

PubMed

Sefton, Elizabeth M; Piekarski, Nadine; Hanken, James

2015-01-01

The impressive morphological diversification of vertebrates was achieved in part by innovation and modification of the pharyngeal skeleton. Extensive fate mapping in amniote models has revealed a primarily cranial neural crest derivation of the pharyngeal skeleton. Although comparable fate maps of amphibians produced over several decades have failed to document a neural crest derivation of ventromedial elements in these vertebrates, a recent report provides evidence of a mesodermal origin of one of these elements, basibranchial 2, in the axolotl. We used a transgenic labeling protocol and grafts of labeled cells between GFP+ and white embryos to derive a fate map that describes contributions of both cranial neural crest and mesoderm to the axolotl pharyngeal skeleton, and we conducted additional experiments that probe the mechanisms that underlie mesodermal patterning. Our fate map confirms a dual embryonic origin of the pharyngeal skeleton in urodeles, including derivation of basibranchial 2 from mesoderm closely associated with the second heart field. Additionally, heterotopic transplantation experiments reveal lineage restriction of mesodermal cells that contribute to pharyngeal cartilage. The mesoderm-derived component of the pharyngeal skeleton appears to be particularly sensitive to retinoic acid (RA): administration of exogenous RA leads to loss of the second basibranchial, but not the first. Neural crest was undoubtedly critical in the evolution of the vertebrate pharyngeal skeleton, but mesoderm may have played a central role in forming ventromedial elements, in particular. When and how many times during vertebrate phylogeny a mesodermal contribution to the pharyngeal skeleton evolved remain to be resolved. © 2015 Wiley Periodicals, Inc.

Systemic treatment of focal brain injury in the rat by human umbilical cord blood cells being at different level of neural commitment.

PubMed

Gornicka-Pawlak, El Bieta; Janowski, Miroslaw; Habich, Aleksandra; Jablonska, Anna; Drela, Katarzyna; Kozlowska, Hanna; Lukomska, Barbara; Sypecka, Joanna; Domanska-Janik, Krystyna

2011-01-01

The aim of the study was to evaluate therapeutic effectiveness of intra-arterial infusion of human umbilical cord blood (HUCB) derived cells at different stages of their neural conversion. Freshly isolated mononuclear cells (D-0), neurally directed progenitors (D-3) and neural-like stem cells derived from umbilical cord blood (NSC) were compared. Focal brain damage was induced in rats by stereotactic injection of ouabain into dorsolateral striatum Three days later 10(7) of different subsets of HUCB cells were infused into the right internal carotid artery. Following surgery rats were housed in enriched environment for 30 days. Behavioral assessment consisted of tests for sensorimotor deficits (walking beam, rotarod, vibrissae elicited forelimb placing, apomorphine induced rotations), cognitive impairments (habit learning and object recognition) and exploratory behavior (open field). Thirty days after surgery the lesion volume was measured and the presence of donor cells was detected in the brain at mRNA level. At the same time immunohistochemical analysis of brain tissue was performed to estimate the local tissue response of ouabain injured rats and its modulation after HUCB cells systemic treatment. Functional effects of different subsets of cord blood cells shared substantial diversity in various behavioral tests. An additional analysis showed that D-0 HUCB cells were the most effective in functional restoration and reduction of brain lesion volume. None of transplanted cord blood derived cell fractions were detected in rat's brains at 30(th) day after treatment. This may suggest that the mechanism(s) underlying positive effects of HUCB derived cell may concern the other than direct neural cell supplementation. In addition increased immunoreactivity of markers indicating local cells proliferation and migration suggests stimulation of endogenous reparative processes by HUCB D-0 cell interarterial infusion.

miR-21 promotes the differentiation of hair follicle-derived neural crest stem cells into Schwann cells

PubMed Central

Ni, Yuxin; Zhang, Kaizhi; Liu, Xuejuan; Yang, Tingting; Wang, Baixiang; Fu, Li; A, Lan; Zhou, Yanmin

2014-01-01

Hair follicle-derived neural crest stem cells can be induced to differentiate into Schwann cells in vivo and in vitro. However, the underlying regulatory mechanism during cell differentiation remains poorly understood. This study isolated neural crest stem cells from human hair follicles and induced them to differentiate into Schwann cells. Quantitative RT-PCR showed that microRNA (miR)-21 expression was gradually increased during the differentiation of neural crest stem cells into Schwann cells. After transfection with the miR-21 agonist (agomir-21), the differentiation capacity of neural crest stem cells was enhanced. By contrast, after transfection with the miR-21 antagonist (antagomir-21), the differentiation capacity was attenuated. Further study results showed that SOX-2 was an effective target of miR-21. Without compromising SOX2 mRNA expression, miR-21 can down-regulate SOX protein expression by binding to the 3′-UTR of miR-21 mRNA. Knocking out the SOX2 gene from the neural crest stem cells significantly reversed the antagomir-21 inhibition of neural crest stem cells differentiating into Schwann cells. The results suggest that miR-21 expression was increased during the differentiation of neural crest stem cells into Schwann cells and miR-21 promoted the differentiation through down-regulating SOX protein expression by binding to the 3′-UTR of SOX2 mRNA. PMID:25206896

Density-based clustering: A 'landscape view' of multi-channel neural data for inference and dynamic complexity analysis.

PubMed

Baglietto, Gabriel; Gigante, Guido; Del Giudice, Paolo

2017-01-01

Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the 'mean-shift' algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters' centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network's state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.

Relations among pure-tone sound stimuli, neural activity, and the loudness sensation

NASA Technical Reports Server (NTRS)

Howes, W. L.

1972-01-01

Both the physiological and psychological responses to pure-tone sound stimuli are used to derive formulas which: (1) relate the loudness, loudness level, and sound-pressure level of pure tones; (2) apply continuously over most of the acoustic regime, including the loudness threshold; and (3) contain no undetermined coefficients. Some of the formulas are fundamental for calculating the loudness of any sound. Power-law formulas relating the pure-tone sound stimulus, neural activity, and loudness are derived from published data.

In vivo differentiation of induced pluripotent stem cells into neural stem cells by chimera formation.

PubMed

Choi, Hyun Woo; Hong, Yean Ju; Kim, Jong Soo; Song, Hyuk; Cho, Ssang Gu; Bae, Hojae; Kim, Changsung; Byun, Sung June; Do, Jeong Tae

2017-01-01

Like embryonic stem cells, induced pluripotent stem cells (iPSCs) can differentiate into all three germ layers in an in vitro system. Here, we developed a new technology for obtaining neural stem cells (NSCs) from iPSCs through chimera formation, in an in vivo environment. iPSCs contributed to the neural lineage in the chimera, which could be efficiently purified and directly cultured as NSCs in vitro. The iPSC-derived, in vivo-differentiated NSCs expressed NSC markers, and their gene-expression pattern more closely resembled that of fetal brain-derived NSCs than in vitro-differentiated NSCs. This system could be applied for differentiating pluripotent stem cells into specialized cell types whose differentiation protocols are not well established.

METAPHOR: Probability density estimation for machine learning based photometric redshifts

NASA Astrophysics Data System (ADS)

Amaro, V.; Cavuoti, S.; Brescia, M.; Vellucci, C.; Tortora, C.; Longo, G.

2017-06-01

We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z's and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF's derived from a traditional SED template fitting method (Le Phare).

Integrator or Coincidence Detector: A Novel Measure Based on the Discrete Reverse Correlation to Determine a Neuron's Operational Mode.

PubMed

Kanev, Jacob; Koutsou, Achilleas; Christodoulou, Chris; Obermayer, Klaus

2016-10-01

In this letter, we propose a definition of the operational mode of a neuron, that is, whether a neuron integrates over its input or detects coincidences. We complete the range of possible operational modes by a new mode we call gap detection, which means that a neuron responds to gaps in its stimulus. We propose a measure consisting of two scalar values, both ranging from -1 to +1: the neural drive, which indicates whether its stimulus excites the neuron, serves as background noise, or inhibits it; the neural mode, which indicates whether the neuron's response is the result of integration over its input, of coincidence detection, or of gap detection; with all three modes possible for all neural drive values. This is a pure spike-based measure and can be applied to measure the influence of either all or subset of a neuron's stimulus. We derive the measure by decomposing the reverse correlation, test it in several artificial and biological settings, and compare it to other measures, finding little or no correlation between them. We relate the results of the measure to neural parameters and investigate the effect of time delay during spike generation. Our results suggest that a neuron can use several different modes simultaneously on different subsets of its stimulus to enable it to respond to its stimulus in a complex manner.

Building a Decision Support System for Inpatient Admission Prediction With the Manchester Triage System and Administrative Check-in Variables.

PubMed

Zlotnik, Alexander; Alfaro, Miguel Cuchí; Pérez, María Carmen Pérez; Gallardo-Antolín, Ascensión; Martínez, Juan Manuel Montero

2016-05-01

The usage of decision support tools in emergency departments, based on predictive models, capable of estimating the probability of admission for patients in the emergency department may give nursing staff the possibility of allocating resources in advance. We present a methodology for developing and building one such system for a large specialized care hospital using a logistic regression and an artificial neural network model using nine routinely collected variables available right at the end of the triage process.A database of 255.668 triaged nonobstetric emergency department presentations from the Ramon y Cajal University Hospital of Madrid, from January 2011 to December 2012, was used to develop and test the models, with 66% of the data used for derivation and 34% for validation, with an ordered nonrandom partition. On the validation dataset areas under the receiver operating characteristic curve were 0.8568 (95% confidence interval, 0.8508-0.8583) for the logistic regression model and 0.8575 (95% confidence interval, 0.8540-0. 8610) for the artificial neural network model. χ Values for Hosmer-Lemeshow fixed "deciles of risk" were 65.32 for the logistic regression model and 17.28 for the artificial neural network model. A nomogram was generated upon the logistic regression model and an automated software decision support system with a Web interface was built based on the artificial neural network model.

Nanoparticle-mediated transcriptional modification enhances neuronal differentiation of human neural stem cells following transplantation in rat brain.

PubMed

Li, Xiaowei; Tzeng, Stephany Y; Liu, Xiaoyan; Tammia, Markus; Cheng, Yu-Hao; Rolfe, Andrew; Sun, Dong; Zhang, Ning; Green, Jordan J; Wen, Xuejun; Mao, Hai-Quan

2016-04-01

Strategies to enhance survival and direct the differentiation of stem cells in vivo following transplantation in tissue repair site are critical to realizing the potential of stem cell-based therapies. Here we demonstrated an effective approach to promote neuronal differentiation and maturation of human fetal tissue-derived neural stem cells (hNSCs) in a brain lesion site of a rat traumatic brain injury model using biodegradable nanoparticle-mediated transfection method to deliver key transcriptional factor neurogenin-2 to hNSCs when transplanted with a tailored hyaluronic acid (HA) hydrogel, generating larger number of more mature neurons engrafted to the host brain tissue than non-transfected cells. The nanoparticle-mediated transcription activation method together with an HA hydrogel delivery matrix provides a translatable approach for stem cell-based regenerative therapy. Copyright © 2016 Elsevier Ltd. All rights reserved.

Ligand Biological Activity Predictions Using Fingerprint-Based Artificial Neural Networks (FANN-QSAR)

PubMed Central

Myint, Kyaw Z.; Xie, Xiang-Qun

2015-01-01

This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research. PMID:25502380

Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoon, Hong-Jun; Alamudun, Folami T.; Hudson, Kathy

Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed thatmore » the CNN classifier is superior compared to alternative classification methods based on macro F1-scores derived from 10-fold cross-validation experiments. Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.« less

Surrogate Modeling of High-Fidelity Fracture Simulations for Real-Time Residual Strength Predictions

NASA Technical Reports Server (NTRS)

Spear, Ashley D.; Priest, Amanda R.; Veilleux, Michael G.; Ingraffea, Anthony R.; Hochhalter, Jacob D.

2011-01-01

A surrogate model methodology is described for predicting in real time the residual strength of flight structures with discrete-source damage. Starting with design of experiment, an artificial neural network is developed that takes as input discrete-source damage parameters and outputs a prediction of the structural residual strength. Target residual strength values used to train the artificial neural network are derived from 3D finite element-based fracture simulations. A residual strength test of a metallic, integrally-stiffened panel is simulated to show that crack growth and residual strength are determined more accurately in discrete-source damage cases by using an elastic-plastic fracture framework rather than a linear-elastic fracture mechanics-based method. Improving accuracy of the residual strength training data would, in turn, improve accuracy of the surrogate model. When combined, the surrogate model methodology and high-fidelity fracture simulation framework provide useful tools for adaptive flight technology.

Adaptive identifier for uncertain complex nonlinear systems based on continuous neural networks.

PubMed

Alfaro-Ponce, Mariel; Cruz, Amadeo Argüelles; Chairez, Isaac

2014-03-01

This paper presents the design of a complex-valued differential neural network identifier for uncertain nonlinear systems defined in the complex domain. This design includes the construction of an adaptive algorithm to adjust the parameters included in the identifier. The algorithm is obtained based on a special class of controlled Lyapunov functions. The quality of the identification process is characterized using the practical stability framework. Indeed, the region where the identification error converges is derived by the same Lyapunov method. This zone is defined by the power of uncertainties and perturbations affecting the complex-valued uncertain dynamics. Moreover, this convergence zone is reduced to its lowest possible value using ideas related to the so-called ellipsoid methodology. Two simple but informative numerical examples are developed to show how the identifier proposed in this paper can be used to approximate uncertain nonlinear systems valued in the complex domain.

Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists

DOE PAGES

Yoon, Hong-Jun; Alamudun, Folami T.; Hudson, Kathy; ...

2018-01-24

Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed thatmore » the CNN classifier is superior compared to alternative classification methods based on macro F1-scores derived from 10-fold cross-validation experiments. Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.« less

Surrogate Modeling of High-Fidelity Fracture Simulations for Real-Time Residual Strength Predictions

NASA Technical Reports Server (NTRS)

Spear, Ashley D.; Priest, Amanda R.; Veilleux, Michael G.; Ingraffea, Anthony R.; Hochhalter, Jacob D.

2011-01-01

A surrogate model methodology is described for predicting, during flight, the residual strength of aircraft structures that sustain discrete-source damage. Starting with design of experiment, an artificial neural network is developed that takes as input discrete-source damage parameters and outputs a prediction of the structural residual strength. Target residual strength values used to train the artificial neural network are derived from 3D finite element-based fracture simulations. Two ductile fracture simulations are presented to show that crack growth and residual strength are determined more accurately in discrete-source damage cases by using an elastic-plastic fracture framework rather than a linear-elastic fracture mechanics-based method. Improving accuracy of the residual strength training data does, in turn, improve accuracy of the surrogate model. When combined, the surrogate model methodology and high fidelity fracture simulation framework provide useful tools for adaptive flight technology.

Sensitivity of hiPSC-derived neural stem cells (NSC) to Pyrroloquinoline quinone depends on their developmental stage.

PubMed

Augustyniak, J; Lenart, J; Zychowicz, M; Lipka, G; Gaj, P; Kolanowska, M; Stepien, P P; Buzanska, L

2017-12-01

Pyrroloquinoline quinone (PQQ) is a factor influencing on the mitochondrial biogenesis. In this study the PQQ effect on viability, total cell number, antioxidant capacity, mitochondrial biogenesis and differentiation potential was investigated in human induced Pluripotent Stem Cells (iPSC) - derived: neural stem cells (NSC), early neural progenitors (eNP) and neural progenitors (NP). Here we demonstrated that sensitivity to PQQ is dependent upon its dose and neural stage of development. Induction of the mitochondrial biogenesis by PQQ at three stages of neural differentiation was evaluated at mtDNA, mRNA and protein level. Changes in NRF1, TFAM and PPARGC1A gene expression were observed at all developmental stages, but only at eNP were correlated with the statistically significant increase in the mtDNA copy numbers and enhancement of SDHA, COX-1 protein level. Thus, the "developmental window" of eNP for PQQ-evoked mitochondrial biogenesis is proposed. This effect was independent of high antioxidant capacity of PQQ, which was confirmed in all tested cell populations, regardless of the stage of hiPSC neural differentiation. Furthermore, a strong induction of GFAP, with down regulation of MAP2 gene expression upon PQQ treatment was observed. This indicates a possibility of shifting the balance of cell differentiation in the favor of astroglia, but more research is needed at this point. Copyright © 2017 Elsevier Ltd. All rights reserved.

Part II: Functional delivery of a neurotherapeutic gene to neural stem cells using minicircle DNA and nanoparticles: Translational advantages for regenerative neurology.

PubMed

Fernandes, Alinda R; Chari, Divya M

2016-09-28

Both neurotrophin-based therapy and neural stem cell (NSC)-based strategies have progressed to clinical trials for treatment of neurological diseases and injuries. Brain-derived neurotrophic factor (BDNF) in particular can confer neuroprotective and neuro-regenerative effects in preclinical studies, complementing the cell replacement benefits of NSCs. Therefore, combining both approaches by genetically-engineering NSCs to express BDNF is an attractive approach to achieve combinatorial therapy for complex neural injuries. Current genetic engineering approaches almost exclusively employ viral vectors for gene delivery to NSCs though safety and scalability pose major concerns for clinical translation and applicability. Magnetofection, a non-viral gene transfer approach deploying magnetic nanoparticles and DNA with magnetic fields offers a safe alternative but significant improvements are required to enhance its clinical application for delivery of large sized therapeutic plasmids. Here, we demonstrate for the first time the feasibility of using minicircles with magnetofection technology to safely engineer NSCs to overexpress BDNF. Primary mouse NSCs overexpressing BDNF generated increased daughter neuronal cell numbers post-differentiation, with accelerated maturation over a four-week period. Based on our findings we highlight the clinical potential of minicircle/magnetofection technology for therapeutic delivery of key neurotrophic agents. Copyright © 2016 Elsevier B.V. All rights reserved.

Optogenetic stimulation of multiwell MEA plates for neural and cardiac applications

NASA Astrophysics Data System (ADS)

Clements, Isaac P.; Millard, Daniel C.; Nicolini, Anthony M.; Preyer, Amanda J.; Grier, Robert; Heckerling, Andrew; Blum, Richard A.; Tyler, Phillip; McSweeney, K. M.; Lu, Yi-Fan; Hall, Diana; Ross, James D.

2016-03-01

Microelectrode array (MEA) technology enables advanced drug screening and "disease-in-a-dish" modeling by measuring the electrical activity of cultured networks of neural or cardiac cells. Recent developments in human stem cell technologies, advancements in genetic models, and regulatory initiatives for drug screening have increased the demand for MEA-based assays. In response, Axion Biosystems previously developed a multiwell MEA platform, providing up to 96 MEA culture wells arrayed into a standard microplate format. Multiwell MEA-based assays would be further enhanced by optogenetic stimulation, which enables selective excitation and inhibition of targeted cell types. This capability for selective control over cell culture states would allow finer pacing and probing of cell networks for more reliable and complete characterization of complex network dynamics. Here we describe a system for independent optogenetic stimulation of each well of a 48-well MEA plate. The system enables finely graded control of light delivery during simultaneous recording of network activity in each well. Using human induced pluripotent stem cell (hiPSC) derived cardiomyocytes and rodent primary neuronal cultures, we demonstrate high channel-count light-based excitation and suppression in several proof-of-concept experimental models. Our findings demonstrate advantages of combining multiwell optical stimulation and MEA recording for applications including cardiac safety screening, neural toxicity assessment, and advanced characterization of complex neuronal diseases.

Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

PubMed

Wan, Ying; Cao, Jinde; Wen, Guanghui

In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.

Translational Neuroscience as a Tool for Intervention Development in the Context of High-Adversity Families

PubMed Central

Fisher, Philip A.

2017-01-01

The use of theory-driven models to develop and evaluate family-based intervention programs has a long history in psychology. Some of the first evidence-based parenting programs to address child problem behavior, developed in the 1970s, were grounded in causal models derived from longitudinal developmental research. The same translational strategies can also be applied to designing programs that leverage emerging scientific knowledge about the effects of early adverse experiences on neurobiological systems to reduce risk and promote well-being. By specifying not only behavioral targets but also affected underlying neural systems, interventions can become more precise and efficient. This chapter describes the development of a program of research focusing on an intervention for young children in foster care. The intervention emerged from social learning theory research and employs a translational neuroscience approach. The conceptual model guiding the research, which incorporates behavioral domains as well as stress-regulatory neural systems, is described. Finally, future directions for translational neuroscience in family-based intervention research are considered. PMID:27589501

Protein Kinase-A Inhibition Is Sufficient to Support Human Neural Stem Cells Self-Renewal.

PubMed

Georges, Pauline; Boissart, Claire; Poulet, Aurélie; Peschanski, Marc; Benchoua, Alexandra

2015-12-01

Human pluripotent stem cell-derived neural stem cells offer unprecedented opportunities for producing specific types of neurons for several biomedical applications. However, to achieve it, protocols of production and amplification of human neural stem cells need to be standardized, cost effective, and safe. This means that small molecules should progressively replace the use of media containing cocktails of protein-based growth factors. Here we have conducted a phenotypical screening to identify pathways involved in the regulation of hNSC self-renewal. We analyzed 80 small molecules acting as kinase inhibitors and identified compounds of the 5-isoquinolinesulfonamide family, described as protein kinase A (PKA) and protein kinase G inhibitors, as candidates to support hNSC self-renewal. Investigating the mode of action of these compounds, we found that modulation of PKA activity was central in controlling the choice between self-renewal or terminal neuronal differentiation of hNSC. We finally demonstrated that the pharmacological inhibition of PKA using the small molecule HA1004 was sufficient to support the full derivation, propagation, and long-term maintenance of stable hNSC in absence of any other extrinsic signals. Our results indicated that tuning of PKA activity is a core mechanism regulating hNSC self-renewal and differentiation and delineate the minimal culture media requirement to maintain undifferentiated hNSC in vitro. © 2015 AlphaMed Press.

Functional screening assays with neurons generated from pluripotent stem cell-derived neural stem cells.

