WorldWideScience

Sample records for biological neural systems

  1. Biological neural networks as model systems for designing future parallel processing computers

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

    One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.

  2. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Science.gov (United States)

    Li, Yongcheng; Sun, Rong; Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  3. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  4. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  5. Biological channel modeling and implantable UWB antenna design for neural recording systems.

    Science.gov (United States)

    Bahrami, Hadi; Mirbozorgi, S Abdollah; Rusch, Leslie A; Gosselin, Benoit

    2015-01-01

    Ultrawideband (UWB) short-range communication systems have proved to be valuable in medical technology, particularly for implanted devices, due to their low-power consumption, low cost, small size, and high data rates. Neural activity monitoring in the brain requires high data rate (800 kb/s per neural sensor), and we target a system supporting a large number of sensors, in particular, aggregate transmission above 430 Mb/s (∼512 sensors). Knowledge of channel behavior is required to determine the maximum allowable power to 1) respect ANSI guidelines for avoiding tissue damage, and 2) respect FCC guidelines on unlicensed transmissions. We utilize a realistic model of the biological channel to inform the design of antennas for the implanted transmitter and the external receiver under these requirements. Antennas placement is examined under two scenarios having contrasting power constraints. Performance of the system within the biological tissues is examined via simulation and experiment. Our miniaturized antennas, 12 mm ×12 mm, need worst-case receiver sensitivities of -38 and -30.5 dBm for the first and second scenarios, respectively. These sensitivities allow us to successfully detect signals transmitted through tissues in the 3.1-10.6-GHz UWB band.

  6. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

    Science.gov (United States)

    Li, Yongcheng; Sun, Rong; Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

    2016-01-01

    We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

  7. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning. Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

  8. Application of Hierarchical Dissociated Neural Network in Closed-Loop Hybrid System Integrating Biological and Mechanical Intelligence

    Science.gov (United States)

    Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including ‘random’ and ‘4Q’ (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the ‘4Q’ cultures presented absolutely different activities, and the robot controlled by the ‘4Q’ network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems. PMID:25992579

  9. Exploring Neural Network Models with Hierarchical Memories and Their Use in Modeling Biological Systems

    Science.gov (United States)

    Pusuluri, Sai Teja

    Energy landscapes are often used as metaphors for phenomena in biology, social sciences and finance. Different methods have been implemented in the past for the construction of energy landscapes. Neural network models based on spin glass physics provide an excellent mathematical framework for the construction of energy landscapes. This framework uses a minimal number of parameters and constructs the landscape using data from the actual phenomena. In the past neural network models were used to mimic the storage and retrieval process of memories (patterns) in the brain. With advances in the field now, these models are being used in machine learning, deep learning and modeling of complex phenomena. Most of the past literature focuses on increasing the storage capacity and stability of stored patterns in the network but does not study these models from a modeling perspective or an energy landscape perspective. This dissertation focuses on neural network models both from a modeling perspective and from an energy landscape perspective. I firstly show how the cellular interconversion phenomenon can be modeled as a transition between attractor states on an epigenetic landscape constructed using neural network models. The model allows the identification of a reaction coordinate of cellular interconversion by analyzing experimental and simulation time course data. Monte Carlo simulations of the model show that the initial phase of cellular interconversion is a Poisson process and the later phase of cellular interconversion is a deterministic process. Secondly, I explore the static features of landscapes generated using neural network models, such as sizes of basins of attraction and densities of metastable states. The simulation results show that the static landscape features are strongly dependent on the correlation strength and correlation structure between patterns. Using different hierarchical structures of the correlation between patterns affects the landscape features

  10. Artificial Neural Networks, and Evolutionary Algorithms as a systems biology approach to a data-base on fetal growth restriction.

    Science.gov (United States)

    Street, Maria E; Buscema, Massimo; Smerieri, Arianna; Montanini, Luisa; Grossi, Enzo

    2013-12-01

    One of the specific aims of systems biology is to model and discover properties of cells, tissues and organisms functioning. A systems biology approach was undertaken to investigate possibly the entire system of intra-uterine growth we had available, to assess the variables of interest, discriminate those which were effectively related with appropriate or restricted intrauterine growth, and achieve an understanding of the systems in these two conditions. The Artificial Adaptive Systems, which include Artificial Neural Networks and Evolutionary Algorithms lead us to the first analyses. These analyses identified the importance of the biochemical variables IL-6, IGF-II and IGFBP-2 protein concentrations in placental lysates, and offered a new insight into placental markers of fetal growth within the IGF and cytokine systems, confirmed they had relationships and offered a critical assessment of studies previously performed. Copyright © 2013 Elsevier Ltd. All rights reserved.

  11. Systems Biology

    Indian Academy of Sciences (India)

    IAS Admin

    Systems biology seeks to study biological systems as a whole, contrary to the reductionist approach that has dominated biology. Such a view of biological systems emanating from strong foundations of molecular level understanding of the individual components in terms of their form, function and interactions is promising to ...

  12. Neural Systems Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...

  13. Models of neural networks temporal aspects of coding and information processing in biological systems

    CERN Document Server

    Hemmen, J; Schulten, Klaus

    1994-01-01

    Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has focused on two paradigms: information coding through coherent firing of the neurons and functional feedback. Information coding through coherent neuronal firing exploits time as a cardinal degree of freedom. This capacity of a neural network rests on the fact that the neuronal action potential is a short, say 1 ms, spike, localized in space and time. Spatial as well as temporal correlations of activity may represent different states of a network. In particular, temporal correlations of activity may express that neurons process the same "object" of, for example, a visual scene by spiking at the very same time. The traditional description of a neural network through a firing rate, the famous S-shaped curve, presupposes a wide time window of, say, at least 100 ms. It thus fails to exploit the capacity to "bind" sets of coherently firing neurons for the purpose of both scene segmentation and figure-ground segregatio...

  14. Systems Biology

    Energy Technology Data Exchange (ETDEWEB)

    Wiley, H S.

    2006-06-01

    The biology revolution over the last 50 years has been driven by the ascendancy of molecular biology. This was enthusiastically embraced by most biologists because it took us into increasingly familiar territory. It took mysterious processes, such as the replication of genetic material and assigned them parts that could be readily understood by the human mind. When we think of ''molecular machines'' as being the underlying basis of life, we are using a paradigm derived from everyday experience. However, the price that we paid was a relentless drive towards reductionism and the attendant balkanization of biology. Now along comes ''systems biology'' that promises us a solution to the problem of ''knowing more and more about less and less''. Unlike molecular biology, systems biology appears to be taking us into unfamiliar intellectual territory, such as statistics, mathematics and computer modeling. Not surprisingly, systems biology has met with widespread skepticism and resistance. Why do we need systems biology anyway and how does this new area of research promise to change the face of biology in the next couple of decades?

  15. Neural plasticity: the biological substrate for neurorehabilitation.

    Science.gov (United States)

    Warraich, Zuha; Kleim, Jeffrey A

    2010-12-01

    Decades of basic science have clearly demonstrated the capacity of the central nervous system (CNS) to structurally and functionally adapt in response to experience. The field of neurorehabilitation has begun to use this body of work to develop neurobiologically informed therapies that harness the key behavioral and neural signals that drive neural plasticity. The present review describes how neural plasticity supports both learning in the intact CNS and functional improvement in the damaged or diseased CNS. A pragmatic, interdisciplinary definition of neural plasticity is presented that may be used by both clinical and basic scientists studying neurorehabilitation. Furthermore, a description of how neural plasticity may act to drive different neural strategies underlying functional improvement after CNS injury or disease is provided. The understanding of the relationship between these different neural strategies, mechanisms of neural plasticity, and changes in behavior may facilitate the development of novel, more effective rehabilitation interventions. Copyright © 2010 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

  16. Neural Expert Systems

    Czech Academy of Sciences Publication Activity Database

    Šíma, Jiří

    1995-01-01

    Roč. 8, č. 2 (1995), s. 261-271 ISSN 0893-6080 R&D Projects: GA ČR GA201/95/0976 Keywords : expert system * knowledge representation * multilayered neural network * back propagation * interval neuron function * incomplete information * explanation Impact factor: 1.262, year: 1995

  17. Neural Flight Control System

    Science.gov (United States)

    Gundy-Burlet, Karen

    2003-01-01

    The Neural Flight Control System (NFCS) was developed to address the need for control systems that can be produced and tested at lower cost, easily adapted to prototype vehicles and for flight systems that can accommodate damaged control surfaces or changes to aircraft stability and control characteristics resulting from failures or accidents. NFCS utilizes on a neural network-based flight control algorithm which automatically compensates for a broad spectrum of unanticipated damage or failures of an aircraft in flight. Pilot stick and rudder pedal inputs are fed into a reference model which produces pitch, roll and yaw rate commands. The reference model frequencies and gains can be set to provide handling quality characteristics suitable for the aircraft of interest. The rate commands are used in conjunction with estimates of the aircraft s stability and control (S&C) derivatives by a simplified Dynamic Inverse controller to produce virtual elevator, aileron and rudder commands. These virtual surface deflection commands are optimally distributed across the aircraft s available control surfaces using linear programming theory. Sensor data is compared with the reference model rate commands to produce an error signal. A Proportional/Integral (PI) error controller "winds up" on the error signal and adds an augmented command to the reference model output with the effect of zeroing the error signal. In order to provide more consistent handling qualities for the pilot, neural networks learn the behavior of the error controller and add in the augmented command before the integrator winds up. In the case of damage sufficient to affect the handling qualities of the aircraft, an Adaptive Critic is utilized to reduce the reference model frequencies and gains to stay within a flyable envelope of the aircraft.

  18. Neural network models: from biology to many - body phenomenology

    International Nuclear Information System (INIS)

    Clark, J.W.

    1993-01-01

    Theoretical work in neural networks has a strange feel for most physicists. In some cases the aspect of design becomes paramount. More comfortable ground at least for many body theorists may be found in realistic biological simulation, although the complexity of most problems is so awesome that incisive results will be hard won. It has also shown the impressive capabilities of artificial networks in pattern recognition and classification may be exploited to solve management problems in experimental physics and for discovery of radically new theoretical description of physical systems. This advance represents an important step towards the ultimate goal of neuro biological paradigm. (A.B.)

  19. Seminars in Cell and Developmental Biology Model Systems for the Study of Heart Development and Disease Cardiac Neural Crest and Conotruncal Malformations

    Science.gov (United States)

    Hutson, Mary R.; Kirby, Margaret L.

    2007-01-01

    Neural crest cells are multipotential cells that delaminate from the dorsal neural tube and migrate widely throughout the body. A subregion of the cranial neural crest originating between the otocyst and somite 3 has been called “cardiac neural crest” because of the importance of these cells in heart development. Much of what we know about the contribution and function of the cardiac neural crest in cardiovascular development has been learned in the chick embryo using quail-chick chimeras to study neural crest migration and derivatives and using ablation of premigratory neural crest cells to study their function. These studies show that cardiac crest cells are absolutely required to form the aorticopulmonary septum dividing the cardiac arterial pole into systemic and pulmonary circulations. They support the normal development and patterning of derivatives of the caudal pharyngeal arches and pouches, including the great arteries and the thymus, thyroid and parathyroids. Recently, cardiac neural crest cells have been shown to modulate signaling in the pharynx during the lengthening of the outflow tract by the secondary heart field. Most of the genes associated with cardiac neural crest function have been identified using mouse models. These studies show that the neural crest cells may not be the direct cause of abnormal cardiovascular development but they are a major component in the complex tissue interactions in the caudal pharynx and outflow tract. Since cardiac neural crest cells span from the caudal pharynx into the outflow tract, they are especially susceptible to any perturbation in or by other cells in these regions. Thus, understanding congenital cardiac outflow malformations in human sequences of malformations as represented by the DiGeorge syndrome will necessarily require understanding development of the cardiac neural crest. PMID:17224285

  20. neural control system

    International Nuclear Information System (INIS)

    Elshazly, A.A.E.

    2002-01-01

    Automatic power stabilization control is the desired objective for any reactor operation , especially, nuclear power plants. A major problem in this area is inevitable gap between a real plant ant the theory of conventional analysis and the synthesis of linear time invariant systems. in particular, the trajectory tracking control of a nonlinear plant is a class of problems in which the classical linear transfer function methods break down because no transfer function can represent the system over the entire operating region . there is a considerable amount of research on the model-inverse approach using feedback linearization technique. however, this method requires a prices plant model to implement the exact linearizing feedback, for nuclear reactor systems, this approach is not an easy task because of the uncertainty in the plant parameters and un-measurable state variables . therefore, artificial neural network (ANN) is used either in self-tuning control or in improving the conventional rule-based exper system.the main objective of this thesis is to suggest an ANN, based self-learning controller structure . this method is capable of on-line reinforcement learning and control for a nuclear reactor with a totally unknown dynamics model. previously, researches are based on back- propagation algorithm . back -propagation (BP), fast back -propagation (FBP), and levenberg-marquardt (LM), algorithms are discussed and compared for reinforcement learning. it is found that, LM algorithm is quite superior

  1. Insights into neural stem cell biology from flies.

    Science.gov (United States)

    Egger, Boris; Chell, James M; Brand, Andrea H

    2008-01-12

    Drosophila neuroblasts are similar to mammalian neural stem cells in their ability to self-renew and to produce many different types of neurons and glial cells. In the past two decades, great advances have been made in understanding the molecular mechanisms underlying embryonic neuroblast formation, the establishment of cell polarity and the temporal regulation of cell fate. It is now a challenge to connect, at the molecular level, the different cell biological events underlying the transition from neural stem cell maintenance to differentiation. Progress has also been made in understanding the later stages of development, when neuroblasts become mitotically inactive, or quiescent, and are then reactivated postembryonically to generate the neurons that make up the adult nervous system. The ability to manipulate the steps leading from quiescence to proliferation and from proliferation to differentiation will have a major impact on the treatment of neurological injury and neurodegenerative disease.

  2. From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

    The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

  3. Neural crest cells: from developmental biology to clinical interventions.

    Science.gov (United States)

    Noisa, Parinya; Raivio, Taneli

    2014-09-01

    Neural crest cells are multipotent cells, which are specified in embryonic ectoderm in the border of neural plate and epiderm during early development by interconnection of extrinsic stimuli and intrinsic factors. Neural crest cells are capable of differentiating into various somatic cell types, including melanocytes, craniofacial cartilage and bone, smooth muscle, and peripheral nervous cells, which supports their promise for cell therapy. In this work, we provide a comprehensive review of wide aspects of neural crest cells from their developmental biology to applicability in medical research. We provide a simplified model of neural crest cell development and highlight the key external stimuli and intrinsic regulators that determine the neural crest cell fate. Defects of neural crest cell development leading to several human disorders are also mentioned, with the emphasis of using human induced pluripotent stem cells to model neurocristopathic syndromes. © 2014 Wiley Periodicals, Inc.

  4. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

    ... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...

  5. Biological conversion system

    Science.gov (United States)

    Scott, C.D.

    A system for bioconversion of organic material comprises a primary bioreactor column wherein a biological active agent (zymomonas mobilis) converts the organic material (sugar) to a product (alcohol), a rejuvenator column wherein the biological activity of said biological active agent is enhanced, and means for circulating said biological active agent between said primary bioreactor column and said rejuvenator column.

  6. Biological Systems Thinking for Control Engineering Design

    Directory of Open Access Journals (Sweden)

    D. J. Murray-Smith

    2004-01-01

    Full Text Available Artificial neural networks and genetic algorithms are often quoted in discussions about the contribution of biological systems thinking to engineering design. This paper reviews work on the neuromuscular system, a field in which biological systems thinking could make specific contributions to the development and design of automatic control systems for mechatronics and robotics applications. The paper suggests some specific areas in which a better understanding of this biological control system could be expected to contribute to control engineering design methods in the future. Particular emphasis is given to the nonlinear nature of elements within the neuromuscular system and to processes of neural signal processing, sensing and system adaptivity. Aspects of the biological system that are of particular significance for engineering control systems include sensor fusion, sensor redundancy and parallelism, together with advanced forms of signal processing for adaptive and learning control. 

  7. Biologically Inspired Modular Neural Control for a Leg-Wheel Hybrid Robot

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Wörgötter, Florentin; Laksanacharoen, Pudit

    2014-01-01

    In this article we present modular neural control for a leg-wheel hybrid robot consisting of three legs with omnidirectional wheels. This neural control has four main modules having their functional origin in biological neural systems. A minimal recurrent control (MRC) module is for sensory signal...... processing and state memorization. Its outputs drive two front wheels while the rear wheel is controlled through a velocity regulating network (VRN) module. In parallel, a neural oscillator network module serves as a central pattern generator (CPG) controls leg movements for sidestepping. Stepping directions...... are achieved by a phase switching network (PSN) module. The combination of these modules generates various locomotion patterns and a reactive obstacle avoidance behavior. The behavior is driven by sensor inputs, to which additional neural preprocessing networks are applied. The complete neural circuitry...

  8. Biologically-inspired Learning in Pulsed Neural Networks

    DEFF Research Database (Denmark)

    Lehmann, Torsten; Woodburn, Robin

    1999-01-01

    Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the `biologically-inspired' approach......, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks. We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...... that useful conclusions as to the future of on-chip learning can be drawn from this work....

  9. 2D neural hardware versus 3D biological ones

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-12-31

    This paper will present important limitations of hardware neural nets as opposed to biological neural nets (i.e. the real ones). The author starts by discussing neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural nets. Going further, the focus will be on hardware constraints. The author will present recent results for three different alternatives of implementing neural networks: digital, threshold gate, and analog, while the area and the delay will be related to neurons' fan-in and weights' precision. Based on all of these, it will be shown why hardware implementations cannot cope with their biological inspiration with respect to their power of computation: the mapping onto silicon lacking the third dimension of biological nets. This translates into reduced fan-in, and leads to reduced precision. The main conclusion is that one is faced with the following alternatives: (1) try to cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow one to use the third dimension, e.g. using optical interconnections.

  10. Systems Biology of Metabolism.

    Science.gov (United States)

    Nielsen, Jens

    2017-06-20

    Metabolism is highly complex and involves thousands of different connected reactions; it is therefore necessary to use mathematical models for holistic studies. The use of mathematical models in biology is referred to as systems biology. In this review, the principles of systems biology are described, and two different types of mathematical models used for studying metabolism are discussed: kinetic models and genome-scale metabolic models. The use of different omics technologies, including transcriptomics, proteomics, metabolomics, and fluxomics, for studying metabolism is presented. Finally, the application of systems biology for analyzing global regulatory structures, engineering the metabolism of cell factories, and analyzing human diseases is discussed.

  11. Neural System Prediction and Identification Challenge

    Directory of Open Access Journals (Sweden)

    Ioannis eVlachos

    2013-12-01

    Full Text Available Can we infer the function of a biological neural network (BNN if we know the connectivity and activity of all its constituent neurons? This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC. We provide the connectivity and activity of all neurons and invite participants (i to infer the functions implemented (hard-wired in spiking neural networks (SNNs by stimulating and recording the activity of neurons and, (ii to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

  12. Neural system prediction and identification challenge.

    Science.gov (United States)

    Vlachos, Ioannis; Zaytsev, Yury V; Spreizer, Sebastian; Aertsen, Ad; Kumar, Arvind

    2013-01-01

    Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons?This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

  13. Neural stem cell biology in vertebrates and invertebrates: more alike than different?

    Science.gov (United States)

    Brand, Andrea H; Livesey, Frederick J

    2011-05-26

    Many of the regulatory mechanisms controlling neural stem cell behavior are proving to be conserved between organisms as diverse as worms and man. Common principles are emerging with respect to the regulation of neural stem cell division and the specification of distinct stem and progenitor cell types. Great progress has been made in recent years in identifying the cellular mechanisms underpinning these processes, thanks in large part to the cross-fertilization of research on different model systems. We review here recent findings that highlight hitherto unappreciated similarities in the cell and molecular biology of neural stem cell self-renewal and differentiation between invertebrates and vertebrates. As well as underscoring the possible conservation of stem cell mechanisms across phyla, these similarities are proving to be practically useful in studying neural stem cell biology in health and disease. Copyright © 2011 Elsevier Inc. All rights reserved.

  14. Teaching systems biology.

    Science.gov (United States)

    Alves, R; Vilaprinyo, E; Sorribas, A

    2011-03-01

    Advances in systems biology are increasingly dependent upon the integration of various types of data and different methodologies to reconstruct how cells work at the systemic level. Thus, teams with a varied array of expertise and people with interdisciplinary training are needed. So far this training was thought to be more productive if aimed at the Masters or PhD level. At this level, multiple specialised and in-depth courses on the different subject matters of systems biology are taught to already well-prepared students. This approach is mostly based on the recognition that systems biology requires a wide background that is hard to find in undergraduate students. Nevertheless, and given the importance of the field, the authors argue that exposition of undergraduate students to the methods and paradigms of systems biology would be advantageous. Here they present and discuss a successful experiment in teaching systems biology to third year undergraduate biotechnology students at the University of Lleida in Spain. The authors' experience, together with that from others, argues for the adequateness of teaching systems biology at the undergraduate level. [Includes supplementary material].

  15. Spiking Neural P Systems With Scheduled Synapses.

    Science.gov (United States)

    Cabarle, Francis George C; Adorna, Henry N; Jiang, Min; Zeng, Xiangxiang

    2017-12-01

    Spiking neural P systems (SN P systems) are models of computation inspired by biological spiking neurons. SN P systems have neurons as spike processors, which are placed on the nodes of a directed and static graph (the edges in the graph are the synapses). In this paper, we introduce a variant called SN P systems with scheduled synapses (SSN P systems). SSN P systems are inspired and motivated by the structural dynamism of biological synapses, while incorporating ideas from nonstatic (i.e., dynamic) graphs and networks. In particular, synapses in SSN P systems are available only at specific durations according to their schedules. The SSN P systems model is a response to the problem of introducing durations to synapses of SN P systems. Since SN P systems are in essence static graphs, it is natural to consider them for dynamic graphs also. We introduce local and global schedule types, also taking inspiration from the above-mentioned sources. We prove that SSN P systems are computationally universal as number generators and acceptors for both schedule types, under a normal form (i.e., a simplifying set of restrictions). The introduction of synapse schedules for either schedule type proves useful in programming the system, despite restrictions in the normal form.

  16. Spiking Neural P Systems with Communication on Request.

    Science.gov (United States)

    Pan, Linqiang; Păun, Gheorghe; Zhang, Gexiang; Neri, Ferrante

    2017-12-01

    Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.

  17. The LILARTI neural network system

    Energy Technology Data Exchange (ETDEWEB)

    Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.

    1992-10-01

    The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.

  18. Systems Biology and Health Systems Complexity in;

    NARCIS (Netherlands)

    Donald Combs, C.; Barham, S.R.; Sloot, P.M.A.

    2016-01-01

    Systems biology addresses interactions in biological systems at different scales of biological organization, from the molecular to the cellular, organ, organism, societal, and ecosystem levels. This chapter expands on the concept of systems biology, explores its implications for individual patients

  19. Nutritional Systems Biology

    DEFF Research Database (Denmark)

    Jensen, Kasper

    and network biology has the potential to increase our understanding of how small molecules affect metabolic pathways and homeostasis, how this perturbation changes at the disease state, and to what extent individual genotypes contribute to this. A fruitful strategy in approaching and exploring the field...... biology research. The paper also shows as a proof-of-concept that a systems biology approach to diet is meaningful and demonstrates some basic principles on how to work with diet systematic. The second chapter of this thesis we developed the resource NutriChem v1.0. A foodchemical database linking...... sites of diet on the disease pathway. We propose a framework for interrogating the critical targets in colon cancer process and identifying plant-based dietary interventions as important modifiers using a systems chemical biology approach. The fifth chapter of the thesis is on discovering of novel anti...

  20. Learning in Artificial Neural Systems

    Science.gov (United States)

    Matheus, Christopher J.; Hohensee, William E.

    1987-01-01

    This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

  1. Plant Systems Biology (editorial)

    Science.gov (United States)

    In June 2003, Plant Physiology published an Arabidopsis special issue devoted to plant systems biology. The intention of Natasha Raikhel and Gloria Coruzzi, the two editors of this first-of-its-kind issue, was ‘‘to help nucleate this new effort within the plant community’’ as they considered that ‘‘...

  2. Systems biology at work

    NARCIS (Netherlands)

    Martins Dos Santos, V.A.P.; Damborsky, J.