PubMed

Efthymiou, Anastasia; Shaltouki, Atossa; Steiner, Joseph P; Jha, Balendu; Heman-Ackah, Sabrina M; Swistowski, Andrzej; Zeng, Xianmin; Rao, Mahendra S; Malik, Nasir

2014-01-01

Rapid and effective drug discovery for neurodegenerative disease is currently impeded by an inability to source primary neural cells for high-throughput and phenotypic screens. This limitation can be addressed through the use of pluripotent stem cells (PSCs), which can be derived from patient-specific samples and differentiated to neural cells for use in identifying novel compounds for the treatment of neurodegenerative diseases. We have developed an efficient protocol to culture pure populations of neurons, as confirmed by gene expression analysis, in the 96-well format necessary for screens. These differentiated neurons were subjected to viability assays to illustrate their potential in future high-throughput screens. We have also shown that organelles such as nuclei and mitochondria could be live-labeled and visualized through fluorescence, suggesting that we should be able to monitor subcellular phenotypic changes. Neurons derived from a green fluorescent protein-expressing reporter line of PSCs were live-imaged to assess markers of neuronal maturation such as neurite length and co-cultured with astrocytes to demonstrate further maturation. These studies confirm that PSC-derived neurons can be used effectively in viability and functional assays and pave the way for high-throughput screens on neurons derived from patients with neurodegenerative disorders.

Physiological Plasticity of Neural-Crest-Derived Stem Cells in the Adult Mammalian Carotid Body.

PubMed

Annese, Valentina; Navarro-Guerrero, Elena; Rodríguez-Prieto, Ismael; Pardal, Ricardo

2017-04-18

Adult stem cell plasticity, or the ability of somatic stem cells to cross boundaries and differentiate into unrelated cell types, has been a matter of debate in the last decade. Neural-crest-derived stem cells (NCSCs) display a remarkable plasticity during development. Whether adult populations of NCSCs retain this plasticity is largely unknown. Herein, we describe that neural-crest-derived adult carotid body stem cells (CBSCs) are able to undergo endothelial differentiation in addition to their reported role in neurogenesis, contributing to both neurogenic and angiogenic processes taking place in the organ during acclimatization to hypoxia. Moreover, CBSC conversion into vascular cell types is hypoxia inducible factor (HIF) dependent and sensitive to hypoxia-released vascular cytokines such as erythropoietin. Our data highlight a remarkable physiological plasticity in an adult population of tissue-specific stem cells and could have impact on the use of these cells for cell therapy. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Discrete-time BAM neural networks with variable delays

NASA Astrophysics Data System (ADS)

Liu, Xin-Ge; Tang, Mei-Lan; Martin, Ralph; Liu, Xin-Bi

2007-07-01

This Letter deals with the global exponential stability of discrete-time bidirectional associative memory (BAM) neural networks with variable delays. Using a Lyapunov functional, and linear matrix inequality techniques (LMI), we derive a new delay-dependent exponential stability criterion for BAM neural networks with variable delays. As this criterion has no extra constraints on the variable delay functions, it can be applied to quite general BAM neural networks with a broad range of time delay functions. It is also easy to use in practice. An example is provided to illustrate the theoretical development.

Neural control of muscle

NASA Technical Reports Server (NTRS)

Max, S. R.; Markelonis, G. J.

1983-01-01

Cholinergic innervation regulates the physiological and biochemical properties of skeletal muscle. The mechanisms that appear to be involved in this regulation include soluble, neurally-derived polypeptides, transmitter-evoked muscle activity and the neurotransmitter, acetylcholine, itself. Despite extensive research, the interacting neural mechanisms that control such macromolecules as acetylcholinesterase, the acetylcholine receptor and glucose 6-phosphate dehydrogenase remain unclear. It may be that more simplified in vitro model systems coupled with recent dramatic advances in the molecular biology of neurally-regulated proteins will begin to allow researchers to unravel the mechanisms controlling the expression and maintenance of these macromolecules.

Direct reprogramming of somatic cells into neural stem cells or neurons for neurological disorders.

PubMed

Hou, Shaoping; Lu, Paul

2016-01-01

Direct reprogramming of somatic cells into neurons or neural stem cells is one of the most important frontier fields in current neuroscience research. Without undergoing the pluripotency stage, induced neurons or induced neural stem cells are a safer and timelier manner resource in comparison to those derived from induced pluripotent stem cells. In this prospective, we review the recent advances in generation of induced neurons and induced neural stem cells in vitro and in vivo and their potential treatments of neurological disorders.

A new upper bound for the norm of interval matrices with application to robust stability analysis of delayed neural networks.

PubMed

Faydasicok, Ozlem; Arik, Sabri

2013-08-01

The main problem with the analysis of robust stability of neural networks is to find the upper bound norm for the intervalized interconnection matrices of neural networks. In the previous literature, the major three upper bound norms for the intervalized interconnection matrices have been reported and they have been successfully applied to derive new sufficient conditions for robust stability of delayed neural networks. One of the main contributions of this paper will be the derivation of a new upper bound for the norm of the intervalized interconnection matrices of neural networks. Then, by exploiting this new upper bound norm of interval matrices and using stability theory of Lyapunov functionals and the theory of homomorphic mapping, we will obtain new sufficient conditions for the existence, uniqueness and global asymptotic stability of the equilibrium point for the class of neural networks with discrete time delays under parameter uncertainties and with respect to continuous and slope-bounded activation functions. The results obtained in this paper will be shown to be new and they can be considered alternative results to previously published corresponding results. We also give some illustrative and comparative numerical examples to demonstrate the effectiveness and applicability of the proposed robust stability condition. Copyright © 2013 Elsevier Ltd. All rights reserved.

Establishment and Characterization of Immortalized Minipig Neural Stem Cell Line

PubMed Central

Choi, Sung S.; Yoon, Seung-Bin; Lee, Sang-Rae; Kim, Sun-Uk; Cha, Young Joo; Lee, Daniel; Kim, Seung U.; Chang, Kyu-Tae; Lee, Hong J.

2017-01-01

Despite the increasing importance of minipigs in biomedical research, there has been relatively little research concerning minipig-derived adult stem cells as a promising research tool that could be used to develop stem cell-based therapies. We first generated immortalized neural stem cells (iNSCs) from primary minipig olfactory bulb cells (pmpOBCs) and defined the characteristics of the cell line. Primary neural cells were prepared from minipig neonate olfactory bulbs and immortalized by infection with retrovirus carrying the v-myc gene. The minipig iNSCs (mpiNSCs) had normal karyotypes and expressed NSC-specific markers, including nestin, vimentin, Musashi1, and SOX2, suggesting a similarity to human NSCs. On the basis of the global gene expression profiles from the microarray analysis, neurogenesis-associated transcript levels were predominantly altered in mpiNSCs compared with pmpOBCs. These findings increase our understanding of minipig stem cells and contribute to the utility of mpiNSCs as resources for immortalized stem cell experiments. PMID:27524466

Self organizing map neural networks approach for lithologic interpretation of nuclear and electrical well logs in basaltic environment, Southern Syria.

PubMed

Asfahani, J; Ahmad, Z; Ghani, B Abdul

2018-07-01

An approach based on self organizing map (SOM) artificial neural networks is proposed herewith oriented towards interpreting nuclear and electrical well logging data. The well logging measurements of Kodana well in Southern Syria have been interpreted by applying the proposed approach. Lithological cross-section model of the basaltic environment has been derived and four different kinds of basalt have been consequently distinguished. The four basalts are hard massive basalt, hard basalt, pyroclastic basalt and the alteration basalt products- clay. The results obtained by SOM artificial neural networks are in a good agreement with the previous published results obtained by other different techniques. The SOM approach is practiced successfully in the case study of the Kodana well logging data, and can be therefore recommended as a suitable and effective approach for handling huge well logging data with higher number of variables required for lithological discrimination purposes. Copyright © 2018 Elsevier Ltd. All rights reserved.

Establishment and Characterization of Immortalized Minipig Neural Stem Cell Line.

PubMed

Choi, Sung S; Yoon, Seung-Bin; Lee, Sang-Rae; Kim, Sun-Uk; Cha, Young Joo; Lee, Daniel; Kim, Seung U; Chang, Kyu-Tae; Lee, Hong J

2017-02-16

Despite the increasing importance of minipigs in biomedical research, there has been relatively little research concerning minipig-derived adult stem cells as a promising research tool that could be used to develop stem cell-based therapies. We first generated immortalized neural stem cells (iNSCs) from primary minipig olfactory bulb cells (pmpOBCs) and defined the characteristics of the cell line. Primary neural cells were prepared from minipig neonate olfactory bulbs and immortalized by infection with retrovirus carrying the v-myc gene. The minipig iNSCs (mpiNSCs) had normal karyotypes and expressed NSC-specific markers, including nestin, vimentin, Musashi1, and SOX2, suggesting a similarity to human NSCs. On the basis of the global gene expression profiles from the microarray analysis, neurogenesis-associated transcript levels were predominantly altered in mpiNSCs compared with pmpOBCs. These findings increase our understanding of minipig stem cells and contribute to the utility of mpiNSCs as resources for immortalized stem cell experiments.

A fast new algorithm for a robot neurocontroller using inverse QR decomposition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morris, A.S.; Khemaissia, S.

2000-01-01

A new adaptive neural network controller for robots is presented. The controller is based on direct adaptive techniques. Unlike many neural network controllers in the literature, inverse dynamical model evaluation is not required. A numerically robust, computationally efficient processing scheme for neutral network weight estimation is described, namely, the inverse QR decomposition (INVQR). The inverse QR decomposition and a weighted recursive least-squares (WRLS) method for neural network weight estimation is derived using Cholesky factorization of the data matrix. The algorithm that performs the efficient INVQR of the underlying space-time data matrix may be implemented in parallel on a triangular array.more » Furthermore, its systolic architecture is well suited for VLSI implementation. Another important benefit is well suited for VLSI implementation. Another important benefit of the INVQR decomposition is that it solves directly for the time-recursive least-squares filter vector, while avoiding the sequential back-substitution step required by the QR decomposition approaches.« less

Neuron’s eye view: Inferring features of complex stimuli from neural responses

PubMed Central

Chen, Xin; Beck, Jeffrey M.

2017-01-01

Experiments that study neural encoding of stimuli at the level of individual neurons typically choose a small set of features present in the world—contrast and luminance for vision, pitch and intensity for sound—and assemble a stimulus set that systematically varies along these dimensions. Subsequent analysis of neural responses to these stimuli typically focuses on regression models, with experimenter-controlled features as predictors and spike counts or firing rates as responses. Unfortunately, this approach requires knowledge in advance about the relevant features coded by a given population of neurons. For domains as complex as social interaction or natural movement, however, the relevant feature space is poorly understood, and an arbitrary a priori choice of features may give rise to confirmation bias. Here, we present a Bayesian model for exploratory data analysis that is capable of automatically identifying the features present in unstructured stimuli based solely on neuronal responses. Our approach is unique within the class of latent state space models of neural activity in that it assumes that firing rates of neurons are sensitive to multiple discrete time-varying features tied to the stimulus, each of which has Markov (or semi-Markov) dynamics. That is, we are modeling neural activity as driven by multiple simultaneous stimulus features rather than intrinsic neural dynamics. We derive a fast variational Bayesian inference algorithm and show that it correctly recovers hidden features in synthetic data, as well as ground-truth stimulus features in a prototypical neural dataset. To demonstrate the utility of the algorithm, we also apply it to cluster neural responses and demonstrate successful recovery of features corresponding to monkeys and faces in the image set. PMID:28827790

Nonlinearly Activated Neural Network for Solving Time-Varying Complex Sylvester Equation.

PubMed

Li, Shuai; Li, Yangming

2013-10-28

The Sylvester equation is often encountered in mathematics and control theory. For the general time-invariant Sylvester equation problem, which is defined in the domain of complex numbers, the Bartels-Stewart algorithm and its extensions are effective and widely used with an O(n³) time complexity. When applied to solving the time-varying Sylvester equation, the computation burden increases intensively with the decrease of sampling period and cannot satisfy continuous realtime calculation requirements. For the special case of the general Sylvester equation problem defined in the domain of real numbers, gradient-based recurrent neural networks are able to solve the time-varying Sylvester equation in real time, but there always exists an estimation error while a recently proposed recurrent neural network by Zhang et al [this type of neural network is called Zhang neural network (ZNN)] converges to the solution ideally. The advancements in complex-valued neural networks cast light to extend the existing real-valued ZNN for solving the time-varying real-valued Sylvester equation to its counterpart in the domain of complex numbers. In this paper, a complex-valued ZNN for solving the complex-valued Sylvester equation problem is investigated and the global convergence of the neural network is proven with the proposed nonlinear complex-valued activation functions. Moreover, a special type of activation function with a core function, called sign-bi-power function, is proven to enable the ZNN to converge in finite time, which further enhances its advantage in online processing. In this case, the upper bound of the convergence time is also derived analytically. Simulations are performed to evaluate and compare the performance of the neural network with different parameters and activation functions. Both theoretical analysis and numerical simulations validate the effectiveness of the proposed method.

The experience of mathematical beauty and its neural correlates

PubMed Central

Zeki, Semir; Romaya, John Paul; Benincasa, Dionigi M. T.; Atiyah, Michael F.

2014-01-01

Many have written of the experience of mathematical beauty as being comparable to that derived from the greatest art. This makes it interesting to learn whether the experience of beauty derived from such a highly intellectual and abstract source as mathematics correlates with activity in the same part of the emotional brain as that derived from more sensory, perceptually based, sources. To determine this, we used functional magnetic resonance imaging (fMRI) to image the activity in the brains of 15 mathematicians when they viewed mathematical formulae which they had individually rated as beautiful, indifferent or ugly. Results showed that the experience of mathematical beauty correlates parametrically with activity in the same part of the emotional brain, namely field A1 of the medial orbito-frontal cortex (mOFC), as the experience of beauty derived from other sources. PMID:24592230

Neural complexity: A graph theoretic interpretation

NASA Astrophysics Data System (ADS)

Barnett, L.; Buckley, C. L.; Bullock, S.

2011-04-01

One of the central challenges facing modern neuroscience is to explain the ability of the nervous system to coherently integrate information across distinct functional modules in the absence of a central executive. To this end, Tononi [Proc. Natl. Acad. Sci. USA.PNASA60027-842410.1073/pnas.91.11.5033 91, 5033 (1994)] proposed a measure of neural complexity that purports to capture this property based on mutual information between complementary subsets of a system. Neural complexity, so defined, is one of a family of information theoretic metrics developed to measure the balance between the segregation and integration of a system’s dynamics. One key question arising for such measures involves understanding how they are influenced by network topology. Sporns [Cereb. Cortex53OPAV1047-321110.1093/cercor/10.2.127 10, 127 (2000)] employed numerical models in order to determine the dependence of neural complexity on the topological features of a network. However, a complete picture has yet to be established. While De Lucia [Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.71.016114 71, 016114 (2005)] made the first attempts at an analytical account of this relationship, their work utilized a formulation of neural complexity that, we argue, did not reflect the intuitions of the original work. In this paper we start by describing weighted connection matrices formed by applying a random continuous weight distribution to binary adjacency matrices. This allows us to derive an approximation for neural complexity in terms of the moments of the weight distribution and elementary graph motifs. In particular, we explicitly establish a dependency of neural complexity on cyclic graph motifs.

Neural activity in relation to empirically derived personality syndromes in depression using a psychodynamic fMRI paradigm

PubMed Central

Taubner, Svenja; Wiswede, Daniel; Kessler, Henrik

2013-01-01

Objective: The heterogeneity between patients with depression cannot be captured adequately with existing descriptive systems of diagnosis and neurobiological models of depression. Furthermore, considering the highly individual nature of depression, the application of general stimuli in past research efforts may not capture the essence of the disorder. This study aims to identify subtypes of depression by using empirically derived personality syndromes, and to explore neural correlates of the derived personality syndromes. Materials and Methods: In the present exploratory study, an individually tailored and psychodynamically based functional magnetic resonance imaging paradigm using dysfunctional relationship patterns was presented to 20 chronically depressed patients. Results from the Shedler–Westen Assessment Procedure (SWAP-200) were analyzed by Q-factor analysis to identify clinically relevant subgroups of depression and related brain activation. Results: The principle component analysis of SWAP-200 items from all 20 patients lead to a two-factor solution: “Depressive Personality” and “Emotional-Hostile-Externalizing Personality.” Both factors were used in a whole-brain correlational analysis but only the second factor yielded significant positive correlations in four regions: a large cluster in the right orbitofrontal cortex (OFC), the left ventral striatum, a small cluster in the left temporal pole, and another small cluster in the right middle frontal gyrus. Discussion: The degree to which patients with depression score high on the factor “Emotional-Hostile-Externalizing Personality” correlated with relatively higher activity in three key areas involved in emotion processing, evaluation of reward/punishment, negative cognitions, depressive pathology, and social knowledge (OFC, ventral striatum, temporal pole). Results may contribute to an alternative description of neural correlates of depression showing differential brain activation dependent on the extent of specific personality syndromes in depression. PMID:24363644

Generation of Regionally Specific Neural Progenitor Cells (NPCs) and Neurons from Human Pluripotent Stem Cells (hPSCs).

PubMed

Cutts, Josh; Brookhouser, Nicholas; Brafman, David A

2016-01-01

Neural progenitor cells (NPCs) derived from human pluripotent stem cells (hPSCs) are a multipotent cell population capable of long-term expansion and differentiation into a variety of neuronal subtypes. As such, NPCs have tremendous potential for disease modeling, drug screening, and regenerative medicine. Current methods for the generation of NPCs results in cell populations homogenous for pan-neural markers such as SOX1 and SOX2 but heterogeneous with respect to regional identity. In order to use NPCs and their neuronal derivatives to investigate mechanisms of neurological disorders and develop more physiologically relevant disease models, methods for generation of regionally specific NPCs and neurons are needed. Here, we describe a protocol in which exogenous manipulation of WNT signaling, through either activation or inhibition, during neural differentiation of hPSCs, promotes the formation of regionally homogenous NPCs and neuronal cultures. In addition, we provide methods to monitor and characterize the efficiency of hPSC differentiation to these regionally specific cell identities.

Cognitive Neuroscience Approaches to Understanding Behavior Change in Alcohol Use Disorder Treatments.

PubMed

Naqvi, Nasir H; Morgenstern, Jon

2015-01-01

Researchers have begun to apply cognitive neuroscience concepts and methods to study behavior change mechanisms in alcohol use disorder (AUD) treatments. This review begins with an examination of the current state of treatment mechanisms research using clinical and social psychological approaches. It then summarizes what is currently understood about the pathophysiology of addiction from a cognitive neuroscience perspective. Finally, it reviews recent efforts to use cognitive neuroscience approaches to understand the neural mechanisms of behavior change in AUD, including studies that use neural functioning to predict relapse and abstinence; studies examining neural mechanisms that operate in current evidence-based behavioral interventions for AUD; as well as research on novel behavioral interventions that are being derived from our emerging understanding of the neural and cognitive mechanisms of behavior change in AUD. The article highlights how the regulation of subcortical regions involved in alcohol incentive motivation by prefrontal cortical regions involved in cognitive control may be a core mechanism that plays a role in these varied forms of behavior change in AUD. We also lay out a multilevel framework for integrating cognitive neuroscience approaches with more traditional methods for examining AUD treatment mechanisms.

Finite time convergent learning law for continuous neural networks.

PubMed

Chairez, Isaac

2014-02-01

This paper addresses the design of a discontinuous finite time convergent learning law for neural networks with continuous dynamics. The neural network was used here to obtain a non-parametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties was the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on discontinuous algorithms was used to adjust the weights of the neural network. The adaptive algorithm was derived by means of a non-standard Lyapunov function that is lower semi-continuous and differentiable in almost the whole space. A compensator term was included in the identifier to reject some specific perturbations using a nonlinear robust algorithm. Two numerical examples demonstrated the improvements achieved by the learning algorithm introduced in this paper compared to classical schemes with continuous learning methods. The first one dealt with a benchmark problem used in the paper to explain how the discontinuous learning law works. The second one used the methane production model to show the benefits in engineering applications of the learning law proposed in this paper. Copyright © 2013 Elsevier Ltd. All rights reserved.

Comparison of the neuropoietic activity of gene-modified versus parental mesenchymal stromal cells and the identification of soluble and extracellular matrix-related neuropoietic mediators

PubMed Central

2014-01-01

Introduction Transplanting mesenchymal stromal cells (MSCs) or their derivatives into a neurodegenerative environment is believed to be beneficial because of the trophic support, migratory guidance, immunosuppression, and neurogenic stimuli they provide. SB623, a cell therapy for the treatment of chronic stroke, currently in a clinical trial, is derived from bone marrow MSCs by using transient transfection with a vector encoding the human Notch1 intracellular domain. This creates a new phenotype, which is effective in experimental stroke, exhibits immunosuppressive and angiogenic activity equal or superior to parental MSCs in vitro, and produces extracellular matrix (ECM) that is exceptionally supportive for neural cell growth. The neuropoietic activity of SB623 and parental MSCs has not been compared, and the SB623-derived neuropoietic mediators have not been identified. Methods SB623 or parental MSCs were cocultured with rat embryonic brain cortex cells on cell-derived ECM in a previously characterized quantitative neuropoiesis assay. Changes in expression of rat neural differentiation markers were quantified by using rat-specific qRT-PCR. Human mediators were identified by using expression profiling, an enzymatic crosslinking activity, and functional interference studies by means of blocking antibodies, biologic inhibitors, and siRNA. Cocultures were immunolabeled for presynaptic vesicular transporters to assess neuronal specialization. Results Among six MSC/SB623 pairs, SB623 induced expression of rat neural precursor, oligodendrocyte, and astrocyte markers on average 2.6 to 3 times stronger than did their parental MSCs. SB623 expressed significantly higher FGF2, FGF1, and BMP4, and lower FGFR1 and FGFR2 levels; and human FGF1, FGF2, BMPs, and HGF were implicated as neuropoietic mediators. Neural precursors grew faster on SB623- than on MSC-derived ECM. SB623 exhibited higher expression levels and crosslinking activity of tissue transglutaminase (TGM2). TGM2 silencing reduced neural precursor growth on SB623-ECM. SB623 also promoted the induction of GABA-ergic, but not glutamatergic, neurons more effectively than did MSCs. Conclusions These data demonstrate that SB623 cells tend to support neural cell growth more effectively than their parental MSCs and identify both soluble and insoluble mediators responsible, at least in part, for enhanced neuropoietic potency of SB623. The neuropoiesis assay is a useful tool for identifying beneficial factors produced by MSCs and their derivatives. PMID:24572070

Differentiating malignant from benign breast tumors on acoustic radiation force impulse imaging using fuzzy-based neural networks with principle component analysis

NASA Astrophysics Data System (ADS)

Liu, Hsiao-Chuan; Chou, Yi-Hong; Tiu, Chui-Mei; Hsieh, Chi-Wen; Liu, Brent; Shung, K. Kirk

2017-03-01

Many modalities have been developed as screening tools for breast cancer. A new screening method called acoustic radiation force impulse (ARFI) imaging was created for distinguishing breast lesions based on localized tissue displacement. This displacement was quantitated by virtual touch tissue imaging (VTI). However, VTIs sometimes express reverse results to intensity information in clinical observation. In the study, a fuzzy-based neural network with principle component analysis (PCA) was proposed to differentiate texture patterns of malignant breast from benign tumors. Eighty VTIs were randomly retrospected. Thirty four patients were determined as BI-RADS category 2 or 3, and the rest of them were determined as BI-RADS category 4 or 5 by two leading radiologists. Morphological method and Boolean algebra were performed as the image preprocessing to acquire region of interests (ROIs) on VTIs. Twenty four quantitative parameters deriving from first-order statistics (FOS), fractal dimension and gray level co-occurrence matrix (GLCM) were utilized to analyze the texture pattern of breast tumors on VTIs. PCA was employed to reduce the dimension of features. Fuzzy-based neural network as a classifier to differentiate malignant from benign breast tumors. Independent samples test was used to examine the significance of the difference between benign and malignant breast tumors. The area Az under the receiver operator characteristic (ROC) curve, sensitivity, specificity and accuracy were calculated to evaluate the performance of the system. Most all of texture parameters present significant difference between malignant and benign tumors with p-value of less than 0.05 except the average of fractal dimension. For all features classified by fuzzy-based neural network, the sensitivity, specificity, accuracy and Az were 95.7%, 97.1%, 95% and 0.964, respectively. However, the sensitivity, specificity, accuracy and Az can be increased to 100%, 97.1%, 98.8% and 0.985, respectively if PCA was performed to reduce the dimension of features. Patterns of breast tumors on VTIs can effectively be recognized by quantitative texture parameters, and differentiated malignant from benign lesions by fuzzy-based neural network with PCA.

Hydrogel formulation determines cell fate of fetal and adult neural progenitor cells

PubMed Central

Wagner, Jennifer L.; Shandas, Robin; Bjugstad, Kimberly B.

2014-01-01

Hydrogels provide a unique tool for neural tissue engineering. These materials can be customized for certain functions, i.e. to provide cell/drug delivery or act as a physical scaffold. Unfortunately, hydrogel complexities can negatively impact their biocompatibility, resulting in unintended consequences. These adverse effects may be combated with a better understanding of hydrogel chemical, physical, and mechanical properties, and how these properties affect encapsulated neural cells. We defined the polymerization and degradation rates and compressive moduli of 25 hydrogels formulated from different concentrations of hyaluronic acid (HA) and poly(ethylene glycol) (PEG). Changes in compressive modulus were driven primarily by the HA concentration. The in vitro biocompatibility of fetal-derived (fNPC) and adult-derived (aNPC) neural progenitor cells was dependent on hydrogel formulation. Acute survival of fNPC benefited from hydrogel encapsulation. NPC differentiation was divergent: fNPC differentiated into mostly glial cells, compared with neuronal differentiation of aNPC. Differentiation was influenced in part by the hydrogel mechanical properties. This study indicates that there can be a wide range of HA and PEG hydrogels compatible with NPC. Additionally, this is the first study comparing hydrogel encapsulation of NPC derived from different aged sources, with data suggesting that fNPC and aNPC respond dissimilarly within the same hydrogel formulation. PMID:24141109

Cauda equina-derived extracellular matrix for fabrication of nanostructured hybrid scaffolds applied to neural tissue engineering.

PubMed

Wen, Xiaoxiao; Wang, Yu; Guo, Zhiyuan; Meng, Haoye; Huang, Jingxiang; Zhang, Li; Zhao, Bin; Zhao, Qing; Zheng, Yudong; Peng, Jiang

2015-03-01

Extracellular matrix (ECM) components have become important candidate materials for use as neural scaffolds for neural tissue engineering. In the current study, we prepared cauda equina-derived ECM materials for the production of scaffolds. Natural porcine cauda equina was decellularized using Triton X-100 and sodium deoxycholate, shattered physically, and made into a suspension by differential centrifugation. The decellularization procedure resulted in the removal of >94% of the nuclear material and preserved the extracellular collagen and sulfated glycosaminoglycan. Immunofluorescent staining confirmed the presence of collagen type I, laminin, and fibronectin in the ECM. The cauda equine-derived ECM was blended with poly(l-lactide-co-glycolide) (PLGA) to fabricate nanostructured scaffolds using electrospinning. The incorporation of the ECM increased the hydrophilicity of the scaffolds. Fourier transform infrared spectroscopy and multiphoton-induced autofluorescence images showed the presence of the ECM in the scaffolds. ECM/PLGA scaffolds were beneficial for the survival of Schwann cells compared with scaffolds consisting of PLGA alone, and the aligned fibers could regulate cell morphologic features by modulating cellular orientation. Axons in the dorsal root ganglia explants extended to a greater extent along ECM/PLGA compared with PLGA-alone fibers. The cauda equina ECM might be a promising material for forming scaffolds for use in neural tissue engineering.

BDNF - A key player in cardiovascular system.

PubMed

Pius-Sadowska, Ewa; Machaliński, Bogusław

2017-09-01

Neurotrophins (NTs) were first identified as target-derived survival factors for neurons of the central and peripheral nervous system (PNS). They are known to control neural cell fate, development and function. Independently of their neuronal properties, NTs exert unique cardiovascular activity. The heart is innervated by sensory, sympathetic and parasympathetic neurons, which require NTs during early development and in the establishment of mature properties, contributing to the maintenance of cardiovascular homeostasis. The identification of molecular mechanisms regulated by NTs and involved in the crosstalk between cardiac sympathetic nerves, cardiomyocytes, cardiac fibroblasts, and vascular cells, has a fundamental importance in both normal heart function and disease. The article aims to review the recent data on the effects of Brain-Derived Neurotrophic Factor (BDNF) on various cardiovascular neuronal and non-neuronal functions such as the modulation of synaptic properties of autonomic neurons, axonal outgrowth and sprouting, formation of the vascular and neural networks, smooth muscle migration, and control of endothelial cell survival and cardiomyocytes. Understanding these mechanisms may be crucial for developing novel therapeutic strategies, including stem cell-based therapies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Reversal of Phenotypic Abnormalities by CRISPR/Cas9-Mediated Gene Correction in Huntington Disease Patient-Derived Induced Pluripotent Stem Cells.

PubMed

Xu, Xiaohong; Tay, Yilin; Sim, Bernice; Yoon, Su-In; Huang, Yihui; Ooi, Jolene; Utami, Kagistia Hana; Ziaei, Amin; Ng, Bryan; Radulescu, Carola; Low, Donovan; Ng, Alvin Yu Jin; Loh, Marie; Venkatesh, Byrappa; Ginhoux, Florent; Augustine, George J; Pouladi, Mahmoud A

2017-03-14

Huntington disease (HD) is a dominant neurodegenerative disorder caused by a CAG repeat expansion in HTT. Here we report correction of HD human induced pluripotent stem cells (hiPSCs) using a CRISPR-Cas9 and piggyBac transposon-based approach. We show that both HD and corrected isogenic hiPSCs can be differentiated into excitable, synaptically active forebrain neurons. We further demonstrate that phenotypic abnormalities in HD hiPSC-derived neural cells, including impaired neural rosette formation, increased susceptibility to growth factor withdrawal, and deficits in mitochondrial respiration, are rescued in isogenic controls. Importantly, using genome-wide expression analysis, we show that a number of apparent gene expression differences detected between HD and non-related healthy control lines are absent between HD and corrected lines, suggesting that these differences are likely related to genetic background rather than HD-specific effects. Our study demonstrates correction of HD hiPSCs and associated phenotypic abnormalities, and the importance of isogenic controls for disease modeling using hiPSCs. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Generation and characterization of Tbx1-AmCyan1 transgenic reporter mouse line that selectively labels developing thymus primordium.

PubMed

Kimura, Wataru; Sharkar, Mohammad Tofael Kabir; Sultana, Nishat; Islam, Mohammod Johirul; Uezato, Tadayoshi; Miura, Naoyuki

2013-06-01

Thymus development is a complicated process that includes highly dynamic morphological changes and reciprocal tissue interactions between endoderm-derived epithelial cells of the anterior foregut and neural crest-derived mesenchymal cells. We generated and characterized a Tbx1-AmCyan1 reporter transgenic mouse to visualize thymus precursor cells during early embryonic development. In transgenic embryos, AmCyan1 fluorescence was specifically detected in the endoderm of the developing 3rd and 4th pharyngeal pouches and later in thymus epithelium until E14.5. Cells expressing AmCyan1 that were isolated based on AmCyan1 fluorescence expressed endodermal, thymic, and parathyroid markers, but they did not express neural crest or endothelial markers; these findings indicated that this transgenic mouse strain could be used to collect thymic or parathyroid precursor cells or both. We also showed that in nude mice, which exhibit defects in thymus development, the thymus precursors were clearly labeled with AmCyan1. In summary, these AmCyan1-fluorescent transgenic mice are useful for investigating early thymus development.

A Voltage-Sensitive Dye-Based Assay for the Identification of Differentiated Neurons Derived from Embryonic Neural Stem Cell Cultures

PubMed Central

Emirandetti, Amanda; Lewicka, Michalina; Hermanson, Ola; Fisahn, André

2010-01-01

Background Pluripotent and multipotent stem cells hold great therapeutical promise for the replacement of degenerated tissue in neurological diseases. To fulfill that promise we have to understand the mechanisms underlying the differentiation of multipotent cells into specific types of neurons. Embryonic stem cell (ESC) and embryonic neural stem cell (NSC) cultures provide a valuable tool to study the processes of neural differentiation, which can be assessed using immunohistochemistry, gene expression, Ca2+-imaging or electrophysiology. However, indirect methods such as protein and gene analysis cannot provide direct evidence of neuronal functionality. In contrast, direct methods such as electrophysiological techniques are well suited to produce direct evidence of neural functionality but are limited to the study of a few cells on a culture plate. Methodology/Principal Findings In this study we describe a novel method for the detection of action potential-capable neurons differentiated from embryonic NSC cultures using fast voltage-sensitive dyes (VSD). We found that the use of extracellularly applied VSD resulted in a more detailed labeling of cellular processes compared to calcium indicators. In addition, VSD changes in fluorescence translated precisely to action potential kinetics as assessed by the injection of simulated slow and fast sodium currents using the dynamic clamp technique. We further demonstrate the use of a finite element model of the NSC culture cover slip for optimizing electrical stimulation parameters. Conclusions/Significance Our method allows for a repeatable fast and accurate stimulation of neurons derived from stem cell cultures to assess their differentiation state, which is capable of monitoring large amounts of cells without harming the overall culture. PMID:21079795

Estimating the functional dimensionality of neural representations.

PubMed

Ahlheim, Christiane; Love, Bradley C

2018-06-07

Recent advances in multivariate fMRI analysis stress the importance of information inherent to voxel patterns. Key to interpreting these patterns is estimating the underlying dimensionality of neural representations. Dimensions may correspond to psychological dimensions, such as length and orientation, or involve other coding schemes. Unfortunately, the noise structure of fMRI data inflates dimensionality estimates and thus makes it difficult to assess the true underlying dimensionality of a pattern. To address this challenge, we developed a novel approach to identify brain regions that carry reliable task-modulated signal and to derive an estimate of the signal's functional dimensionality. We combined singular value decomposition with cross-validation to find the best low-dimensional projection of a pattern of voxel-responses at a single-subject level. Goodness of the low-dimensional reconstruction is measured as Pearson correlation with a test set, which allows to test for significance of the low-dimensional reconstruction across participants. Using hierarchical Bayesian modeling, we derive the best estimate and associated uncertainty of underlying dimensionality across participants. We validated our method on simulated data of varying underlying dimensionality, showing that recovered dimensionalities match closely true dimensionalities. We then applied our method to three published fMRI data sets all involving processing of visual stimuli. The results highlight three possible applications of estimating the functional dimensionality of neural data. Firstly, it can aid evaluation of model-based analyses by revealing which areas express reliable, task-modulated signal that could be missed by specific models. Secondly, it can reveal functional differences across brain regions. Thirdly, knowing the functional dimensionality allows assessing task-related differences in the complexity of neural patterns. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Genetic reprogramming of human amniotic cells with episomal vectors: neural rosettes as sentinels in candidate selection for validation assays.

PubMed

Wilson, Patricia G; Payne, Tiffany

2014-01-01

The promise of genetic reprogramming has prompted initiatives to develop banks of induced pluripotent stem cells (iPSCs) from diverse sources. Sentinel assays for pluripotency could maximize available resources for generating iPSCs. Neural rosettes represent a primitive neural tissue that is unique to differentiating PSCs and commonly used to identify derivative neural/stem progenitors. Here, neural rosettes were used as a sentinel assay for pluripotency in selection of candidates to advance to validation assays. Candidate iPSCs were generated from independent populations of amniotic cells with episomal vectors. Phase imaging of living back up cultures showed neural rosettes in 2 of the 5 candidate populations. Rosettes were immunopositive for the Sox1, Sox2, Pax6 and Pax7 transcription factors that govern neural development in the earliest stage of development and for the Isl1/2 and Otx2 transcription factors that are expressed in the dorsal and ventral domains, respectively, of the neural tube in vivo. Dissociation of rosettes produced cultures of differentiation competent neural/stem progenitors that generated immature neurons that were immunopositive for βIII-tubulin and glia that were immunopositive for GFAP. Subsequent validation assays of selected candidates showed induced expression of endogenous pluripotency genes, epigenetic modification of chromatin and formation of teratomas in immunodeficient mice that contained derivatives of the 3 embryonic germ layers. Validated lines were vector-free and maintained a normal karyotype for more than 60 passages. The credibility of rosette assembly as a sentinel assay for PSCs is supported by coordinate loss of nuclear-localized pluripotency factors Oct4 and Nanog in neural rosettes that emerge spontaneously in cultures of self-renewing validated lines. Taken together, these findings demonstrate value in neural rosettes as sentinels for pluripotency and selection of promising candidates for advance to validation assays.

Genetic reprogramming of human amniotic cells with episomal vectors: neural rosettes as sentinels in candidate selection for validation assays

PubMed Central

Payne, Tiffany

2014-01-01

The promise of genetic reprogramming has prompted initiatives to develop banks of induced pluripotent stem cells (iPSCs) from diverse sources. Sentinel assays for pluripotency could maximize available resources for generating iPSCs. Neural rosettes represent a primitive neural tissue that is unique to differentiating PSCs and commonly used to identify derivative neural/stem progenitors. Here, neural rosettes were used as a sentinel assay for pluripotency in selection of candidates to advance to validation assays. Candidate iPSCs were generated from independent populations of amniotic cells with episomal vectors. Phase imaging of living back up cultures showed neural rosettes in 2 of the 5 candidate populations. Rosettes were immunopositive for the Sox1, Sox2, Pax6 and Pax7 transcription factors that govern neural development in the earliest stage of development and for the Isl1/2 and Otx2 transcription factors that are expressed in the dorsal and ventral domains, respectively, of the neural tube in vivo. Dissociation of rosettes produced cultures of differentiation competent neural/stem progenitors that generated immature neurons that were immunopositive for βIII-tubulin and glia that were immunopositive for GFAP. Subsequent validation assays of selected candidates showed induced expression of endogenous pluripotency genes, epigenetic modification of chromatin and formation of teratomas in immunodeficient mice that contained derivatives of the 3 embryonic germ layers. Validated lines were vector-free and maintained a normal karyotype for more than 60 passages. The credibility of rosette assembly as a sentinel assay for PSCs is supported by coordinate loss of nuclear-localized pluripotency factors Oct4 and Nanog in neural rosettes that emerge spontaneously in cultures of self-renewing validated lines. Taken together, these findings demonstrate value in neural rosettes as sentinels for pluripotency and selection of promising candidates for advance to validation assays. PMID:25426336

Convergence behavior of delayed discrete cellular neural network without periodic coefficients.

PubMed

Wang, Jinling; Jiang, Haijun; Hu, Cheng; Ma, Tianlong

2014-05-01

In this paper, we study convergence behaviors of delayed discrete cellular neural networks without periodic coefficients. Some sufficient conditions are derived to ensure all solutions of delayed discrete cellular neural network without periodic coefficients converge to a periodic function, by applying mathematical analysis techniques and the properties of inequalities. Finally, some examples showing the effectiveness of the provided criterion are given. Copyright © 2014 Elsevier Ltd. All rights reserved.

Obstacle detection by recognizing binary expansion patterns

NASA Technical Reports Server (NTRS)

Baram, Yoram; Barniv, Yair

1993-01-01

This paper describes a technique for obstacle detection, based on the expansion of the image-plane projection of a textured object, as its distance from the sensor decreases. Information is conveyed by vectors whose components represent first-order temporal and spatial derivatives of the image intensity, which are related to the time to collision through the local divergence. Such vectors may be characterized as patterns corresponding to 'safe' or 'dangerous' situations. We show that essential information is conveyed by single-bit vector components, representing the signs of the relevant derivatives. We use two recently developed, high capacity classifiers, employing neural learning techniques, to recognize the imminence of collision from such patterns.

Induced pluripotent stem cell-derived neural cells survive and mature in the nonhuman primate brain.

PubMed

Emborg, Marina E; Liu, Yan; Xi, Jiajie; Zhang, Xiaoqing; Yin, Yingnan; Lu, Jianfeng; Joers, Valerie; Swanson, Christine; Holden, James E; Zhang, Su-Chun

2013-03-28

The generation of induced pluripotent stem cells (iPSCs) opens up the possibility for personalized cell therapy. Here, we show that transplanted autologous rhesus monkey iPSC-derived neural progenitors survive for up to 6 months and differentiate into neurons, astrocytes, and myelinating oligodendrocytes in the brains of MPTP-induced hemiparkinsonian rhesus monkeys with a minimal presence of inflammatory cells and reactive glia. This finding represents a significant step toward personalized regenerative therapies. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Regenerative medicine for Parkinson's disease using differentiated nerve cells derived from human buccal fat pad stem cells.

PubMed

Takahashi, Haruka; Ishikawa, Hiroshi; Tanaka, Akira

2017-04-01

The purpose of this study was to evaluate the utility of human adipose stem cells derived from the buccal fat pad (hBFP-ASCs) for nerve regeneration. Parkinson's disease (PD) is a neurodegenerative disorder characterized by progressive death of dopaminergic neurons. PD is a candidate disease for cell replacement therapy because it has no fundamental therapeutic methods. We examined the properties of neural-related cells induced from hBFP-ASCs as a cell source for PD treatment. hBFP-ASCs were cultured in neurogenic differentiation medium for about 2 weeks. After the morphology of hBFP-ASCs changed to neural-like cells, the medium was replaced with neural maintenance medium. Cells differentiated from hBFP-ASCs showed neuron-like structures and expressed neuron markers (β3-tubulin, neurofilament 200, and microtubule-associated protein 2), an astrocyte marker (glial fibrillary acidic protein), or dopaminergic neuron-related marker (tyrosine hydroxylase). Induced neural cells were transplanted into a 6-hydroxydopamine (6-OHDA)-lesioned rat hemi-parkinsonian model. At 4 weeks after transplantation, 6-OHDA-lesioned rats were subjected to apomorphine-induced rotation analysis. The transplanted cells survived in the brain of rats as dopaminergic neural cells. No tumor formation was found after cell transplantation. We demonstrated differentiation of hBFP-ASCs into neural cells, and that transplantation of these neural cells improved the symptoms of model rats. Our results suggest that neurons differentiated from hBFP-ASCs would be applicable to cell replacement therapy of PD.

Roles of neural stem cells in the repair of peripheral nerve injury.

PubMed

Wang, Chong; Lu, Chang-Feng; Peng, Jiang; Hu, Cheng-Dong; Wang, Yu

2017-12-01

Currently, researchers are using neural stem cell transplantation to promote regeneration after peripheral nerve injury, as neural stem cells play an important role in peripheral nerve injury repair. This article reviews recent research progress of the role of neural stem cells in the repair of peripheral nerve injury. Neural stem cells can not only differentiate into neurons, astrocytes and oligodendrocytes, but can also differentiate into Schwann-like cells, which promote neurite outgrowth around the injury. Transplanted neural stem cells can differentiate into motor neurons that innervate muscles and promote the recovery of neurological function. To promote the repair of peripheral nerve injury, neural stem cells secrete various neurotrophic factors, including brain-derived neurotrophic factor, fibroblast growth factor, nerve growth factor, insulin-like growth factor and hepatocyte growth factor. In addition, neural stem cells also promote regeneration of the axonal myelin sheath, angiogenesis, and immune regulation. It can be concluded that neural stem cells promote the repair of peripheral nerve injury through a variety of ways.

Stability analysis of fractional-order Hopfield neural networks with time delays.

PubMed

Wang, Hu; Yu, Yongguang; Wen, Guoguang

2014-07-01

This paper investigates the stability for fractional-order Hopfield neural networks with time delays. Firstly, the fractional-order Hopfield neural networks with hub structure and time delays are studied. Some sufficient conditions for stability of the systems are obtained. Next, two fractional-order Hopfield neural networks with different ring structures and time delays are developed. By studying the developed neural networks, the corresponding sufficient conditions for stability of the systems are also derived. It is shown that the stability conditions are independent of time delays. Finally, numerical simulations are given to illustrate the effectiveness of the theoretical results obtained in this paper. Copyright © 2014 Elsevier Ltd. All rights reserved.