    2010-01-01

    In his editorial overview for the 2008 Special Issue on this topic, the late Jaroslav Stark pointedly noted that systems biology is no longer a niche pursuit, but a recognized discipline in its own right “noisily” coming of age [1]. Whilst general underlying principles and basic techniques are now

  3. Neural network based system for equipment surveillance

    Science.gov (United States)

    Vilim, R.B.; Gross, K.C.; Wegerich, S.W.

    1998-04-28

    A method and system are disclosed for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process. 33 figs.

  4. Neural Control of the Immune System

    Science.gov (United States)

    Sundman, Eva; Olofsson, Peder S.

    2014-01-01

    Neural reflexes support homeostasis by modulating the function of organ systems. Recent advances in neuroscience and immunology have revealed that neural reflexes also regulate the immune system. Activation of the vagus nerve modulates leukocyte cytokine production and alleviates experimental shock and autoimmune disease, and recent data have…

  5. 7th Annual Systems Biology Symposium: Systems Biology and Engineering

    Energy Technology Data Exchange (ETDEWEB)

    Galitski, Timothy P.

    2008-04-01

    Systems biology recognizes the complex multi-scale organization of biological systems, from molecules to ecosystems. The International Symposium on Systems Biology has been hosted by the Institute for Systems Biology in Seattle, Washington, since 2002. The annual two-day event gathers the most influential researchers transforming biology into an integrative discipline investingating complex systems. Engineering and application of new technology is a central element of systems biology. Genome-scale, or very small-scale, biological questions drive the enigneering of new technologies, which enable new modes of experimentation and computational analysis, leading to new biological insights and questions. Concepts and analytical methods in engineering are now finding direct applications in biology. Therefore, the 2008 Symposium, funded in partnership with the Department of Energy, featured global leaders in "Systems Biology and Engineering."

  6. Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.

    Science.gov (United States)

    Kriegeskorte, Nikolaus

    2015-11-24

    Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

  7. Integrative radiation systems biology

    International Nuclear Information System (INIS)

    Unger, Kristian

    2014-01-01

    Maximisation of the ratio of normal tissue preservation and tumour cell reduction is the main concept of radiotherapy alone or combined with chemo-, immuno- or biologically targeted therapy. The foremost parameter influencing this ratio is radiation sensitivity and its modulation towards a more efficient killing of tumour cells and a better preservation of normal tissue at the same time is the overall aim of modern therapy schemas. Nevertheless, this requires a deep understanding of the molecular mechanisms of radiation sensitivity in order to identify its key players as potential therapeutic targets. Moreover, the success of conventional approaches that tried to statistically associate altered radiation sensitivity with any molecular phenotype such as gene expression proofed to be somewhat limited since the number of clinically used targets is rather sparse. However, currently a paradigm shift is taking place from pure frequentistic association analysis to the rather holistic systems biology approach that seeks to mathematically model the system to be investigated and to allow the prediction of an altered phenotype as the function of one single or a signature of biomarkers. Integrative systems biology also considers the data from different molecular levels such as the genome, transcriptome or proteome in order to partially or fully comprehend the causal chain of molecular mechanisms. An example for the application of this concept currently carried out at the Clinical Cooperation Group “Personalized Radiotherapy in Head and Neck Cancer” of the Helmholtz-Zentrum München and the LMU Munich is described. This review article strives for providing a compact overview on the state of the art of systems biology, its actual challenges, potential applications, chances and limitations in radiation oncology research working towards improved personalised therapy concepts using this relatively new methodology

  8. Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

    Science.gov (United States)

    Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

    2015-01-01

    Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis.

  9. Echoes in correlated neural systems

    International Nuclear Information System (INIS)

    Helias, M; Tetzlaff, T; Diesmann, M

    2013-01-01

    Correlations are employed in modern physics to explain microscopic and macroscopic phenomena, like the fractional quantum Hall effect and the Mott insulator state in high temperature superconductors and ultracold atoms. Simultaneously probed neurons in the intact brain reveal correlations between their activity, an important measure to study information processing in the brain that also influences the macroscopic signals of neural activity, like the electroencephalogram (EEG). Networks of spiking neurons differ from most physical systems: the interaction between elements is directed, time delayed, mediated by short pulses and each neuron receives events from thousands of neurons. Even the stationary state of the network cannot be described by equilibrium statistical mechanics. Here we develop a quantitative theory of pairwise correlations in finite-sized random networks of spiking neurons. We derive explicit analytic expressions for the population-averaged cross correlation functions. Our theory explains why the intuitive mean field description fails, how the echo of single action potentials causes an apparent lag of inhibition with respect to excitation and how the size of the network can be scaled while maintaining its dynamical state. Finally, we derive a new criterion for the emergence of collective oscillations from the spectrum of the time-evolution propagator. (paper)

  10. Systems biology in animal sciences

    NARCIS (Netherlands)

    Woelders, H.; Pas, te M.F.W.; Bannink, A.; Veerkamp, R.F.; Smits, M.A.

    2011-01-01

    Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes

  11. Logical analysis of biological systems

    DEFF Research Database (Denmark)

    Mardare, Radu Iulian

    2005-01-01

    R. Mardare, Logical analysis of biological systems. Fundamenta Informaticae, N 64:271-285, 2005.......R. Mardare, Logical analysis of biological systems. Fundamenta Informaticae, N 64:271-285, 2005....

  12. Stable Neural Control of Uncertain Multivariable Systems

    National Research Council Canada - National Science Library

    Mears, Mark

    2001-01-01

    ... that the trajectories remain bonded. Lyapunov analysis is used to derive equations for the sliding mode control, neural network training, and to show uniform ultimate boundedness of the closed loop systems...

  13. Constraints of Biological Neural Networks and Their Consideration in AI Applications

    Directory of Open Access Journals (Sweden)

    Richard Stafford

    2010-01-01

    Full Text Available Biological organisms do not evolve to perfection, but to out compete others in their ecological niche, and therefore survive and reproduce. This paper reviews the constraints imposed on imperfect organisms, particularly on their neural systems and ability to capture and process information accurately. By understanding biological constraints of the physical properties of neurons, simpler and more efficient artificial neural networks can be made (e.g., spiking networks will transmit less information than graded potential networks, spikes only occur in nature due to limitations of carrying electrical charges over large distances. Furthermore, understanding the behavioural and ecological constraints on animals allows an understanding of the limitations of bio-inspired solutions, but also an understanding of why bio-inspired solutions may fail and how to correct these failures.

  14. A biologically inspired neural network controller for ballistic arm movements

    Directory of Open Access Journals (Sweden)

    Schmid Maurizio

    2007-09-01

    Full Text Available Abstract Background In humans, the implementation of multijoint tasks of the arm implies a highly complex integration of sensory information, sensorimotor transformations and motor planning. Computational models can be profitably used to better understand the mechanisms sub-serving motor control, thus providing useful perspectives and investigating different control hypotheses. To this purpose, the use of Artificial Neural Networks has been proposed to represent and interpret the movement of upper limb. In this paper, a neural network approach to the modelling of the motor control of a human arm during planar ballistic movements is presented. Methods The developed system is composed of three main computational blocks: 1 a parallel distributed learning scheme that aims at simulating the internal inverse model in the trajectory formation process; 2 a pulse generator, which is responsible for the creation of muscular synergies; and 3 a limb model based on two joints (two degrees of freedom and six muscle-like actuators, that can accommodate for the biomechanical parameters of the arm. The learning paradigm of the neural controller is based on a pure exploration of the working space with no feedback signal. Kinematics provided by the system have been compared with those obtained in literature from experimental data of humans. Results The model reproduces kinematics of arm movements, with bell-shaped wrist velocity profiles and approximately straight trajectories, and gives rise to the generation of synergies for the execution of movements. The model allows achieving amplitude and direction errors of respectively 0.52 cm and 0.2 radians. Curvature values are similar to those encountered in experimental measures with humans. The neural controller also manages environmental modifications such as the insertion of different force fields acting on the end-effector. Conclusion The proposed system has been shown to properly simulate the development of

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

    Science.gov (United States)

    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.

  16. Theory of Neural Information Processing Systems

    International Nuclear Information System (INIS)

    Galla, Tobias

    2006-01-01

    It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 10 11 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kuehn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the

  17. Advanced models of neural networks nonlinear dynamics and stochasticity in biological neurons

    CERN Document Server

    Rigatos, Gerasimos G

    2015-01-01

    This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory. It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.

  18. Optimal synchronization in small-world biological neural networks with time-varying weights

    International Nuclear Information System (INIS)

    Zheng Hongyu; Luo Xiaoshu

    2009-01-01

    In this paper, a new model of small-world biological neural networks based on biophysical Hodgkin-Huxley neurons with time-varying weights is proposed. Then the synchronization phenomenon of small-world biological neural networks evoked by the learning rate is studied. The study shows that there exists an optimal synchronization state by changing the learning rate.

  19. The Physics behind Systems Biology

    Directory of Open Access Journals (Sweden)

    Radde Nicole E.

    2016-12-01

    Full Text Available Systems Biology is a young and rapidly evolving research field, which combines experimental techniques and mathematical modeling in order to achieve a mechanistic understanding of processes underlying the regulation and evolution of living systems. Systems Biology is often associated with an Engineering approach: The purpose is to formulate a data-rich, detailed simulation model that allows to perform numerical (‘in silico’ experiments and then draw conclusions about the biological system. While methods from Engineering may be an appropriate approach to extending the scope of biological investigations to experimentally inaccessible realms and to supporting data-rich experimental work, it may not be the best strategy in a search for design principles of biological systems and the fundamental laws underlying Biology. Physics has a long tradition of characterizing and understanding emergent collective behaviors in systems of interacting units and searching for universal laws. Therefore, it is natural that many concepts used in Systems Biology have their roots in Physics. With an emphasis on Theoretical Physics, we will here review the ‘Physics core’ of Systems Biology, show how some success stories in Systems Biology can be traced back to concepts developed in Physics, and discuss how Systems Biology can further benefit from its Theoretical Physics foundation.

  20. Application of neural networks in CRM systems

    Directory of Open Access Journals (Sweden)

    Bojanowska Agnieszka

    2017-01-01

    Full Text Available The central aim of this study is to investigate how to apply artificial neural networks in Customer Relationship Management (CRM. The paper presents several business applications of neural networks in software systems designed to aid CRM, e.g. in deciding on the profitability of building a relationship with a given customer. Furthermore, a framework for a neural-network based CRM software tool is developed. Building beneficial relationships with customers is generating considerable interest among various businesses, and is often mentioned as one of the crucial objectives of enterprises, next to their key aim: to bring satisfactory profit. There is a growing tendency among businesses to invest in CRM systems, which together with an organisational culture of a company aid managing customer relationships. It is the sheer amount of gathered data as well as the need for constant updating and analysis of this breadth of information that may imply the suitability of neural networks for the application in question. Neural networks exhibit considerably higher computational capabilities than sequential calculations because the solution to a problem is obtained without the need for developing a special algorithm. In the majority of presented CRM applications neural networks constitute and are presented as a managerial decision-taking optimisation tool.

  1. Mammalian Synthetic Biology: Engineering Biological Systems.

    Science.gov (United States)

    Black, Joshua B; Perez-Pinera, Pablo; Gersbach, Charles A

    2017-06-21

    The programming of new functions into mammalian cells has tremendous application in research and medicine. Continued improvements in the capacity to sequence and synthesize DNA have rapidly increased our understanding of mechanisms of gene function and regulation on a genome-wide scale and have expanded the set of genetic components available for programming cell biology. The invention of new research tools, including targetable DNA-binding systems such as CRISPR/Cas9 and sensor-actuator devices that can recognize and respond to diverse chemical, mechanical, and optical inputs, has enabled precise control of complex cellular behaviors at unprecedented spatial and temporal resolution. These tools have been critical for the expansion of synthetic biology techniques from prokaryotic and lower eukaryotic hosts to mammalian systems. Recent progress in the development of genome and epigenome editing tools and in the engineering of designer cells with programmable genetic circuits is expanding approaches to prevent, diagnose, and treat disease and to establish personalized theranostic strategies for next-generation medicines. This review summarizes the development of these enabling technologies and their application to transforming mammalian synthetic biology into a distinct field in research and medicine.

  2. Electromagnetic fields in biological systems

    National Research Council Canada - National Science Library

    Lin, James C

    2012-01-01

    "Focusing on exposure, induced fields, and absorbed energy, this volume covers the interaction of electromagnetic fields and waves with biological systems, spanning static fields to terahertz waves...

  3. Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control.

    Science.gov (United States)

    Kocaturk, Mehmet; Gulcur, Halil Ozcan; Canbeyli, Resit

    2015-01-01

    In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain-machine interface (BMI) controllers, which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons, which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two-target reaching task in one-dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based, and all-in-one solution for developing neurally inspired BMI controllers. We believe that the BNDE is the first implementation, which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.

  4. Mapping biological systems to network systems

    CERN Document Server

    Rathore, Heena

    2016-01-01

    The book presents the challenges inherent in the paradigm shift of network systems from static to highly dynamic distributed systems – it proposes solutions that the symbiotic nature of biological systems can provide into altering networking systems to adapt to these changes. The author discuss how biological systems – which have the inherent capabilities of evolving, self-organizing, self-repairing and flourishing with time – are inspiring researchers to take opportunities from the biology domain and map them with the problems faced in network domain. The book revolves around the central idea of bio-inspired systems -- it begins by exploring why biology and computer network research are such a natural match. This is followed by presenting a broad overview of biologically inspired research in network systems -- it is classified by the biological field that inspired each topic and by the area of networking in which that topic lies. Each case elucidates how biological concepts have been most successfully ...

  5. [A method of recognizing biology surface spectrum using cascade-connection artificial neural nets].

    Science.gov (United States)

    Shi, Wei-Jie; Yao, Yong; Zhang, Tie-Qiang; Meng, Xian-Jiang

    2008-05-01

    A method of recognizing the visible spectrum of micro-areas on the biological surface with cascade-connection artificial neural nets is presented in the present paper. The visible spectra of spots on apples' pericarp, ranging from 500 to 730 nm, were obtained with a fiber-probe spectrometer, and a new spectrum recognition system consisting of three-level cascade-connection neural nets was set up. The experiments show that the spectra of rotten, scar and bumped spot on an apple's pericarp can be recognized by the spectrum recognition system, and the recognition accuracy is higher than 85% even when noise level is 15%. The new recognition system overcomes the disadvantages of poor accuracy and poor anti-noise with the traditional system based on single cascade neural nets. Finally, a new method of expression of recognition results was proved. The method is based on the conception of degree of membership in fuzzing mathematics, and through it the recognition results can be expressed exactly and objectively.

  6. Kannada character recognition system using neural network

    Science.gov (United States)

    Kumar, Suresh D. S.; Kamalapuram, Srinivasa K.; Kumar, Ajay B. R.

    2013-03-01

    Handwriting recognition has been one of the active and challenging research areas in the field of pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. As there is no sufficient number of works on Indian language character recognition especially Kannada script among 15 major scripts in India. In this paper an attempt is made to recognize handwritten Kannada characters using Feed Forward neural networks. A handwritten Kannada character is resized into 20x30 Pixel. The resized character is used for training the neural network. Once the training process is completed the same character is given as input to the neural network with different set of neurons in hidden layer and their recognition accuracy rate for different Kannada characters has been calculated and compared. The results show that the proposed system yields good recognition accuracy rates comparable to that of other handwritten character recognition systems.

  7. Telemetry System of Biological Parameters

    Directory of Open Access Journals (Sweden)

    Jan Spisak

    2005-01-01

    Full Text Available The mobile telemetry system of biological parameters serves for reading and wireless data transfer of measured values of selected biological parameters to an outlying computer. It concerns basically long time monitoring of vital function of car pilot.The goal of this projects is to propose mobile telemetry system for reading, wireless transfer and processing of biological parameters of car pilot during physical and psychical stress. It has to be made with respect to minimal consumption, weight and maximal device mobility. This system has to eliminate signal noise, which is created by biological artifacts and disturbances during the data transfer.

  8. Excited states in biological systems

    International Nuclear Information System (INIS)

    Cilento, G.; Zinner, K.; Bechara, E.J.H.; Duran, N.; Baptista, R.C. de; Shimizu, Y.; Augusto, O.; Faljoni-Alario, A.; Vidigal, C.C.C.; Oliveira, O.M.M.F.; Haun, M.

    1979-01-01

    Some aspects of bioluminescence related to bioenergetics are discussed: 1. chemical generation of excited species, by means of two general processes: electron transference and cyclic - and linear peroxide cleavage; 2. biological systems capable of generating excited states and 3. biological functions of these states, specially the non-emissive ones (tripletes). The production and the role of non-emissive excited states in biological systems are analysed, the main purpose of the study being the search for non-emissive states. Experiences carried out in biological systems are described; results and conclusions are given. (M.A.) [pt

  9. Biologically plausible learning in neural networks with modulatory feedback.

    Science.gov (United States)

    Grant, W Shane; Tanner, James; Itti, Laurent

    2017-04-01

    Although Hebbian learning has long been a key component in understanding neural plasticity, it has not yet been successful in modeling modulatory feedback connections, which make up a significant portion of connections in the brain. We develop a new learning rule designed around the complications of learning modulatory feedback and composed of three simple concepts grounded in physiologically plausible evidence. Using border ownership as a prototypical example, we show that a Hebbian learning rule fails to properly learn modulatory connections, while our proposed rule correctly learns a stimulus-driven model. To the authors' knowledge, this is the first time a border ownership network has been learned. Additionally, we show that the rule can be used as a drop-in replacement for a Hebbian learning rule to learn a biologically consistent model of orientation selectivity, a network which lacks any modulatory connections. Our results predict that the mechanisms we use are integral for learning modulatory connections in the brain and furthermore that modulatory connections have a strong dependence on inhibition. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  10. Integrated Neural Flight and Propulsion Control System

    Science.gov (United States)

    Kaneshige, John; Gundy-Burlet, Karen; Norvig, Peter (Technical Monitor)

    2001-01-01

    This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.

  11. A neural systems analysis of adaptive navigation.

    Science.gov (United States)

    Mizumori, S J; Cooper, B G; Leutgeb, S; Pratt, W E

    2000-01-01

    In the field of the neurobiology of learning, significant emphasis has been placed on understanding neural plasticity within a single structure (or synapse type) as it relates to a particular type of learning mediated by a particular brain area. To appreciate fully the breadth of the plasticity responsible for complex learning phenomena, it is imperative that we also examine the neural mechanisms of the behavioral instantiation of learned information, how motivational systems interact, and how past memories affect the learning process. To address this issue, we describe a model of complex learning (rodent adaptive navigation) that could be used to study dynamically interactive neural systems. Adaptive navigation depends on the efficient integration of external and internal sensory information with motivational systems to arrive at the most effective cognitive and/or behavioral strategies. We present evidence consistent with the view that during navigation: 1) the limbic thalamus and limbic cortex is primarily responsible for the integration of current and expected sensory information, 2) the hippocampal-septal-hypothalamic system provides a mechanism whereby motivational perspectives bias sensory processing, and 3) the amygdala-prefrontal-striatal circuit allows animals to evaluate the expected reinforcement consequences of context-dependent behavioral responses. Although much remains to be determined regarding the nature of the interactions among neural systems, new insights have emerged regarding the mechanisms that underlie flexible and adaptive behavioral responses.

  12. Systems Biology and Livestock Science

    NARCIS (Netherlands)

    Pas, te M.F.W.; Woelders, H.; Bannink, A.

    2011-01-01

    Systems Biology is an interdisciplinary approach to the study of life made possible through the explosion of molecular data made available through the genome revolution and the simultaneous development of computational technologies that allow us to interpret these large data sets. Systems Biology

  13. Static Analysis for Systems Biology

    DEFF Research Database (Denmark)

    Nielson, Flemming; Nielson, Hanne Riis; Rosa, D. Schuch da

    2004-01-01

    This paper shows how static analysis techniques can help understanding biological systems. Based on a simple example we illustrate the outcome of performing three different analyses extracting information of increasing precision. We conclude by reporting on the potential impact and exploitation o...... of these techniques in systems biology....

  14. Nonlinear behavior in small neural systems

    Science.gov (United States)

    Wheeler, Diek Winters

    This work addresses the nonlinear behavior of one or two model neurons under the influence of different stimuli, whether they be forms of chaos control or varieties of added noise. This is a step towards the ultimate objective of exploring the notion that a neural system might utilize a mechanism such as a memory-searching chaotic attractor to locate and retrieve stable-memory limit cycles. The biological realism of the Hopfield neuron models is discussed, and the concept of an ``effective'' neuron is introduced. The dynamical effects of adding inertial/inductance terms to an effective-neuron system are presented along with arguments for the biological relevance of such terms. A two neuron system with one or two inertial terms added is shown to exhibit chaos. The chaos is confirmed by Lyapunov exponents, power spectra, and phase-space plots. The effects of multiplicative and additive noise on the dynamics of a two effective-neuron system are investigated. One of the neurons possesses an added inertial term so the system is able to generate chaotic dynamics. The multiplicative noise is added to the connection parameter J 11, and the additive noise is added to the equation for U 2 like an external driving force. Using J11 as a bifurcation parameter, the system is examined as it passes from limit cycle dynamics to chaotic dynamics. Both types of noise are found to lower the bifurcation point with respect to its deterministic value, and both cause the dynamics to expand in phase space. For equivalent levels of noise, additive noise is found to have a stronger effect on the dynamics than multiplicative noise. The bifurcation points are explored by means of ensembles of the largest Lyapunov exponents derived from the stochastic dynamics. A brief overview is presented of the current state of control theory in chaotic systems. One control method, Hübler's [74] technique of using aperiodic forces to drive nonlinear oscillators to resonance, is analyzed. The technique is

  15. Frequency-difference-dependent stochastic resonance in neural systems

    Science.gov (United States)

    Guo, Daqing; Perc, Matjaž; Zhang, Yangsong; Xu, Peng; Yao, Dezhong

    2017-08-01

    Biological neurons receive multiple noisy oscillatory signals, and their dynamical response to the superposition of these signals is of fundamental importance for information processing in the brain. Here we study the response of neural systems to the weak envelope modulation signal, which is superimposed by two periodic signals with different frequencies. We show that stochastic resonance occurs at the beat frequency in neural systems at the single-neuron as well as the population level. The performance of this frequency-difference-dependent stochastic resonance is influenced by both the beat frequency and the two forcing frequencies. Compared to a single neuron, a population of neurons is more efficient in detecting the information carried by the weak envelope modulation signal at the beat frequency. Furthermore, an appropriate fine-tuning of the excitation-inhibition balance can further optimize the response of a neural ensemble to the superimposed signal. Our results thus introduce and provide insights into the generation and modulation mechanism of the frequency-difference-dependent stochastic resonance in neural systems.

  16. Quantum Effects in Biological Systems

    CERN Document Server

    2016-01-01

    Since the last decade the study of quantum mechanical phenomena in biological systems has become a vibrant field of research. Initially sparked by evidence of quantum effects in energy transport that is instrumental for photosynthesis, quantum biology asks the question of how methods and models from quantum theory can help us to understand fundamental mechanisms in living organisms. This approach entails a paradigm change challenging the related disciplines: The successful framework of quantum theory is taken out of its low-temperature, microscopic regimes and applied to hot and dense macroscopic environments, thereby extending the toolbox of biology and biochemistry at the same time. The Quantum Effects in Biological Systems conference is a platform for researchers from biology, chemistry and physics to present and discuss the latest developments in the field of quantum biology. After meetings in Lisbon (2009), Harvard (2010), Ulm (2011), Berkeley (2012), Vienna (2013), Singapore (2014) and Florence (2015),...

  17. System and method for determining stability of a neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2011-01-01

    Disclosed are methods, systems, and computer-readable media for determining stability of a neural system. The method includes tracking a function world line of an N element neural system within at least one behavioral space, determining whether the tracking function world line is approaching a psychological stability surface, and implementing a quantitative solution that corrects instability if the tracked function world line is approaching the psychological stability surface.

  18. Electromagnetic fields in biological systems

    CERN Document Server

    Lin, James C

    2016-01-01

    As wireless technology becomes more sophisticated and accessible to more users, the interactions of electromagnetic fields with biological systems have captured the interest not only of the scientific community but also the general public. Unintended or deleterious biological effects of electromagnetic fields and radiation may indicate grounds for health and safety precautions in their use. Spanning static fields to terahertz waves, Electromagnetic Fields in Biological Systems explores the range of consequences these fields have on the human body. With contributions by an array of experts, topics discussed include: Essential interactions and field coupling phenomena, highlighting their importance in research on biological effects and in scientific, industrial, and medical applications Electric field interactions in cells, focusing on ultrashort, pulsed high-intensity fields The effect of exposure to naturally occurring and human-made static, low-frequency, and pulsed magnetic fields in biological systems Dosi...

  19. Correntropy-Based Evolving Fuzzy Neural System

    OpenAIRE

    Bao, Rongjing; Rong, Haijun; Angelov, Plamen Parvanov; Chen, Badong; Wong, Pak Kin

    2017-01-01

    In this paper, a correntropy-based evolving fuzzy neural system (correntropy-EFNS) is proposed for approximation of nonlinear systems. Different from the commonly used meansquare error criterion, correntropy has a strong outliers rejection ability through capturing the higher moments of the error distribution. Considering the merits of correntropy, this paper brings contributions to build EFNS based on the correntropy concept to achieve a more stable evolution of the rule base and update of t...

  20. Validation of systems biology models

    NARCIS (Netherlands)

    Hasdemir, D.

    2015-01-01

    The paradigm shift from qualitative to quantitative analysis of biological systems brought a substantial number of modeling approaches to the stage of molecular biology research. These include but certainly are not limited to nonlinear kinetic models, static network models and models obtained by the

  1. Analysis of complex systems using neural networks

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1992-01-01

    The application of neural networks, alone or in conjunction with other advanced technologies (expert systems, fuzzy logic, and/or genetic algorithms), to some of the problems of complex engineering systems has the potential to enhance the safety, reliability, and operability of these systems. Typically, the measured variables from the systems are analog variables that must be sampled and normalized to expected peak values before they are introduced into neural networks. Often data must be processed to put it into a form more acceptable to the neural network (e.g., a fast Fourier transformation of the time-series data to produce a spectral plot of the data). Specific applications described include: (1) Diagnostics: State of the Plant (2) Hybrid System for Transient Identification, (3) Sensor Validation, (4) Plant-Wide Monitoring, (5) Monitoring of Performance and Efficiency, and (6) Analysis of Vibrations. Although specific examples described deal with nuclear power plants or their subsystems, the techniques described can be applied to a wide variety of complex engineering systems

  2. Systems biology of human atherosclerosis.

    Science.gov (United States)

    Shalhoub, Joseph; Sikkel, Markus B; Davies, Kerry J; Vorkas, Panagiotis A; Want, Elizabeth J; Davies, Alun H

    2014-01-01

    Systems biology describes a holistic and integrative approach to understand physiology and pathology. The "omic" disciplines include genomics, transcriptomics, proteomics, and metabolic profiling (metabonomics and metabolomics). By adopting a stance, which is opposing (yet complimentary) to conventional research techniques, systems biology offers an overview by assessing the "net" biological effect imposed by a disease or nondisease state. There are a number of different organizational levels to be understood, from DNA to protein, metabolites, cells, organs and organisms, even beyond this to an organism's context. Systems biology relies on the existence of "nodes" and "edges." Nodes are the constituent part of the system being studied (eg, proteins in the proteome), while the edges are the way these constituents interact. In future, it will be increasingly important to collaborate, collating data from multiple studies to improve data sets, making them freely available and undertaking integrative analyses.

  3. Text mining for systems biology.

    Science.gov (United States)

    Fluck, Juliane; Hofmann-Apitius, Martin

    2014-02-01

    Scientific communication in biomedicine is, by and large, still text based. Text mining technologies for the automated extraction of useful biomedical information from unstructured text that can be directly used for systems biology modelling have been substantially improved over the past few years. In this review, we underline the importance of named entity recognition and relationship extraction as fundamental approaches that are relevant to systems biology. Furthermore, we emphasize the role of publicly organized scientific benchmarking challenges that reflect the current status of text-mining technology and are important in moving the entire field forward. Given further interdisciplinary development of systems biology-orientated ontologies and training corpora, we expect a steadily increasing impact of text-mining technology on systems biology in the future. Copyright © 2013 Elsevier Ltd. All rights reserved.

  4. Compositional Modeling of Biological Systems

    OpenAIRE

    Zámborszky, Judit

    2010-01-01

    Molecular interactions are wired in a fascinating way resulting in complex behavior of bio-logical systems. Theoretical modeling provides us a useful framework for understanding the dynamics and the function of such networks. The complexity of the biological systems calls for conceptual tools that manage the combinatorial explosion of the set of possible interac-tions. A suitable conceptual tool to attack complexity is compositionality, already success-fully used in the process algebra field ...

  5. Biologically based neural circuit modelling for the study of fear learning and extinction

    Science.gov (United States)

    Nair, Satish S.; Paré, Denis; Vicentic, Aleksandra

    2016-11-01

    The neuronal systems that promote protective defensive behaviours have been studied extensively using Pavlovian conditioning. In this paradigm, an initially neutral-conditioned stimulus is paired with an aversive unconditioned stimulus leading the subjects to display behavioural signs of fear. Decades of research into the neural bases of this simple behavioural paradigm uncovered that the amygdala, a complex structure comprised of several interconnected nuclei, is an essential part of the neural circuits required for the acquisition, consolidation and expression of fear memory. However, emerging evidence from the confluence of electrophysiological, tract tracing, imaging, molecular, optogenetic and chemogenetic methodologies, reveals that fear learning is mediated by multiple connections between several amygdala nuclei and their distributed targets, dynamical changes in plasticity in local circuit elements as well as neuromodulatory mechanisms that promote synaptic plasticity. To uncover these complex relations and analyse multi-modal data sets acquired from these studies, we argue that biologically realistic computational modelling, in conjunction with experiments, offers an opportunity to advance our understanding of the neural circuit mechanisms of fear learning and to address how their dysfunction may lead to maladaptive fear responses in mental disorders.

  6. IMPLEMENTATION OF NEURAL - CRYPTOGRAPHIC SYSTEM USING FPGA

    Directory of Open Access Journals (Sweden)

    KARAM M. Z. OTHMAN

    2011-08-01

    Full Text Available Modern cryptography techniques are virtually unbreakable. As the Internet and other forms of electronic communication become more prevalent, electronic security is becoming increasingly important. Cryptography is used to protect e-mail messages, credit card information, and corporate data. The design of the cryptography system is a conventional cryptography that uses one key for encryption and decryption process. The chosen cryptography algorithm is stream cipher algorithm that encrypt one bit at a time. The central problem in the stream-cipher cryptography is the difficulty of generating a long unpredictable sequence of binary signals from short and random key. Pseudo random number generators (PRNG have been widely used to construct this key sequence. The pseudo random number generator was designed using the Artificial Neural Networks (ANN. The Artificial Neural Networks (ANN providing the required nonlinearity properties that increases the randomness statistical properties of the pseudo random generator. The learning algorithm of this neural network is backpropagation learning algorithm. The learning process was done by software program in Matlab (software implementation to get the efficient weights. Then, the learned neural network was implemented using field programmable gate array (FPGA.

  7. Simulating neural systems with Xyce.

    Energy Technology Data Exchange (ETDEWEB)

    Schiek, Richard Louis; Thornquist, Heidi K.; Mei, Ting; Warrender, Christina E.; Aimone, James Bradley; Teeter, Corinne; Duda, Alex M.

    2012-12-01

    Sandias parallel circuit simulator, Xyce, can address large scale neuron simulations in a new way extending the range within which one can perform high-fidelity, multi-compartment neuron simulations. This report documents the implementation of neuron devices in Xyce, their use in simulation and analysis of neuron systems.

  8. Ten questions about systems biology

    DEFF Research Database (Denmark)

    Joyner, Michael J; Pedersen, Bente K

    2011-01-01

    In this paper we raise 'ten questions' broadly related to 'omics', the term systems biology, and why the new biology has failed to deliver major therapeutic advances for many common diseases, especially diabetes and cardiovascular disease. We argue that a fundamentally narrow and reductionist...... to understand how whole animals adapt to the real world. We argue that a lack of fluency in these concepts is a major stumbling block for what has been narrowly defined as 'systems biology' by some of its leading advocates. We also point out that it is a failure of regulation at multiple levels that causes many...

  9. Informing biological design by integration of systems and synthetic biology.

    Science.gov (United States)

    Smolke, Christina D; Silver, Pamela A

    2011-03-18

    Synthetic biology aims to make the engineering of biology faster and more predictable. In contrast, systems biology focuses on the interaction of myriad components and how these give rise to the dynamic and complex behavior of biological systems. Here, we examine the synergies between these two fields. Copyright © 2011 Elsevier Inc. All rights reserved.

  10. Study Under AC Stimulation on Excitement Properties of Weighted Small-World Biological Neural Networks with Side-Restrain Mechanism

    International Nuclear Information System (INIS)

    Yuan Wujie; Luo Xiaoshu; Jiang Pinqun

    2007-01-01

    In this paper, we propose a new model of weighted small-world biological neural networks based on biophysical Hodgkin-Huxley neurons with side-restrain mechanism. Then we study excitement properties of the model under alternating current (AC) stimulation. The study shows that the excitement properties in the networks are preferably consistent with the behavior properties of a brain nervous system under different AC stimuli, such as refractory period and the brain neural excitement response induced by different intensities of noise and coupling. The results of the study have reference worthiness for the brain nerve electrophysiology and epistemological science.

  11. Neural network based facial recognition system

    Science.gov (United States)

    Luebbers, Paul G.; Uwechue, Okechukwu A.; Pandya, Abhijit S.

    1994-03-01

    Researchers have for many years tried to develop machine recognition systems using video images of the human face as the input, with limited success. This paper presents a technique for recognizing individuals based on facial features using a novel multi-layer neural network architecture called `PWRNET'. We envision a real-time version of this technique to be used for high security applications. Two systems are proposed. One involves taking a grayscale video image and using it directly, the other involves decomposing the grayscale image into a series of binary images using the isodensity regions of the image. Isodensity regions are the areas within an image where the intensity is within a certain range. The binary image is produced by setting the pixels inside this intensity range to one, and the rest of the pixels in the image to zero. Features based on moments are subsequently extracted from these grayscale images. These features are then used for classification of the image. The classification is accomplished using an artificial neural network called `PWRNET', which produces a polynomial expression of the trained network. There is one neural network for each individual to be identified, with an output value which is either positive or negative identification. A detailed development of the design is presented, and identification for small population of individuals is presented. It is shown that the system is effective for variations in both scale and translation, which are considered to be reasonable variations for this type of facial identification.

  12. An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

    Science.gov (United States)

    1996-01-01

    An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems appli...

  13. Development trend of radiation biology research-systems radiation biology

    International Nuclear Information System (INIS)

    Min Rui

    2010-01-01

    Radiation biology research has past 80 years. We have known much more about fundamentals, processes and results of biology effects induced by radiation and various factors that influence biology effects wide and deep, however many old and new scientific problems occurring in the field of radiation biology research remain to be illustrated. To explore and figure these scientific problems need systemic concept, methods and multi dimension view on the base of considerations of complexity of biology system, diversity of biology response, temporal and spatial process of biological effects during occurrence, and complex feed back network of biological regulations. (authors)

  14. Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator.

    Science.gov (United States)

    Hoellinger, Thomas; Petieau, Mathieu; Duvinage, Matthieu; Castermans, Thierry; Seetharaman, Karthik; Cebolla, Ana-Maria; Bengoetxea, Ana; Ivanenko, Yuri; Dan, Bernard; Cheron, Guy

    2013-01-01

    The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum, or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996) was recently modeled (Barliya et al., 2009) by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator (CPG) processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN) mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank, and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications.

  15. Hybrid LQG-Neural Controller for Inverted Pendulum System

    OpenAIRE

    Sazonov, E. S.; Klinkhachorn, P.; Klein, R. L.

    2003-01-01

    The paper presents a hybrid system controller, incorporating a neural and an LQG controller. The neural controller has been optimized by genetic algorithms directly on the inverted pendulum system. The failure free optimization process stipulated a relatively small region of the asymptotic stability of the neural controller, which is concentrated around the regulation point. The presented hybrid controller combines benefits of a genetically optimized neural controller and an LQG controller in...

  16. Non-Linear Systems Identification Using Neural Networks

    OpenAIRE

    Chen, S.; Billings, S.A.; Grant, P.M.

    1989-01-01

    Multi-layered neural networks offer an exciting alternative for modelling complex non-linear systems. This paper investigates the identification of discrete-time non-linear systems using neural networks with a single hidden layer. New parameter estimation algorithms are derived for the neural network model based on a prediction error formulation and the application to both simulated and real data is included to demonstrate the effectiveness of the neural network approach.

  17. Inverse problems in systems biology

    International Nuclear Information System (INIS)

    Engl, Heinz W; Lu, James; Müller, Stefan; Flamm, Christoph; Schuster, Peter; Kügler, Philipp

    2009-01-01

    Systems biology is a new discipline built upon the premise that an understanding of how cells and organisms carry out their functions cannot be gained by looking at cellular components in isolation. Instead, consideration of the interplay between the parts of systems is indispensable for analyzing, modeling, and predicting systems' behavior. Studying biological processes under this premise, systems biology combines experimental techniques and computational methods in order to construct predictive models. Both in building and utilizing models of biological systems, inverse problems arise at several occasions, for example, (i) when experimental time series and steady state data are used to construct biochemical reaction networks, (ii) when model parameters are identified that capture underlying mechanisms or (iii) when desired qualitative behavior such as bistability or limit cycle oscillations is engineered by proper choices of parameter combinations. In this paper we review principles of the modeling process in systems biology and illustrate the ill-posedness and regularization of parameter identification problems in that context. Furthermore, we discuss the methodology of qualitative inverse problems and demonstrate how sparsity enforcing regularization allows the determination of key reaction mechanisms underlying the qualitative behavior. (topical review)

  18. Olfactory instruction for fear: neural system analysis.

    Directory of Open Access Journals (Sweden)

    Newton Sabino Canteras

    2015-08-01

    Full Text Available Studies using cat odor have led to detailed mapping of neural sites engaged in innate and contextual fear responses. Here, we reviewed three lines of work examining the dynamics of the neural systems that organize innate and learned fear responses to cat odor. In the first, we explored the neural systems involved in innate fear responses and in the different stages of fear conditioning to cat odor (i.e., acquisition and expression, with a particular emphasis on the role of the dorsal premammillary nucleus (PMd and the dorsolateral periaqueductal gray (PAGdl as key sites that influence innate and contextual conditioning. In the second line of studies, we reviewed how chemical stimulation of these sites (i.e., the PMd and PAGdl may serve as a useful unconditioned stimulus in an olfactory fear conditioning paradigm; these experiments provide an interesting perspective for the understanding of learned fear to predator odor. Finally, in the third line of studies, we explored the fact that neutral odors that acquire an aversive valence in a shock-paired conditioning paradigm may mimic predator odor and mobilize elements of the hypothalamic predator-responsive circuit.

  19. 3-D components of a biological neural network visualized in computer generated imagery. II - Macular neural network organization

    Science.gov (United States)

    Ross, Muriel D.; Meyer, Glenn; Lam, Tony; Cutler, Lynn; Vaziri, Parshaw

    1990-01-01

    Computer-assisted reconstructions of small parts of the macular neural network show how the nerve terminals and receptive fields are organized in 3-dimensional space. This biological neural network is anatomically organized for parallel distributed processing of information. Processing appears to be more complex than in computer-based neural network, because spatiotemporal factors figure into synaptic weighting. Serial reconstruction data show anatomical arrangements which suggest that (1) assemblies of cells analyze and distribute information with inbuilt redundancy, to improve reliability; (2) feedforward/feedback loops provide the capacity for presynaptic modulation of output during processing; (3) constrained randomness in connectivities contributes to adaptability; and (4) local variations in network complexity permit differing analyses of incoming signals to take place simultaneously. The last inference suggests that there may be segregation of information flow to central stations subserving particular functions.

  20. Nanoscale technology in biological systems

    CERN Document Server

    Greco, Ralph S; Smith, R Lane

    2004-01-01

    Reviewing recent accomplishments in the field of nanobiology Nanoscale Technology in Biological Systems introduces the application of nanoscale matrices to human biology. It focuses on the applications of nanotechnology fabrication to biomedical devices and discusses new physical methods for cell isolation and manipulation and intracellular communication at the molecular level. It also explores the application of nanobiology to cardiovascular diseases, oncology, transplantation, and a range of related disciplines. This book build a strong background in nanotechnology and nanobiology ideal for

  1. Embodied Learning of a Generative Neural Model for Biological Motion Perception and Inference

    Directory of Open Access Journals (Sweden)

    Fabian eSchrodt

    2015-07-01

    Full Text Available Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons.

  2. Systems biology approach to bioremediation

    Energy Technology Data Exchange (ETDEWEB)

    Chakraborty, Romy; Wu, Cindy H.; Hazen, Terry C.

    2012-06-01

    Bioremediation has historically been approached as a ‘black box’ in terms of our fundamental understanding. Thus it succeeds and fails, seldom without a complete understanding of why. Systems biology is an integrated research approach to study complex biological systems, by investigating interactions and networks at the molecular, cellular, community, and ecosystem level. The knowledge of these interactions within individual components is fundamental to understanding the dynamics of the ecosystem under investigation. Finally, understanding and modeling functional microbial community structure and stress responses in environments at all levels have tremendous implications for our fundamental understanding of hydrobiogeochemical processes and the potential for making bioremediation breakthroughs and illuminating the ‘black box’.

  3. Systems biology: the reincarnation of systems theory applied in biology?

    Science.gov (United States)

    Wolkenhauer, O

    2001-09-01

    With the availability of quantitative data on the transcriptome and proteome level, there is an increasing interest in formal mathematical models of gene expression and regulation. International conferences, research institutes and research groups concerned with systems biology have appeared in recent years and systems theory, the study of organisation and behaviour per se, is indeed a natural conceptual framework for such a task. This is, however, not the first time that systems theory has been applied in modelling cellular processes. Notably in the 1960s systems theory and biology enjoyed considerable interest among eminent scientists, mathematicians and engineers. Why did these early attempts vanish from research agendas? Here we shall review the domain of systems theory, its application to biology and the lessons that can be learned from the work of Robert Rosen. Rosen emerged from the early developments in the 1960s as a main critic but also developed a new alternative perspective to living systems, a concept that deserves a fresh look in the post-genome era of bioinformatics.

  4. Systems biology of cellular rhythms.

    Science.gov (United States)

    Goldbeter, A; Gérard, C; Gonze, D; Leloup, J-C; Dupont, G

    2012-08-31

    Rhythms abound in biological systems, particularly at the cellular level where they originate from the feedback loops present in regulatory networks. Cellular rhythms can be investigated both by experimental and modeling approaches, and thus represent a prototypic field of research for systems biology. They have also become a major topic in synthetic biology. We review advances in the study of cellular rhythms of biochemical rather than electrical origin by considering a variety of oscillatory processes such as Ca++ oscillations, circadian rhythms, the segmentation clock, oscillations in p53 and NF-κB, synthetic oscillators, and the oscillatory dynamics of cyclin-dependent kinases driving the cell cycle. Finally we discuss the coupling between cellular rhythms and their robustness with respect to molecular noise.

  5. Systems Biology of Industrial Microorganisms

    Science.gov (United States)

    Papini, Marta; Salazar, Margarita; Nielsen, Jens

    The field of industrial biotechnology is expanding rapidly as the chemical industry is looking towards more sustainable production of chemicals that can be used as fuels or building blocks for production of solvents and materials. In connection with the development of sustainable bioprocesses, it is a major challenge to design and develop efficient cell factories that can ensure cost efficient conversion of the raw material into the chemical of interest. This is achieved through metabolic engineering, where the metabolism of the cell factory is engineered such that there is an efficient conversion of sugars, the typical raw materials in the fermentation industry, into the desired product. However, engineering of cellular metabolism is often challenging due to the complex regulation that has evolved in connection with adaptation of the different microorganisms to their ecological niches. In order to map these regulatory structures and further de-regulate them, as well as identify ingenious metabolic engineering strategies that full-fill mass balance constraints, tools from systems biology can be applied. This involves both high-throughput analysis tools like transcriptome, proteome and metabolome analysis, as well as the use of mathematical modeling to simulate the phenotypes resulting from the different metabolic engineering strategies. It is in fact expected that systems biology may substantially improve the process of cell factory development, and we therefore propose the term Industrial Systems Biology for how systems biology will enhance the development of industrial biotechnology for sustainable chemical production.

  6. Radiation interactions with biological systems.

    Science.gov (United States)

    Islam, Muhammad Torequl

    2017-05-01

    The use of radiation, especially ionizing radiation (IR), is currently attracting great attention in the field of medical sciences. However, it should be mentioned that IR has both beneficial and harmful effects in biological systems. This review aims to focus on IR-mediated physiological events in a mechanistic way. Evidence from the databases, mainly from PUBMED and SCIENCE DIRECT were considered. IR directly and/or with their lyses products (indirect) causes oxidative stresses to biological systems. These activities may be localized and systematic. Otherwise, IR-induced non-/multi-targeted effects are also evident. IR in diagnosis and cancer radiotherapy is well-known. Reactive species produced by IR are not only beneficial, but also can exert harmful effects in a biological system such as aging, genetic instability and mutagenicity, membrane lysis and cell death, alteration of enzymatic activity and metabolic events, mitochondrial dysfunction, and even cancer. Additionally, DNA adducts formation, after IR-induced DNA breakage, is a cause of blockage of DNA repair capability with an increase in cellular radiosensitivity. These may allow cellular ruin even at low IR levels. Dependent on the dose, duration of action and quality, IR plays diverse roles in biological systems.

  7. Tunable promoters in systems biology

    DEFF Research Database (Denmark)

    Mijakovic, Ivan; Petranovic, Dina; Jensen, Peter Ruhdal

    2005-01-01

    The construction of synthetic promoter libraries has represented a major breakthrough in systems biology, enabling the subtle tuning of enzyme activities. A number of tools are now available that allow the modulation of gene expression and the detection of changes in expression patterns. But, how...

  8. Visual gravity cues in the interpretation of biological movements: neural correlates in humans.

    Science.gov (United States)

    Maffei, Vincenzo; Indovina, Iole; Macaluso, Emiliano; Ivanenko, Yuri P; A Orban, Guy; Lacquaniti, Francesco

    2015-01-01

    Our visual system takes into account the effects of Earth gravity to interpret biological motion (BM), but the neural substrates of this process remain unclear. Here we measured functional magnetic resonance (fMRI) signals while participants viewed intact or scrambled stick-figure animations of walking, running, hopping, and skipping recorded at normal or reduced gravity. We found that regions sensitive to BM configuration in the occipito-temporal cortex (OTC) were more active for reduced than normal gravity but with intact stimuli only. Effective connectivity analysis suggests that predictive coding of gravity effects underlies BM interpretation. This process might be implemented by a family of snapshot neurons involved in action monitoring. Copyright © 2014 Elsevier Inc. All rights reserved.

  9. Biologically-inspired On-chip Learning in Pulsed Neural Networks

    DEFF Research Database (Denmark)

    Lehmann, Torsten; Woodburn, Robin

    1999-01-01

    Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the "biologically-inspired" approach......, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks, We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...... that useful conclusions as to the future of on-chip learning can be drawn from this work....

  10. Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

    Science.gov (United States)

    Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

    2017-08-01

    Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a

  11. Biological Networks Entropies: Examples in Neural Memory Networks, Genetic Regulation Networks and Social Epidemic Networks

    Directory of Open Access Journals (Sweden)

    Jacques Demongeot

    2018-01-01

    Full Text Available Networks used in biological applications at different scales (molecule, cell and population are of different types: neuronal, genetic, and social, but they share the same dynamical concepts, in their continuous differential versions (e.g., non-linear Wilson-Cowan system as well as in their discrete Boolean versions (e.g., non-linear Hopfield system; in both cases, the notion of interaction graph G(J associated to its Jacobian matrix J, and also the concepts of frustrated nodes, positive or negative circuits of G(J, kinetic energy, entropy, attractors, structural stability, etc., are relevant and useful for studying the dynamics and the robustness of these systems. We will give some general results available for both continuous and discrete biological networks, and then study some specific applications of three new notions of entropy: (i attractor entropy, (ii isochronal entropy and (iii entropy centrality; in three domains: a neural network involved in the memory evocation, a genetic network responsible of the iron control and a social network accounting for the obesity spread in high school environment.