Disease and Stem Cell-Based Analysis of the 2014 ASNTR Meeting

PubMed Central

Eve, David J.

2015-01-01

A wide variety of subjects are presented at the annual American Society of Neural Therapy and Repair meeting every year, as typified by this summary of the 2014 meeting. Parkinson’s disease-related presentations were again the most popular topic, with traumatic brain injury, spinal cord injury, and stroke being close behind. Other disorders included Huntington’s disease, brain cancer, and bipolar disorders. Several studies were related to multiple diseases, and many studies attempted to reveal more about the disease process. The use of scaffolds, drugs, and gene therapy as disease models and/or potential therapies were also featured. An increasing proportion of presentations related to stem cells, with the study of multiple stem cell types being the most common. Induced pluripotent stem cells were increasingly popular, including two presentations each on a muscle-derived dedifferentiated cell type and cells derived from bipolar patients. Other stem cells, including neural stem cells, mesenchymal stem cells, umbilical cord blood cells, and embryonic stem cells, were featured. More than 55% of the stem cell studies involved transplantation, with human-derived cells being the most frequently transplanted, while rats were the most common recipient. Two human autologous studies for spinal cord injury and hypoxia-derived encephalopathy, while a further three allogenic studies for stroke and spinal cord injury, were also featured. This year’s meeting highlights the increasing promise of stem cells and other therapies for the treatment of neurodegenerative disorders. PMID:26858901

Femtosecond laser dissection in C. elegans neural circuits

NASA Astrophysics Data System (ADS)

Samuel, Aravinthan D. T.; Chung, Samuel H.; Clark, Damon A.; Gabel, Christopher V.; Chang, Chieh; Murthy, Venkatesh; Mazur, Eric

2006-02-01

The nematode C. elegans, a millimeter-long roundworm, is a well-established model organism for studies of neural development and behavior, however physiological methods to manipulate and monitor the activity of its neural network have lagged behind the development of powerful methods in genetics and molecular biology. The small size and transparency of C. elegans make the worm an ideal test-bed for the development of physiological methods derived from optics and microscopy. We present the development and application of a new physiological tool: femtosecond laser dissection, which allows us to selectively ablate segments of individual neural fibers within live C. elegans. Femtosecond laser dissection provides a scalpel with submicrometer resolution, and we discuss its application in studies of neural growth, regenerative growth, and the neural basis of behavior.

Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.

PubMed

Schwalger, Tilo; Deger, Moritz; Gerstner, Wulfram

2017-04-01

Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50-2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations.

An analysis of neural receptive field plasticity by point process adaptive filtering

PubMed Central

Brown, Emery N.; Nguyen, David P.; Frank, Loren M.; Wilson, Matthew A.; Solo, Victor

2001-01-01

Neural receptive fields are plastic: with experience, neurons in many brain regions change their spiking responses to relevant stimuli. Analysis of receptive field plasticity from experimental measurements is crucial for understanding how neural systems adapt their representations of relevant biological information. Current analysis methods using histogram estimates of spike rate functions in nonoverlapping temporal windows do not track the evolution of receptive field plasticity on a fine time scale. Adaptive signal processing is an established engineering paradigm for estimating time-varying system parameters from experimental measurements. We present an adaptive filter algorithm for tracking neural receptive field plasticity based on point process models of spike train activity. We derive an instantaneous steepest descent algorithm by using as the criterion function the instantaneous log likelihood of a point process spike train model. We apply the point process adaptive filter algorithm in a study of spatial (place) receptive field properties of simulated and actual spike train data from rat CA1 hippocampal neurons. A stability analysis of the algorithm is sketched in the Appendix. The adaptive algorithm can update the place field parameter estimates on a millisecond time scale. It reliably tracked the migration, changes in scale, and changes in maximum firing rate characteristic of hippocampal place fields in a rat running on a linear track. Point process adaptive filtering offers an analytic method for studying the dynamics of neural receptive fields. PMID:11593043

Prediction of Aerodynamic Coefficients using Neural Networks for Sparse Data

NASA Technical Reports Server (NTRS)

Rajkumar, T.; Bardina, Jorge; Clancy, Daniel (Technical Monitor)

2002-01-01

Basic aerodynamic coefficients are modeled as functions of angles of attack and sideslip with vehicle lateral symmetry and compressibility effects. Most of the aerodynamic parameters can be well-fitted using polynomial functions. In this paper a fast, reliable way of predicting aerodynamic coefficients is produced using a neural network. The training data for the neural network is derived from wind tunnel test and numerical simulations. The coefficients of lift, drag, pitching moment are expressed as a function of alpha (angle of attack) and Mach number. The results produced from preliminary neural network analysis are very good.

A Neural Network Model for K(λ) Retrieval and Application to Global K par Monitoring

PubMed Central

Chen, Jun; Zhu, Yuanli; Wu, Yongsheng; Cui, Tingwei; Ishizaka, Joji; Ju, Yongtao

2015-01-01

Accurate estimation of diffuse attenuation coefficients in the visible wavelengths K d(λ) from remotely sensed data is particularly challenging in global oceanic and coastal waters. The objectives of the present study are to evaluate the applicability of a semi-analytical K d(λ) retrieval model (SAKM) and Jamet’s neural network model (JNNM), and then develop a new neural network K d(λ) retrieval model (NNKM). Based on the comparison of K d(λ) predicted by these models with in situ measurements taken from the global oceanic and coastal waters, all of the NNKM, SAKM, and JNNM models work well in K d(λ) retrievals, but the NNKM model works more stable and accurate than both SAKM and JNNM models. The near-infrared band-based and shortwave infrared band-based combined model is used to remove the atmospheric effects on MODIS data. The K d(λ) data was determined from the atmospheric corrected MODIS data using the NNKM, JNNM, and SAKM models. The results show that the NNKM model produces <30% uncertainty in deriving K d(λ) from global oceanic and coastal waters, which is 4.88-17.18% more accurate than SAKM and JNNM models. Furthermore, we employ an empirical approach to calculate K par from the NNKM model-derived diffuse attenuation coefficient at visible bands (443, 488, 555, and 667 nm). The results show that our model presents a satisfactory performance in deriving K par from the global oceanic and coastal waters with 20.2% uncertainty. The K par are quantified from MODIS data atmospheric correction using our model. Comparing with field measurements, our model produces ~31.0% uncertainty in deriving K par from Bohai Sea. Finally, the applicability of our model for general oceanographic studies is briefly illuminated by applying it to climatological monthly mean remote sensing reflectance for time ranging from July, 2002- July 2014 at the global scale. The results indicate that the high K d(λ) and K par values are usually found around the coastal zones in the high latitude regions, while low K d(λ) and K par values are usually found in the open oceans around the low-latitude regions. These results could improve our knowledge about the light field under waters at either the global or basin scales, and be potentially used into general circulation models to estimate the heat flux between atmosphere and ocean. PMID:26083341

Adaptive Neural Network Control for the Trajectory Tracking of the Furuta Pendulum.

PubMed

Moreno-Valenzuela, Javier; Aguilar-Avelar, Carlos; Puga-Guzman, Sergio A; Santibanez, Victor

2016-12-01

The purpose of this paper is to introduce a novel adaptive neural network-based control scheme for the Furuta pendulum, which is a two degree-of-freedom underactuated system. Adaptation laws for the input and output weights are also provided. The proposed controller is able to guarantee tracking of a reference signal for the arm while the pendulum remains in the upright position. The key aspect of the derivation of the controller is the definition of an output function that depends on the position and velocity errors. The internal and external dynamics are rigorously analyzed, thereby proving the uniform ultimate boundedness of the error trajectories. By using real-time experiments, the new scheme is compared with other control methodologies, therein demonstrating the improved performance of the proposed adaptive algorithm.

Development of drug-loaded polymer microcapsules for treatment of epilepsy.

PubMed

Chen, Yu; Gu, Qi; Yue, Zhilian; Crook, Jeremy M; Moulton, Simon E; Cook, Mark J; Wallace, Gordon G

2017-09-26

Despite significant progress in developing new drugs for seizure control, epilepsy still affects 1% of the global population and is drug-resistant in more than 30% of cases. To improve the therapeutic efficacy of epilepsy medication, a promising approach is to deliver anti-epilepsy drugs directly to affected brain areas using local drug delivery systems. The drug delivery systems must meet a number of criteria, including high drug loading efficiency, biodegradability, neuro-cytocompatibility and predictable drug release profiles. Here we report the development of fibre- and sphere-based microcapsules that exhibit controllable uniform morphologies and drug release profiles as predicted by mathematical modelling. Importantly, both forms of fabricated microcapsules are compatible with human brain derived neural stem cells and differentiated neurons and neuroglia, indicating clinical compliance for neural implantation and therapeutic drug delivery.

Global asymptotic stability of hybrid bidirectional associative memory neural networks with time delays

NASA Astrophysics Data System (ADS)

Arik, Sabri

2006-02-01

This Letter presents a sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with distributed time delays. The results impose constraint conditions on the network parameters of neural system independently of the delay parameter, and they are applicable to all bounded continuous non-monotonic neuron activation functions. The results are also compared with the previous results derived in the literature.

mSEL-1L (Suppressor/Enhancer Lin12-like) Protein Levels Influence Murine Neural Stem Cell Self-renewal and Lineage Commitment*

PubMed Central

Cardano, Marina; Diaferia, Giuseppe R.; Cattaneo, Monica; Dessì, Sara S.; Long, Qiaoming; Conti, Luciano; DeBlasio, Pasquale; Cattaneo, Elena; Biunno, Ida

2011-01-01

Murine SEL-1L (mSEL-1L) is a key component of the endoplasmic reticulum-associated degradation pathway. It is essential during development as revealed by the multi-organ dysfunction and in uterus lethality occurring in homozygous mSEL-1L-deficient mice. Here we show that mSEL-1L is highly expressed in pluripotent embryonic stem cells and multipotent neural stem cells (NSCs) but silenced in all mature neural derivatives (i.e. astrocytes, oligodendrocytes, and neurons) by mmu-miR-183. NSCs derived from homozygous mSEL-1L-deficient embryos (mSEL-1L−/− NSCs) fail to proliferate in vitro, show a drastic reduction of the Notch effector HES-5, and reveal a significant down-modulation of the early neural progenitor markers PAX-6 and OLIG-2, when compared with the wild type (mSEL-1L+/+ NSCs) counterpart. Furthermore, these cells are almost completely deprived of the neural marker Nestin, display a significant decrease of SOX-2 expression, and rapidly undergo premature astrocytic commitment and apoptosis. The data suggest severe self-renewal defects occurring in these cells probably mediated by misregulation of the Notch signaling. The results reported here denote mSEL-1L as a primitive marker with a possible involvement in the regulation of neural progenitor stemness maintenance and lineage determination. PMID:21454627

mSEL-1L (Suppressor/enhancer Lin12-like) protein levels influence murine neural stem cell self-renewal and lineage commitment.

PubMed

Cardano, Marina; Diaferia, Giuseppe R; Cattaneo, Monica; Dessì, Sara S; Long, Qiaoming; Conti, Luciano; Deblasio, Pasquale; Cattaneo, Elena; Biunno, Ida

2011-05-27

Murine SEL-1L (mSEL-1L) is a key component of the endoplasmic reticulum-associated degradation pathway. It is essential during development as revealed by the multi-organ dysfunction and in uterus lethality occurring in homozygous mSEL-1L-deficient mice. Here we show that mSEL-1L is highly expressed in pluripotent embryonic stem cells and multipotent neural stem cells (NSCs) but silenced in all mature neural derivatives (i.e. astrocytes, oligodendrocytes, and neurons) by mmu-miR-183. NSCs derived from homozygous mSEL-1L-deficient embryos (mSEL-1L(-/-) NSCs) fail to proliferate in vitro, show a drastic reduction of the Notch effector HES-5, and reveal a significant down-modulation of the early neural progenitor markers PAX-6 and OLIG-2, when compared with the wild type (mSEL-1L(+/+) NSCs) counterpart. Furthermore, these cells are almost completely deprived of the neural marker Nestin, display a significant decrease of SOX-2 expression, and rapidly undergo premature astrocytic commitment and apoptosis. The data suggest severe self-renewal defects occurring in these cells probably mediated by misregulation of the Notch signaling. The results reported here denote mSEL-1L as a primitive marker with a possible involvement in the regulation of neural progenitor stemness maintenance and lineage determination.

Changes of neural markers expression during late neurogenic differentiation of human adipose-derived stem cells

PubMed Central

Razavi, Shahnaz; Khosravizadeh, Zahra; Bahramian, Hamid; Kazemi, Mohammad

2015-01-01

Background: Different studies have been done to obtain sufficient number of neural cells for treatment of neurodegenerative diseases, spinal cord, and traumatic brain injury because neural stem cells are limited in central nerves system. Recently, several studies have shown that adipose-derived stem cells (ADSCs) are the appropriate source of multipotent stem cells. Furthermore, these cells are found in large quantities. The aim of this study was an assessment of proliferation and potential of neurogenic differentiation of ADSCs with passing time. Materials and Methods: Neurosphere formation was used for neural induction in isolated human ADSCs (hADSCs). The rate of proliferation was determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay and potential of neural differentiation of induced hADSCs was evaluated by immunocytochemical and real-time reverse transcription polymerase chain reaction analysis after 10 and 14 days post-induction. Results: The rate of proliferation of induced hADSCs increased after 14 days while the expression of nestin, glial fibrillary acidic protein, and microtubule-associated protein 2 was decreased with passing time during neurogenic differentiation. Conclusion: These findings showed that the proliferation of induced cells increased with passing time, but in early neurogenic differentiation of hADSCs, neural expression was higher than late of differentiation. Thus, using of induced cells in early differentiation may be suggested for in vivo application. PMID:26605238

Predictive Coding of Dynamical Variables in Balanced Spiking Networks

PubMed Central

Boerlin, Martin; Machens, Christian K.; Denève, Sophie

2013-01-01

Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated. PMID:24244113

Melanoma genetics and the development of rational therapeutics

PubMed Central

Chudnovsky, Yakov; Khavari, Paul A.; Adams, Amy E.

2005-01-01

Melanoma is a cancer of the neural crest–derived cells that provide pigmentation to skin and other tissues. Over the past 4 decades, the incidence of melanoma has increased more rapidly than that of any other malignancy in the United States. No current treatments substantially enhance patient survival once metastasis has occurred. This review focuses on recent insights into melanoma genetics and new therapeutic approaches being developed based on these advances. PMID:15841168

A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization.

PubMed

Liu, Qingshan; Guo, Zhishan; Wang, Jun

2012-02-01

In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Rapid generation of OPC-like cells from human pluripotent stem cells for treating spinal cord injury.

PubMed

Kim, Dae-Sung; Jung, Se Jung; Lee, Jae Souk; Lim, Bo Young; Kim, Hyun Ah; Yoo, Jeong-Eun; Kim, Dong-Wook; Leem, Joong Woo

2017-07-28

Remyelination via the transplantation of oligodendrocyte precursor cells (OPCs) has been considered as a strategy to improve the locomotor deficits caused by traumatic spinal cord injury (SCI). To date, enormous efforts have been made to derive OPCs from human pluripotent stem cells (hPSCs), and significant progress in the transplantation of such cells in SCI animal models has been reported. The current methods generally require a long period of time (>2 months) to obtain transplantable OPCs, which hampers their clinical utility for patients with SCI. Here we demonstrate a rapid and efficient method to differentiate hPSCs into neural progenitors that retain the features of OPCs (referred to as OPC-like cells). We used cell sorting to select A2B5-positive cells from hPSC-derived neural rosettes and cultured the selected cells in the presence of signaling cues, including sonic hedgehog, PDGF and insulin-like growth factor-1. This method robustly generated neural cells positive for platelet-derived growth factor receptor-α (PDGFRα) and NG2 (~90%) after 4 weeks of differentiation. Behavioral tests revealed that the transplantation of the OPC-like cells into the spinal cords of rats with contusive SCI at the thoracic level significantly improved hindlimb locomotor function. Electrophysiological assessment revealed enhanced neural conduction through the injury site. Histological examination showed increased numbers of axon with myelination at the injury site and graft-derived myelin formation with no evidence of tumor formation. Our method provides a cell source from hPSCs that has the potential to recover motor function following SCI.

Rapid generation of OPC-like cells from human pluripotent stem cells for treating spinal cord injury

PubMed Central

Kim, Dae-Sung; Jung, Se Jung; Lee, Jae Souk; Lim, Bo Young; Kim, Hyun Ah; Yoo, Jeong-Eun; Kim, Dong-Wook; Leem, Joong Woo

2017-01-01

Remyelination via the transplantation of oligodendrocyte precursor cells (OPCs) has been considered as a strategy to improve the locomotor deficits caused by traumatic spinal cord injury (SCI). To date, enormous efforts have been made to derive OPCs from human pluripotent stem cells (hPSCs), and significant progress in the transplantation of such cells in SCI animal models has been reported. The current methods generally require a long period of time (>2 months) to obtain transplantable OPCs, which hampers their clinical utility for patients with SCI. Here we demonstrate a rapid and efficient method to differentiate hPSCs into neural progenitors that retain the features of OPCs (referred to as OPC-like cells). We used cell sorting to select A2B5-positive cells from hPSC-derived neural rosettes and cultured the selected cells in the presence of signaling cues, including sonic hedgehog, PDGF and insulin-like growth factor-1. This method robustly generated neural cells positive for platelet-derived growth factor receptor-α (PDGFRα) and NG2 (~90%) after 4 weeks of differentiation. Behavioral tests revealed that the transplantation of the OPC-like cells into the spinal cords of rats with contusive SCI at the thoracic level significantly improved hindlimb locomotor function. Electrophysiological assessment revealed enhanced neural conduction through the injury site. Histological examination showed increased numbers of axon with myelination at the injury site and graft-derived myelin formation with no evidence of tumor formation. Our method provides a cell source from hPSCs that has the potential to recover motor function following SCI. PMID:28751784

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perea, Philip Michael

I have performed a search for t-channel single top quark production in pmore » $$\\bar{p}$$ single number sub collisions at 1.96 TeV on a 366 pb -1 dataset collected with the D0 detector from 2002-2005. The analysis is restricted to the leptonic decay of the W boson from the top quark to an electron or muon, tq$$\\bar{b}$$ → lv lb q$$\\bar{b}$$ (l = e,μ). A powerful b-quark tagging algorithm derived from neural networks is used to identify b jets and significantly reduce background. I further use neural networks to discriminate signal from background, and apply a binned likelihood calculation to the neural network output distributions to derive the final limits. No direct observation of single top quark production has been made, and I report expected/measured 95% confidence level limits of 3.5/8.0 pb.« less

Integration of donor mesenchymal stem cell-derived neuron-like cells into host neural network after rat spinal cord transection.

PubMed

Zeng, Xiang; Qiu, Xue-Cheng; Ma, Yuan-Huan; Duan, Jing-Jing; Chen, Yuan-Feng; Gu, Huai-Yu; Wang, Jun-Mei; Ling, Eng-Ang; Wu, Jin-Lang; Wu, Wutian; Zeng, Yuan-Shan

2015-06-01

Functional deficits following spinal cord injury (SCI) primarily attribute to loss of neural connectivity. We therefore tested if novel tissue engineering approaches could enable neural network repair that facilitates functional recovery after spinal cord transection (SCT). Rat bone marrow-derived mesenchymal stem cells (MSCs), genetically engineered to overexpress TrkC, receptor of neurotrophin-3 (NT-3), were pre-differentiated into cells carrying neuronal features via co-culture with NT-3 overproducing Schwann cells in 3-dimensional gelatin sponge (GS) scaffold for 14 days in vitro. Intra-GS formation of MSC assemblies emulating neural network (MSC-GS) were verified morphologically via electron microscopy (EM) and functionally by whole-cell patch clamp recording of spontaneous post-synaptic currents. The differentiated MSCs still partially maintained prototypic property with the expression of some mesodermal cytokines. MSC-GS or GS was then grafted acutely into a 2 mm-wide transection gap in the T9-T10 spinal cord segments of adult rats. Eight weeks later, hindlimb function of the MSC-GS-treated SCT rats was significantly improved relative to controls receiving the GS or lesion only as indicated by BBB score. The MSC-GS transplantation also significantly recovered cortical motor evoked potential (CMEP). Histologically, MSC-derived neuron-like cells maintained their synapse-like structures in vivo; they additionally formed similar connections with host neurites (i.e., mostly serotonergic fibers plus a few corticospinal axons; validated by double-labeled immuno-EM). Moreover, motor cortex electrical stimulation triggered c-fos expression in the grafted and lumbar spinal cord cells of the treated rats only. Our data suggest that MSC-derived neuron-like cells resulting from NT-3-TrkC-induced differentiation can partially integrate into transected spinal cord and this strategy should be further investigated for reconstructing disrupted neural circuits. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Immunogenicity and Immune Tolerance of Pluripotent Stem Cell Derivatives

PubMed Central

Liu, Xin; Li, Wenjuan; Fu, Xuemei; Xu, Yang

2017-01-01

Human embryonic stem cells (hESCs) can undergo unlimited self-renewal and differentiate into all cell types in human body, and therefore hold great potential for cell therapy of currently incurable diseases including neural degenerative diseases, heart failure, and macular degeneration. This potential is further underscored by the promising safety and efficacy data from the ongoing clinical trials of hESC-based therapy of macular degeneration. However, one main challenge for the clinical application of hESC-based therapy is the allogeneic immune rejection of hESC-derived cells by the recipient. The breakthrough of the technology to generate autologous-induced pluripotent stem cells (iPSCs) by nuclear reprogramming of patient’s somatic cells raised the possibility that autologous iPSC-derived cells can be transplanted into the patients without the concern of immune rejection. However, accumulating data indicate that certain iPSC-derived cells can be immunogenic. In addition, the genomic instability associated with iPSCs raises additional safety concern to use iPSC-derived cells in human cell therapy. In this review, we will discuss the mechanism underlying the immunogenicity of the pluripotent stem cells and recent progress in developing immune tolerance strategies of human pluripotent stem cell (hPSC)-derived allografts. The successful development of safe and effective immune tolerance strategy will greatly facilitate the clinical development of hPSC-based cell therapy. PMID:28626459

LIN28A enhances the therapeutic potential of cultured neural stem cells in a Parkinson's disease model.