  12. Biologically driven neural platform invoking parallel electrophoretic separation and urinary metabolite screening.

    Science.gov (United States)

    Page, Tessa; Nguyen, Huong Thi Huynh; Hilts, Lindsey; Ramos, Lorena; Hanrahan, Grady

    2012-06-01

    This work reveals a computational framework for parallel electrophoretic separation of complex biological macromolecules and model urinary metabolites. More specifically, the implementation of a particle swarm optimization (PSO) algorithm on a neural network platform for multiparameter optimization of multiplexed 24-capillary electrophoresis technology with UV detection is highlighted. Two experimental systems were examined: (1) separation of purified rabbit metallothioneins and (2) separation of model toluene urinary metabolites and selected organic acids. Results proved superior to the use of neural networks employing standard back propagation when examining training error, fitting response, and predictive abilities. Simulation runs were obtained as a result of metaheuristic examination of the global search space with experimental responses in good agreement with predicted values. Full separation of selected analytes was realized after employing optimal model conditions. This framework provides guidance for the application of metaheuristic computational tools to aid in future studies involving parallel chemical separation and screening. Adaptable pseudo-code is provided to enable users of varied software packages and modeling framework to implement the PSO algorithm for their desired use.

  13. Systems Biology as an Integrated Platform for Bioinformatics, Systems Synthetic Biology, and Systems Metabolic Engineering

    Science.gov (United States)

    Chen, Bor-Sen; Wu, Chia-Chou

    2013-01-01

    Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering. PMID:24709875

  14. Systems Biology as an Integrated Platform for Bioinformatics, Systems Synthetic Biology, and Systems Metabolic Engineering

    Directory of Open Access Journals (Sweden)

    Bor-Sen Chen

    2013-10-01

    Full Text Available Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

  15. Systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

    Science.gov (United States)

    Chen, Bor-Sen; Wu, Chia-Chou

    2013-10-11

    Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

  16. Convergent evolution of neural systems in ctenophores.

    Science.gov (United States)

    Moroz, Leonid L

    2015-02-15

    Neurons are defined as polarized secretory cells specializing in directional propagation of electrical signals leading to the release of extracellular messengers - features that enable them to transmit information, primarily chemical in nature, beyond their immediate neighbors without affecting all intervening cells en route. Multiple origins of neurons and synapses from different classes of ancestral secretory cells might have occurred more than once during ~600 million years of animal evolution with independent events of nervous system centralization from a common bilaterian/cnidarian ancestor without the bona fide central nervous system. Ctenophores, or comb jellies, represent an example of extensive parallel evolution in neural systems. First, recent genome analyses place ctenophores as a sister group to other animals. Second, ctenophores have a smaller complement of pan-animal genes controlling canonical neurogenic, synaptic, muscle and immune systems, and developmental pathways than most other metazoans. However, comb jellies are carnivorous marine animals with a complex neuromuscular organization and sophisticated patterns of behavior. To sustain these functions, they have evolved a number of unique molecular innovations supporting the hypothesis of massive homoplasies in the organization of integrative and locomotory systems. Third, many bilaterian/cnidarian neuron-specific genes and 'classical' neurotransmitter pathways are either absent or, if present, not expressed in ctenophore neurons (e.g. the bilaterian/cnidarian neurotransmitter, γ-amino butyric acid or GABA, is localized in muscles and presumed bilaterian neuron-specific RNA-binding protein Elav is found in non-neuronal cells). Finally, metabolomic and pharmacological data failed to detect either the presence or any physiological action of serotonin, dopamine, noradrenaline, adrenaline, octopamine, acetylcholine or histamine - consistent with the hypothesis that ctenophore neural systems evolved

  17. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

    2018-02-01

    Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  18. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

    2018-02-01

    Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  19. Biological and bionic hands: natural neural coding and artificial perception.

    Science.gov (United States)

    Bensmaia, Sliman J

    2015-09-19

    The first decade and a half of the twenty-first century brought about two major innovations in neuroprosthetics: the development of anthropomorphic robotic limbs that replicate much of the function of a native human arm and the refinement of algorithms that decode intended movements from brain activity. However, skilled manipulation of objects requires somatosensory feedback, for which vision is a poor substitute. For upper-limb neuroprostheses to be clinically viable, they must therefore provide for the restoration of touch and proprioception. In this review, I discuss efforts to elicit meaningful tactile sensations through stimulation of neurons in somatosensory cortex. I focus on biomimetic approaches to sensory restoration, which leverage our current understanding about how information about grasped objects is encoded in the brain of intact individuals. I argue that not only can sensory neuroscience inform the development of sensory neuroprostheses, but also that the converse is true: stimulating the brain offers an exceptional opportunity to causally interrogate neural circuits and test hypotheses about natural neural coding.

  20. [Circadian rhythms and systems biology].

    Science.gov (United States)

    Goldbeter, Albert; Gérard, Claude; Leloup, Jean-Christophe

    2010-01-01

    Cellular rhythms represent a field of choice for studies in system biology. The examples of circadian rhythms and of the cell cycle show how the experimental and modeling approaches contribute to clarify the conditions in which periodic behavior spontaneously arises in regulatory networks at the cellular level. Circadian rhythms originate from intertwined positive and negative feedback loops controlling the expression of several clock genes. Models can be used to address the dynamical bases of physiological disorders related to dysfunctions of the mammalian circadian clock. The cell cycle is driven by a network of cyclin-dependent kinases (Cdks). Modeled in the form of four modules coupled through multiple regulatory interactions, the Cdk network operates in an oscillatory manner in the presence of sufficient amounts of growth factor. For circadian rhythms and the cell cycle, as for other recently observed cellular rhythms, periodic behavior represents an emergent property of biological systems related to their regulatory structure.

  1. Ten questions about systems biology

    DEFF Research Database (Denmark)

    Joyner, Michael J; Pedersen, Bente K

    2011-01-01

    In this paper we raise 'ten questions' broadly related to 'omics', the term systems biology, and why the new biology has failed to deliver major therapeutic advances for many common diseases, especially diabetes and cardiovascular disease. We argue that a fundamentally narrow and reductionist...... perspective about the contribution of genes and genetic variants to disease is a key reason 'omics' has failed to deliver the anticipated breakthroughs. We then point out the critical utility of key concepts from physiology like homeostasis, regulated systems and redundancy as major intellectual tools...... common diseases. Finally, we attempt to integrate our critique of reductionism into a broader social framework about so-called translational research in specific and the root causes of common diseases in general. Throughout we offer ideas and suggestions that might be incorporated into the current...

  2. Carbon nanomaterials in biological systems

    Energy Technology Data Exchange (ETDEWEB)

    Pu Chun Ke [Laboratory of Single-Molecule Biophysics and Polymer Physics, Department of Physics and Astronomy, Clemson University, Clemson, SC 29634 (United States); Qiao Rui [Department of Mechanical Engineering, Clemson University, Clemson, SC 29634 (United States)

    2007-09-19

    This paper intends to reflect, from the biophysical viewpoint, our current understanding on interfacing nanomaterials, such as carbon nanotubes and fullerenes, with biological systems. Strategies for improving the solubility, and therefore, the bioavailability of nanomaterials in aqueous solutions are summarized. In particular, the underlining mechanisms of attaching biomacromolecules (DNA, RNA, proteins) and lysophospholipids onto carbon nanotubes and gallic acids onto fullerenes are analyzed. The diffusion and the cellular delivery of RNA-coated carbon nanotubes are characterized using fluorescence microscopy. The translocation of fullerenes across cell membranes is simulated using molecular dynamics to offer new insight into the complex issue of nanotoxicity. To assess the fate of nanomaterials in the environment, the biomodification of lipid-coated carbon nanotubes by the aquatic organism Daphnia magna is discussed. The aim of this paper is to illuminate the need for adopting multidisciplinary approaches in the field study of nanomaterials in biological systems and in the environment. (topical review)

  3. Carbon nanomaterials in biological systems

    International Nuclear Information System (INIS)

    Pu Chun Ke; Qiao Rui

    2007-01-01

    This paper intends to reflect, from the biophysical viewpoint, our current understanding on interfacing nanomaterials, such as carbon nanotubes and fullerenes, with biological systems. Strategies for improving the solubility, and therefore, the bioavailability of nanomaterials in aqueous solutions are summarized. In particular, the underlining mechanisms of attaching biomacromolecules (DNA, RNA, proteins) and lysophospholipids onto carbon nanotubes and gallic acids onto fullerenes are analyzed. The diffusion and the cellular delivery of RNA-coated carbon nanotubes are characterized using fluorescence microscopy. The translocation of fullerenes across cell membranes is simulated using molecular dynamics to offer new insight into the complex issue of nanotoxicity. To assess the fate of nanomaterials in the environment, the biomodification of lipid-coated carbon nanotubes by the aquatic organism Daphnia magna is discussed. The aim of this paper is to illuminate the need for adopting multidisciplinary approaches in the field study of nanomaterials in biological systems and in the environment. (topical review)

  4. Spacecraft Neural Network Control System Design using FPGA

    OpenAIRE

    Hanaa T. El-Madany; Faten H. Fahmy; Ninet M. A. El-Rahman; Hassen T. Dorrah

    2011-01-01

    Designing and implementing intelligent systems has become a crucial factor for the innovation and development of better products of space technologies. A neural network is a parallel system, capable of resolving paradigms that linear computing cannot. Field programmable gate array (FPGA) is a digital device that owns reprogrammable properties and robust flexibility. For the neural network based instrument prototype in real time application, conventional specific VLSI neural chip design suffer...

  5. Biological Potential in Serpentinizing Systems

    Science.gov (United States)

    Hoehler, Tori M.

    2016-01-01

    Generation of the microbial substrate hydrogen during serpentinization, the aqueous alteration of ultramafic rocks, has focused interest on the potential of serpentinizing systems to support biological communities or even the origin of life. However the process also generates considerable alkalinity, a challenge to life, and both pH and hydrogen concentrations vary widely across natural systems as a result of different host rock and fluid composition and differing physical and hydrogeologic conditions. Biological potential is expected to vary in concert. We examined the impact of such variability on the bioenergetics of an example metabolism, methanogenesis, using a cell-scale reactive transport model to compare rates of metabolic energy generation as a function of physicochemical environment. Potential rates vary over more than 5 orders of magnitude, including bioenergetically non-viable conditions, across the range of naturally occurring conditions. In parallel, we assayed rates of hydrogen metabolism in wells associated with the actively serpentinizing Coast Range Ophiolite, which includes conditions more alkaline and considerably less reducing than is typical of serpentinizing systems. Hydrogen metabolism is observed at pH approaching 12 but, consistent with the model predictions, biological methanogenesis is not observed.

  6. Anion binding in biological systems

    Energy Technology Data Exchange (ETDEWEB)

    Feiters, Martin C [Department of Organic Chemistry, Institute for Molecules and Materials, Faculty of Science, Radboud University Nijmegen, Heyendaalseweg 135, 6525 AJ Nijmegen (Netherlands); Meyer-Klaucke, Wolfram [EMBL Hamburg Outstation at DESY, Notkestrasse 85, D-22607 Hamburg (Germany); Kostenko, Alexander V; Soldatov, Alexander V [Faculty of Physics, Southern Federal University, Sorge 5, Rostov-na-Donu, 344090 (Russian Federation); Leblanc, Catherine; Michel, Gurvan; Potin, Philippe [Centre National de la Recherche Scientifique and Universite Pierre et Marie Curie Paris-VI, Station Biologique de Roscoff, Place Georges Teissier, BP 74, F-29682 Roscoff cedex, Bretagne (France); Kuepper, Frithjof C [Scottish Association for Marine Science, Dunstaffnage Marine Laboratory, Oban, Argyll PA37 1QA, Scotland (United Kingdom); Hollenstein, Kaspar; Locher, Kaspar P [Institute of Molecular Biology and Biophysics, ETH Zuerich, Schafmattstrasse 20, Zuerich, 8093 (Switzerland); Bevers, Loes E; Hagedoorn, Peter-Leon; Hagen, Wilfred R, E-mail: m.feiters@science.ru.n [Department of Biotechnology, Delft University of Technology, Julianalaan 67, 2628 BC Delft (Netherlands)

    2009-11-15

    We compare aspects of biological X-ray absorption spectroscopy (XAS) studies of cations and anions, and report on some examples of anion binding in biological systems. Brown algae such as Laminaria digitata (oarweed) are effective accumulators of I from seawater, with tissue concentrations exceeding 50 mM, and the vanadate-containing enzyme haloperoxidase is implicated in halide accumulation. We have studied the chemical state of iodine and its biological role in Laminaria at the I K edge, and bromoperoxidase from Ascophyllum nodosum (knotted wrack) at the Br K edge. Mo is essential for many forms of life; W only for certain archaea, such as Archaeoglobus fulgidus and the hyperthermophilic archaeon Pyrococcus furiosus, and some bacteria. The metals are bound and transported as their oxo-anions, molybdate and tungstate, which are similar in size. The transport protein WtpA from P. furiosus binds tungstate more strongly than molybdate, and is related in sequence to Archaeoglobus fulgidus ModA, of which a crystal structure is known. We have measured A. fulgidus ModA with tungstate at the W L{sub 3} (2p{sub 3/2}) edge, and compared the results with the refined crystal structure. XAS studies of anion binding are feasible even if only weak interactions are present, are biologically relevant, and give new insights in the spectroscopy.

  7. Anion binding in biological systems

    International Nuclear Information System (INIS)

    Feiters, Martin C; Meyer-Klaucke, Wolfram; Kostenko, Alexander V; Soldatov, Alexander V; Leblanc, Catherine; Michel, Gurvan; Potin, Philippe; Kuepper, Frithjof C; Hollenstein, Kaspar; Locher, Kaspar P; Bevers, Loes E; Hagedoorn, Peter-Leon; Hagen, Wilfred R

    2009-01-01

    We compare aspects of biological X-ray absorption spectroscopy (XAS) studies of cations and anions, and report on some examples of anion binding in biological systems. Brown algae such as Laminaria digitata (oarweed) are effective accumulators of I from seawater, with tissue concentrations exceeding 50 mM, and the vanadate-containing enzyme haloperoxidase is implicated in halide accumulation. We have studied the chemical state of iodine and its biological role in Laminaria at the I K edge, and bromoperoxidase from Ascophyllum nodosum (knotted wrack) at the Br K edge. Mo is essential for many forms of life; W only for certain archaea, such as Archaeoglobus fulgidus and the hyperthermophilic archaeon Pyrococcus furiosus, and some bacteria. The metals are bound and transported as their oxo-anions, molybdate and tungstate, which are similar in size. The transport protein WtpA from P. furiosus binds tungstate more strongly than molybdate, and is related in sequence to Archaeoglobus fulgidus ModA, of which a crystal structure is known. We have measured A. fulgidus ModA with tungstate at the W L 3 (2p 3/2 ) edge, and compared the results with the refined crystal structure. XAS studies of anion binding are feasible even if only weak interactions are present, are biologically relevant, and give new insights in the spectroscopy.

  8. Anion binding in biological systems

    Science.gov (United States)

    Feiters, Martin C.; Meyer-Klaucke, Wolfram; Kostenko, Alexander V.; Soldatov, Alexander V.; Leblanc, Catherine; Michel, Gurvan; Potin, Philippe; Küpper, Frithjof C.; Hollenstein, Kaspar; Locher, Kaspar P.; Bevers, Loes E.; Hagedoorn, Peter-Leon; Hagen, Wilfred R.

    2009-11-01

    We compare aspects of biological X-ray absorption spectroscopy (XAS) studies of cations and anions, and report on some examples of anion binding in biological systems. Brown algae such as Laminaria digitata (oarweed) are effective accumulators of I from seawater, with tissue concentrations exceeding 50 mM, and the vanadate-containing enzyme haloperoxidase is implicated in halide accumulation. We have studied the chemical state of iodine and its biological role in Laminaria at the I K edge, and bromoperoxidase from Ascophyllum nodosum (knotted wrack) at the Br K edge. Mo is essential for many forms of life; W only for certain archaea, such as Archaeoglobus fulgidus and the hyperthermophilic archaeon Pyrococcus furiosus, and some bacteria. The metals are bound and transported as their oxo-anions, molybdate and tungstate, which are similar in size. The transport protein WtpA from P. furiosus binds tungstate more strongly than molybdate, and is related in sequence to Archaeoglobus fulgidus ModA, of which a crystal structure is known. We have measured A. fulgidus ModA with tungstate at the W L3 (2p3/2) edge, and compared the results with the refined crystal structure. XAS studies of anion binding are feasible even if only weak interactions are present, are biologically relevant, and give new insights in the spectroscopy.

  9. Nonlinear dynamics in biological systems

    CERN Document Server

    Carballido-Landeira, Jorge

    2016-01-01

    This book presents recent research results relating to applications of nonlinear dynamics, focusing specifically on four topics of wide interest: heart dynamics, DNA/RNA, cell mobility, and proteins. The book derives from the First BCAM Workshop on Nonlinear Dynamics in Biological Systems, held in June 2014 at the Basque Center of Applied Mathematics (BCAM). At this international meeting, researchers from different but complementary backgrounds, including molecular dynamics, physical chemistry, bio-informatics and biophysics, presented their most recent results and discussed the future direction of their studies using theoretical, mathematical modeling and experimental approaches. Such was the level of interest stimulated that the decision was taken to produce this publication, with the organizers of the event acting as editors. All of the contributing authors are researchers working on diverse biological problems that can be approached using nonlinear dynamics. The book will appeal especially to applied math...

  10. Neural Network for Optimization of Existing Control Systems

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1995-01-01

    The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....

  11. Biological assessments of multifunctional hydrogel-decorated implantable neural cuff electrode for clinical neurology application.

    Science.gov (United States)

    Kim, Han-Jun; Heo, Dong Nyoung; Lee, Yi Jae; Lee, Sang Jin; Kang, Ji Yoon; Lee, Soo Hyun; Kwon, I I Keun; Do, Sun Hee

    2017-11-10

    The implantable cuff electrode is an effective neuroprosthetic device in current nerve tissue engineering. However, biocompatibility and stability are still a serious dispute in terms of in vivo function and continuous monitoring. In this regard, assessing the host's biological response to biomaterials is one of the key factors of chronic implantation. In this article, we analyzed the peripheral nerve specific-biological responses to the application of multi-functional hydrogel-coated electrodes. The surface of the cuff electrode was modified using a multifunctional hydrogel composed of PEG hydrogel, cyclosporin A(CsA)-microsphere(MS) and electrodeposited PEDOT:PSS. Through our approach, we have found that the multifunctional hydrogel coatings improve the neural electrode function, such as peak-to-peak amplitude increase. Additionally, the multifunctional hydrogel coated electrodes exhibited improved biocompatibility, such as reduced apoptotic properties and increased axonal myelination. Furthermore, 12 genes (BDNF, Gfra1, IL-6, Sox 10, S100B, P75 NTR , GAP43, MBP, MPZ, NrCAM, NE-FL, CB1) were upregulated at 5 weeks post-implant. Finally, double immunofluorescence revealed the effect of endocannabinoid system on neuroprotective properties and tissue remodeling of peripheral nerves during cuff electrode implantation. These results clearly confirmed that multifunctional hydrogel coatings could improve electrode function and biocompatibility by enhancing neuroprotective properties, which may provide a valuable paradigm for clinical neurology application.

  12. The Effects of GABAergic Polarity Changes on Episodic Neural Network Activity in Developing Neural Systems

    Directory of Open Access Journals (Sweden)

    Wilfredo Blanco

    2017-09-01

    Full Text Available Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons.” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes

  13. Tunable promoters in systems biology

    DEFF Research Database (Denmark)

    Mijakovic, Ivan; Petranovic, Dina; Jensen, Peter Ruhdal

    2005-01-01

    The construction of synthetic promoter libraries has represented a major breakthrough in systems biology, enabling the subtle tuning of enzyme activities. A number of tools are now available that allow the modulation of gene expression and the detection of changes in expression patterns. But, how...... does one choose the correct promoter and what are the appropriate methods for reading promoter strength? Furthermore, how fine should the tuning of gene expression be for some specific applications and how can the simultaneous and individual tuning of multiple genes be achieved? Some recent studies...

  14. Decision Making in Biological Systems

    DEFF Research Database (Denmark)

    Tian, Chengzhe

    -dormancy transition is primarily mediated by (p)ppGpp fluctuation. In the second topic, we discuss the transition paths between two stable steady states. We construct a simple model of coupled bistable gene circuits and demonstrate the possibility of bifurcation of transition path in biology. We then construct...... a theory to predict whether a general coupled bistable system exhibits bifurcated path or not and verify the theory through numerical simulation. We also show that a primary function of bifurcated paths is to facilitate transition by lowering the associated action. In the third topic, we discuss...

  15. Systems biology of Microbial Communities

    Energy Technology Data Exchange (ETDEWEB)

    Navid, A; Ghim, C; Fenley, A; Yoon, S; Lee, S; Almaas, E

    2008-04-11

    Microbes exist naturally in a wide range of environments, spanning the extremes of high acidity and high temperature to soil and the ocean, in communities where their interactions are significant. We present a practical discussion of three different approaches for modeling microbial communities: rate equations, individual-based modeling, and population dynamics. We illustrate the approaches with detailed examples. Each approach is best fit to different levels of system representation, and they have different needs for detailed biological input. Thus, this set of approaches is able to address the operation and function of microbial communities on a wide range of organizational levels.

  16. Neural Network Based Intelligent Sootblowing System

    Energy Technology Data Exchange (ETDEWEB)

    Mark Rhode

    2005-04-01

    . Due to the composition of coal, particulate matter is also a by-product of coal combustion. Modern day utility boilers are usually fitted with electrostatic precipitators to aid in the collection of particulate matter. Although extremely efficient, these devices are sensitive to rapid changes in inlet mass concentration as well as total mass loading. Traditionally, utility boilers are equipped with devices known as sootblowers, which use, steam, water or air to dislodge and clean the surfaces within the boiler and are operated based upon established rule or operator's judgment. Poor sootblowing regimes can influence particulate mass loading to the electrostatic precipitators. The project applied a neural network intelligent sootblowing system in conjunction with state-of-the-art controls and instruments to optimize the operation of a utility boiler and systematically control boiler slagging/fouling. This optimization process targeted reduction of NOx of 30%, improved efficiency of 2% and a reduction in opacity of 5%. The neural network system proved to be a non-invasive system which can readily be adapted to virtually any utility boiler. Specific conclusions from this neural network application are listed below. These conclusions should be used in conjunction with the specific details provided in the technical discussions of this report to develop a thorough understanding of the process.

  17. Novel recurrent neural network for modelling biological networks: oscillatory p53 interaction dynamics.

    Science.gov (United States)

    Ling, Hong; Samarasinghe, Sandhya; Kulasiri, Don

    2013-12-01

    Understanding the control of cellular networks consisting of gene and protein interactions and their emergent properties is a central activity of Systems Biology research. For this, continuous, discrete, hybrid, and stochastic methods have been proposed. Currently, the most common approach to modelling accurate temporal dynamics of networks is ordinary differential equations (ODE). However, critical limitations of ODE models are difficulty in kinetic parameter estimation and numerical solution of a large number of equations, making them more suited to smaller systems. In this article, we introduce a novel recurrent artificial neural network (RNN) that addresses above limitations and produces a continuous model that easily estimates parameters from data, can handle a large number of molecular interactions and quantifies temporal dynamics and emergent systems properties. This RNN is based on a system of ODEs representing molecular interactions in a signalling network. Each neuron represents concentration change of one molecule represented by an ODE. Weights of the RNN correspond to kinetic parameters in the system and can be adjusted incrementally during network training. The method is applied to the p53-Mdm2 oscillation system - a crucial component of the DNA damage response pathways activated by a damage signal. Simulation results indicate that the proposed RNN can successfully represent the behaviour of the p53-Mdm2 oscillation system and solve the parameter estimation problem with high accuracy. Furthermore, we presented a modified form of the RNN that estimates parameters and captures systems dynamics from sparse data collected over relatively large time steps. We also investigate the robustness of the p53-Mdm2 system using the trained RNN under various levels of parameter perturbation to gain a greater understanding of the control of the p53-Mdm2 system. Its outcomes on robustness are consistent with the current biological knowledge of this system. As more

  18. Systems biology and biomarker discovery

    Energy Technology Data Exchange (ETDEWEB)

    Rodland, Karin D.