PubMed

Rhee, Yong-Hee; Kim, Tae-Ho; Jo, A-Young; Chang, Mi-Yoon; Park, Chang-Hwan; Kim, Sang-Mi; Song, Jae-Jin; Oh, Sang-Min; Yi, Sang-Hoon; Kim, Hyeon Ho; You, Bo-Hyun; Nam, Jin-Wu; Lee, Sang-Hun

2016-10-01

The original properties of tissue-specific stem cells, regardless of their tissue origins, are inevitably altered during in vitro culturing, lessening the clinical and research utility of stem cell cultures. Specifically, neural stem cells derived from the ventral midbrain lose their dopamine neurogenic potential, ventral midbrain-specific phenotypes, and repair capacity during in vitro cell expansion, all of which are critical concerns in using the cultured neural stem cells in therapeutic approaches for Parkinson's disease. In this study, we observed that the culture-dependent changes of neural stem cells derived from the ventral midbrain coincided with loss of RNA-binding protein LIN28A expression. When LIN28A expression was forced and sustained during neural stem cell expansion using an inducible expression-vector system, loss of dopamine neurogenic potential and midbrain phenotypes after long-term culturing was blocked. Furthermore, dopamine neurons that differentiated from neural stem cells exhibited remarkable survival and resistance against toxic insults. The observed effects were not due to a direct action of LIN28A on the differentiated dopamine neurons, but rather its action on precursor neural stem cells as exogene expression was switched off in the differentiating/differentiated cultures. Remarkable and reproducible behavioural recovery was shown in all Parkinson's disease rats grafted with neural stem cells expanded with LIN28A expression, along with extensive engraftment of dopamine neurons expressing mature neuronal and midbrain-specific markers. These findings suggest that LIN28A expression during stem cell expansion could be used to prepare therapeutically competent donor cells. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Cardiovascular Development and the Colonizing Cardiac Neural Crest Lineage

PubMed Central

Snider, Paige; Olaopa, Michael; Firulli, Anthony B.; Conway, Simon J.

2007-01-01

Although it is well established that transgenic manipulation of mammalian neural crest-related gene expression and microsurgical removal of premigratory chicken and Xenopus embryonic cardiac neural crest progenitors results in a wide spectrum of both structural and functional congenital heart defects, the actual functional mechanism of the cardiac neural crest cells within the heart is poorly understood. Neural crest cell migration and appropriate colonization of the pharyngeal arches and outflow tract septum is thought to be highly dependent on genes that regulate cell-autonomous polarized movement (i.e., gap junctions, cadherins, and noncanonical Wnt1 pathway regulators). Once the migratory cardiac neural crest subpopulation finally reaches the heart, they have traditionally been thought to participate in septation of the common outflow tract into separate aortic and pulmonary arteries. However, several studies have suggested these colonizing neural crest cells may also play additional unexpected roles during cardiovascular development and may even contribute to a crest-derived stem cell population. Studies in both mice and chick suggest they can also enter the heart from the venous inflow as well as the usual arterial outflow region, and may contribute to the adult semilunar and atrioventricular valves as well as part of the cardiac conduction system. Furthermore, although they are not usually thought to give rise to the cardiomyocyte lineage, neural crest cells in the zebrafish (Danio rerio) can contribute to the myocardium and may have different functions in a species-dependent context. Intriguingly, both ablation of chick and Xenopus premigratory neural crest cells, and a transgenic deletion of mouse neural crest cell migration or disruption of the normal mammalian neural crest gene expression profiles, disrupts ventral myocardial function and/or cardiomyocyte proliferation. Combined, this suggests that either the cardiac neural crest secrete factor/s that regulate myocardial proliferation, can signal to the epicardium to subsequently secrete a growth factor/s, or may even contribute directly to the heart. Although there are species differences between mouse, chick, and Xenopus during cardiac neural crest cell morphogenesis, recent data suggest mouse and chick are more similar to each other than to the zebrafish neural crest cell lineage. Several groups have used the genetically defined Pax3 (splotch) mutant mice model to address the role of the cardiac neural crest lineage. Here we review the current literature, the neural crest-related role of the Pax3 transcription factor, and discuss potential function/s of cardiac neural crest-derived cells during cardiovascular developmental remodeling. PMID:17619792

A Hopfield neural network for image change detection.

PubMed

Pajares, Gonzalo

2006-09-01

This paper outlines an optimization relaxation approach based on the analog Hopfield neural network (HNN) for solving the image change detection problem between two images. A difference image is obtained by subtracting pixel by pixel both images. The network topology is built so that each pixel in the difference image is a node in the network. Each node is characterized by its state, which determines if a pixel has changed. An energy function is derived, so that the network converges to stable states. The analog Hopfield's model allows each node to take on analog state values. Unlike most widely used approaches, where binary labels (changed/unchanged) are assigned to each pixel, the analog property provides the strength of the change. The main contribution of this paper is reflected in the customization of the analog Hopfield neural network to derive an automatic image change detection approach. When a pixel is being processed, some existing image change detection procedures consider only interpixel relations on its neighborhood. The main drawback of such approaches is the labeling of this pixel as changed or unchanged according to the information supplied by its neighbors, where its own information is ignored. The Hopfield model overcomes this drawback and for each pixel allows a tradeoff between the influence of its neighborhood and its own criterion. This is mapped under the energy function to be minimized. The performance of the proposed method is illustrated by comparative analysis against some existing image change detection methods.

The effect of magnetic nanoparticles on neuronal differentiation of induced pluripotent stem cell-derived neural precursors

PubMed Central

Jiráková, Klára; Šeneklová, Monika; Jirák, Daniel; Turnovcová, Karolína; Vosmanská, Magda; Babič, Michal; Horák, Daniel; Veverka, Pavel; Jendelová, Pavla

2016-01-01

Introduction Magnetic resonance (MR) imaging is suitable for noninvasive long-term tracking. We labeled human induced pluripotent stem cell-derived neural precursors (iPSC-NPs) with two types of iron-based nanoparticles, silica-coated cobalt zinc ferrite nanoparticles (CZF) and poly-l-lysine-coated iron oxide superparamagnetic nanoparticles (PLL-coated γ-Fe2O3) and studied their effect on proliferation and neuronal differentiation. Materials and methods We investigated the effect of these two contrast agents on neural precursor cell proliferation and differentiation capability. We further defined the intracellular localization and labeling efficiency and analyzed labeled cells by MR. Results Cell proliferation was not affected by PLL-coated γ-Fe2O3 but was slowed down in cells labeled with CZF. Labeling efficiency, iron content and relaxation rates measured by MR were lower in cells labeled with CZF when compared to PLL-coated γ-Fe2O3. Cytoplasmic localization of both types of nanoparticles was confirmed by transmission electron microscopy. Flow cytometry and immunocytochemical analysis of specific markers expressed during neuronal differentiation did not show any significant differences between unlabeled cells or cells labeled with both magnetic nanoparticles. Conclusion Our results show that cells labeled with PLL-coated γ-Fe2O3 are suitable for MR detection, did not affect the differentiation potential of iPSC-NPs and are suitable for in vivo cell therapies in experimental models of central nervous system disorders. PMID:27920532

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

PubMed

Shim, Yoonsik; Philippides, Andrew; Staras, Kevin; Husbands, Phil

2016-10-01

We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP

PubMed Central

Staras, Kevin

2016-01-01

We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture. PMID:27760125

Prediction of agriculture derived groundwater nitrate distribution in North China Plain with GIS-based BPNN

NASA Astrophysics Data System (ADS)

Wang, M. X.; Liu, G. D.; Wu, W. L.; Bao, Y. H.; Liu, W. N.

2006-07-01

In recent years, nitrate contamination of groundwater has become a growing concern for people in rural areas in North China Plain (NCP) where groundwater is used as drinking water. The objective of this study was to simulate agriculture derived groundwater nitrate pollution patterns with artificial neural network (ANN), which has been proved to be an effective tool for prediction in many branches of hydrology when data are not sufficient to understand the physical process of the systems but relative accurate predictions is needed. In our study, a back propagation neural network (BPNN) was developed to simulate spatial distribution of NO3-N concentrations in groundwater with land use information and site-specific hydrogeological properties in Huantai County, a typical agriculture dominated region of NCP. Geographic information system (GIS) tools were used in preparing and processing input-output vectors data for the BPNN. The circular buffer zones centered on the sampling wells were designated so as to consider the nitrate contamination of groundwater due to neighboring field. The result showed that the GIS-based BPNN simulated groundwater NO3-N concentration efficiently and captured the general trend of groundwater nitrate pollution patterns. The optimal result was obtained with a learning rate of 0.02, a 4-7-1 architecture and a buffer zone radius of 400 m. Nitrogen budget combined with GIS-based BPNN can serve as a cost-effective tool for prediction and management of groundwater nitrate pollution in an agriculture dominated regions in North China Plain.

Reduced Synchronization Persistence in Neural Networks Derived from Atm-Deficient Mice

PubMed Central

Levine-Small, Noah; Yekutieli, Ziv; Aljadeff, Jonathan; Boccaletti, Stefano; Ben-Jacob, Eshel; Barzilai, Ari

2011-01-01

Many neurodegenerative diseases are characterized by malfunction of the DNA damage response. Therefore, it is important to understand the connection between system level neural network behavior and DNA. Neural networks drawn from genetically engineered animals, interfaced with micro-electrode arrays allowed us to unveil connections between networks’ system level activity properties and such genome instability. We discovered that Atm protein deficiency, which in humans leads to progressive motor impairment, leads to a reduced synchronization persistence compared to wild type synchronization, after chemically imposed DNA damage. Not only do these results suggest a role for DNA stability in neural network activity, they also establish an experimental paradigm for empirically determining the role a gene plays on the behavior of a neural network. PMID:21519382

On Extended Dissipativity of Discrete-Time Neural Networks With Time Delay.

PubMed

Feng, Zhiguang; Zheng, Wei Xing

2015-12-01

In this brief, the problem of extended dissipativity analysis for discrete-time neural networks with time-varying delay is investigated. The definition of extended dissipativity of discrete-time neural networks is proposed, which unifies several performance measures, such as the H∞ performance, passivity, l2 - l∞ performance, and dissipativity. By introducing a triple-summable term in Lyapunov function, the reciprocally convex approach is utilized to bound the forward difference of the triple-summable term and then the extended dissipativity criterion for discrete-time neural networks with time-varying delay is established. The derived condition guarantees not only the extended dissipativity but also the stability of the neural networks. Two numerical examples are given to demonstrate the reduced conservatism and effectiveness of the obtained results.

Neural Crossroads in the Hematopoietic Stem Cell Niche.

PubMed

Agarwala, Sobhika; Tamplin, Owen J

2018-05-29

The hematopoietic stem cell (HSC) niche supports steady-state hematopoiesis and responds to changing needs during stress and disease. The nervous system is an important regulator of the niche, and its influence is established early in development when stem cells are specified. Most research has focused on direct innervation of the niche, however recent findings show there are different modes of neural control, including globally by the central nervous system (CNS) and hormone release, locally by neural crest-derived mesenchymal stem cells, and intrinsically by hematopoietic cells that express neural receptors and neurotransmitters. Dysregulation between neural and hematopoietic systems can contribute to disease, however new therapeutic opportunities may be found among neuroregulator drugs repurposed to support hematopoiesis. Copyright © 2018 Elsevier Ltd. All rights reserved.

Global robust stability of bidirectional associative memory neural networks with multiple time delays.

PubMed

Senan, Sibel; Arik, Sabri

2007-10-01

This correspondence presents a sufficient condition for the existence, uniqueness, and global robust asymptotic stability of the equilibrium point for bidirectional associative memory neural networks with discrete time delays. The results impose constraint conditions on the network parameters of the neural system independently of the delay parameter, and they are applicable to all bounded continuous nonmonotonic neuron activation functions. Some numerical examples are given to compare our results with the previous robust stability results derived in the literature.

A study on ?-dissipative synchronisation of coupled reaction-diffusion neural networks with time-varying delays

NASA Astrophysics Data System (ADS)

Ali, M. Syed; Zhu, Quanxin; Pavithra, S.; Gunasekaran, N.

2018-03-01

This study examines the problem of dissipative synchronisation of coupled reaction-diffusion neural networks with time-varying delays. This paper proposes a complex dynamical network consisting of N linearly and diffusively coupled identical reaction-diffusion neural networks. By constructing a suitable Lyapunov-Krasovskii functional (LKF), utilisation of Jensen's inequality and reciprocally convex combination (RCC) approach, strictly ?-dissipative conditions of the addressed systems are derived. Finally, a numerical example is given to show the effectiveness of the theoretical results.

Your Brain on the Movies: A Computational Approach for Predicting Box-office Performance from Viewer’s Brain Responses to Movie Trailers

PubMed Central

Christoforou, Christoforos; Papadopoulos, Timothy C.; Constantinidou, Fofi; Theodorou, Maria

2017-01-01

The ability to anticipate the population-wide response of a target audience to a new movie or TV series, before its release, is critical to the film industry. Equally important is the ability to understand the underlying factors that drive or characterize viewer’s decision to watch a movie. Traditional approaches (which involve pilot test-screenings, questionnaires, and focus groups) have reached a plateau in their ability to predict the population-wide responses to new movies. In this study, we develop a novel computational approach for extracting neurophysiological electroencephalography (EEG) and eye-gaze based metrics to predict the population-wide behavior of movie goers. We further, explore the connection of the derived metrics to the underlying cognitive processes that might drive moviegoers’ decision to watch a movie. Towards that, we recorded neural activity—through the use of EEG—and eye-gaze activity from a group of naive individuals while watching movie trailers of pre-selected movies for which the population-wide preference is captured by the movie’s market performance (i.e., box-office ticket sales in the US). Our findings show that the neural based metrics, derived using the proposed methodology, carry predictive information about the broader audience decisions to watch a movie, above and beyond traditional methods. In particular, neural metrics are shown to predict up to 72% of the variance of the films’ performance at their premiere and up to 67% of the variance at following weekends; which corresponds to a 23-fold increase in prediction accuracy compared to current neurophysiological or traditional methods. We discuss our findings in the context of existing literature and hypothesize on the possible connection of the derived neurophysiological metrics to cognitive states of focused attention, the encoding of long-term memory, and the synchronization of different components of the brain’s rewards network. Beyond the practical implication in predicting and understanding the behavior of moviegoers, the proposed approach can facilitate the use of video stimuli in neuroscience research; such as the study of individual differences in attention-deficit disorders, and the study of desensitization to media violence. PMID:29311885

Your Brain on the Movies: A Computational Approach for Predicting Box-office Performance from Viewer's Brain Responses to Movie Trailers.

PubMed

Christoforou, Christoforos; Papadopoulos, Timothy C; Constantinidou, Fofi; Theodorou, Maria

2017-01-01

The ability to anticipate the population-wide response of a target audience to a new movie or TV series, before its release, is critical to the film industry. Equally important is the ability to understand the underlying factors that drive or characterize viewer's decision to watch a movie. Traditional approaches (which involve pilot test-screenings, questionnaires, and focus groups) have reached a plateau in their ability to predict the population-wide responses to new movies. In this study, we develop a novel computational approach for extracting neurophysiological electroencephalography (EEG) and eye-gaze based metrics to predict the population-wide behavior of movie goers. We further, explore the connection of the derived metrics to the underlying cognitive processes that might drive moviegoers' decision to watch a movie. Towards that, we recorded neural activity-through the use of EEG-and eye-gaze activity from a group of naive individuals while watching movie trailers of pre-selected movies for which the population-wide preference is captured by the movie's market performance (i.e., box-office ticket sales in the US). Our findings show that the neural based metrics, derived using the proposed methodology, carry predictive information about the broader audience decisions to watch a movie, above and beyond traditional methods. In particular, neural metrics are shown to predict up to 72% of the variance of the films' performance at their premiere and up to 67% of the variance at following weekends; which corresponds to a 23-fold increase in prediction accuracy compared to current neurophysiological or traditional methods. We discuss our findings in the context of existing literature and hypothesize on the possible connection of the derived neurophysiological metrics to cognitive states of focused attention, the encoding of long-term memory, and the synchronization of different components of the brain's rewards network. Beyond the practical implication in predicting and understanding the behavior of moviegoers, the proposed approach can facilitate the use of video stimuli in neuroscience research; such as the study of individual differences in attention-deficit disorders, and the study of desensitization to media violence.

The Neural Cell Adhesion Molecule-Derived (NCAM)-Peptide FG Loop (FGL) Mobilizes Endogenous Neural Stem Cells and Promotes Endogenous Regenerative Capacity after Stroke.

PubMed

Klein, Rebecca; Mahlberg, Nicolas; Ohren, Maurice; Ladwig, Anne; Neumaier, Bernd; Graf, Rudolf; Hoehn, Mathias; Albrechtsen, Morten; Rees, Stephen; Fink, Gereon Rudolf; Rueger, Maria Adele; Schroeter, Michael

2016-12-01

The neural cell adhesion molecule (NCAM)-derived peptide FG loop (FGL) modulates synaptogenesis, neurogenesis, and stem cell proliferation, enhances cognitive capacities, and conveys neuroprotection after stroke. Here we investigated the effect of subcutaneously injected FGL on cellular compartments affected by degeneration and regeneration after stroke due to middle cerebral artery occlusion (MCAO), namely endogenous neural stem cells (NSC), oligodendrocytes, and microglia. In addition to immunohistochemistry, we used non-invasive positron emission tomography (PET) imaging with the tracer [ 18 F]-fluoro-L-thymidine ([ 18 F]FLT) to visualize endogenous NSC in vivo. FGL significantly increased endogenous NSC mobilization in the neurogenic niches as evidenced by in vivo and ex vivo methods, and it induced remyelination. Moreover, FGL affected neuroinflammation. Extending previous in vitro results, our data show that the NCAM mimetic peptide FGL mobilizes endogenous NSC after focal ischemia and enhances regeneration by amplifying remyelination and modulating neuroinflammation via affecting microglia. Results suggest FGL as a promising candidate to promote recovery after stroke.

A comparative study of a theoretical neural net model with MEG data from epileptic patients and normal individuals.

PubMed

Kotini, A; Anninos, P; Anastasiadis, A N; Tamiolakis, D

2005-09-07

The aim of this study was to compare a theoretical neural net model with MEG data from epileptic patients and normal individuals. Our experimental study population included 10 epilepsy sufferers and 10 healthy subjects. The recordings were obtained with a one-channel biomagnetometer SQUID in a magnetically shielded room. Using the method of x2-fitting it was found that the MEG amplitudes in epileptic patients and normal subjects had Poisson and Gauss distributions respectively. The Poisson connectivity derived from the theoretical neural model represents the state of epilepsy, whereas the Gauss connectivity represents normal behavior. The MEG data obtained from epileptic areas had higher amplitudes than the MEG from normal regions and were comparable with the theoretical magnetic fields from Poisson and Gauss distributions. Furthermore, the magnetic field derived from the theoretical model had amplitudes in the same order as the recorded MEG from the 20 participants. The approximation of the theoretical neural net model with real MEG data provides information about the structure of the brain function in epileptic and normal states encouraging further studies to be conducted.

Effects of light emitting diode irradiation on neural differentiation of human umbilical cord-derived mesenchymal cells.

PubMed

Dehghani-Soltani, Samereh; Shojaee, Mohammad; Jalalkamali, Mahshid; Babaee, Abdolreza; Nematollahi-Mahani, Seyed Noureddin

2017-08-30

Recently, light emitting diodes (LEDs) have been introduced as a potential physical factor for proliferation and differentiation of various stem cells. Among the mesenchymal stem cells human umbilical cord matrix-derived mesenchymal (hUCM) cells are easily propagated in the laboratory and their low immunogenicity make them more appropriate for regenerative medicine procedures. We aimed at this study to evaluate the effect of red and green light emitted from LED on the neural lineage differentiation of hUCM cells in the presence or absence of retinoic acid (RA). Harvested hUCM cells exhibited mesenchymal and stemness properties. Irradiation of these cells by green and red LED with or without RA pre-treatment successfully differentiated them into neural lineage when the morphology of the induced cells, gene expression pattern (nestin, β-tubulin III and Olig2) and protein synthesis (anti-nestin, anti-β-tubulin III, anti-GFAP and anti-O4 antibodies) was evaluated. These data point for the first time to the fact that LED irradiation and optogenetic technology may be applied for neural differentiation and neuronal repair in regenerative medicine.

Functional Comparison of Neuronal Cells Differentiated from Human Induced Pluripotent Stem Cell-Derived Neural Stem Cells under Different Oxygen and Medium Conditions.

PubMed

Yamazaki, Kazuto; Fukushima, Kazuyuki; Sugawara, Michiko; Tabata, Yoshikuni; Imaizumi, Yoichi; Ishihara, Yasuharu; Ito, Masashi; Tsukahara, Kappei; Kohyama, Jun; Okano, Hideyuki

2016-12-01

Because neurons are difficult to obtain from humans, generating functional neurons from human induced pluripotent stem cells (hiPSCs) is important for establishing physiological or disease-relevant screening systems for drug discovery. To examine the culture conditions leading to efficient differentiation of functional neural cells, we investigated the effects of oxygen stress (2% or 20% O 2 ) and differentiation medium (DMEM/F12:Neurobasal-based [DN] or commercial [PhoenixSongs Biologicals; PS]) on the expression of genes related to neural differentiation, glutamate receptor function, and the formation of networks of neurons differentiated from hiPSCs (201B7) via long-term self-renewing neuroepithelial-like stem (lt-NES) cells. Expression of genes related to neural differentiation occurred more quickly in PS and/or 2% O 2 than in DN and/or 20% O 2 , resulting in high responsiveness of neural cells to glutamate, N-methyl-d-aspartate (NMDA), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionate (AMPA), and ( S)-3,5-dihydroxyphenylglycine (an agonist for mGluR 1/5 ), as revealed by calcium imaging assays. NMDA receptors, AMPA receptors, mGluR 1 , and mGluR 5 were functionally validated by using the specific antagonists MK-801, NBQX, JNJ16259685, and 2-methyl-6-(phenylethynyl)-pyridine, respectively. Multielectrode array analysis showed that spontaneous firing occurred earlier in cells cultured in 2% O 2 than in 20% O 2 . Optimization of O 2 tension and culture medium for neural differentiation of hiPSCs can efficiently generate physiologically relevant cells for screening systems.