    2010-12-01

    Medical practitioners have always relied on surrogate markers of inaccessible biological processes to make their diagnosis, whether it was the pallor of shock, the flush of inflammation, or the jaundice of liver failure. Obviously, the current implementation of biomarkers for disease is far more sophisticated, relying on highly reproducible, quantitative measurements of molecules that are often mechanistically associated with the disease in question, as in glycated hemoglobin for the diagnosis of diabetes [1] or the presence of cardiac troponins in the blood for confirmation of myocardial infarcts [2]. In cancer, where the initial symptoms are often subtle and the consequences of delayed diagnosis often drastic for disease management, the impetus to discover readily accessible, reliable, and accurate biomarkers for early detection is compelling. Yet despite years of intense activity, the stable of clinically validated, cost-effective biomarkers for early detection of cancer is pathetically small and still dominated by a handful of markers (CA-125, CEA, PSA) first discovered decades ago. It is time, one could argue, for a fresh approach to the discovery and validation of disease biomarkers, one that takes full advantage of the revolution in genomic technologies and in the development of computational tools for the analysis of large complex datasets. This issue of Disease Markers is dedicated to one such new approach, loosely termed the 'Systems Biology of Biomarkers'. What sets the Systems Biology approach apart from other, more traditional approaches, is both the types of data used, and the tools used for data analysis - and both reflect the revolution in high throughput analytical methods and high throughput computing that has characterized the start of the twenty first century.

  19. Radical production in biological systems

    International Nuclear Information System (INIS)

    Johnson, J.R.; Akabani, G.

    1994-10-01

    This paper describes our effort to develop a metric for radiation exposure that is more fundamental than adsorbed dose and upon which a metric for exposure to chemicals could be based. This metric is based on the production of radicals by the two agents. Radicals produced by radiation in biological systems commonly assumed to be the same as those produced in water despite the presence of a variety of complex molecules. This may explain why the extensive efforts to describe the relationship between energy deposition (track structure) and molecular damage to DNA, based on the spectrum of radicals produced, have not been successful in explaining simple biological effects such as cell killing. Current models assume that DNA and its basic elements are immersed in water-like media and only model the production and diffusion of water-based radicals and their interaction with DNA structures; these models lack the cross sections associated with each macro-component of DNA and only treat water-based radicals. It has been found that such models are not realistic because DNA is not immersed in pure water. A computer code capable of simulating electron tracks, low-energy electrons, energy deposition in small molecules, and radical production and diffusion in water like media has been developed. This code is still in at a primitive stage and development is continuing. It is being used to study radical production by radiation, and radical diffusion and interactions in simple molecular systems following their production. We are extending the code to radical production by chemicals to complement our PBPK modeling efforts. It therefore has been developed primarily for use with radionuclides that are in biological materials, and not for radiation fields

  20. Systems biology of fungal infection

    Directory of Open Access Journals (Sweden)

    Fabian eHorn

    2012-04-01

    Full Text Available Elucidation of pathogenicity mechanisms of the most important human pathogenic fungi, Aspergillus fumigatus and Candida albicans, has gained great interest in the light of the steadily increasing number of cases of invasive fungal infections.A key feature of these infections is the interaction of the different fungal morphotypes with epithelial and immune effector cells in the human host. Because of the high level of complexity, it is necessary to describe and understand invasive fungal infection by taking a systems biological approach, i.e., by a comprehensive quantitative analysis of the non-linear and selective interactions of a large number of functionally diverse, and frequently multifunctional, sets of elements, e.g., genes, proteins, metabolites, which produce coherent and emergent behaviours in time and space. The recent advances in systems biology will now make it possible to uncover the structure and dynamics of molecular and cellular cause-effect relationships within these pathogenic interactions.We review current efforts to integrate omics and image-based data of host-pathogen interactions into network and spatio-temporal models. The modelling will help to elucidate pathogenicity mechanisms and to identify diagnostic biomarkers and potential drug targets for therapy and could thus pave the way for novel intervention strategies based on novel antifungal drugs and cell therapy.

  1. The ctenophore genome and the evolutionary origins of neural systems

    NARCIS (Netherlands)

    Moroz, Leonid L.; Kocot, Kevin M.; Citarella, Mathew R.; Dosung, Sohn; Norekian, Tigran P.; Povolotskaya, Inna S.; Grigorenko, Anastasia P.; Dailey, Christopher; Berezikov, Eugene; Buckley, Katherine M.; Ptitsyn, Andrey; Reshetov, Denis; Mukherjee, Krishanu; Moroz, Tatiana P.; Bobkova, Yelena; Yu, Fahong; Kapitonov, Vladimir V.; Jurka, Jerzy; Bobkov, Yuri V.; Swore, Joshua J.; Girardo, David O.; Fodor, Alexander; Gusev, Fedor; Sanford, Rachel; Bruders, Rebecca; Kittler, Ellen; Mills, Claudia E.; Rast, Jonathan P.; Derelle, Romain; Solovyev, Victor V.; Kondrashov, Fyodor A.; Swalla, Billie J.; Sweedler, Jonathan V.; Rogaev, Evgeny I.; Halanych, Kenneth M.; Kohn, Andrea B.

    2014-01-01

    The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores (comb jellies) have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here we

  2. Dynamical systems in population biology

    CERN Document Server

    Zhao, Xiao-Qiang

    2017-01-01

    This research monograph provides an introduction to the theory of nonautonomous semiflows with applications to population dynamics. It develops dynamical system approaches to various evolutionary equations such as difference, ordinary, functional, and partial differential equations, and pays more attention to periodic and almost periodic phenomena. The presentation includes persistence theory, monotone dynamics, periodic and almost periodic semiflows, basic reproduction ratios, traveling waves, and global analysis of prototypical population models in ecology and epidemiology. Research mathematicians working with nonlinear dynamics, particularly those interested in applications to biology, will find this book useful. It may also be used as a textbook or as supplementary reading for a graduate special topics course on the theory and applications of dynamical systems. Dr. Xiao-Qiang Zhao is a University Research Professor at Memorial University of Newfoundland, Canada. His main research interests involve applied...

  3. System Identification for Nonlinear Control Using Neural Networks

    Science.gov (United States)

    Stengel, Robert F.; Linse, Dennis J.

    1990-01-01

    An approach to incorporating artificial neural networks in nonlinear, adaptive control systems is described. The controller contains three principal elements: a nonlinear inverse dynamic control law whose coefficients depend on a comprehensive model of the plant, a neural network that models system dynamics, and a state estimator whose outputs drive the control law and train the neural network. Attention is focused on the system identification task, which combines an extended Kalman filter with generalized spline function approximation. Continual learning is possible during normal operation, without taking the system off line for specialized training. Nonlinear inverse dynamic control requires smooth derivatives as well as function estimates, imposing stringent goals on the approximating technique.

  4. Neural networks for nonlinear dynamic system modelling and identification

    OpenAIRE

    Chen, S.; Billings, S. A.

    1992-01-01

    Many real-world systems exhibit complex non-linear characteristics and cannot be treated satisfactorily using linear systems theory. A neural network which has the ability to learn sophisticated non-linear relationships provides an ideal means of modelling complicated non-linear systems. This paper addresses the issues related to the identification of non-linear discrete-time dynamic systems using neural networks..........

  5. System Identification of X-33 Neural Network

    Science.gov (United States)

    Aggarwal, Shiv

    2003-01-01

    present attempt, as a start, focuses only on the entry phase. Since the main engine remains cut off in this phase, there is no thrust acting on the system. This considerably simplifies the equations of motion. We introduce another simplification by assuming the system to be linear after some non-linearities are removed analytically from our consideration. Under these assumptions, the problem could be solved by Classical Statistics by employing the least sum of squares approach. Instead we chose to use the Neural Network method. This method has many advantages. It is modern, more efficient, can be adapted to work even when the assumptions are diluted. In fact, Neural Networks try to model the human brain and are capable of pattern recognition.

  6. Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.

    Science.gov (United States)

    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.

  7. Short-term synaptic plasticity and heterogeneity in neural systems

    Science.gov (United States)

    Mejias, J. F.; Kappen, H. J.; Longtin, A.; Torres, J. J.

    2013-01-01

    We review some recent results on neural dynamics and information processing which arise when considering several biophysical factors of interest, in particular, short-term synaptic plasticity and neural heterogeneity. The inclusion of short-term synaptic plasticity leads to enhanced long-term memory capacities, a higher robustness of memory to noise, and irregularity in the duration of the so-called up cortical states. On the other hand, considering some level of neural heterogeneity in neuron models allows neural systems to optimize information transmission in rate coding and temporal coding, two strategies commonly used by neurons to codify information in many brain areas. In all these studies, analytical approximations can be made to explain the underlying dynamics of these neural systems.

  8. Biological modelling of a computational spiking neural network with neuronal avalanches

    Science.gov (United States)

    Li, Xiumin; Chen, Qing; Xue, Fangzheng

    2017-05-01

    In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance. This article is part of the themed issue `Mathematical methods in medicine: neuroscience, cardiology and pathology'.

  9. Verification and Validation of Neural Networks for Aerospace Systems

    Science.gov (United States)

    Mackall, Dale; Nelson, Stacy; Schumann, Johann

    2002-01-01

    The Dryden Flight Research Center V&V working group and NASA Ames Research Center Automated Software Engineering (ASE) group collaborated to prepare this report. The purpose is to describe V&V processes and methods for certification of neural networks for aerospace applications, particularly adaptive flight control systems like Intelligent Flight Control Systems (IFCS) that use neural networks. This report is divided into the following two sections: Overview of Adaptive Systems and V&V Processes/Methods.

  10. Fostering synergy between cell biology and systems biology.

    Science.gov (United States)

    Eddy, James A; Funk, Cory C; Price, Nathan D

    2015-08-01

    In the shared pursuit of elucidating detailed mechanisms of cell function, systems biology presents a natural complement to ongoing efforts in cell biology. Systems biology aims to characterize biological systems through integrated and quantitative modeling of cellular information. The process of model building and analysis provides value through synthesizing and cataloging information about cells and molecules, predicting mechanisms and identifying generalizable themes, generating hypotheses and guiding experimental design, and highlighting knowledge gaps and refining understanding. In turn, incorporating domain expertise and experimental data is crucial for building towards whole cell models. An iterative cycle of interaction between cell and systems biologists advances the goals of both fields and establishes a framework for mechanistic understanding of the genome-to-phenome relationship. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

  11. Neurale Netwerken en Radarsystemen (Neural Networks and Radar Systems)

    Science.gov (United States)

    1989-08-01

    architectures that are currently in use, do not meet the requirements of future radar systems. Man and animals show very convincingly that real-time signal...antstaan wederom "ghost items", items die spontaan ontstaan door een vorm van Interferentie in bet gebeugen. Figuur 5.22 last zien hoe dit magelijk is

  12. Editorial overview : Systems biology for biotechnology

    NARCIS (Netherlands)

    Heinemann, Matthias; Pilpel, Yitzhak

    About 15 years ago, systems biology was introduced as a novel approach to biological research. On the one side, its introduction was a result of the recognition that through solely the reductionist approach, we would ulti- mately not be able to understand how biological systems function as a whole.

  13. Magnetic Actuation of Biological Systems

    Science.gov (United States)

    Lauback, Stephanie D.

    Central to the advancement of many biomedical and nanotechnology capabilities is the capacity to precisely control the motion of micro and nanostructures. These applications range from single molecule experiments to cell isolation and separation, to drug delivery and nanomachine manipulation. This dissertation focuses on actuation of biological micro- and nano-entities through the use of weak external magnetic fields, superparamagnetic beads, and ferromagnetic thin films. The magnetic platform presents an excellent method for actuation of biological systems due to its ability to directly control the motion of an array of micro and nanostructures in real-time with calibrated picoNewton forces. The energy landscape of two ferromagnetic thin film patterns (disks and zigzag wires) is experimentally explored and compared to corresponding theoretical models to quantify the applied forces and trajectories of superparamagnetic beads due to the magnetic traps. A magnetic method to directly actuate DNA nanomachines in real-time with nanometer resolution and sub-second response times using micromagnetic control was implemented through the use of stiff DNA micro-levers which bridged the large length scale mismatch between the micro-actuator and the nanomachine. Compared to current alternative methods which are limited in the actuation speeds and the number of reconfiguration states of DNA constructs, this magnetic approach enables fast actuation (˜ milliseconds) and reconfigurable conformations achieved through a continuous range of finely tuned steps. The system was initially tested through actuation of the stiff arm tethered to the surface, and two prototype DNA nanomachines (rotor and hinge) were successfully actuated using the stiff mechanical lever. These results open new possibilities in the development of functional robotic systems at the molecular scale. In exploiting the use of DNA stiff levers, a new technique was also developed to investigate the emergence of the

  14. An online neural network triggering system for the Tile Calorimeter

    CERN Document Server

    Damazio, D O; Magacho, P V

    2002-01-01

    For the hadronic calorimeter of ATLAS, TileCal, neural processing is used to establish an efficient methodology for the online particle identification in beam tests of calorimeter prototypes. Although beam purity is usually very good for a selected particle type, background from wrong-type particles cannot be avoided and is routinely identified in the offline analysis. The proposed neural system is trained online to identify electrons, pions, and muons at different energy levels and it achieves more than 90% efficiency in terms of particle identification. The neural system is being implemented by integrating it to the readout drive (ROD) of the TileCal. (12 refs).

  15. Dopamine system: Manager of neural pathways

    Directory of Open Access Journals (Sweden)

    Simon eHong

    2013-12-01

    Full Text Available There are a growing number of roles that midbrain dopamine (DA neurons assume, such as, reward, aversion, alerting and vigor. Here I propose a theory that may be able to explain why the suggested functions of DA came about. It has been suggested that largely parallel cortico-basal ganglia-thalamo-cortico loops exist to control different aspects of behavior. I propose that (1 the midbrain DA system is organized in a similar manner, with different groups of DA neurons corresponding to these parallel neural pathways (NPs. The DA system can be viewed as the manager of these parallel NPs in that it recruits and activates only the task-relevant NPs when they are needed. It is likely that the functions of those NPs that have been consistently activated by the corresponding DA groups are facilitated. I also propose that (2 there are two levels of DA roles: the How and What roles. The How role is encoded in tonic and phasic DA neuron firing patterns and gives a directive to its target NP: how vigorously its function needs to be carried out. The tonic DA firing is to maintain a certain level of DA in the target NPs to support their expected behavioral and mental functions; it is only when a sudden unexpected boost or suppression of activity is required by the relevant target NP that DA neurons in the corresponding NP act in a phasic manner. The What role is the implementational aspect of the role of DA in the target NP, such as binding to D1 receptors to boost working memory. This What aspect of DA explains why DA seems to assume different functions depending on the region of the brain in which it is involved. In terms of the role of the lateral habenula (LHb, the LHb is expected to suppress maladaptive behaviors and mental processes by controlling the DA system. The demand-based smart management by the DA system may have given animals an edge in evolution with adaptive behaviors and a better survival rate in resource-scarce situations.

  16. Optical production systems using neural networks and symbolic substitution

    Science.gov (United States)

    Botha, Elizabeth; Casasent, David; Barnard, Etienne

    1988-01-01

    Two optical implementations of production systems are advanced. The production systems operate on a knowledge base where facts and rules are encoded as formulas in propositional calculus. The first implementation is a binary neural network. An analog neural network is used to include reasoning with uncertainties. The second implementation uses a new optical symbolic substitution correlator. This implementation is useful when a set of similar situations has to be handled in parallel on one processor.

  17. Diagnostic Neural Network Systems for the Electronic Circuits

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2014-01-01

    Neural Networks is one of the most important artificial intelligent approaches for solving the diagnostic processes. This research concerns with uses the neural networks for diagnosis of the electronic circuits. Modern electronic systems contain both the analog and digital circuits. But, diagnosis of the analog circuits suffers from great complexity due to their nonlinearity. To overcome this problem, the proposed system introduces a diagnostic system that uses the neural network to diagnose both the digital and analog circuits. So, it can face the new requirements for the modern electronic systems. A fault dictionary method was implemented in the system. Experimental results are presented on three electronic systems. They are: artificial kidney, wireless network and personal computer systems. The proposed system has improved the performance of the diagnostic systems when applied for these practical cases

  18. Vein matching using artificial neural network in vein authentication systems

    Science.gov (United States)

    Noori Hoshyar, Azadeh; Sulaiman, Riza

    2011-10-01

    Personal identification technology as security systems is developing rapidly. Traditional authentication modes like key; password; card are not safe enough because they could be stolen or easily forgotten. Biometric as developed technology has been applied to a wide range of systems. According to different researchers, vein biometric is a good candidate among other biometric traits such as fingerprint, hand geometry, voice, DNA and etc for authentication systems. Vein authentication systems can be designed by different methodologies. All the methodologies consist of matching stage which is too important for final verification of the system. Neural Network is an effective methodology for matching and recognizing individuals in authentication systems. Therefore, this paper explains and implements the Neural Network methodology for finger vein authentication system. Neural Network is trained in Matlab to match the vein features of authentication system. The Network simulation shows the quality of matching as 95% which is a good performance for authentication system matching.

  19. Use of neural networks in the analysis of complex systems

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1992-01-01

    The application of neural networks, alone or in conjunction with other advanced technologies (expert systems, fuzzy logic, and/or genetic algorithms) to some of the problems of complex engineering systems has the potential to enhance the safety reliability and operability of these systems. The work described here deals with complex systems or parts of such systems that can be isolated from the total system. Typically, the measured variables from the systems are analog variables that must be sampled and normalized to expected peak values before they are introduced into neural networks. Often data must be processed to put it into a form more acceptable to the neural network. The neural networks are usually simulated on modern high-speed computers that carry out the calculations serially. However, it is possible to implement neural networks using specially designed microchips where the network calculations are truly carried out in parallel, thereby providing virtually instantaneous outputs for each set of inputs. Specific applications described include: Diagnostics: State of the Plant; Hybrid System for Transient Identification; Detection of Change of Mode in Complex Systems; Sensor Validation; Plant-Wide Monitoring; Monitoring of Performance and Efficiency; and Analysis of Vibrations. Although the specific examples described deal with nuclear power plants or their subsystems, the techniques described can be applied to a wide variety of complex engineering systems

  20. Genetic learning in rule-based and neural systems

    Science.gov (United States)

    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.

  1. Applicability of Computational Systems Biology in Toxicology

    DEFF Research Database (Denmark)

    Kongsbak, Kristine Grønning; Hadrup, Niels; Audouze, Karine Marie Laure

    2014-01-01

    be used to establish hypotheses on links between the chemical and human diseases. Such information can also be applied for designing more intelligent animal/cell experiments that can test the established hypotheses. Here, we describe how and why to apply an integrative systems biology method......Systems biology as a research field has emerged within the last few decades. Systems biology, often defined as the antithesis of the reductionist approach, integrates information about individual components of a biological system. In integrative systems biology, large data sets from various sources...... and databases are used to model and predict effects of chemicals on, for instance, human health. In toxicology, computational systems biology enables identification of important pathways and molecules from large data sets; tasks that can be extremely laborious when performed by a classical literature search...

  2. Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.

    Science.gov (United States)

    Spoerer, Courtney J; McClure, Patrick; Kriegeskorte, Nikolaus

    2017-01-01

    Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and non-human primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognizing objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognize objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance.

  3. Introducing systems biology for nursing science.

    Science.gov (United States)

    Founds, Sandra A

    2009-07-01

    Systems biology expands on general systems theory as the "omics'' era rapidly progresses. Although systems biology has been institutionalized as an interdisciplinary framework in the biosciences, it is not yet apparent in nursing. This article introduces systems biology for nursing science by presenting an overview of the theory. This framework for the study of organisms from molecular to environmental levels includes iterations of computational modeling, experimentation, and theory building. Synthesis of complex biological processes as whole systems rather than isolated parts is emphasized. Pros and cons of systems biology are discussed, and relevance of systems biology to nursing is described. Nursing research involving molecular, physiological, or biobehavioral questions may be guided by and contribute to the developing science of systems biology. Nurse scientists can proactively incorporate systems biology into their investigations as a framework for advancing the interdisciplinary science of human health care. Systems biology has the potential to advance the research and practice goals of the National Institute for Nursing Research in the National Institutes of Health Roadmap initiative.

  4. BOOK REVIEW: Theory of Neural Information Processing Systems

    Science.gov (United States)

    Galla, Tobias

    2006-04-01

    It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 1011 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kühn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the

  5. System Identification, Prediction, Simulation and Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System...... Identification, Prediction, Simulation and Control of a dynamic, non-linear and noisy process. Further, the difficulties to control a practical non-linear laboratory process in a satisfactory way by using a traditional controller are overcomed by using a trained neural network to perform non-linear System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...

  6. Philosophy of Systems and Synthetic Biology

    DEFF Research Database (Denmark)

    Green, Sara

    2017-01-01

    This entry aims to clarify how systems and synthetic biology contribute to and extend discussions within philosophy of science. Unlike fields such as developmental biology or molecular biology, systems and synthetic biology are not easily demarcated by a focus on a specific subject area or level...... computational approaches, about the relation between living and artificial systems, and about the implications of interdisciplinary research for science and society. The entry can be openly accessed at the webpage of the Stanford Encyclopaedia of Philosophy: https://plato.stanford.edu/entries/systems-synthetic-biology/...... of organization. Rather, they are characterized by the development and application of mathematical, computational, and synthetic modeling strategies in response to complex problems and challenges within the life sciences. Proponents of systems and synthetic biology often stress the necessity of a perspective...

  7. Systemic Modeling of Biological Functions in Consideration of Physiome Project

    Science.gov (United States)

    Minamitani, Haruyuki

    Emerging of the physiome project provides various influences on the medical, biological and pharmaceutical development. In this paper, as an example of physiome research, neural network model analysis providing the conduction mechanisms of pain and tactile sensations was presented, and the functional relations between neural activities of the network cells and stimulus intensity applied on the peripheral receptive fields were described. The modeling presented here is based on the various assumptions made by the results of physiological and anatomical studies reported in the literature. The functional activities of spinothalamic and thalamocortical cells show a good agreement with the physiological and psychophysical functions of somatosensory system that are very instructive for covering the gap between physiologically and psychophysically aspects of pain and tactile sensation.

  8. The Criticality Hypothesis in Neural Systems

    Science.gov (United States)

    Karimipanah, Yahya

    There is mounting evidence that neural networks of the cerebral cortex exhibit scale invariant dynamics. At the larger scale, fMRI recordings have shown evidence for spatiotemporal long range correlations. On the other hand, at the smaller scales this scale invariance is marked by the power law distribution of the size and duration of spontaneous bursts of activity, which are referred as neuronal avalanches. The existence of such avalanches has been confirmed by several studies in vitro and in vivo, among different species and across multiple scales, from spatial scale of MEG and EEG down to single cell resolution. This prevalent scale free nature of cortical activity suggests the hypothesis that the cortex resides at a critical state between two phases of order (short-lasting activity) and disorder (long-lasting activity). In addition, it has been shown, both theoretically and experimentally, that being at criticality brings about certain functional advantages for information processing. However, despite the plenty of evidence and plausibility of the neural criticality hypothesis, still very little is known on how the brain may leverage such criticality to facilitate neural coding. Moreover, the emergent functions that may arise from critical dynamics is poorly understood. In the first part of this thesis, we review several pieces of evidence for the neural criticality hypothesis at different scales, as well as some of the most popular theories of self-organized criticality (SOC). Thereafter, we will focus on the most prominent evidence from small scales, namely neuronal avalanches. We will explore the effect of adaptation and how it can maintain scale free dynamics even at the presence of external stimuli. Using calcium imaging we also experimentally demonstrate the existence of scale free activity at the cellular resolution in vivo. Moreover, by exploring the subsampling issue in neural data, we will find some fundamental constraints of the conventional methods

  9. Capacity of UWB wireless channel for neural recording systems.

    Science.gov (United States)

    El Khaled, Mohamad; Bahrami, Hadi; Fortier, Paul; Gosselin, Benoit; Rusch, Leslie Ann

    2014-01-01

    Ultra wide-band (UWB) short-range communication systems are valuable in medical technology, particularly for implanted devices, due to their low-power consumption, low cost, small size and high data rates. Monitoring of neural responses in the brain requires high data rate if we target a system supporting a large number of sensors. In this work, we are interested in the evaluation of the capacity of the ultra wide-band (UWB) channel that we could exploit using a realistic model of the biological channel. The channel characteristics are examined under two scenarios that are related to TX antenna placements. Using optimal power spectrum allocation (OPSA) at the transmitter side, we have computed this capacity by taking into account the fading characteristics of the channel. The results show the pertinence of the optimal power spectrum allocation for this type of channel. An improvement by a factor of 2 to 3 over a uniform power spectrum allocation (UPSA) when the SNR 40 dB, both approaches give similar results. Antennas placement is examined under two scenarios having contrasting power constraints.