Cell surface glycan engineering of neural stem cells augments neurotropism and improves recovery in a murine model of multiple sclerosis

PubMed Central

Merzaban, Jasmeen S; Imitola, Jaime; Starossom, Sarah C; Zhu, Bing; Wang, Yue; Lee, Jack; Ali, Amal J; Olah, Marta; Abuelela, Ayman F; Khoury, Samia J; Sackstein, Robert

2015-01-01

Neural stem cell (NSC)-based therapies offer potential for neural repair in central nervous system (CNS) inflammatory and degenerative disorders. Typically, these conditions present with multifocal CNS lesions making it impractical to inject NSCs locally, thus mandating optimization of vascular delivery of the cells to involved sites. Here, we analyzed NSCs for expression of molecular effectors of cell migration and found that these cells are natively devoid of E-selectin ligands. Using glycosyltransferase-programmed stereosubstitution (GPS), we glycan engineered the cell surface of NSCs (“GPS-NSCs”) with resultant enforced expression of the potent E-selectin ligand HCELL (hematopoietic cell E-/L-selectin ligand) and of an E-selectin-binding glycoform of neural cell adhesion molecule (“NCAM-E”). Following intravenous (i.v.) injection, short-term homing studies demonstrated that, compared with buffer-treated (control) NSCs, GPS-NSCs showed greater neurotropism. Administration of GPS-NSC significantly attenuated the clinical course of experimental autoimmune encephalomyelitis (EAE), with markedly decreased inflammation and improved oligodendroglial and axonal integrity, but without evidence of long-term stem cell engraftment. Notably, this effect of NSC is not a universal property of adult stem cells, as administration of GPS-engineered mouse hematopoietic stem/progenitor cells did not improve EAE clinical course. These findings highlight the utility of cell surface glycan engineering to boost stem cell delivery in neuroinflammatory conditions and indicate that, despite the use of a neural tissue-specific progenitor cell population, neural repair in EAE results from endogenous repair and not from direct, NSC-derived cell replacement. PMID:26153105

Predictions of psychophysical measurements for sinusoidal amplitude modulated (SAM) pulse-train stimuli from a stochastic model.

PubMed

Xu, Yifang; Collins, Leslie M

2007-08-01

Two approaches have been proposed to reduce the synchrony of the neural response to electrical stimuli in cochlear implants. One approach involves adding noise to the pulse-train stimulus, and the other is based on using a high-rate pulse-train carrier. Hypotheses regarding the efficacy of the two approaches can be tested using computational models of neural responsiveness prior to time-intensive psychophysical studies. In our previous work, we have used such models to examine the effects of noise on several psychophysical measures important to speech recognition. However, to date there has been no parallel analytic solution investigating the neural response to the high-rate pulse-train stimuli and their effect on psychophysical measures. This work investigates the properties of the neural response to high-rate pulse-train stimuli with amplitude modulated envelopes using a stochastic auditory nerve model. The statistics governing the neural response to each pulse are derived using a recursive method. The agreement between the theoretical predictions and model simulations is demonstrated for sinusoidal amplitude modulated (SAM) high rate pulse-train stimuli. With our approach, predicting the neural response in modern implant devices becomes tractable. Psychophysical measurements are also predicted using the stochastic auditory nerve model for SAM high-rate pulse-train stimuli. Changes in dynamic range (DR) and intensity discrimination are compared with that observed for noise-modulated pulse-train stimuli. Modulation frequency discrimination is also studied as a function of stimulus level and pulse rate. Results suggest that high rate carriers may positively impact such psychophysical measures.

High-frequency neural activity and human cognition: past, present and possible future of intracranial EEG research

PubMed Central

Lachaux, Jean-Philippe; Axmacher, Nikolai; Mormann, Florian; Halgren, Eric; Crone, Nathan E.

2013-01-01

Human intracranial EEG (iEEG) recordings are primarily performed in epileptic patients for presurgical mapping. When patients perform cognitive tasks, iEEG signals reveal high-frequency neural activities (HFA, between around 40 Hz and 150 Hz) with exquisite anatomical, functional and temporal specificity. Such HFA were originally interpreted in the context of perceptual or motor binding, in line with animal studies on gamma-band (‘40Hz’) neural synchronization. Today, our understanding of HFA has evolved into a more general index of cortical processing: task-induced HFA reveals, with excellent spatial and time resolution, the participation of local neural ensembles in the task-at-hand, and perhaps the neural communication mechanisms allowing them to do so. This review promotes the claim that studying HFA with iEEG provides insights into the neural bases of cognition that cannot be derived as easily from other approaches, such as fMRI. We provide a series of examples supporting that claim, drawn from studies on memory, language and default-mode networks, and successful attempts of real-time functional mapping. These examples are followed by several guidelines for HFA research, intended for new groups interested by this approach. Overall, iEEG research on HFA should play an increasing role in cognitive neuroscience in humans, because it can be explicitly linked to basic research in animals. We conclude by discussing the future evolution of this field, which might expand that role even further, for instance through the use of multi-scale electrodes and the fusion of iEEG with MEG and fMRI. PMID:22750156

Modeling Neuropsychiatric and Neurodegenerative Diseases With Induced Pluripotent Stem Cells.

PubMed

LaMarca, Elizabeth A; Powell, Samuel K; Akbarian, Schahram; Brennand, Kristen J

2018-01-01

Human-induced pluripotent stem cells (hiPSCs) have revolutionized our ability to model neuropsychiatric and neurodegenerative diseases, and recent progress in the field is paving the way for improved therapeutics. In this review, we discuss major advances in generating hiPSC-derived neural cells and cutting-edge techniques that are transforming hiPSC technology, such as three-dimensional "mini-brains" and clustered, regularly interspersed short palindromic repeats (CRISPR)-Cas systems. We examine specific examples of how hiPSC-derived neural cells are being used to uncover the pathophysiology of schizophrenia and Parkinson's disease, and consider the future of this groundbreaking research.

Process-Based Expansion and Neural Differentiation of Human Pluripotent Stem Cells for Transplantation and Disease Modeling

PubMed Central

Stover, Alexander E.; Brick, David J.; Nethercott, Hubert E.; Banuelos, Maria G.; Sun, Lei; O’Dowd, Diane K.; Schwartz, Philip H.

2014-01-01

Robust strategies for developing patient-specific, human, induced pluripotent stem cell (iPSC)-based therapies of the brain require an ability to derive large numbers of highly defined neural cells. Recent progress in iPSC culture techniques includes partial-to-complete elimination of feeder layers, use of defined media, and single-cell passaging. However, these techniques still require embryoid body formation or coculture for differentiation into neural stem cells (NSCs). In addition, none of the published methodologies has employed all of the advances in a single culture system. Here we describe a reliable method for long-term, single-cell passaging of PSCs using a feeder-free, defined culture system that produces confluent, adherent PSCs that can be differentiated into NSCs. To provide a basis for robust quality control, we have devised a system of cellular nomenclature that describes an accurate genotype and phenotype of the cells at specific stages in the process. We demonstrate that this protocol allows for the efficient, large-scale, cGMP-compliant production of transplantable NSCs from all lines tested. We also show that NSCs generated from iPSCs produced with the process described are capable of forming both glia defined by their expression of S100β and neurons that fire repetitive action potentials. PMID:23893392

Mittag-Leffler synchronization of fractional neural networks with time-varying delays and reaction-diffusion terms using impulsive and linear controllers.

PubMed

Stamova, Ivanka; Stamov, Gani

2017-12-01

In this paper, we propose a fractional-order neural network system with time-varying delays and reaction-diffusion terms. We first develop a new Mittag-Leffler synchronization strategy for the controlled nodes via impulsive controllers. Using the fractional Lyapunov method sufficient conditions are given. We also study the global Mittag-Leffler synchronization of two identical fractional impulsive reaction-diffusion neural networks using linear controllers, which was an open problem even for integer-order models. Since the Mittag-Leffler stability notion is a generalization of the exponential stability concept for fractional-order systems, our results extend and improve the exponential impulsive control theory of neural network system with time-varying delays and reaction-diffusion terms to the fractional-order case. The fractional-order derivatives allow us to model the long-term memory in the neural networks, and thus the present research provides with a conceptually straightforward mathematical representation of rather complex processes. Illustrative examples are presented to show the validity of the obtained results. We show that by means of appropriate impulsive controllers we can realize the stability goal and to control the qualitative behavior of the states. An image encryption scheme is extended using fractional derivatives. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cholinergic and dopaminergic neuronal differentiation of human adipose tissue derived mesenchymal stem cells.

PubMed

Marei, Hany El Sayed; El-Gamal, Aya; Althani, Asma; Afifi, Nahla; Abd-Elmaksoud, Ahmed; Farag, Amany; Cenciarelli, Carlo; Thomas, Caceci; Anwarul, Hasan

2018-02-01

Mesenchymal stem cells (MSCs) are multipotent cells that can differentiate into various cell types such as cartilage, bone, and fat cells. Recent studies have shown that induction of MSCs in vitro by growth factors including epidermal growth factor (EGF) and fibroblast growth factor (FGF2) causes them to differentiate into neural like cells. These cultures also express ChAT, a cholinergic marker; and TH, a dopaminergic marker for neural cells. To establish a protocol with maximum differentiation potential, we examined MSCs under three experimental culture conditions using neural induction media containing FGF2, EGF, BMP-9, retinoic acid, and heparin. Adipose-derived MSCs were extracted and expanded in vitro for 3 passages after reaching >80% confluency, for a total duration of 9 days. Cells were then characterized by flow cytometry for CD markers as CD44 positive and CD45 negative. MSCs were then treated with neural induction media and were characterized by morphological changes and Q-PCR. Differentiated MSCs expressed markers for immature and mature neurons; β Tubulin III (TUBB3) and MAP2, respectively, showing the neural potential of these cells to differentiate into functional neurons. Improved protocols for MSCs induction will facilitate and ensure the reproducibility and standard production of MSCs for therapeutic applications in neurodegenerative diseases. © 2017 Wiley Periodicals, Inc.

Neural network-based QSAR and insecticide discovery: spinetoram

NASA Astrophysics Data System (ADS)

Sparks, Thomas C.; Crouse, Gary D.; Dripps, James E.; Anzeveno, Peter; Martynow, Jacek; DeAmicis, Carl V.; Gifford, James

2008-06-01

Improvements in the efficacy and spectrum of the spinosyns, novel fermentation derived insecticide, has long been a goal within Dow AgroSciences. As large and complex fermentation products identifying specific modifications to the spinosyns likely to result in improved activity was a difficult process, since most modifications decreased the activity. A variety of approaches were investigated to identify new synthetic directions for the spinosyn chemistry including several explorations of the quantitative structure activity relationships (QSAR) of spinosyns, which initially were unsuccessful. However, application of artificial neural networks (ANN) to the spinosyn QSAR problem identified new directions for improved activity in the chemistry, which subsequent synthesis and testing confirmed. The ANN-based analogs coupled with other information on substitution effects resulting from spinosyn structure activity relationships lead to the discovery of spinetoram (XDE-175). Launched in late 2007, spinetoram provides both improved efficacy and an expanded spectrum while maintaining the exceptional environmental and toxicological profile already established for the spinosyn chemistry.

New results on global exponential dissipativity analysis of memristive inertial neural networks with distributed time-varying delays.

PubMed

Zhang, Guodong; Zeng, Zhigang; Hu, Junhao

2018-01-01

This paper is concerned with the global exponential dissipativity of memristive inertial neural networks with discrete and distributed time-varying delays. By constructing appropriate Lyapunov-Krasovskii functionals, some new sufficient conditions ensuring global exponential dissipativity of memristive inertial neural networks are derived. Moreover, the globally exponential attractive sets and positive invariant sets are also presented here. In addition, the new proposed results here complement and extend the earlier publications on conventional or memristive neural network dynamical systems. Finally, numerical simulations are given to illustrate the effectiveness of obtained results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Biological characteristics of human-urine-derived stem cells: potential for cell-based therapy in neurology.

PubMed

Guan, Jun-Jie; Niu, Xin; Gong, Fei-Xiang; Hu, Bin; Guo, Shang-Chun; Lou, Yuan-Lei; Zhang, Chang-Qing; Deng, Zhi-Feng; Wang, Yang

2014-07-01

Stem cells in human urine have gained attention in recent years; however, urine-derived stem cells (USCs) are far from being well elucidated. In this study, we compared the biological characteristics of USCs with adipose-derived stem cells (ASCs) and investigated whether USCs could serve as a potential cell source for neural tissue engineering. USCs were isolated from voided urine with a modified culture medium. Through a series of experiments, we examined the growth rate, surface antigens, and differentiation potential of USCs, and compared them with ASCs. USCs showed robust proliferation ability. After serial propagation, USCs retained normal karyotypes. Cell surface antigen expression of USCs was similar to ASCs. With lineage-specific induction factors, USCs could differentiate toward the osteogenic, chondrogenic, adipogenic, and neurogenic lineages. To assess the ability of USCs to survive, differentiate, and migrate, they were seeded onto hydrogel scaffold and transplanted into rat brain. The results showed that USCs were able to survive in the lesion site, migrate to other areas, and express proteins that were associated with neural phenotypes. The results of our study demonstrate that USCs possess similar biological characteristics with ASCs and have multilineage differentiation potential. Moreover USCs can differentiate to neuron-like cells in rat brain. The present study shows that USCs are a promising cell source for tissue engineering and regenerative medicine.

Stem cell treatment of degenerative eye disease.

PubMed

Mead, Ben; Berry, Martin; Logan, Ann; Scott, Robert A H; Leadbeater, Wendy; Scheven, Ben A

2015-05-01

Stem cell therapies are being explored extensively as treatments for degenerative eye disease, either for replacing lost neurons, restoring neural circuits or, based on more recent evidence, as paracrine-mediated therapies in which stem cell-derived trophic factors protect compromised endogenous retinal neurons from death and induce the growth of new connections. Retinal progenitor phenotypes induced from embryonic stem cells/induced pluripotent stem cells (ESCs/iPSCs) and endogenous retinal stem cells may replace lost photoreceptors and retinal pigment epithelial (RPE) cells and restore vision in the diseased eye, whereas treatment of injured retinal ganglion cells (RGCs) has so far been reliant on mesenchymal stem cells (MSC). Here, we review the properties of non-retinal-derived adult stem cells, in particular neural stem cells (NSCs), MSC derived from bone marrow (BMSC), adipose tissues (ADSC) and dental pulp (DPSC), together with ESC/iPSC and discuss and compare their potential advantages as therapies designed to provide trophic support, repair and replacement of retinal neurons, RPE and glia in degenerative retinal diseases. We conclude that ESCs/iPSCs have the potential to replace lost retinal cells, whereas MSC may be a useful source of paracrine factors that protect RGC and stimulate regeneration of their axons in the optic nerve in degenerate eye disease. NSC may have potential as both a source of replacement cells and also as mediators of paracrine treatment. Copyright © 2015. Published by Elsevier B.V.

Two-dimensional shape classification using generalized Fourier representation and neural networks

NASA Astrophysics Data System (ADS)

Chodorowski, Artur; Gustavsson, Tomas; Mattsson, Ulf

2000-04-01

A shape-based classification method is developed based upon the Generalized Fourier Representation (GFR). GFR can be regarded as an extension of traditional polar Fourier descriptors, suitable for description of closed objects, both convex and concave, with or without holes. Explicit relations of GFR coefficients to regular moments, moment invariants and affine moment invariants are given in the paper. The dual linear relation between GFR coefficients and regular moments was used to compare shape features derive from GFR descriptors and Hu's moment invariants. the GFR was then applied to a clinical problem within oral medicine and used to represent the contours of the lesions in the oral cavity. The lesions studied were leukoplakia and different forms of lichenoid reactions. Shape features were extracted from GFR coefficients in order to classify potentially cancerous oral lesions. Alternative classifiers were investigated based on a multilayer perceptron with different architectures and extensions. The overall classification accuracy for recognition of potentially cancerous oral lesions when using neural network classifier was 85%, while the classification between leukoplakia and reticular lichenoid reactions gave 96% (5-fold cross-validated) recognition rate.

Multiple μ-stability of neural networks with unbounded time-varying delays.

PubMed

Wang, Lili; Chen, Tianping

2014-05-01

In this paper, we are concerned with a class of recurrent neural networks with unbounded time-varying delays. Based on the geometrical configuration of activation functions, the phase space R(n) can be divided into several Φη-type subsets. Accordingly, a new set of regions Ωη are proposed, and rigorous mathematical analysis is provided to derive the existence of equilibrium point and its local μ-stability in each Ωη. It concludes that the n-dimensional neural networks can exhibit at least 3(n) equilibrium points and 2(n) of them are μ-stable. Furthermore, due to the compatible property, a set of new conditions are presented to address the dynamics in the remaining 3(n)-2(n) subset regions. As direct applications of these results, we can get some criteria on the multiple exponential stability, multiple power stability, multiple log-stability, multiple log-log-stability and so on. In addition, the approach and results can also be extended to the neural networks with K-level nonlinear activation functions and unbounded time-varying delays, in which there can store (2K+1)(n) equilibrium points, (K+1)(n) of them are locally μ-stable. Numerical examples are given to illustrate the effectiveness of our results. Copyright © 2014 Elsevier Ltd. All rights reserved.

TRPM7 maintains progenitor-like features of neuroblastoma cells: implications for metastasis formation

PubMed Central

Middelbeek, Jeroen; Kamermans, Alwin; Kuipers, Arthur J.; Hoogerbrugge, Peter M.; Jalink, Kees; van Leeuwen, Frank N.

2015-01-01

Neuroblastoma is an embryonal tumor derived from poorly differentiated neural crest cells. Current research is aimed at identifying the molecular mechanisms that maintain the progenitor state of neuroblastoma cells and to develop novel therapeutic strategies that induce neuroblastoma cell differentiation. Mechanisms controlling neural crest development are typically dysregulated during neuroblastoma progression, and provide an appealing starting point for drug target discovery. Transcriptional programs involved in neural crest development act as a context dependent gene regulatory network. In addition to BMP, Wnt and Notch signaling, activation of developmental gene expression programs depends on the physical characteristics of the tissue microenvironment. TRPM7, a mechanically regulated TRP channel with kinase activity, was previously found essential for embryogenesis and the maintenance of undifferentiated neural crest progenitors. Hence, we hypothesized that TRPM7 may preserve progenitor-like, metastatic features of neuroblastoma cells. Using multiple neuroblastoma cell models, we demonstrate that TRPM7 expression closely associates with the migratory and metastatic properties of neuroblastoma cells in vitro and in vivo. Moreover, microarray-based expression profiling on control and TRPM7 shRNA transduced neuroblastoma cells indicates that TRPM7 controls a developmental transcriptional program involving the transcription factor SNAI2. Overall, our data indicate that TRPM7 contributes to neuroblastoma progression by maintaining progenitor-like features. PMID:25797249

Generation of diverse neuronal subtypes in cloned populations of stem-like cells

PubMed Central

Varga, Balázs V; Hádinger, Nóra; Gócza, Elen; Dulberg, Vered; Demeter, Kornél; Madarász, Emília; Herberth, Balázs

2008-01-01

Background The central nervous tissue contains diverse subtypes of neurons with characteristic morphological and physiological features and different neurotransmitter phenotypes. The generation of neurons with defined neurotransmitter phenotypes seems to be governed by factors differently expressed along the anterior-posterior and dorsal-ventral body axes. The mechanisms of the cell-type determination, however, are poorly understood. Selected neuronal phenotypes had been generated from embryonic stem (ES) cells, but similar results were not obtained on more restricted neural stem cells, presumably due to the lack of homogeneous neural stem cell populations as a starting material. Results In the presented work, the establishment of different neurotransmitter phenotypes was investigated in the course of in vitro induced neural differentiation of a one-cell derived neuroectodermal cell line, in conjunction with the activation of various region-specific genes. For comparison, similar studies were carried out on the R1 embryonic stem (ES) and P19 multipotent embryonic carcinoma (EC) cells. In response to a short treatment with all-trans retinoic acid, all cell lines gave rise to neurons and astrocytes. Non-induced neural stem cells and self-renewing cells persisting in differentiated cultures, expressed "stemness genes" along with early embryonic anterior-dorsal positional genes, but did not express the investigated CNS region-specific genes. In differentiating stem-like cell populations, on the other hand, different region-specific genes, those expressed in non-overlapping regions along the body axes were activated. The potential for diverse regional specifications was induced in parallel with the initiation of neural tissue-type differentiation. In accordance with the wide regional specification potential, neurons with different neurotransmitter phenotypes developed. Mechanisms inherent to one-cell derived neural stem cell populations were sufficient to establish glutamatergic and GABAergic neuronal phenotypes but failed to manifest cathecolaminergic neurons. Conclusion The data indicate that genes involved in positional determination are activated along with pro-neuronal genes in conditions excluding any outside influences. Interactions among progenies of one cell derived neural stem cells are sufficient for the activation of diverse region specific genes and initiate different routes of neuronal specification. PMID:18808670

Biologically inspired emotion recognition from speech

NASA Astrophysics Data System (ADS)

Caponetti, Laura; Buscicchio, Cosimo Alessandro; Castellano, Giovanna

2011-12-01

Emotion recognition has become a fundamental task in human-computer interaction systems. In this article, we propose an emotion recognition approach based on biologically inspired methods. Specifically, emotion classification is performed using a long short-term memory (LSTM) recurrent neural network which is able to recognize long-range dependencies between successive temporal patterns. We propose to represent data using features derived from two different models: mel-frequency cepstral coefficients (MFCC) and the Lyon cochlear model. In the experimental phase, results obtained from the LSTM network and the two different feature sets are compared, showing that features derived from the Lyon cochlear model give better recognition results in comparison with those obtained with the traditional MFCC representation.