  10. Anomaly detection in an automated safeguards system using neural networks

    International Nuclear Information System (INIS)

    Whiteson, R.; Howell, J.A.

    1992-01-01

    An automated safeguards system must be able to detect an anomalous event, identify the nature of the event, and recommend a corrective action. Neural networks represent a new way of thinking about basic computational mechanisms for intelligent information processing. In this paper, we discuss the issues involved in applying a neural network model to the first step of this process: anomaly detection in materials accounting systems. We extend our previous model to a 3-tank problem and compare different neural network architectures and algorithms. We evaluate the computational difficulties in training neural networks and explore how certain design principles affect the problems. The issues involved in building a neural network architecture include how the information flows, how the network is trained, how the neurons in a network are connected, how the neurons process information, and how the connections between neurons are modified. Our approach is based on the demonstrated ability of neural networks to model complex, nonlinear, real-time processes. By modeling the normal behavior of the processes, we can predict how a system should be behaving and, therefore, detect when an abnormality occurs

  11. Variable neural adaptive robust control: a switched system approach.

    Science.gov (United States)

    Lian, Jianming; Hu, Jianghai; Żak, Stanislaw H

    2015-05-01

    Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multiinput multioutput uncertain systems. The controllers incorporate a novel variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. It can determine the network structure online dynamically by adding or removing RBFs according to the tracking performance. The structure variation is systematically considered in the stability analysis of the closed-loop system using a switched system approach with the piecewise quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.

  12. Introduction to Network Analysis in Systems Biology

    OpenAIRE

    Ma’ayan, Avi

    2011-01-01

    This Teaching Resource provides lecture notes, slides, and a problem set for a set of three lectures from a course entitled “Systems Biology: Biomedical Modeling.” The materials are from three separate lectures introducing applications of graph theory and network analysis in systems biology. The first lecture describes different types of intracellular networks, methods for constructing biological networks, and different types of graphs used to represent regulatory intracellular networks. The ...

  13. A Novel CPU/GPU Simulation Environment for Large-Scale Biologically-Realistic Neural Modeling

    Directory of Open Access Journals (Sweden)

    Roger V Hoang

    2013-10-01

    Full Text Available Computational Neuroscience is an emerging field that provides unique opportunities to studycomplex brain structures through realistic neural simulations. However, as biological details are added tomodels, the execution time for the simulation becomes longer. Graphics Processing Units (GPUs are now being utilized to accelerate simulations due to their ability to perform computations in parallel. As such, they haveshown significant improvement in execution time compared to Central Processing Units (CPUs. Most neural simulators utilize either multiple CPUs or a single GPU for better performance, but still show limitations in execution time when biological details are not sacrificed. Therefore, we present a novel CPU/GPU simulation environment for large-scale biological networks,the NeoCortical Simulator version 6 (NCS6. NCS6 is a free, open-source, parallelizable, and scalable simula-tor, designed to run on clusters of multiple machines, potentially with high performance computing devicesin each of them. It has built-in leaky-integrate-and-fire (LIF and Izhikevich (IZH neuron models, but usersalso have the capability to design their own plug-in interface for different neuron types as desired. NCS6is currently able to simulate one million cells and 100 million synapses in quasi real time by distributing dataacross these heterogeneous clusters of CPUs and GPUs.

  14. Neural network training by Kalman filtering in process system monitoring

    International Nuclear Information System (INIS)

    Ciftcioglu, Oe.

    1996-03-01

    Kalman filtering approach for neural network training is described. Its extended form is used as an adaptive filter in a nonlinear environment of the form a feedforward neural network. Kalman filtering approach generally provides fast training as well as avoiding excessive learning which results in enhanced generalization capability. The network is used in a process monitoring application where the inputs are measurement signals. Since the measurement errors are also modelled in Kalman filter the approach yields accurate training with the implication of accurate neural network model representing the input and output relationships in the application. As the process of concern is a dynamic system, the input source of information to neural network is time dependent so that the training algorithm presents an adaptive form for real-time operation for the monitoring task. (orig.)

  15. High speed digital interfacing for a neural data acquisition system

    Directory of Open Access Journals (Sweden)

    Bahr Andreas

    2016-09-01

    Full Text Available Diseases like schizophrenia and genetic epilepsy are supposed to be caused by disorders in the early development of the brain. For the further investigation of these relationships a custom designed application specific integrated circuit (ASIC was developed that is optimized for the recording from neonatal mice [Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. 16 Channel Neural Recording Integrated Circuit with SPI Interface and Error Correction Coding. Proc. 9th BIOSTEC 2016. Biodevices: Rome, Italy, 2016; 1: 263; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. Development of a neural recording mixed signal integrated circuit for biomedical signal acquisition. Biomed Eng Biomed Tech Abstracts 2015; 60(S1: 298–299; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider WH. 16 Channel Neural Recording Mixed Signal ASIC. CDNLive EMEA 2015 Conference Proceedings, 2015.]. To enable the live display of the neural signals a multichannel neural data acquisition system with live display functionality is presented. It implements a high speed data transmission from the ASIC to a computer with a live display functionality. The system has been successfully implemented and was used in a neural recording of a head-fixed mouse.

  16. Nonlinear signal processing using neural networks: Prediction and system modelling

    Energy Technology Data Exchange (ETDEWEB)

    Lapedes, A.; Farber, R.

    1987-06-01

    The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting two common topics in signal processing, prediction and system modelling, and show that nonlinear applications can be handled extremely well by using neural networks. The formalism is a natural, nonlinear extension of the linear Least Mean Squares algorithm commonly used in adaptive signal processing. Simulations are presented that document the additional performance achieved by using nonlinear neural networks. First, we demonstrate that the formalism may be used to predict points in a highly chaotic time series with orders of magnitude increase in accuracy over conventional methods including the Linear Predictive Method and the Gabor-Volterra-Weiner Polynomial Method. Deterministic chaos is thought to be involved in many physical situations including the onset of turbulence in fluids, chemical reactions and plasma physics. Secondly, we demonstrate the use of the formalism in nonlinear system modelling by providing a graphic example in which it is clear that the neural network has accurately modelled the nonlinear transfer function. It is interesting to note that the formalism provides explicit, analytic, global, approximations to the nonlinear maps underlying the various time series. Furthermore, the neural net seems to be extremely parsimonious in its requirements for data points from the time series. We show that the neural net is able to perform well because it globally approximates the relevant maps by performing a kind of generalized mode decomposition of the maps. 24 refs., 13 figs.

  17. Marine biological data and information management system

    Digital Repository Service at National Institute of Oceanography (India)

    Sarupria, J.S.

    Indian National Oceanographic Data Centre (INODC) is engaged in developing a marine biological data and information management system (BIODIMS). This system will contain the information on zooplankton in the water column, zoobenthic biomass...

  18. Systems biology in critical-care nursing.

    Science.gov (United States)

    Schallom, Lynn; Thimmesch, Amanda R; Pierce, Janet D

    2011-01-01

    Systems biology applies advances in technology and new fields of study including genomics, transcriptomics, proteomics, and metabolomics to the development of new treatments and approaches of care for the critically ill and injured patient. An understanding of systems biology enhances a nurse's ability to implement evidence-based practice and to educate patients and families on novel testing and therapies. Systems biology is an integrated and holistic view of humans in relationship with the environment. Biomarkers are used to measure the presence and severity of disease and are rapidly expanding in systems biology endeavors. A systems biology approach using predictive, preventive, and participatory involvement is being utilized in a plethora of conditions of critical illness and injury including sepsis, cancer, pulmonary disease, and traumatic injuries.

  19. Plant systems biology: insights, advances and challenges.

    Science.gov (United States)

    Sheth, Bhavisha P; Thaker, Vrinda S

    2014-07-01

    Plants dwelling at the base of biological food chain are of fundamental significance in providing solutions to some of the most daunting ecological and environmental problems faced by our planet. The reductionist views of molecular biology provide only a partial understanding to the phenotypic knowledge of plants. Systems biology offers a comprehensive view of plant systems, by employing a holistic approach integrating the molecular data at various hierarchical levels. In this review, we discuss the basics of systems biology including the various 'omics' approaches and their integration, the modeling aspects and the tools needed for the plant systems research. A particular emphasis is given to the recent analytical advances, updated published examples of plant systems biology studies and the future trends.

  20. Why Traditional Expository Teaching-Learning Approaches May Founder? An Experimental Examination of Neural Networks in Biology Learning

    Science.gov (United States)

    Lee, Jun-Ki; Kwon, Yong-Ju

    2011-01-01

    Using functional magnetic resonance imaging (fMRI), this study investigates and discusses neurological explanations for, and the educational implications of, the neural network activations involved in hypothesis-generating and hypothesis-understanding for biology education. Two sets of task paradigms about biological phenomena were designed:…

  1. Estimating the behavior of RC beams strengthened with NSM system using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Seyed Rohollah Hosseini Vaez

    2017-12-01

    Full Text Available In the last decade, conventional materials such as steel and concrete are being replaced by fiber reinforced polymer (FRP materials for the strengthening of concrete structures. Among the strengthening techniques based on Fiber Reinforced Polymer composites, the use of near-surface mounted (NSM FRP rods is emerging as a promising technology for increasing flexural and shear strength of deficient concrete, masonry and timber members. An artificial neural network is an information processing tool that is inspired by the way biological nervous systems (such as the brain process the information. The key element of this tool is the novel structure of the information processing system. In engineering applications, a neural network can be a vector mapper which maps an input vector to an output one. In the present study, a new approach is developed to predict the behavior of strengthened concrete beam using a large number of experimental data by applying artificial neural networks. Having parameters used as input nodes in ANN modeling such as elastic modulus of the FRP reinforcement, the ratio of the steel longitudinal reinforcement, dimensions of the beam section, the ratio of the NSM-FRP reinforcement and characteristics of concrete, the output node was the flexural strength of beams. The idealized neural network was employed to generate empirical charts and equations to be used in design. The aim of this study is to investigate the behavior of strengthened RC beam using artificial neural networks.

  2. Feedback dynamics and cell function: Why systems biology is called Systems Biology.

    Science.gov (United States)

    Wolkenhauer, Olaf; Mesarovic, Mihajlo

    2005-05-01

    A new paradigm, like Systems Biology, should challenge the way research has been conducted previously. This Opinion article aims to present Systems Biology, not as the application of engineering principles to biology but as a merger of systems- and control theory with molecular- and cell biology. In our view, the central dogma of Systems Biology is that it is system dynamics that gives rise to the functioning and function of cells. The concepts of feedback regulation and control of pathways and the coordination of cell function are emphasized as an important area of Systems Biology research. The hurdles and risks for this area are discussed from the perspective of dynamic pathway modelling. Most of all, the aim of this article is to promote mathematical modelling and simulation as a part of molecular- and cell biology. Systems Biology is a success if it is widely accepted that there is nothing more practical than a good theory.

  3. Dimensionality reduction of bistable biological systems.

    Science.gov (United States)

    Zakharova, A; Nikoloski, Z; Koseska, A

    2013-03-01

    Time hierarchies, arising as a result of interactions between system's components, represent a ubiquitous property of dynamical biological systems. In addition, biological systems have been attributed switch-like properties modulating the response to various stimuli across different organisms and environmental conditions. Therefore, establishing the interplay between these features of system dynamics renders itself a challenging question of practical interest in biology. Existing methods are suitable for systems with one stable steady state employed as a well-defined reference. In such systems, the characterization of the time hierarchies has already been used for determining the components that contribute to the dynamics of biological systems. However, the application of these methods to bistable nonlinear systems is impeded due to their inherent dependence on the reference state, which in this case is no longer unique. Here, we extend the applicability of the reference-state analysis by proposing, analyzing, and applying a novel method, which allows investigation of the time hierarchies in systems exhibiting bistability. The proposed method is in turn used in identifying the components, other than reactions, which determine the systemic dynamical properties. We demonstrate that in biological systems of varying levels of complexity and spanning different biological levels, the method can be effectively employed for model simplification while ensuring preservation of qualitative dynamical properties (i.e., bistability). Finally, by establishing a connection between techniques from nonlinear dynamics and multivariate statistics, the proposed approach provides the basis for extending reference-based analysis to bistable systems.

  4. Genomes, Phylogeny, and Evolutionary Systems Biology

    Energy Technology Data Exchange (ETDEWEB)

    Medina, Monica

    2005-03-25

    With the completion of the human genome and the growing number of diverse genomes being sequenced, a new age of evolutionary research is currently taking shape. The myriad of technological breakthroughs in biology that are leading to the unification of broad scientific fields such as molecular biology, biochemistry, physics, mathematics and computer science are now known as systems biology. Here I present an overview, with an emphasis on eukaryotes, of how the postgenomics era is adopting comparative approaches that go beyond comparisons among model organisms to shape the nascent field of evolutionary systems biology.

  5. Graphics processing units in bioinformatics, computational biology and systems biology.

    Science.gov (United States)

    Nobile, Marco S; Cazzaniga, Paolo; Tangherloni, Andrea; Besozzi, Daniela

    2017-09-01

    Several studies in Bioinformatics, Computational Biology and Systems Biology rely on the definition of physico-chemical or mathematical models of biological systems at different scales and levels of complexity, ranging from the interaction of atoms in single molecules up to genome-wide interaction networks. Traditional computational methods and software tools developed in these research fields share a common trait: they can be computationally demanding on Central Processing Units (CPUs), therefore limiting their applicability in many circumstances. To overcome this issue, general-purpose Graphics Processing Units (GPUs) are gaining an increasing attention by the scientific community, as they can considerably reduce the running time required by standard CPU-based software, and allow more intensive investigations of biological systems. In this review, we present a collection of GPU tools recently developed to perform computational analyses in life science disciplines, emphasizing the advantages and the drawbacks in the use of these parallel architectures. The complete list of GPU-powered tools here reviewed is available at http://bit.ly/gputools. © The Author 2016. Published by Oxford University Press.

  6. AN INTELLIGENT HYBRID NEURAL NETWORK MODEL IN RENEWABLE ENERGY SYSTEMS

    Directory of Open Access Journals (Sweden)

    K. Gnana Sheela

    2012-07-01

    Full Text Available This paper presents a hybrid neural network approach to predict wind speed automatically in renewable energy systems. Wind energy is one of the renewable energy systems with lowest cost of production of electricity with largest resources available. By the reason of the fluctuation and volatility in wind, the wind speed prediction provides the challenges in the stability of renewable energy system. The aim is to compute predicted wind speed based on hybrid model which integrates a Self Organizing Map (SOM and Back propagation (BP neural network. The simulation result shows that the proposed approach provides significant result of wind speed prediction with less error rates. Due to seasonality, single computing models have some disadvantages such as fluctuality, randomness and unstable. These disadvantages are rectified by using hybrid computing neural network models. Wind speed prediction is an important in the field of wind power plants.

  7. Role of neural network models for developing speech systems

    Indian Academy of Sciences (India)

    These prosody models are further examined for applications such as text to speech synthesis, speech recognition, speaker recognition and language identification. Neural network models in voice conversion system are explored for capturing the mapping functions between source and target speakers at source, system and ...

  8. Neural expert decision support system for stroke diagnosis

    Science.gov (United States)

    Kupershtein, Leonid M.; Martyniuk, Tatiana B.; Krencin, Myhail D.; Kozhemiako, Andriy V.; Bezsmertnyi, Yurii; Bezsmertna, Halyna; Kolimoldayev, Maksat; Smolarz, Andrzej; Weryńska-Bieniasz, RóŻa; Uvaysova, Svetlana

    2017-08-01

    In the work the hybrid expert system for stroke diagnosis was presented. The base of expert system consists of neural network and production rules. This program can quickly and accurately set to the patient preliminary and final diagnoses, get examination and treatment plans, print data of patient, analyze statistics data and perform parameterized search for patients.

  9. NNSYSID - toolbox for system identification with neural networks

    DEFF Research Database (Denmark)

    Norgaard, M.; Ravn, Ole; Poulsen, Niels Kjølstad

    2002-01-01

    The NNSYSID toolset for System Identification has been developed as an add on to MATLAB(R). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains a number of nonlinear model structures based on neural networks, effective training algorithms...

  10. Classical Conditioning with Pulsed Integrated Neural Networks: Circuits and System

    DEFF Research Database (Denmark)

    Lehmann, Torsten

    1998-01-01

    In this paper we investigate on-chip learning for pulsed, integrated neural networks. We discuss the implementational problems the technology imposes on learning systems and we find that abiologically inspired approach using simple circuit structures is most likely to bring success. We develop...... a suitable learning algorithm -- a continuous-time version of a temporal differential Hebbian learning algorithm for pulsed neural systems with non-linear synapses -- as well as circuits for the electronic implementation. Measurements from an experimental CMOS chip are presented. Finally, we use our test...

  11. Decarboxylation mechanisms in biological system.

    Science.gov (United States)

    Li, Tingfeng; Huo, Lu; Pulley, Christopher; Liu, Aimin

    2012-08-01

    This review examines the mechanisms propelling cofactor-independent, organic cofactor-dependent and metal-dependent decarboxylase chemistry. Decarboxylation, the removal of carbon dioxide from organic acids, is a fundamentally important reaction in biology. Numerous decarboxylase enzymes serve as key components of aerobic and anaerobic carbohydrate metabolism and amino acid conversion. In the past decade, our knowledge of the mechanisms enabling these crucial decarboxylase reactions has continued to expand and inspire. This review focuses on the organic cofactors biotin, flavin, NAD, pyridoxal 5'-phosphate, pyruvoyl, and thiamin pyrophosphate as catalytic centers. Significant attention is also placed on the metal-dependent decarboxylase mechanisms. Copyright © 2012 Elsevier Inc. All rights reserved.

  12. Reliability Modeling of Microelectromechanical Systems Using Neural Networks

    Science.gov (United States)

    Perera. J. Sebastian

    2000-01-01

    Microelectromechanical systems (MEMS) are a broad and rapidly expanding field that is currently receiving a great deal of attention because of the potential to significantly improve the ability to sense, analyze, and control a variety of processes, such as heating and ventilation systems, automobiles, medicine, aeronautical flight, military surveillance, weather forecasting, and space exploration. MEMS are very small and are a blend of electrical and mechanical components, with electrical and mechanical systems on one chip. This research establishes reliability estimation and prediction for MEMS devices at the conceptual design phase using neural networks. At the conceptual design phase, before devices are built and tested, traditional methods of quantifying reliability are inadequate because the device is not in existence and cannot be tested to establish the reliability distributions. A novel approach using neural networks is created to predict the overall reliability of a MEMS device based on its components and each component's attributes. The methodology begins with collecting attribute data (fabrication process, physical specifications, operating environment, property characteristics, packaging, etc.) and reliability data for many types of microengines. The data are partitioned into training data (the majority) and validation data (the remainder). A neural network is applied to the training data (both attribute and reliability); the attributes become the system inputs and reliability data (cycles to failure), the system output. After the neural network is trained with sufficient data. the validation data are used to verify the neural networks provided accurate reliability estimates. Now, the reliability of a new proposed MEMS device can be estimated by using the appropriate trained neural networks developed in this work.

  13. Tunable promoters in synthetic and systems biology

    DEFF Research Database (Denmark)

    Dehli, Tore; Solem, Christian; Jensen, Peter Ruhdal

    2012-01-01

    in synthetic biology. A number of tools exist to manipulate the steps in between gene sequence and functional protein in living cells, but out of these the most straight-forward approach is to alter the gene expression level by manipulating the promoter sequence. Some of the promoter tuning tools available......Synthetic and systems biologists need standardized, modular and orthogonal tools yielding predictable functions in vivo. In systems biology such tools are needed to quantitatively analyze the behavior of biological systems while the efficient engineering of artificial gene networks is central...

  14. Computational intelligence approaches for pattern discovery in biological systems.

    Science.gov (United States)

    Fogel, Gary B

    2008-07-01

    Biology, chemistry and medicine are faced by tremendous challenges caused by an overwhelming amount of data and the need for rapid interpretation. Computational intelligence (CI) approaches such as artificial neural networks, fuzzy systems and evolutionary computation are being used with increasing frequency to contend with this problem, in light of noise, non-linearity and temporal dynamics in the data. Such methods can be used to develop robust models of processes either on their own or in combination with standard statistical approaches. This is especially true for database mining, where modeling is a key component of scientific understanding. This review provides an introduction to current CI methods, their application to biological problems, and concludes with a commentary about the anticipated impact of these approaches in bioinformatics.

  15. Metabolic systems biology: a brief primer.

    Science.gov (United States)

    Edwards, Lindsay M

    2017-05-01

    In the early to mid-20th century, reductionism as a concept in biology was challenged by key thinkers, including Ludwig von Bertalanffy. He proposed that living organisms were specific examples of complex systems and, as such, they should display characteristics including hierarchical organisation and emergent behaviour. Yet the true study of complete biological systems (for example, metabolism) was not possible until technological advances that occurred 60 years later. Technology now exists that permits the measurement of complete levels of the biological hierarchy, for example the genome and transcriptome. The complexity and scale of these data require computational models for their interpretation. The combination of these - systems thinking, high-dimensional data and computation - defines systems biology, typically accompanied by some notion of iterative model refinement. Only sequencing-based technologies, however, offer full coverage. Other 'omics' platforms trade coverage for sensitivity, although the densely connected nature of biological networks suggests that full coverage may not be necessary. Systems biology models are often characterised as either 'bottom-up' (mechanistic) or 'top-down' (statistical). This distinction can mislead, as all models rely on data and all are, to some degree, 'middle-out'. Systems biology has matured as a discipline, and its methods are commonplace in many laboratories. However, many challenges remain, especially those related to large-scale data integration. © 2016 The Authors. The Journal of Physiology © 2016 The Physiological Society.

  16. An engineering design approach to systems biology.

    Science.gov (United States)

    Janes, Kevin A; Chandran, Preethi L; Ford, Roseanne M; Lazzara, Matthew J; Papin, Jason A; Peirce, Shayn M; Saucerman, Jeffrey J; Lauffenburger, Douglas A

    2017-07-17

    Measuring and modeling the integrated behavior of biomolecular-cellular networks is central to systems biology. Over several decades, systems biology has been shaped by quantitative biologists, physicists, mathematicians, and engineers in different ways. However, the basic and applied versions of systems biology are not typically distinguished, which blurs the separate aspirations of the field and its potential for real-world impact. Here, we articulate an engineering approach to systems biology, which applies educational philosophy, engineering design, and predictive models to solve contemporary problems in an age of biomedical Big Data. A concerted effort to train systems bioengineers will provide a versatile workforce capable of tackling the diverse challenges faced by the biotechnological and pharmaceutical sectors in a modern, information-dense economy.

  17. A NEURAL NETWORK BASED IRIS RECOGNITION SYSTEM FOR PERSONAL IDENTIFICATION

    Directory of Open Access Journals (Sweden)

    Usham Dias

    2010-10-01

    Full Text Available This paper presents biometric personal identification based on iris recognition using artificial neural networks. Personal identification system consists of localization of the iris region, normalization, enhancement and then iris pattern recognition using neural network. In this paper, through results obtained, we have shown that a person’s left and right eye are unique. In this paper, we also show that the network is sensitive to the initial weights and that over-training gives bad results. We also propose a fast algorithm for the localization of the inner and outer boundaries of the iris region. Results of simulations illustrate the effectiveness of the neural system in personal identification. Finally a hardware iris recognition model is proposed and implementation aspects are discussed.

  18. Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems.

    Science.gov (United States)

    Wang, Sheng-Jun; Ouyang, Guang; Guang, Jing; Zhang, Mingsha; Wong, K Y Michael; Zhou, Changsong

    2016-01-08

    Self-organized critical states (SOCs) and stochastic oscillations (SOs) are simultaneously observed in neural systems, which appears to be theoretically contradictory since SOCs are characterized by scale-free avalanche sizes but oscillations indicate typical scales. Here, we show that SOs can emerge in SOCs of small size systems due to temporal correlation between large avalanches at the finite-size cutoff, resulting from the accumulation-release process in SOCs. In contrast, the critical branching process without accumulation-release dynamics cannot exhibit oscillations. The reconciliation of SOCs and SOs is demonstrated both in the sandpile model and robustly in biologically plausible neuronal networks. The oscillations can be suppressed if external inputs eliminate the prominent slow accumulation process, providing a potential explanation of the widely studied Berger effect or event-related desynchronization in neural response. The features of neural oscillations and suppression are confirmed during task processing in monkey eye-movement experiments. Our results suggest that finite-size, columnar neural circuits may play an important role in generating neural oscillations around the critical states, potentially enabling functional advantages of both SOCs and oscillations for sensitive response to transient stimuli.