Local and Global Gestalt Laws: A Neurally Based Spectral Approach.

PubMed

Favali, Marta; Citti, Giovanna; Sarti, Alessandro

2017-02-01

This letter presents a mathematical model of figure-ground articulation that takes into account both local and global gestalt laws and is compatible with the functional architecture of the primary visual cortex (V1). The local gestalt law of good continuation is described by means of suitable connectivity kernels that are derived from Lie group theory and quantitatively compared with long-range connectivity in V1. Global gestalt constraints are then introduced in terms of spectral analysis of a connectivity matrix derived from these kernels. This analysis performs grouping of local features and individuates perceptual units with the highest salience. Numerical simulations are performed, and results are obtained by applying the technique to a number of stimuli.

Chemical Library Screening for Potential Therapeutics Using Novel Cell Based Models of ALS

DTIC Science & Technology

2017-06-01

Major Task 1, Subtask 1. Our first major activity in year 1 was the design and engineering of a custom inducible plasmid for expressing fluorescently...back to that construct. Other achievements during this first major activity derived from investigating why Broccoli RNA aptamer did not fold...These are outlined below in the CHANGES/PROBLEMS section. Major Task 1, Subtask 2. Our second major activity in year 1 was to generate human neural

A neural network-based input shaping for swing suppression of an overhead crane under payload hoisting and mass variations

NASA Astrophysics Data System (ADS)

Ramli, Liyana; Mohamed, Z.; Jaafar, H. I.

2018-07-01

This paper proposes an improved input shaping for minimising payload swing of an overhead crane with payload hoisting and payload mass variations. A real time unity magnitude zero vibration (UMZV) shaper is designed by using an artificial neural network trained by particle swarm optimisation. The proposed technique could predict and directly update the shaper's parameters in real time to handle the effects of time-varying parameters during the crane operation with hoisting. To evaluate the performances of the proposed method, experiments are conducted on a laboratory overhead crane with a payload hoisting, different payload masses and two different crane motions. The superiority of the proposed method is confirmed by reductions of at least 38.9% and 91.3% in the overall and residual swing responses, respectively over a UMZV shaper designed using an average operating frequency and a robust shaper namely Zero Vibration Derivative-Derivative (ZVDD). The proposed method also demonstrates a significant residual swing suppression as compared to a ZVDD shaper designed based on varying frequency. In addition, the significant reductions are achieved with a less shaper duration resulting in a satisfactory speed of response. It is envisaged that the proposed method can be used for designing effective input shapers for payload swing suppression of a crane with time-varying parameters and for a crane that employ finite actuation states.

Survival curves of Listeria monocytogenes in chorizos modeled with artificial neural networks.

PubMed

Hajmeer, M; Basheer, I; Cliver, D O

2006-09-01

Using artificial neural networks (ANNs), a highly accurate model was developed to simulate survival curves of Listeria monocytogenes in chorizos as affected by the initial water activity (a(w0)) of the sausage formulation, temperature (T), and air inflow velocity (F) where the sausages are stored. The ANN-based survival model (R(2)=0.970) outperformed the regression-based cubic model (R(2)=0.851), and as such was used to derive other models (using regression) that allow prediction of the times needed to drop count by 1, 2, 3, and 4 logs (i.e., nD-values, n=1, 2, 3, 4). The nD-value regression models almost perfectly predicted the various times derived from a number of simulated survival curves exhibiting a wide variety of the operating conditions (R(2)=0.990-0.995). The nD-values were found to decrease with decreasing a(w0), and increasing T and F. The influence of a(w0) on nD-values seems to become more significant at some critical value of a(w0), below which the variation is negligible (0.93 for 1D-value, 0.90 for 2D-value, and <0.85 for 3D- and 4D-values). There is greater influence of storage T and F on 3D- and 4D-values than on 1D- and 2D-values.

Removal of BCG artifacts using a non-Kirchhoffian overcomplete representation.

PubMed

Dyrholm, Mads; Goldman, Robin; Sajda, Paul; Brown, Truman R

2009-02-01

We present a nonlinear unmixing approach for extracting the ballistocardiogram (BCG) from EEG recorded in an MR scanner during simultaneous acquisition of functional MRI (fMRI). First, an overcomplete basis is identified in the EEG based on a custom multipath EEG electrode cap. Next, the overcomplete basis is used to infer non-Kirchhoffian latent variables that are not consistent with a conservative electric field. Neural activity is strictly Kirchhoffian while the BCG artifact is not, and the representation can hence be used to remove the artifacts from the data in a way that does not attenuate the neural signals needed for optimal single-trial classification performance. We compare our method to more standard methods for BCG removal, namely independent component analysis and optimal basis sets, by looking at single-trial classification performance for an auditory oddball experiment. We show that our overcomplete representation method for removing BCG artifacts results in better single-trial classification performance compared to the conventional approaches, indicating that the derived neural activity in this representation retains the complex information in the trial-to-trial variability.

Human neural stem cell-derived cultures in three-dimensional substrates form spontaneously functional neuronal networks.

PubMed

Smith, Imogen; Silveirinha, Vasco; Stein, Jason L; de la Torre-Ubieta, Luis; Farrimond, Jonathan A; Williamson, Elizabeth M; Whalley, Benjamin J

2017-04-01

Differentiated human neural stem cells were cultured in an inert three-dimensional (3D) scaffold and, unlike two-dimensional (2D) but otherwise comparable monolayer cultures, formed spontaneously active, functional neuronal networks that responded reproducibly and predictably to conventional pharmacological treatments to reveal functional, glutamatergic synapses. Immunocytochemical and electron microscopy analysis revealed a neuronal and glial population, where markers of neuronal maturity were observed in the former. Oligonucleotide microarray analysis revealed substantial differences in gene expression conferred by culturing in a 3D vs a 2D environment. Notable and numerous differences were seen in genes coding for neuronal function, the extracellular matrix and cytoskeleton. In addition to producing functional networks, differentiated human neural stem cells grown in inert scaffolds offer several significant advantages over conventional 2D monolayers. These advantages include cost savings and improved physiological relevance, which make them better suited for use in the pharmacological and toxicological assays required for development of stem cell-based treatments and the reduction of animal use in medical research. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Object based technique for delineating and mapping 15 tree species using VHR WorldView-2 imagery

NASA Astrophysics Data System (ADS)

Mustafa, Yaseen T.; Habeeb, Hindav N.

2014-10-01

Monitoring and analyzing forests and trees are required task to manage and establish a good plan for the forest sustainability. To achieve such a task, information and data collection of the trees are requested. The fastest way and relatively low cost technique is by using satellite remote sensing. In this study, we proposed an approach to identify and map 15 tree species in the Mangish sub-district, Kurdistan Region-Iraq. Image-objects (IOs) were used as the tree species mapping unit. This is achieved using the shadow index, normalized difference vegetation index and texture measurements. Four classification methods (Maximum Likelihood, Mahalanobis Distance, Neural Network, and Spectral Angel Mapper) were used to classify IOs using selected IO features derived from WorldView-2 imagery. Results showed that overall accuracy was increased 5-8% using the Neural Network method compared with other methods with a Kappa coefficient of 69%. This technique gives reasonable results of various tree species classifications by means of applying the Neural Network method with IOs techniques on WorldView-2 imagery.

Low-complexity nonlinear adaptive filter based on a pipelined bilinear recurrent neural network.

PubMed

Zhao, Haiquan; Zeng, Xiangping; He, Zhengyou

2011-09-01

To reduce the computational complexity of the bilinear recurrent neural network (BLRNN), a novel low-complexity nonlinear adaptive filter with a pipelined bilinear recurrent neural network (PBLRNN) is presented in this paper. The PBLRNN, inheriting the modular architectures of the pipelined RNN proposed by Haykin and Li, comprises a number of BLRNN modules that are cascaded in a chained form. Each module is implemented by a small-scale BLRNN with internal dynamics. Since those modules of the PBLRNN can be performed simultaneously in a pipelined parallelism fashion, it would result in a significant improvement of computational efficiency. Moreover, due to nesting module, the performance of the PBLRNN can be further improved. To suit for the modular architectures, a modified adaptive amplitude real-time recurrent learning algorithm is derived on the gradient descent approach. Extensive simulations are carried out to evaluate the performance of the PBLRNN on nonlinear system identification, nonlinear channel equalization, and chaotic time series prediction. Experimental results show that the PBLRNN provides considerably better performance compared to the single BLRNN and RNN models.

Modulation of cultured neural networks using neurotrophin release from hydrogel-coated microelectrode arrays

NASA Astrophysics Data System (ADS)

Jun, Sang Beom; Hynd, Matthew R.; Dowell-Mesfin, Natalie M.; Al-Kofahi, Yousef; Roysam, Badrinath; Shain, William; Kim, Sung June

2008-06-01

Polyacrylamide and poly(ethylene glycol) diacrylate hydrogels were synthesized and characterized for use as drug release and substrates for neuron cell culture. Protein release kinetics was determined by incorporating bovine serum albumin (BSA) into hydrogels during polymerization. To determine if hydrogel incorporation and release affect bioactivity, alkaline phosphatase was incorporated into hydrogels and a released enzyme activity determined using the fluorescence-based ELF-97 assay. Hydrogels were then used to deliver a brain-derived neurotrophic factor (BDNF) from hydrogels polymerized over planar microelectrode arrays (MEAs). Primary hippocampal neurons were cultured on both control and neurotrophin-containing hydrogel-coated MEAs. The effect of released BDNF on neurite length and process arborization was investigated using automated image analysis. An increased spontaneous activity as a response to the released BDNF was recorded from the neurons cultured on the top of hydrogel layers. These results demonstrate that proteins of biological interest can be incorporated into hydrogels to modulate development and function of cultured neural networks. These results also set the stage for development of hydrogel-coated neural prosthetic devices for local delivery of various biologically active molecules.

Contact-force distribution optimization and control for quadruped robots using both gradient and adaptive neural networks.

PubMed

Li, Zhijun; Ge, Shuzhi Sam; Liu, Sibang

2014-08-01

This paper investigates optimal feet forces' distribution and control of quadruped robots under external disturbance forces. First, we formulate a constrained dynamics of quadruped robots and derive a reduced-order dynamical model of motion/force. Consider an external wrench on quadruped robots; the distribution of required forces and moments on the supporting legs of a quadruped robot is handled as a tip-point force distribution and used to equilibrate the external wrench. Then, a gradient neural network is adopted to deal with the optimized objective function formulated as to minimize this quadratic objective function subjected to linear equality and inequality constraints. For the obtained optimized tip-point force and the motion of legs, we propose the hybrid motion/force control based on an adaptive neural network to compensate for the perturbations in the environment and approximate feedforward force and impedance of the leg joints. The proposed control can confront the uncertainties including approximation error and external perturbation. The verification of the proposed control is conducted using a simulation.

Global Mittag-Leffler stability and synchronization analysis of fractional-order quaternion-valued neural networks with linear threshold neurons.

PubMed

Yang, Xujun; Li, Chuandong; Song, Qiankun; Chen, Jiyang; Huang, Junjian

2018-05-04

This paper talks about the stability and synchronization problems of fractional-order quaternion-valued neural networks (FQVNNs) with linear threshold neurons. On account of the non-commutativity of quaternion multiplication resulting from Hamilton rules, the FQVNN models are separated into four real-valued neural network (RVNN) models. Consequently, the dynamic analysis of FQVNNs can be realized by investigating the real-valued ones. Based on the method of M-matrix, the existence and uniqueness of the equilibrium point of the FQVNNs are obtained without detailed proof. Afterwards, several sufficient criteria ensuring the global Mittag-Leffler stability for the unique equilibrium point of the FQVNNs are derived by applying the Lyapunov direct method, the theory of fractional differential equation, the theory of matrix eigenvalue, and some inequality techniques. In the meanwhile, global Mittag-Leffler synchronization for the drive-response models of the addressed FQVNNs are investigated explicitly. Finally, simulation examples are designed to verify the feasibility and availability of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

Enhanced robust finite-time passivity for Markovian jumping discrete-time BAM neural networks with leakage delay.

PubMed

Sowmiya, C; Raja, R; Cao, Jinde; Rajchakit, G; Alsaedi, Ahmed

2017-01-01

This paper is concerned with the problem of enhanced results on robust finite-time passivity for uncertain discrete-time Markovian jumping BAM delayed neural networks with leakage delay. By implementing a proper Lyapunov-Krasovskii functional candidate, the reciprocally convex combination method together with linear matrix inequality technique, several sufficient conditions are derived for varying the passivity of discrete-time BAM neural networks. An important feature presented in our paper is that we utilize the reciprocally convex combination lemma in the main section and the relevance of that lemma arises from the derivation of stability by using Jensen's inequality. Further, the zero inequalities help to propose the sufficient conditions for finite-time boundedness and passivity for uncertainties. Finally, the enhancement of the feasible region of the proposed criteria is shown via numerical examples with simulation to illustrate the applicability and usefulness of the proposed method.

Two neural network algorithms for designing optimal terminal controllers with open final time

NASA Technical Reports Server (NTRS)

Plumer, Edward S.

1992-01-01

Multilayer neural networks, trained by the backpropagation through time algorithm (BPTT), have been used successfully as state-feedback controllers for nonlinear terminal control problems. Current BPTT techniques, however, are not able to deal systematically with open final-time situations such as minimum-time problems. Two approaches which extend BPTT to open final-time problems are presented. In the first, a neural network learns a mapping from initial-state to time-to-go. In the second, the optimal number of steps for each trial run is found using a line-search. Both methods are derived using Lagrange multiplier techniques. This theoretical framework is used to demonstrate that the derived algorithms are direct extensions of forward/backward sweep methods used in N-stage optimal control. The two algorithms are tested on a Zermelo problem and the resulting trajectories compare favorably to optimal control results.

Generation of inner ear sensory cells from bone marrow-derived human mesenchymal stem cells.

PubMed

Durán Alonso, M Beatriz; Feijoo-Redondo, Ana; Conde de Felipe, Magnolia; Carnicero, Estela; García, Ana Sánchez; García-Sancho, Javier; Rivolta, Marcelo N; Giráldez, Fernando; Schimmang, Thomas

2012-11-01

Hearing loss is the most common sensory disorder in humans, its main cause being the loss of cochlear hair cells. We studied the potential of human mesenchymal stem cells (hMSCs) to differentiate towards hair cells and auditory neurons. hMSCs were first differentiated to neural progenitors and subsequently to hair cell- or auditory neuron-like cells using in vitro culture methods. Differentiation of hMSCs to an intermediate neural progenitor stage was critical for obtaining inner ear sensory lineages. hMSCs generated hair cell-like cells only when neural progenitors derived from nonadherent hMSC cultures grown in serum-free medium were exposed to EGF and retinoic acid. Auditory neuron-like cells were obtained when treated with retinoic acid, and in the presence of defined growth factor combinations containing Sonic Hedgehog. The results show the potential of hMSCs to give rise to inner ear sensory cells.

Environmental estrogens alter early development in Xenopus laevis.

PubMed

Bevan, Cassandra L; Porter, Donna M; Prasad, Anita; Howard, Marthe J; Henderson, Leslie P

2003-04-01

A growing number of environmental toxicants found in pesticides, herbicides, and industrial solvents are believed to have deleterious effects on development by disrupting hormone-sensitive processes. We exposed Xenopus laevis embryos at early gastrula to the commonly encountered environmental estrogens nonylphenol, octylphenol, and methoxychlor, the antiandrogen, p,p-DDE, or the synthetic androgen, 17 alpha-methyltestosterone at concentrations ranging from 10 nM to 10 microM and examined them at tailbud stages (approximately 48 hr of treatment). Exposure to the three environmental estrogens, as well as to the natural estrogen 17 beta-estradiol, increased mortality, induced morphologic deformations, increased apoptosis, and altered the deposition and differentiation of neural crest-derived melanocytes in tailbud stage embryos. Although neural crest-derived melanocytes were markedly altered in embryos treated with estrogenic toxicants, expression of the early neural crest maker Xslug, a factor that regulates both the induction and subsequent migration of neural crest cells, was not affected, suggesting that the disruption induced by these compounds with respect to melanocyte development may occur at later stages of their differentiation. Co-incubation of embryos with the pure antiestrogen ICI 182,780 blocked the ability of nonylphenol to induce abnormalities in body shape and in melanocyte differentiation but did not block the effects of methoxychlor. Our data indicate not only that acute exposure to these environmental estrogens induces deleterious effects on early vertebrate development but also that different environmental estrogens may alter the fate of a specific cell type via different mechanisms. Finally, our data suggest that the differentiation of neural crest-derived melanocytes may be particularly sensitive to the disruptive actions of these ubiquitous chemical contaminants.

Conditionally immortalized stem cell lines from human spinal cord retain regional identity and generate functional V2a interneurons and motorneurons.

PubMed

Cocks, Graham; Romanyuk, Nataliya; Amemori, Takashi; Jendelova, Pavla; Forostyak, Oksana; Jeffries, Aaron R; Perfect, Leo; Thuret, Sandrine; Dayanithi, Govindan; Sykova, Eva; Price, Jack

2013-06-07

The use of immortalized neural stem cells either as models of neural development in vitro or as cellular therapies in central nervous system (CNS) disorders has been controversial. This controversy has centered on the capacity of immortalized cells to retain characteristic features of the progenitor cells resident in the tissue of origin from which they were derived, and the potential for tumorogenicity as a result of immortalization. Here, we report the generation of conditionally immortalized neural stem cell lines from human fetal spinal cord tissue, which addresses these issues. Clonal neural stem cell lines were derived from 10-week-old human fetal spinal cord and conditionally immortalized with an inducible form of cMyc. The derived lines were karyotyped, transcriptionally profiled by microarray, and assessed against a panel of spinal cord progenitor markers with immunocytochemistry. In addition, the lines were differentiated and assessed for the presence of neuronal fate markers and functional calcium channels. Finally, a clonal line expressing eGFP was grafted into lesioned rat spinal cord and assessed for survival, differentiation characteristics, and tumorogenicity. We demonstrate that these clonal lines (a) retain a clear transcriptional signature of ventral spinal cord progenitors and a normal karyotype after extensive propagation in vitro, (b) differentiate into relevant ventral neuronal subtypes with functional T-, L-, N-, and P/Q-type Ca(2+) channels and spontaneous calcium oscillations, and (c) stably engraft into lesioned rat spinal cord without tumorogenicity. We propose that these cells represent a useful tool both for the in vitro study of differentiation into ventral spinal cord neuronal subtypes, and for examining the potential of conditionally immortalized neural stem cells to facilitate functional recovery after spinal cord injury or disease.

A cortical neural prosthesis for restoring and enhancing memory

NASA Astrophysics Data System (ADS)

Berger, Theodore W.; Hampson, Robert E.; Song, Dong; Goonawardena, Anushka; Marmarelis, Vasilis Z.; Deadwyler, Sam A.

2011-08-01

A primary objective in developing a neural prosthesis is to replace neural circuitry in the brain that no longer functions appropriately. Such a goal requires artificial reconstruction of neuron-to-neuron connections in a way that can be recognized by the remaining normal circuitry, and that promotes appropriate interaction. In this study, the application of a specially designed neural prosthesis using a multi-input/multi-output (MIMO) nonlinear model is demonstrated by using trains of electrical stimulation pulses to substitute for MIMO model derived ensemble firing patterns. Ensembles of CA3 and CA1 hippocampal neurons, recorded from rats performing a delayed-nonmatch-to-sample (DNMS) memory task, exhibited successful encoding of trial-specific sample lever information in the form of different spatiotemporal firing patterns. MIMO patterns, identified online and in real-time, were employed within a closed-loop behavioral paradigm. Results showed that the model was able to predict successful performance on the same trial. Also, MIMO model-derived patterns, delivered as electrical stimulation to the same electrodes, improved performance under normal testing conditions and, more importantly, were capable of recovering performance when delivered to animals with ensemble hippocampal activity compromised by pharmacologic blockade of synaptic transmission. These integrated experimental-modeling studies show for the first time that, with sufficient information about the neural coding of memories, a neural prosthesis capable of real-time diagnosis and manipulation of the encoding process can restore and even enhance cognitive, mnemonic processes.

Transcriptional regulation of cranial sensory placode development

PubMed Central

Moody, Sally A.; LaMantia, Anthony-Samuel

2015-01-01

Cranial sensory placodes derive from discrete patches of the head ectoderm, and give rise to numerous sensory structures. During gastrulation, a specialized “neural border zone” forms around the neural plate in response to interactions between the neural and non-neural ectoderm and signals from adjacent mesodermal and/or endodermal tissues. This zone subsequently gives rise to two distinct precursor populations of the peripheral nervous system: the neural crest and the pre-placodal ectoderm (PPE). The PPE is a common field from which all cranial sensory placodes arise (adenohypophyseal, olfactory, lens, trigeminal, epibranchial, otic). Members of the Six family of transcription factors are major regulators of PPE specification, in partnership with co-factor proteins such as Eya. Six gene activity also maintains tissue boundaries between the PPE, neural crest and epidermis by repressing genes that specify the fates of those adjacent ectodermally-derived domains. As the embryo acquires anterior-posterior identity, the PPE becomes transcriptionally regionalized, and it subsequently subdivides into specific placodes with distinct developmental fates in response to signaling from adjacent tissues. Each placode is characterized by a unique transcriptional program that leads to the differentiation of highly specialized cells, such as neurosecretory cells, somatic sensory receptor cells, chemosensory neurons, peripheral glia and supporting cells. In this review, we summarize the transcriptional and signaling factors that regulate key steps of placode development, influence subsequent sensory neuron specification, and discuss what is known about mutations in some of the essential PPE genes that underlie human congenital syndromes. PMID:25662264

Penetration and differentiation of cephalic neural crest-derived cells in the developing mouse telencephalon.

PubMed

Yamanishi, Emiko; Takahashi, Masanori; Saga, Yumiko; Osumi, Noriko

2012-12-01

Neural crest (NC) cells originate from the neural folds and migrate into the various embryonic regions where they differentiate into multiple cell types. A population of cephalic neural crest-derived cells (NCDCs) penetrates back into the developing forebrain to differentiate into microvascular pericytes, but little is known about when and how cephalic NCDCs invade the telencephalon and differentiate into pericytes. Using a transgenic mouse line in which NCDCs are genetically labeled with enhanced green fluorescent protein (EGFP), we observed that NCDCs started to invade the telencephalon together with endothelial cells from embryonic day (E) 9.5. A majority of NCDCs located in the telencephalon expressed pericyte markers, that is, PDGFRβ and NG2, and differentiated into pericytes around E11.5. Surprisingly, many of the NC-derived pericytes express p75, an undifferentiated NCDC marker at E11.5, as well as NCDCs in the mesenchyme. At the same time, a minor population of NCDCs that located separately from blood vessels in the telencephalon were NG2-negative and some of these NCDCs also expressed p75. Proliferation and differentiation of pericytes appeared to occur in a specific mesenchymal region where blood vessels penetrated into the telencephalon. These results indicate that (i) NCDCs penetrate back into the telencephalon in parallel with angiogenesis, (ii) many NC-derived pericytes may be still in pre-mature states even though after differentiation into pericytes in the early developing stages, (iii) a small minority of NCDCs may retain undifferentiated states in the developing telencephalon, and (iv) a majority of NCDCs proliferate and differentiate into pericytes in the mesenchyme around the telencephalon. © 2012 The Authors Development, Growth & Differentiation © 2012 Japanese Society of Developmental Biologists.

Development of a sensor coordinated kinematic model for neural network controller training

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C.

1990-01-01

A robotic benchmark problem useful for evaluating alternative neural network controllers is presented. Specifically, it derives two camera models and the kinematic equations of a multiple degree of freedom manipulator whose end effector is under observation. The mapping developed include forward and inverse translations from binocular images to 3-D target position and the inverse kinematics of mapping point positions into manipulator commands in joint space. Implementation is detailed for a three degree of freedom manipulator with one revolute joint at the base and two prismatic joints on the arms. The example is restricted to operate within a unit cube with arm links of 0.6 and 0.4 units respectively. The development is presented in the context of more complex simulations and a logical path for extension of the benchmark to higher degree of freedom manipulators is presented.

Controller design for global fixed-time synchronization of delayed neural networks with discontinuous activations.

PubMed

Wang, Leimin; Zeng, Zhigang; Hu, Junhao; Wang, Xiaoping

2017-03-01

This paper addresses the controller design problem for global fixed-time synchronization of delayed neural networks (DNNs) with discontinuous activations. To solve this problem, adaptive control and state feedback control laws are designed. Then based on the two controllers and two lemmas, the error system is proved to be globally asymptotically stable and even fixed-time stable. Moreover, some sufficient and easy checked conditions are derived to guarantee the global synchronization of drive and response systems in fixed time. It is noted that the settling time functional for fixed-time synchronization is independent on initial conditions. Our fixed-time synchronization results contain the finite-time results as the special cases by choosing different values of the two controllers. Finally, theoretical results are supported by numerical simulations. Copyright © 2016 Elsevier Ltd. All rights reserved.

A neural fuzzy controller learning by fuzzy error propagation

NASA Technical Reports Server (NTRS)

Nauck, Detlef; Kruse, Rudolf

1992-01-01

In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

Impulsive control of stochastic systems with applications in chaos control, chaos synchronization, and neural networks.

PubMed

Li, Chunguang; Chen, Luonan; Aihara, Kazuyuki

2008-06-01

Real systems are often subject to both noise perturbations and impulsive effects. In this paper, we study the stability and stabilization of systems with both noise perturbations and impulsive effects. In other words, we generalize the impulsive control theory from the deterministic case to the stochastic case. The method is based on extending the comparison method to the stochastic case. The method presented in this paper is general and easy to apply. Theoretical results on both stability in the pth mean and stability with disturbance attenuation are derived. To show the effectiveness of the basic theory, we apply it to the impulsive control and synchronization of chaotic systems with noise perturbations, and to the stability of impulsive stochastic neural networks. Several numerical examples are also presented to verify the theoretical results.

H∞ state estimation for discrete-time memristive recurrent neural networks with stochastic time-delays

NASA Astrophysics Data System (ADS)

Liu, Hongjian; Wang, Zidong; Shen, Bo; Alsaadi, Fuad E.

2016-07-01

This paper deals with the robust H∞ state estimation problem for a class of memristive recurrent neural networks with stochastic time-delays. The stochastic time-delays under consideration are governed by a Bernoulli-distributed stochastic sequence. The purpose of the addressed problem is to design the robust state estimator such that the dynamics of the estimation error is exponentially stable in the mean square, and the prescribed ? performance constraint is met. By utilizing the difference inclusion theory and choosing a proper Lyapunov-Krasovskii functional, the existence condition of the desired estimator is derived. Based on it, the explicit expression of the estimator gain is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is employed to demonstrate the effectiveness and applicability of the proposed estimation approach.

Robust passivity analysis for discrete-time recurrent neural networks with mixed delays

NASA Astrophysics Data System (ADS)

Huang, Chuan-Kuei; Shu, Yu-Jeng; Chang, Koan-Yuh; Shou, Ho-Nien; Lu, Chien-Yu

2015-02-01

This article considers the robust passivity analysis for a class of discrete-time recurrent neural networks (DRNNs) with mixed time-delays and uncertain parameters. The mixed time-delays that consist of both the discrete time-varying and distributed time-delays in a given range are presented, and the uncertain parameters are norm-bounded. The activation functions are assumed to be globally Lipschitz continuous. Based on new bounding technique and appropriate type of Lyapunov functional, a sufficient condition is investigated to guarantee the existence of the desired robust passivity condition for the DRNNs, which can be derived in terms of a family of linear matrix inequality (LMI). Some free-weighting matrices are introduced to reduce the conservatism of the criterion by using the bounding technique. A numerical example is given to illustrate the effectiveness and applicability.

Neural activity during natural viewing of Sesame Street statistically predicts test scores in early childhood.

PubMed

Cantlon, Jessica F; Li, Rosa

2013-01-01

It is not currently possible to measure the real-world thought process that a child has while observing an actual school lesson. However, if it could be done, children's neural processes would presumably be predictive of what they know. Such neural measures would shed new light on children's real-world thought. Toward that goal, this study examines neural processes that are evoked naturalistically, during educational television viewing. Children and adults all watched the same Sesame Street video during functional magnetic resonance imaging (fMRI). Whole-brain intersubject correlations between the neural timeseries from each child and a group of adults were used to derive maps of "neural maturity" for children. Neural maturity in the intraparietal sulcus (IPS), a region with a known role in basic numerical cognition, predicted children's formal mathematics abilities. In contrast, neural maturity in Broca's area correlated with children's verbal abilities, consistent with prior language research. Our data show that children's neural responses while watching complex real-world stimuli predict their cognitive abilities in a content-specific manner. This more ecologically natural paradigm, combined with the novel measure of "neural maturity," provides a new method for studying real-world mathematics development in the brain.

Nuclear translocation of PKCα isoenzyme is involved in neurogenic commitment of human neural crest-derived periodontal ligament stem cells.

PubMed

Trubiani, Oriana; Guarnieri, Simone; Diomede, Francesca; Mariggiò, Maria A; Merciaro, Ilaria; Morabito, Caterina; Cavalcanti, Marcos F X B; Cocco, Lucio; Ramazzotti, Giulia

2016-11-01

Stem cells isolated from human adult tissue niche represent a promising source for neural differentiation. Human Periodontal Ligament Stem Cells (hPDLSCs) originating from the neural crest are particularly suitable for induction of neural commitment. In this study, under xeno-free culture conditions, in undifferentiated hPDLSCs and in hPDLSCs induced to neuronal differentiation by basic Fibroblast Growth Factor, the level of some neural markers have been analyzed. The hPDLSCs spontaneously express Nestin, a neural progenitor marker. In these cells, the neurogenic process induced to rearrange the cytoskeleton, form neurospheres and express higher levels of Nestin and Tyrosine Hydroxylase, indicating neural induction. Protein Kinase C (PKC) is highly expressed in neural tissue and has a key role in neuronal functions. In particular the Ca(2+) and diacylglycerol-dependent activation of PKCα isozyme is involved in the regulation of neuronal differentiation. Another main component of the pathways controlling neuronal differentiation is the Growth Associated Protein-43 (GAP-43), whose activity is strictly regulated by PKC. The aim of this study is to investigate the role of PKCα/GAP-43 nuclear signal transduction pathway during neuronal commitment of hPDLSCs. During hPDLSCs neurogenic commitment the levels of p-PKC and p-GAP-43 increased both in cytoplasmic and nuclear compartment. PKCα nuclear translocation induced GAP-43 movement to the cytoplasm, where it is known to regulate growth cone dynamics and neuronal differentiation. Moreover, the degree of cytosolic Ca(2+) mobilization appeared to be more pronounced in differentiated hPDLSCs than in undifferentiated cells. This study provides evidences of a new PKCα/GAP-43 nuclear signalling pathway that controls neuronal differentiation in hPDLSCs, leading the way to a potential use of these cells in cell-based therapy in neurodegenerative diseases. Copyright © 2016 Elsevier Inc. All rights reserved.

Signatures of criticality arise from random subsampling in simple population models.

PubMed

Nonnenmacher, Marcel; Behrens, Christian; Berens, Philipp; Bethge, Matthias; Macke, Jakob H

2017-10-01

The rise of large-scale recordings of neuronal activity has fueled the hope to gain new insights into the collective activity of neural ensembles. How can one link the statistics of neural population activity to underlying principles and theories? One attempt to interpret such data builds upon analogies to the behaviour of collective systems in statistical physics. Divergence of the specific heat-a measure of population statistics derived from thermodynamics-has been used to suggest that neural populations are optimized to operate at a "critical point". However, these findings have been challenged by theoretical studies which have shown that common inputs can lead to diverging specific heat. Here, we connect "signatures of criticality", and in particular the divergence of specific heat, back to statistics of neural population activity commonly studied in neural coding: firing rates and pairwise correlations. We show that the specific heat diverges whenever the average correlation strength does not depend on population size. This is necessarily true when data with correlations is randomly subsampled during the analysis process, irrespective of the detailed structure or origin of correlations. We also show how the characteristic shape of specific heat capacity curves depends on firing rates and correlations, using both analytically tractable models and numerical simulations of a canonical feed-forward population model. To analyze these simulations, we develop efficient methods for characterizing large-scale neural population activity with maximum entropy models. We find that, consistent with experimental findings, increases in firing rates and correlation directly lead to more pronounced signatures. Thus, previous reports of thermodynamical criticality in neural populations based on the analysis of specific heat can be explained by average firing rates and correlations, and are not indicative of an optimized coding strategy. We conclude that a reliable interpretation of statistical tests for theories of neural coding is possible only in reference to relevant ground-truth models.

A New Wnt1-CRE TomatoRosa Embryonic Stem Cell Line: A Tool for Studying Neural Crest Cell Integration Capacity.

PubMed

Acuna-Mendoza, Soledad; Martin, Sabrina; Kuchler-Bopp, Sabine; Ribes, Sandy; Thalgott, Jérémy; Chaussain, Catherine; Creuzet, Sophie; Lesot, Hervé; Lebrin, Franck; Poliard, Anne

2017-12-01

Neural crest (NC) cells are a migratory, multipotent population giving rise to numerous lineages in the embryo. Their plasticity renders attractive their use in tissue engineering-based therapies, but further knowledge on their in vivo behavior is required before clinical transfer may be envisioned. We here describe the isolation and characterization of a new mouse embryonic stem (ES) line derived from Wnt1-CRE-R26 Rosa TomatoTdv blastocyst and show that it displays the characteristics of typical ES cells. Further, these cells can be efficiently directed toward an NC stem cell-like phenotype as attested by concomitant expression of NC marker genes and Tomato fluorescence. As native NC progenitors, they are capable of differentiating toward typical derivative phenotypes and interacting with embryonic tissues to participate in the formation of neo-structures. Their specific fluorescence allows purification and tracking in vivo. This cellular tool should facilitate a better understanding of the mechanisms driving NC fate specification and help identify the key interactions developed within a tissue after in vivo implantation. Altogether, this novel model may provide important knowledge to optimize NC stem cell graft conditions, which are required for efficient tissue repair.

Transcription factor-based modulation of neural stem cell differentiation using direct protein transduction

PubMed Central

Stock, Kristin; Nolden, Lars; Edenhofer, Frank; Quandel, Tamara

2010-01-01

In contrast to conventional gene transfer strategies, the direct introduction of recombinant proteins into cells bypasses the risk of insertional mutagenesis and offers an alternative to genetic intervention. Here, we explore whether protein transduction of the gliogenic transcription factor Nkx2.2 can be used to promote oligodendroglial differentiation of mouse embryonic stem cell (ESC)-derived neural stem cells (NSC). To that end, a recombinant cell-permeant form of Nkx2.2 protein was generated. Exposure of ESC-derived NSC to the recombinant protein and initiation of differentiation resulted in a two-fold increase in the number of oligodendrocytes. Furthermore, Nkx2.2-transduced cells exhibited a more mature oligodendroglial phenotype. Comparative viral gene transfer studies showed that the biological effect of Nkx2.2 protein transduction is comparable to that obtained by lentiviral transduction. The results of this proof-of-concept study depict direct intracellular delivery of transcription factors as alternative modality to control lineage differentiation in NSC cultures without genetic modification. Electronic supplementary material The online version of this article (doi:10.1007/s00018-010-0347-1) contains supplementary material, which is available to authorized users. PMID:20352468

Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size

PubMed Central

Gerstner, Wulfram

2017-01-01

Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50–2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations. PMID:28422957

Search for the Missing lncs: Gene Regulatory Networks in Neural Crest Development and Long Non-coding RNA Biomarkers of Hirschsprung's Disease

EPA Science Inventory

Hirschsprung’s disease (HSCR), a birth defect characterized by variable aganglionosis of the gut, affects about 1 in 5000 births, and is a consequence of abnormal development of neural crest cells, from which enteric ganglia derive. In the companion article in this issue (Shen et...

The Reference Ability Neural Network Study: Life-time stability of reference-ability neural networks derived from task maps of young adults.

PubMed

Habeck, C; Gazes, Y; Razlighi, Q; Steffener, J; Brickman, A; Barulli, D; Salthouse, T; Stern, Y

2016-01-15

Analyses of large test batteries administered to individuals ranging from young to old have consistently yielded a set of latent variables representing reference abilities (RAs) that capture the majority of the variance in age-related cognitive change: Episodic Memory, Fluid Reasoning, Perceptual Processing Speed, and Vocabulary. In a previous paper (Stern et al., 2014), we introduced the Reference Ability Neural Network Study, which administers 12 cognitive neuroimaging tasks (3 for each RA) to healthy adults age 20-80 in order to derive unique neural networks underlying these 4 RAs and investigate how these networks may be affected by aging. We used a multivariate approach, linear indicator regression, to derive a unique covariance pattern or Reference Ability Neural Network (RANN) for each of the 4 RAs. The RANNs were derived from the neural task data of 64 younger adults of age 30 and below. We then prospectively applied the RANNs to fMRI data from the remaining sample of 227 adults of age 31 and above in order to classify each subject-task map into one of the 4 possible reference domains. Overall classification accuracy across subjects in the sample age 31 and above was 0.80±0.18. Classification accuracy by RA domain was also good, but variable; memory: 0.72±0.32; reasoning: 0.75±0.35; speed: 0.79±0.31; vocabulary: 0.94±0.16. Classification accuracy was not associated with cross-sectional age, suggesting that these networks, and their specificity to the respective reference domain, might remain intact throughout the age range. Higher mean brain volume was correlated with increased overall classification accuracy; better overall performance on the tasks in the scanner was also associated with classification accuracy. For the RANN network scores, we observed for each RANN that a higher score was associated with a higher corresponding classification accuracy for that reference ability. Despite the absence of behavioral performance information in the derivation of these networks, we also observed some brain-behavioral correlations, notably for the fluid-reasoning network whose network score correlated with performance on the memory and fluid-reasoning tasks. While age did not influence the expression of this RANN, the slope of the association between network score and fluid-reasoning performance was negatively associated with higher ages. These results provide support for the hypothesis that a set of specific, age-invariant neural networks underlies these four RAs, and that these networks maintain their cognitive specificity and level of intensity across age. Activation common to all 12 tasks was identified as another activation pattern resulting from a mean-contrast Partial-Least-Squares technique. This common pattern did show associations with age and some subject demographics for some of the reference domains, lending support to the overall conclusion that aspects of neural processing that are specific to any cognitive reference ability stay constant across age, while aspects that are common to all reference abilities differ across age. Copyright © 2015 Elsevier Inc. All rights reserved.

Finite-time convergent recurrent neural network with a hard-limiting activation function for constrained optimization with piecewise-linear objective functions.

PubMed

Liu, Qingshan; Wang, Jun

2011-04-01

This paper presents a one-layer recurrent neural network for solving a class of constrained nonsmooth optimization problems with piecewise-linear objective functions. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on a derived lower bound of a single gain parameter in the model. The number of neurons in the neural network is the same as the number of decision variables of the optimization problem. Compared with existing neural networks for optimization, the proposed neural network has a couple of salient features such as finite-time convergence and a low model complexity. Specific models for two important special cases, namely, linear programming and nonsmooth optimization, are also presented. In addition, applications to the shortest path problem and constrained least absolute deviation problem are discussed with simulation results to demonstrate the effectiveness and characteristics of the proposed neural network.

Chromatin Remodeling BAF (SWI/SNF) Complexes in Neural Development and Disorders

PubMed Central

Sokpor, Godwin; Xie, Yuanbin; Rosenbusch, Joachim; Tuoc, Tran

2017-01-01

The ATP-dependent BRG1/BRM associated factor (BAF) chromatin remodeling complexes are crucial in regulating gene expression by controlling chromatin dynamics. Over the last decade, it has become increasingly clear that during neural development in mammals, distinct ontogenetic stage-specific BAF complexes derived from combinatorial assembly of their subunits are formed in neural progenitors and post-mitotic neural cells. Proper functioning of the BAF complexes plays critical roles in neural development, including the establishment and maintenance of neural fates and functionality. Indeed, recent human exome sequencing and genome-wide association studies have revealed that mutations in BAF complex subunits are linked to neurodevelopmental disorders such as Coffin-Siris syndrome, Nicolaides-Baraitser syndrome, Kleefstra's syndrome spectrum, Hirschsprung's disease, autism spectrum disorder, and schizophrenia. In this review, we focus on the latest insights into the functions of BAF complexes during neural development and the plausible mechanistic basis of how mutations in known BAF subunits are associated with certain neurodevelopmental disorders. PMID:28824374

Chromatin Remodeling BAF (SWI/SNF) Complexes in Neural Development and Disorders.

PubMed

Sokpor, Godwin; Xie, Yuanbin; Rosenbusch, Joachim; Tuoc, Tran

2017-01-01

The ATP-dependent BRG1/BRM associated factor (BAF) chromatin remodeling complexes are crucial in regulating gene expression by controlling chromatin dynamics. Over the last decade, it has become increasingly clear that during neural development in mammals, distinct ontogenetic stage-specific BAF complexes derived from combinatorial assembly of their subunits are formed in neural progenitors and post-mitotic neural cells. Proper functioning of the BAF complexes plays critical roles in neural development, including the establishment and maintenance of neural fates and functionality. Indeed, recent human exome sequencing and genome-wide association studies have revealed that mutations in BAF complex subunits are linked to neurodevelopmental disorders such as Coffin-Siris syndrome, Nicolaides-Baraitser syndrome, Kleefstra's syndrome spectrum, Hirschsprung's disease, autism spectrum disorder, and schizophrenia. In this review, we focus on the latest insights into the functions of BAF complexes during neural development and the plausible mechanistic basis of how mutations in known BAF subunits are associated with certain neurodevelopmental disorders.

Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect

PubMed Central

Bush, Keith A.; Inman, Cory S.; Hamann, Stephan; Kilts, Clinton D.; James, G. Andrew

2017-01-01

Recent evidence suggests that emotions have a distributed neural representation, which has significant implications for our understanding of the mechanisms underlying emotion regulation and dysregulation as well as the potential targets available for neuromodulation-based emotion therapeutics. This work adds to this evidence by testing the distribution of neural representations underlying the affective dimensions of valence and arousal using representational models that vary in both the degree and the nature of their distribution. We used multi-voxel pattern classification (MVPC) to identify whole-brain patterns of functional magnetic resonance imaging (fMRI)-derived neural activations that reliably predicted dimensional properties of affect (valence and arousal) for visual stimuli viewed by a normative sample (n = 32) of demographically diverse, healthy adults. Inter-subject leave-one-out cross-validation showed whole-brain MVPC significantly predicted (p < 0.001) binarized normative ratings of valence (positive vs. negative, 59% accuracy) and arousal (high vs. low, 56% accuracy). We also conducted group-level univariate general linear modeling (GLM) analyses to identify brain regions whose response significantly differed for the contrasts of positive versus negative valence or high versus low arousal. Multivoxel pattern classifiers using voxels drawn from all identified regions of interest (all-ROIs) exhibited mixed performance; arousal was predicted significantly better than chance but worse than the whole-brain classifier, whereas valence was not predicted significantly better than chance. Multivoxel classifiers derived using individual ROIs generally performed no better than chance. Although performance of the all-ROI classifier improved with larger ROIs (generated by relaxing the clustering threshold), performance was still poorer than the whole-brain classifier. These findings support a highly distributed model of neural processing for the affective dimensions of valence and arousal. Finally, joint error analyses of the MVPC hyperplanes encoding valence and arousal identified regions within the dimensional affect space where multivoxel classifiers exhibited the greatest difficulty encoding brain states – specifically, stimuli of moderate arousal and high or low valence. In conclusion, we highlight new directions for characterizing affective processing for mechanistic and therapeutic applications in affective neuroscience. PMID:28959198

Self-Organizing Maps-based ocean currents forecasting system.

PubMed

Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir

2016-03-16

An ocean surface currents forecasting system, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training.