  19. Bionic models for identification of biological systems

    Science.gov (United States)

    Gerget, O. M.

    2017-01-01

    This article proposes a clinical decision support system that processes biomedical data. For this purpose a bionic model has been designed based on neural networks, genetic algorithms and immune systems. The developed system has been tested on data from pregnant women. The paper focuses on the approach to enable selection of control actions that can minimize the risk of adverse outcome. The control actions (hyperparameters of a new type) are further used as an additional input signal. Its values are defined by a hyperparameter optimization method. A software developed with Python is briefly described.

  20. Optical neural network system for pose determination of spinning satellites

    Science.gov (United States)

    Lee, Andrew; Casasent, David

    1990-01-01

    An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.

  1. Neural computing for numeric-to-symbolic conversion in control systems

    Science.gov (United States)

    Passino, Kevin M.; Sartori, Michael A.; Antsaklis, Panos J.

    1989-01-01

    A type of neural network, the multilayer perceptron, is used to classify numeric data and assign appropriate symbols to various classes. This numeric-to-symbolic conversion results in a type of information extraction, which is similar to what is called data reduction in pattern recognition. The use of the neural network as a numeric-to-symbolic converter is introduced, its application in autonomous control is discussed, and several applications are studied. The perceptron is used as a numeric-to-symbolic converter for a discrete-event system controller supervising a continuous variable dynamic system. It is also shown how the perceptron can implement fault trees, which provide useful information (alarms) in a biological system and information for failure diagnosis and control purposes in an aircraft example.

  2. Emergent latent symbol systems in recurrent neural networks

    Science.gov (United States)

    Monner, Derek; Reggia, James A.

    2012-12-01

    Fodor and Pylyshyn [(1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28(1-2), 3-71] famously argued that neural networks cannot behave systematically short of implementing a combinatorial symbol system. A recent response from Frank et al. [(2009). Connectionist semantic systematicity. Cognition, 110(3), 358-379] claimed to have trained a neural network to behave systematically without implementing a symbol system and without any in-built predisposition towards combinatorial representations. We believe systems like theirs may in fact implement a symbol system on a deeper and more interesting level: one where the symbols are latent - not visible at the level of network structure. In order to illustrate this possibility, we demonstrate our own recurrent neural network that learns to understand sentence-level language in terms of a scene. We demonstrate our model's learned understanding by testing it on novel sentences and scenes. By paring down our model into an architecturally minimal version, we demonstrate how it supports combinatorial computation over distributed representations by using the associative memory operations of Vector Symbolic Architectures. Knowledge of the model's memory scheme gives us tools to explain its errors and construct superior future models. We show how the model designs and manipulates a latent symbol system in which the combinatorial symbols are patterns of activation distributed across the layers of a neural network, instantiating a hybrid of classical symbolic and connectionist representations that combines advantages of both.

  3. EURASIP journal on bioinformatics & systems biology

    National Research Council Canada - National Science Library

    2006-01-01

    "The overall aim of "EURASIP Journal on Bioinformatics and Systems Biology" is to publish research results related to signal processing and bioinformatics theories and techniques relevant to a wide...

  4. A framework for evolutionary systems biology.

    Science.gov (United States)

    Loewe, Laurence

    2009-02-24

    Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects. Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions in silico. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism. EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications.

  5. Arabic morphology in the neural language system.

    Science.gov (United States)

    Boudelaa, Sami; Pulvermüller, Friedemann; Hauk, Olaf; Shtyrov, Yury; Marslen-Wilson, William

    2010-05-01

    There are two views about morphology, the aspect of language concerned with the internal structure of words. One view holds that morphology is a domain of knowledge with a specific type of neurocognitive representation supported by specific brain mechanisms lateralized to left fronto-temporal cortex. The alternate view characterizes morphological effects as being a by-product of the correlation between form and meaning and where no brain area is predicted to subserve morphological processing per se. Here we provided evidence from Arabic that morphemes do have specific memory traces, which differ as a function of their functional properties. In an MMN study, we showed that the abstract consonantal root, which conveys semantic meaning (similarly to monomorphemic content words in English), elicits an MMN starting from 160 msec after the deviation point, whereas the abstract vocalic word pattern, which plays a range of grammatical roles, elicits an MMN response starting from 250 msec after the deviation point. Topographically, the root MMN has a symmetric fronto-central distribution, whereas the word pattern MMN lateralizes significantly to the left, indicating stronger involvement of left peri-sylvian areas. In languages with rich morphologies, morphemic processing seems to be supported by distinct neural networks, thereby providing evidence for a specific neuronal basis for morphology as part of the cerebral language machinery.

  6. Using fuzzy logic to integrate neural networks and knowledge-based systems

    Science.gov (United States)

    Yen, John

    1991-01-01

    Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

  7. Neural network based system for script identification in Indian ...

    Indian Academy of Sciences (India)

    2016-08-26

    Aug 26, 2016 ... The paper describes a neural network-based script identification system which can be used in the machine reading of documents written in English, Hindi and Kannada language scripts. Script identification is a basic requirement in automation of document processing, in multi-script, multi-lingual ...

  8. Classical Conditioning with Pulsed Integrated Neural Networks: Circuits and System

    DEFF Research Database (Denmark)

    Lehmann, Torsten

    1998-01-01

    In this paper we investigate on-chip learning for pulsed, integrated neural networks. We discuss the implementational problems the technology imposes on learning systems and we find that abiologically inspired approach using simple circuit structures is most likely to bring success. We develop a ...... chip to solve simple classical conditioning tasks, thus verifying the design methodologies put forward in the paper....

  9. Dissipative rendering and neural network control system design

    Science.gov (United States)

    Gonzalez, Oscar R.

    1995-01-01

    Model-based control system designs are limited by the accuracy of the models of the plant, plant uncertainty, and exogenous signals. Although better models can be obtained with system identification, the models and control designs still have limitations. One approach to reduce the dependency on particular models is to design a set of compensators that will guarantee robust stability to a set of plants. Optimization over the compensator parameters can then be used to get the desired performance. Conservativeness of this approach can be reduced by integrating fundamental properties of the plant models. This is the approach of dissipative control design. Dissipative control designs are based on several variations of the Passivity Theorem, which have been proven for nonlinear/linear and continuous-time/discrete-time systems. These theorems depend not on a specific model of a plant, but on its general dissipative properties. Dissipative control design has found wide applicability in flexible space structures and robotic systems that can be configured to be dissipative. Currently, there is ongoing research to improve the performance of dissipative control designs. For aircraft systems that are not dissipative active control may be used to make them dissipative and then a dissipative control design technique can be used. It is also possible that rendering a system dissipative and dissipative control design may be combined into one step. Furthermore, the transformation of a non-dissipative system to dissipative can be done robustly. One sequential design procedure for finite dimensional linear time-invariant systems has been developed. For nonlinear plants that cannot be controlled adequately with a single linear controller, model-based techniques have additional problems. Nonlinear system identification is still a research topic. Lacking analytical models for model-based design, artificial neural network algorithms have recently received considerable attention. Using

  10. A Philosophical Perspective on Evolutionary Systems Biology.

    Science.gov (United States)

    O'Malley, Maureen A; Soyer, Orkun S; Siegal, Mark L

    2015-03-01

    Evolutionary systems biology (ESB) is an emerging hybrid approach that integrates methods, models, and data from evolutionary and systems biology. Drawing on themes that arose at a cross-disciplinary meeting on ESB in 2013, we discuss in detail some of the explanatory friction that arises in the interaction between evolutionary and systems biology. These tensions appear because of different modeling approaches, diverse explanatory aims and strategies, and divergent views about the scope of the evolutionary synthesis. We locate these discussions in the context of long-running philosophical deliberations on explanation, modeling, and theoretical synthesis. We show how many of the issues central to ESB's progress can be understood as general philosophical problems. The benefits of addressing these philosophical issues feed back into philosophy too, because ESB provides excellent examples of scientific practice for the development of philosophy of science and philosophy of biology.

  11. Neural mechanisms of selective attention in the somatosensory system

    Science.gov (United States)

    Hysaj, Kristjana; Niebur, Ernst

    2016-01-01

    Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates. PMID:27334956

  12. Neural mechanisms of selective attention in the somatosensory system.

    Science.gov (United States)

    Gomez-Ramirez, Manuel; Hysaj, Kristjana; Niebur, Ernst

    2016-09-01

    Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates. Copyright © 2016 the American Physiological Society.

  13. SECLAF: A Webserver and Deep Neural Network Design Tool for Hierarchical Biological Sequence Classification.

    Science.gov (United States)

    Szalkai, Balázs; Grolmusz, Vince

    2018-02-27

    Artificial intelligence (AI) tools are gaining more and more ground each year in bioinformatics. Learning algorithms can be taught for specific tasks by using the existing enormous biological databases, and the resulting models can be used for the high-quality classification of novel, un-categorized data in numerous areas, including biological sequence analysis. Here we introduce SECLAF, a webserver that uses deep neural networks for hierarchical biological sequence classification. By applying SECLAF for residue-sequences, we have reported (Methods (2018), https://doi.org/10.1016/j.ymeth.2017.06.034) the most accurate multi-label protein classifier to date (UniProt -into 698 classes- AUC 99.99%; Gene Ontology -into 983 classes- AUC 99.45%). Our framework SECLAF can be applied for other sequence classification tasks, as we describe in the present contribution. The program SECLAF is implemented in Python, and is available for download, with example datasets at the website https://pitgroup.org/seclaf/. For Gene Ontology and UniProt based classifications a webserver is also available at the address above. grolmusz@pitgroup.org and szalkai@pitgroup.org.

  14. Systems biology: leading the revolution in ecotoxicology.

    Science.gov (United States)

    Garcia-Reyero, Natàlia; Perkins, Edward J

    2011-02-01

    The rapid development of new technologies such as transcriptomics, proteomics, and metabolomics (Omics) are changing the way ecotoxicology is practiced. The data deluge has begun with genomes of over 65 different aquatic species that are currently being sequenced, and many times that number with at least some level of transcriptome sequencing. Integrating these top-down methodologies is an essential task in the field of systems biology. Systems biology is a biology-based interdisciplinary field that focuses on complex interactions in biological systems, with the intent to model and discover emergent properties of the system. Recent studies demonstrate that Omics technologies provide valuable insight into ecotoxicity, both in laboratory exposures with model organisms and with animals exposed in the field. However, these approaches require a context of the whole animal and population to be relevant. Powerful approaches using reverse engineering to determine interacting networks of genes, proteins, or biochemical reactions are uncovering unique responses to toxicants. Modeling efforts in aquatic animals are evolving to interrelate the interacting networks of a system and the flow of information linking these elements. Just as is happening in medicine, systems biology approaches that allow the integration of many different scales of interaction and information are already driving a revolution in understanding the impacts of pollutants on aquatic systems. © 2010 SETAC.

  15. Carbon-13 NMR spectroscopy of biological systems

    CERN Document Server

    Beckmann, Nicolau

    1995-01-01

    This book is intended to provide an in-depth understanding of 13C NMR as a tool in biological research. 13C NMR has provided unique information concerning complex biological systems, from proteins and nucleic acids to animals and humans. The subjects addressed include multidimensional heteronuclear techniques for structural studies of molecules in the liquid and solid states, the investigation of interactions in model membranes, the elucidation of metabolic pathwaysin vitro and in vivo on animals, and noninvasive metabolic studies performed on humans. The book is a unique mix of NMR methods and biological applications which makes it a convenient reference for those interested in research in this interdisciplinary area of physics, chemistry, biology, and medicine.Key Features* An interdisciplinary text with emphasis on both 13C NMR methodology and the relevant biological and biomedical issues* State-of-the-art 13C NMR techniques are described; Whenever possible, their advantages over other approaches are empha...

  16. Cognitive training for impaired neural systems in neuropsychiatric illness.

    Science.gov (United States)

    Vinogradov, Sophia; Fisher, Melissa; de Villers-Sidani, Etienne

    2012-01-01

    Neuropsychiatric illnesses are associated with dysfunction in distributed prefrontal neural systems that underlie perception, cognition, social interactions, emotion regulation, and motivation. The high degree of learning-dependent plasticity in these networks-combined with the availability of advanced computerized technology-suggests that we should be able to engineer very specific training programs that drive meaningful and enduring improvements in impaired neural systems relevant to neuropsychiatric illness. However, cognitive training approaches for mental and addictive disorders must take into account possible inherent limitations in the underlying brain 'learning machinery' due to pathophysiology, must grapple with the presence of complex overlearned maladaptive patterns of neural functioning, and must find a way to ally with developmental and psychosocial factors that influence response to illness and to treatment. In this review, we briefly examine the current state of knowledge from studies of cognitive remediation in psychiatry and we highlight open questions. We then present a systems neuroscience rationale for successful cognitive training for neuropsychiatric illnesses, one that emphasizes the distributed nature of neural assemblies that support cognitive and affective processing, as well as their plasticity. It is based on the notion that, during successful learning, the brain represents the relevant perceptual and cognitive/affective inputs and action outputs with disproportionately larger and more coordinated populations of neurons that are distributed (and that are interacting) across multiple levels of processing and throughout multiple brain regions. This approach allows us to address limitations found in earlier research and to introduce important principles for the design and evaluation of the next generation of cognitive training for impaired neural systems. We summarize work to date using such neuroscience-informed methods and indicate some

  17. Distributed Adaptive Neural Control for Stochastic Nonlinear Multiagent Systems.

    Science.gov (United States)

    Wang, Fang; Chen, Bing; Lin, Chong; Li, Xuehua

    2016-11-14

    In this paper, a consensus tracking problem of nonlinear multiagent systems is investigated under a directed communication topology. All the followers are modeled by stochastic nonlinear systems in nonstrict feedback form, where nonlinearities and stochastic disturbance terms are totally unknown. Based on the structural characteristic of neural networks (in Lemma 4), a novel distributed adaptive neural control scheme is put forward. The raised control method not only effectively handles unknown nonlinearities in nonstrict feedback systems, but also copes with the interactions among agents and coupling terms. Based on the stochastic Lyapunov functional method, it is indicated that all the signals of the closed-loop system are bounded in probability and all followers' outputs are convergent to a neighborhood of the output of leader. At last, the efficiency of the control method is testified by a numerical example.

  18. Synthesis of recurrent neural networks for dynamical system simulation.

    Science.gov (United States)

    Trischler, Adam P; D'Eleuterio, Gabriele M T

    2016-08-01

    We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Three neural network based sensor systems for environmental monitoring

    International Nuclear Information System (INIS)

    Keller, P.E.; Kouzes, R.T.; Kangas, L.J.

    1994-05-01

    Compact, portable systems capable of quickly identifying contaminants in the field are of great importance when monitoring the environment. One of the missions of the Pacific Northwest Laboratory is to examine and develop new technologies for environmental restoration and waste management at the Hanford Site. In this paper, three prototype sensing systems are discussed. These prototypes are composed of sensing elements, data acquisition system, computer, and neural network implemented in software, and are capable of automatically identifying contaminants. The first system employs an array of tin-oxide gas sensors and is used to identify chemical vapors. The second system employs an array of optical sensors and is used to identify the composition of chemical dyes in liquids. The third system contains a portable gamma-ray spectrometer and is used to identify radioactive isotopes. In these systems, the neural network is used to identify the composition of the sensed contaminant. With a neural network, the intense computation takes place during the training process. Once the network is trained, operation consists of propagating the data through the network. Since the computation involved during operation consists of vector-matrix multiplication and application of look-up tables unknown samples can be rapidly identified in the field

  20. Communications and control for electric power systems: Power system stability applications of artificial neural networks

    Science.gov (United States)

    Toomarian, N.; Kirkham, Harold

    1994-01-01

    This report investigates the application of artificial neural networks to the problem of power system stability. The field of artificial intelligence, expert systems, and neural networks is reviewed. Power system operation is discussed with emphasis on stability considerations. Real-time system control has only recently been considered as applicable to stability, using conventional control methods. The report considers the use of artificial neural networks to improve the stability of the power system. The networks are considered as adjuncts and as replacements for existing controllers. The optimal kind of network to use as an adjunct to a generator exciter is discussed.

  1. Neural multigrid for gauge theories and other disordered systems

    International Nuclear Information System (INIS)

    Baeker, M.; Kalkreuter, T.; Mack, G.; Speh, M.

    1992-09-01

    We present evidence that multigrid works for wave equations in disordered systems, e.g. in the presence of gauge fields, no matter how strong the disorder, but one needs to introduce a 'neural computations' point of view into large scale simulations: First, the system must learn how to do the simulations efficiently, then do the simulation (fast). The method can also be used to provide smooth interpolation kernels which are needed in multigrid Monte Carlo updates. (orig.)

  2. Systems Biology and Stem Cell Pluripotency

    DEFF Research Database (Denmark)

    Mashayekhi, Kaveh; Hall, Vanessa Jane; Freude, Kristine

    2016-01-01

    Recent breakthroughs in stem cell biology have accelerated research in the area of regenerative medicine. Over the past years, it has become possible to derive patient-specific stem cells which can be used to generate different cell populations for potential cell therapy. Systems biological...... improve systems biology and its uses in the field. In this chapter, we first give a general background on stem cell biology and regenerative medicine. Stem cell potency is introduced together with the hierarchy of stem cells ranging from pluripotent embryonic stem cells (ESCs) and induced pluripotent stem...... modeling of stem cell pluripotency and differentiation have largely been based on prior knowledge of signaling pathways, gene regulatory networks, and epigenetic factors. However, there is a great need to extend the complexity of the modeling and to integrate different types of data, which would further...

  3. Parametric motion control of robotic arms: A biologically based approach using neural networks

    Science.gov (United States)

    Bock, O.; D'Eleuterio, G. M. T.; Lipitkas, J.; Grodski, J. J.

    1993-01-01

    A neural network based system is presented which is able to generate point-to-point movements of robotic manipulators. The foundation of this approach is the use of prototypical control torque signals which are defined by a set of parameters. The parameter set is used for scaling and shaping of these prototypical torque signals to effect a desired outcome of the system. This approach is based on neurophysiological findings that the central nervous system stores generalized cognitive representations of movements called synergies, schemas, or motor programs. It has been proposed that these motor programs may be stored as torque-time functions in central pattern generators which can be scaled with appropriate time and magnitude parameters. The central pattern generators use these parameters to generate stereotypical torque-time profiles, which are then sent to the joint actuators. Hence, only a small number of parameters need to be determined for each point-to-point movement instead of the entire torque-time trajectory. This same principle is implemented for controlling the joint torques of robotic manipulators where a neural network is used to identify the relationship between the task requirements and the torque parameters. Movements are specified by the initial robot position in joint coordinates and the desired final end-effector position in Cartesian coordinates. This information is provided to the neural network which calculates six torque parameters for a two-link system. The prototypical torque profiles (one per joint) are then scaled by those parameters. After appropriate training of the network, our parametric control design allowed the reproduction of a trained set of movements with relatively high accuracy, and the production of previously untrained movements with comparable accuracy. We conclude that our approach was successful in discriminating between trained movements and in generalizing to untrained movements.

  4. Integrative Systems Biology Visualization with MAYDAY

    Directory of Open Access Journals (Sweden)

    Symonsy Stephan

    2010-12-01

    Full Text Available Visualization is pivotal for gaining insight in systems biology data. As the size and complexity of datasets and supplemental information increases, an efficient, integrated framework for general and specialized views is necessary. MAYDAY is an application for analysis and visualization of general ‘omics’ data. It follows a trifold approach for data visualization, consisting of flexible data preprocessing, highly customizable data perspective plots for general purpose visualization and systems based plots. Here, we introduce two new systems biology visualization tools for MAYDAY. Efficiently implemented genomic viewers allow the display of variables associated with genomic locations. Multiple variables can be viewed using our new track-based ChromeTracks tool. A functional perspective is provided by visualizing metabolic pathways either in KEGG or BioPax format. Multiple options of displaying pathway components are available, including Systems Biology Graphical Notation (SBGN glyphs. Furthermore, pathways can be viewed together with gene expression data either as heatmaps or profiles.

  5. Statistical Model Checking for Biological Systems

    DEFF Research Database (Denmark)

    David, Alexandre; Larsen, Kim Guldstrand; Legay, Axel

    2014-01-01

    Statistical Model Checking (SMC) is a highly scalable simulation-based verification approach for testing and estimating the probability that a stochastic system satisfies a given linear temporal property. The technique has been applied to (discrete and continuous time) Markov chains, stochastic...... timed automata and most recently hybrid systems using the tool Uppaal SMC. In this paper we enable the application of SMC to complex biological systems, by combining Uppaal SMC with ANIMO, a plugin of the tool Cytoscape used by biologists, as well as with SimBiology®, a plugin of Matlab to simulate...

  6. Parameter estimation in space systems using recurrent neural networks

    Science.gov (United States)

    Parlos, Alexander G.; Atiya, Amir F.; Sunkel, John W.

    1991-01-01

    The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.

  7. Dynamical systems, attractors, and neural circuits [version 1; referees: 3 approved

    Directory of Open Access Journals (Sweden)

    Paul Miller

    2016-05-01

    Full Text Available Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic—they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

  8. Analysis of the DWPF glass pouring system using neural networks

    International Nuclear Information System (INIS)

    Calloway, T.B. Jr.; Jantzen, C.M.

    1997-01-01

    Neural networks were used to determine the sensitivity of 39 selected Melter/Melter Off Gas and Melter Feed System process parameters as related to the Defense Waste Processing Facility (DWPF) Melter Pour Spout Pressure during the overall analysis and resolution of the DWPF glass production and pouring issues. Two different commercial neural network software packages were used for this analysis. Models were developed and used to determine the critical parameters which accurately describe the DWPF Pour Spout Pressure. The model created using a low-end software package has a root mean square error of ± 0.35 inwc ( 2 = 0.77) with respect to the plant data used to validate and test the model. The model created using a high-end software package has a R 2 = 0.97 with respect to the plant data used to validate and test the model. The models developed for this application identified the key process parameters which contribute to the control of the DWPF Melter Pour Spout pressure during glass pouring operations. The relative contribution and ranking of the selected parameters was determined using the modeling software. Neural network computing software was determined to be a cost-effective software tool for process engineers performing troubleshooting and system performance monitoring activities. In remote high-level waste processing environments, neural network software is especially useful as a replacement for sensors which have failed and are costly to replace. The software can be used to accurately model critical remotely installed plant instrumentation. When the instrumentation fails, the software can be used to provide a soft sensor to replace the actual sensor, thereby decreasing the overall operating cost. Additionally, neural network software tools require very little training and are especially useful in mining or selecting critical variables from the vast amounts of data collected from process computers

  9. Ins and outs of systems biology vis-à-vis molecular biology: continuation or clear cut?

    Science.gov (United States)

    De Backer, Philippe; De Waele, Danny; Van Speybroeck, Linda

    2010-03-01

    The comprehension of living organisms in all their complexity poses a major challenge to the biological sciences. Recently, systems biology has been proposed as a new candidate in the development of such a comprehension. The main objective of this paper is to address what systems biology is and how it is practised. To this end, the basic tools of a systems biological approach are explored and illustrated. In addition, it is questioned whether systems biology 'revolutionizes' molecular biology and 'transcends' its assumed reductionism. The strength of this claim appears to depend on how molecular and systems biology are characterised and on how reductionism is interpreted. Doing credit to molecular biology and to methodological reductionism, it is argued that the distinction between molecular and systems biology is gradual rather than sharp. As such, the classical challenge in biology to manage, interpret and integrate biological data into functional wholes is further intensified by systems biology's use of modelling and bioinformatics, and by its scale enlargement.

  10. Photosynthetic system as a biological functional element

    International Nuclear Information System (INIS)

    Zakhidov, E.A.; Zakhidova, M.A.; Kasymdzhanov, M.A.; Kurbanov, S.S.; Nematov, Sh.K.; Khabibullaev, P.K.

    2005-01-01

    Photosynthetic apparatus of high plants and photosynthetic bacteria is essentially autonomic system in terms of genetics and structural -functional properties located in specific medium, a bio-membrane. Processes of light absorption and exciton migration in light harvesting antenna, separation and further transfer of charges in reaction centers have specific features, which may be used for application of these objects as key elements in construction of future biological functional elements. Progress in study and genetic modification of photosynthetic membranes achieved during the last decade opens great prospects in development biological functional elements and systems. The main characteristics of photosynthetic system for these purposes are: (i) energy conversion processes in the first light phase of the photosynthesis have very short periods, up to picoseconds, which indicates possibility of creation of ultrafast functional elements on their basis; (ii) characteristics sizes of photosynthetic units, 10-100 nm, and possibility to arrange regularly disposed elements in relevant membranes could be prospective point for creation of nano structures and on their basis relevant biologic functional elements; (iii) elements based on modified photosynthetic apparatus and bio-membranes might be efficiently created by methods of gene engineering and manipulation, that open huge opportunities for development of read biological functional systems. In the paper structural-functional properties and characteristics of high plants and purple photosynthetic bacteria, which may be useful for creation of future biological functional elements are considered. (author)

  11. Neural networks for control of nuclear reactors systems; Redes neurais para controle de sistemas de reatores nucleares

    Energy Technology Data Exchange (ETDEWEB)

    Baptista Filho, Benedito Dias

    1998-07-01

    A new architecture of artificial neural networks was developed with the development of new control systems for nuclear facilities and robotics. The new concepts introduced with this architecture were based in the design of task dedicated networks, in the use of multiple synaptic contacts with new transfer functions, and in the use of synaptic plasticity processes similar to that of biological organisms. These concepts take advantage of the evolutionary aspects of biological systems in their architecture, signal transmission, and memory and learning processes, given rise to a generalization capacity not obtained with classical feed-forward (F-F) neural networks. The new concepts were applied to the control of a planar two-link robotic manipulator and to the control of a natural circulation water loop. Its efficiency and generalization capacity were compared with a classic neural network in F-F, trained with the back propagation algorithm. The excellent performance obtained, mainly in the manipulator's problem, that characterizes a system of dynamic more complex, demonstrated that the use of the new task specialized network, produces much better results than the results obtained with use of generic F-F networks. The training task in the new proposed neural network was 150 times faster than the F-F neural network training with back-propagation. The results showed that the positioning errors with the new model were up to 60 times smaller than the errors found with the F-F network. (author)

  12. Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Biaobiao Zhang

    2011-01-01

    Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

  13. Neural Mechanisms and Information Processing in Recognition Systems

    Directory of Open Access Journals (Sweden)

    Mamiko Ozaki

    2014-10-01

    Full Text Available Nestmate recognition is a hallmark of social insects. It is based on the match/mismatch of an identity signal carried by members of the society with that of the perceiving individual. While the behavioral response, amicable or aggressive, is very clear, the neural systems underlying recognition are not fully understood. Here we contrast two alternative hypotheses for the neural mechanisms that are responsible for the perception and information processing in recognition. We focus on recognition via chemical signals, as the common modality in social insects. The first, classical, hypothesis states that upon perception of recognition cues by the sensory system the information is passed as is to the antennal lobes and to higher brain centers where the information is deciphered and compared to a neural template. Match or mismatch information is then transferred to some behavior-generating centers where the appropriate response is elicited. An alternative hypothesis, that of “pre-filter mechanism”, posits that the decision as to whether to pass on the information to the central nervous system takes place in the peripheral sensory system. We suggest that, through sensory adaptation, only alien signals are passed on to the brain, specifically to an “aggressive-behavior-switching center”, where the response is generated if the signal is above a certain threshold.

  14. Quantum Processes and Dynamic Networks in Physical and Biological Systems.

    Science.gov (United States)

    Dudziak, Martin Joseph

    Quantum theory since its earliest formulations in the Copenhagen Interpretation has been difficult to integrate with general relativity and with classical Newtonian physics. There has been traditionally a regard for quantum phenomena as being a limiting case for a natural order that is fundamentally classical except for microscopic extrema where quantum mechanics must be applied, more as a mathematical reconciliation rather than as a description and explanation. Macroscopic sciences including the study of biological neural networks, cellular energy transports and the broad field of non-linear and chaotic systems point to a quantum dimension extending across all scales of measurement and encompassing all of Nature as a fundamentally quantum universe. Theory and observation lead to a number of hypotheses all of which point to dynamic, evolving networks of fundamental or elementary processes as the underlying logico-physical structure (manifestation) in Nature and a strongly quantized dimension to macroscalar processes such as are found in biological, ecological and social systems. The fundamental thesis advanced and presented herein is that quantum phenomena may be the direct consequence of a universe built not from objects and substance but from interacting, interdependent processes collectively operating as sets and networks, giving rise to systems that on microcosmic or macroscopic scales function wholistically and organically, exhibiting non-locality and other non -classical phenomena. The argument is made that such effects as non-locality are not aberrations or departures from the norm but ordinary consequences of the process-network dynamics of Nature. Quantum processes are taken to be the fundamental action-events within Nature; rather than being the exception quantum theory is the rule. The argument is also presented that the study of quantum physics could benefit from the study of selective higher-scale complex systems, such as neural processes in the brain

  15. Combustion optimization using an expert system and neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Levy, E.; Pfahler, J.; Miles, J.; Keller, F.; Lee, P.; Woldehanna, S. [Lehigh Univ., Bethlehem, PA (United States). Energy Research Center; Williams, S. [Potomac Electric Power Co., Upper Marlboro, MD (United States)

    1996-12-31

    Lehigh University`s Energy Research Center and the Potomac Electric Power Company have been developing software for use by plant personnel in turning a pulverized coal-fired boiler to achieve optimized combustion. The software is based on the interactions of an expert system, neural network, and a mathematical optimization algorithm. It uses the expert system to safely guide the plant engineer through a series of parametric boiler tests and gather a data base which characterizes boiler operation over a wide range of conditions. The neural network portion develops non-linear mapping functions between the outputs of NO{sub x}, heat rate, LOI, opacity, and the controllable boiler input parameters. These mapping functions are then analyzed by the mathematical optimization algorithm and the optimal boiler operating conditions are identified. This paper describes the application of the software to corner-fired boilers with either conventional or low NO{sub x} burners and overfire air.

  16. Nonlinear dynamical system approaches towards neural prosthesis

    International Nuclear Information System (INIS)

    Torikai, Hiroyuki; Hashimoto, Sho

    2011-01-01

    An asynchronous discrete-state spiking neurons is a wired system of shift registers that can mimic nonlinear dynamics of an ODE-based neuron model. The control parameter of the neuron is the wiring pattern among the registers and thus they are suitable for on-chip learning. In this paper an asynchronous discrete-state spiking neuron is introduced and its typical nonlinear phenomena are demonstrated. Also, a learning algorithm for a set of neurons is presented and it is demonstrated that the algorithm enables the set of neurons to reconstruct nonlinear dynamics of another set of neurons with unknown parameter values. The learning function is validated by FPGA experiments.

  17. Discovery of Chemical Toxicity via Biological Networks and Systems Biology

    Energy Technology Data Exchange (ETDEWEB)

    Perkins, Edward; Habib, Tanwir; Guan, Xin; Escalon, Barbara; Falciani, Francesco; Chipman, J.K.; Antczak, Philipp; Edwards, Stephen; Taylor, Ronald C.; Vulpe, Chris; Loguinov, Alexandre; Van Aggelen, Graham; Villeneuve, Daniel L.; Garcia-Reyero, Natalia

    2010-09-30

    Both soldiers and animals are exposed to many chemicals as the result of military activities. Tools are needed to understand the hazards and risks that chemicals and new materials pose to soldiers and the environment. We have investigated the potential of global gene regulatory networks in understanding the impact of chemicals on reproduction. We characterized effects of chemicals on ovaries of the model animal system, the Fathead minnow (Pimopheles promelas) connecting chemical impacts on gene expression to circulating blood levels of the hormones testosterone and estradiol in addition to the egg yolk protein vitellogenin. We describe the application of reverse engineering complex interaction networks from high dimensional gene expression data to characterize chemicals that disrupt the hypothalamus-pituitary-gonadal endocrine axis that governs reproduction in fathead minnows. The construction of global gene regulatory networks provides deep insights into how drugs and chemicals effect key organs and biological pathways.

  18. Engineering neural systems for high-level problem solving.

    Science.gov (United States)

    Sylvester, Jared; Reggia, James

    2016-07-01

    There is a long-standing, sometimes contentious debate in AI concerning the relative merits of a symbolic, top-down approach vs. a neural, bottom-up approach to engineering intelligent machine behaviors. While neurocomputational methods excel at lower-level cognitive tasks (incremental learning for pattern classification, low-level sensorimotor control, fault tolerance and processing of noisy data, etc.), they are largely non-competitive with top-down symbolic methods for tasks involving high-level cognitive problem solving (goal-directed reasoning, metacognition, planning, etc.). Here we take a step towards addressing this limitation by developing a purely neural framework named galis. Our goal in this work is to integrate top-down (non-symbolic) control of a neural network system with more traditional bottom-up neural computations. galis is based on attractor networks that can be "programmed" with temporal sequences of hand-crafted instructions that control problem solving by gating the activity retention of, communication between, and learning done by other neural networks. We demonstrate the effectiveness of this approach by showing that it can be applied successfully to solve sequential card matching problems, using both human performance and a top-down symbolic algorithm as experimental controls. Solving this kind of problem makes use of top-down attention control and the binding together of visual features in ways that are easy for symbolic AI systems but not for neural networks to achieve. Our model can not only be instructed on how to solve card matching problems successfully, but its performance also qualitatively (and sometimes quantitatively) matches the performance of both human subjects that we had perform the same task and the top-down symbolic algorithm that we used as an experimental control. We conclude that the core principles underlying the galis framework provide a promising approach to engineering purely neurocomputational systems for problem

  19. Heavy ion action on biological systems

    Energy Technology Data Exchange (ETDEWEB)

    Kiefer, J. [Giessen Univ. (Germany). Zentrum fuer Radiologie; Brend`amour, M. [Giessen Univ. (Germany). Zentrum fuer Radiologie; Stoll, U. [Giessen Univ. (Germany). Zentrum fuer Radiologie

    1996-02-01

    Life is governed by molecular processes, particularly involving the expression and conservation of genetic information. Heavy ions deposit large amount of energy at very small scale comparable to the essential molecular structures of biological systems. This paper illustrates the special aspects of heavy ion radiobiology from a fundamental point of view. After a short summary of the structure and function of biological systems, concentrating on the cell and its constituents, the pattern of energy deposition by heavy ions is discussed. Experimental examples are the induction of molecular changes in deoxyribonucleic acid, cell killing and the formation of mutations. It is shown that a close link exists between the physical parameters and the inactivation of biological functions that is not restricted to direct particle traversals but may also be brought about by the action of far-reaching secondary electrons. (orig.).

  20. Generalized Predictive and Neural Generalized Predictive Control of Aerospace Systems

    Science.gov (United States)

    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.

  1. Concepts in context: processing mental state concepts with internal or external focus involves different neural systems

    NARCIS (Netherlands)

    Oosterwijk, S.; Mackey, S.; Wilson-Mendenhall, C.; Winkielman, P.; Paulus, M.P.

    2015-01-01

    According to embodied cognition theories, concepts are contextually situated and grounded in neural systems that produce experiential states. This view predicts that processing mental state concepts recruits neural regions associated with different aspects of experience depending on the context in

  2. Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

    Directory of Open Access Journals (Sweden)

    Bahita Mohamed

    2011-01-01

    Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

  3. A biologically inspired neural model for visual and proprioceptive integration including sensory training.

    Science.gov (United States)

    Saidi, Maryam; Towhidkhah, Farzad; Gharibzadeh, Shahriar; Lari, Abdolaziz Azizi

    2013-12-01

    Humans perceive the surrounding world by integration of information through different sensory modalities. Earlier models of multisensory integration rely mainly on traditional Bayesian and causal Bayesian inferences for single causal (source) and two causal (for two senses such as visual and auditory systems), respectively. In this paper a new recurrent neural model is presented for integration of visual and proprioceptive information. This model is based on population coding which is able to mimic multisensory integration of neural centers in the human brain. The simulation results agree with those achieved by casual Bayesian inference. The model can also simulate the sensory training process of visual and proprioceptive information in human. Training process in multisensory integration is a point with less attention in the literature before. The effect of proprioceptive training on multisensory perception was investigated through a set of experiments in our previous study. The current study, evaluates the effect of both modalities, i.e., visual and proprioceptive training and compares them with each other through a set of new experiments. In these experiments, the subject was asked to move his/her hand in a circle and estimate its position. The experiments were performed on eight subjects with proprioception training and eight subjects with visual training. Results of the experiments show three important points: (1) visual learning rate is significantly more than that of proprioception; (2) means of visual and proprioceptive errors are decreased by training but statistical analysis shows that this decrement is significant for proprioceptive error and non-significant for visual error, and (3) visual errors in training phase even in the beginning of it, is much less than errors of the main test stage because in the main test, the subject has to focus on two senses. The results of the experiments in this paper is in agreement with the results of the neural model

  4. Systems Biology for Organotypic Cell Cultures

    Energy Technology Data Exchange (ETDEWEB)

    Grego, Sonia [RTI International, Research Triangle Park, NC (United States); Dougherty, Edward R. [Texas A & M Univ., College Station, TX (United States); Alexander, Francis J. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Auerbach, Scott S. [National Inst. of Environmental Health Sciences, Research Triangle Park, NC (United States); Berridge, Brian R. [GlaxoSmithKline, Research Triangle Park, NC (United States); Bittner, Michael L. [Translational Genomics Research Inst., Phoenix, AZ (United States); Casey, Warren [National Inst. of Environmental Health Sciences, Research Triangle Park, NC (United States); Cooley, Philip C. [RTI International, Research Triangle Park, NC (United States); Dash, Ajit [HemoShear Therapeutics, Charlottesville, VA (United States); Ferguson, Stephen S. [National Inst. of Environmental Health Sciences, Research Triangle Park, NC (United States); Fennell, Timothy R. [RTI International, Research Triangle Park, NC (United States); Hawkins, Brian T. [RTI International, Research Triangle Park, NC (United States); Hickey, Anthony J. [RTI International, Research Triangle Park, NC (United States); Kleensang, Andre [Johns Hopkins Univ., Baltimore, MD (United States). Center for Alternatives to Animal Testing; Liebman, Michael N. [IPQ Analytics, Kennett Square, PA (United States); Martin, Florian [Phillip Morris International, Neuchatel (Switzerland); Maull, Elizabeth A. [National Inst. of Environmental Health Sciences, Research Triangle Park, NC (United States); Paragas, Jason [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Qiao, Guilin [Defense Threat Reduction Agency, Ft. Belvoir, VA (United States); Ramaiahgari, Sreenivasa [National Inst. of Environmental Health Sciences, Research Triangle Park, NC (United States); Sumner, Susan J. [RTI International, Research Triangle Park, NC (United States); Yoon, Miyoung [The Hamner Inst. for Health Sciences, Research Triangle Park, NC (United States); ScitoVation, Research Triangle Park, NC (United States)

    2016-08-04

    Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomic data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data. This consensus report summarizes the discussions held.

  5. Radiological/biological/aerosol removal system

    Science.gov (United States)

    Haslam, Jeffery J

    2015-03-17

    An air filter replacement system for existing buildings, vehicles, arenas, and other enclosed airspaces includes a replacement air filter for replacing a standard air filter. The replacement air filter has dimensions and air flow specifications that allow it to replace the standard air filter. The replacement air filter includes a filter material that removes radiological or biological or aerosol particles.

  6. Studies on Semantic Systems Chemical Biology

    Science.gov (United States)

    Chen, Bin

    2012-01-01

    Current "one disease, one target and one drug" drug development paradigm is under question as relatively few drugs have reached the market in the last two decades. Increasingly research focus is being placed on the study of drug action against biological systems as a whole rather than against a single component (called "Systems…

  7. Modular microfluidic system for biological sample preparation

    Science.gov (United States)

    Rose, Klint A.; Mariella, Jr., Raymond P.; Bailey, Christopher G.; Ness, Kevin Dean

    2015-09-29

    A reconfigurable modular microfluidic system for preparation of a biological sample including a series of reconfigurable modules for automated sample preparation adapted to selectively include a) a microfluidic acoustic focusing filter module, b) a dielectrophoresis bacteria filter module, c) a dielectrophoresis virus filter module, d) an isotachophoresis nucleic acid filter module, e) a lyses module, and f) an isotachophoresis-based nucleic acid filter.

  8. Nutritional Systems Biology: Definitions and Approaches

    DEFF Research Database (Denmark)

    Panagiotou, Gianni; Nielsen, Jens

    2009-01-01

    waiting for a predictive knowledge of genetic variation. It is widely recognized that systems and network biology has the potential to increase our understanding of how nutrition influences metabolic pathways and homeostasis, how this regulation is disturbed in a diet-related disease, and to what extent...... individual genotypes contribute to such diseases....

  9. How do biological systems escape 'chaotic' state?

    Indian Academy of Sciences (India)

    B J Rao

    2018-02-13

    Feb 13, 2018 ... triggering 'escape from homeostasis' (Nijhout et al. 2014) where phenotypes begin to become less stable and eventually turn into fully unstable state, the start of 'chaos' in a system. Therefore, the key to biological designs is to stay close to or within the 'homeostatic plateau' and resist drifting into 'chaos'.

  10. Notions of radiation chemistry in biological systems

    International Nuclear Information System (INIS)

    Mastro, N.L. del.

    1989-10-01

    The present paper examines some aspects of the direct and indirect biological radiation effects: pair formation, free radicals, superoxide ion, hydrogen peroxide, hydroxyl radical, oxygen singlet together with the endogen radioprotector mechanisms of organisms and the ways in which an improved radioresistance of biochemical systems can be achieved. (author) [pt

  11. Quantifying electron transfer reactions in biological systems

    DEFF Research Database (Denmark)

    Sjulstok, Emil Sjulstok; Olsen, Jógvan Magnus Haugaard; Solov'yov, Ilia A

    2015-01-01

    to deduce the driving force for the electron transfer reaction and to establish those interactions that play the major role in propelling the electron. The suggested approach is seen as a general recipe to treat electron transfer events in biological systems computationally, and we utilize it to describe...

  12. Micromechanics of engineered and biological systems

    Indian Academy of Sciences (India)

    Microsystems are good examples of integrated engineered systems of small size. Although this .... In develop- mental biology, the application of controlled forces on growing embryos is shown to help in under- standing ..... Optimization is a useful tool for synthesis. Many optimal synthesis methods have been developed for.

  13. Sender-receiver systems and applying information theory for quantitative synthetic biology.

    Science.gov (United States)

    Barcena Menendez, Diego; Senthivel, Vivek Raj; Isalan, Mark

    2015-02-01

    Sender-receiver (S-R) systems abound in biology, with communication systems sending information in various forms. Information theory provides a quantitative basis for analysing these processes and is being applied to study natural genetic, enzymatic and neural networks. Recent advances in synthetic biology are providing us with a wealth of artificial S-R systems, giving us quantitative control over networks with a finite number of well-characterised components. Combining the two approaches can help to predict how to maximise signalling robustness, and will allow us to make increasingly complex biological computers. Ultimately, pushing the boundaries of synthetic biology will require moving beyond engineering the flow of information and towards building more sophisticated circuits that interpret biological meaning. Copyright © 2014. Published by Elsevier Ltd.

  14. Mathematical Modeling of Complex Biological Systems

    OpenAIRE

    Fischer, Hans Peter

    2008-01-01

    To understand complex biological systems such as cells, tissues, or even the human body, it is not sufficient to identify and characterize the individual molecules in the system. It also is necessary to obtain a thorough understanding of the interaction between molecules and pathways. This is even truer for understanding complex diseases such as cancer, Alzheimer’s disease, or alcoholism. With recent technological advances enabling researchers to monitor complex cellular processes on the mole...

  15. Visualizing dimensionality reduction of systems biology data

    OpenAIRE

    Lehrmann, Andreas; Huber, Michael; Polatkan, Aydin C.; Pritzkau, Albert; Nieselt, Kay

    2012-01-01

    One of the challenges in analyzing high-dimensional expression data is the detection of important biological signals. A common approach is to apply a dimension reduction method, such as principal component analysis. Typically, after application of such a method the data is projected and visualized in the new coordinate system, using scatter plots or profile plots. These methods provide good results if the data have certain properties which become visible in the new coordinate system and which...

  16. Dynamics of a neural system with a multiscale architecture

    Science.gov (United States)

    Breakspear, Michael; Stam, Cornelis J

    2005-01-01

    The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales—neurons, minicolumns, cortical columns, functional brain regions, and so on. It is important to consider that the processes governing neural dynamics at any given scale are not only determined by the behaviour of other neural structures at that scale, but also by the emergent behaviour of smaller scales, and the constraining influence of activity at larger scales. In this paper, we introduce a theoretical framework for neural systems in which the dynamics are nested within a multiscale architecture. In essence, the dynamics at each scale are determined by a coupled ensemble of nonlinear oscillators, which embody the principle scale-specific neurobiological processes. The dynamics at larger scales are ‘slaved’ to the emergent behaviour of smaller scales through a coupling function that depends on a multiscale wavelet decomposition. The approach is first explicated mathematically. Numerical examples are then given to illustrate phenomena such as between-scale bifurcations, and how synchronization in small-scale structures influences the dynamics in larger structures in an intuitive manner that cannot be captured by existing modelling approaches. A framework for relating the dynamical behaviour of the system to measured observables is presented and further extensions to capture wave phenomena and mode coupling are suggested. PMID:16087448

  17. Intelligent systems II complete approximation by neural network operators

    CERN Document Server

    Anastassiou, George A

    2016-01-01

    This monograph is the continuation and completion of the monograph, “Intelligent Systems: Approximation by Artificial Neural Networks” written by the same author and published 2011 by Springer. The book you hold in hand presents the complete recent and original work of the author in approximation by neural networks. Chapters are written in a self-contained style and can be read independently. Advanced courses and seminars can be taught out of this brief book. All necessary background and motivations are given per chapter. A related list of references is given also per chapter. The book’s results are expected to find applications in many areas of applied mathematics, computer science and engineering. As such this monograph is suitable for researchers, graduate students, and seminars of the above subjects, also for all science and engineering libraries.  .

  18. Artificial Neural Network for Location Estimation in Wireless Communication Systems

    Directory of Open Access Journals (Sweden)

    Chien-Sheng Chen

    2012-03-01

    Full Text Available In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS. To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA measurements and the angle of arrival (AOA information to locate MS when three base stations (BSs are available. Artificial neural networks (ANN are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line, based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.

  19. Artificial neural network for location estimation in wireless communication systems.

    Science.gov (United States)

    Chen, Chien-Sheng

    2012-01-01

    In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS). To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA) measurements and the angle of arrival (AOA) information to locate MS when three base stations (BSs) are available. Artificial neural networks (ANN) are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line), based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS) environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.

  20. Motivation alters impression formation and related neural systems

    Science.gov (United States)

    Zaki, Jamil; Ambady, Nalini

    2017-01-01

    Abstract Observers frequently form impressions of other people based on complex or conflicting information. Rather than being objective, these impressions are often biased by observers’ motives. For instance, observers often downplay negative information they learn about ingroup members. Here, we characterize the neural systems associated with biased impression formation. Participants learned positive and negative information about ingroup and outgroup social targets. Following this information, participants worsened their impressions of outgroup, but not ingroup, targets. This tendency was associated with a failure to engage neural structures including lateral prefrontal cortex, dorsal anterior cingulate cortex, temporoparietal junction, Insula and Precuneus when processing negative information about ingroup (but not outgroup) targets. To the extent that participants engaged these regions while learning negative information about ingroup members, they exhibited less ingroup bias in their impressions. These data are consistent with a model of ‘effortless bias’, under which perceivers fail to process goal-inconsistent information in order to maintain desired conclusions. PMID:27798250

  1. Parallel and interrelated neural systems underlying adaptive navigation.

    Science.gov (United States)

    Mizumori, Sheri J Y; Canfield, James G; Yeshenko, Oksana

    2005-06-01

    The ability to process in parallel multiple forms of sensory information, and link sensory-sensory associations to behavior, presumably allows for the opportunistic use of the most reliable and predictive sensory modalities in diverse behavioral contexts. Evolutionary considerations indicate that such processing may represent a fundamental operating principle underlying complex sensory associations and sensory-motor integration. Here, we suggest that animal navigation is a particularly useful model of such opportunistic use of sensory and motor information because it is possible to study directly the effects of memory on neural system functions. First, comparative evidence for parallel processing across multiple brain structures during navigation is provided from the literatures on fish and rodent navigation. Then, based on neurophysiological evidence of coordinated, multiregional processing, we provide a neurobiological explanation of learning and memory effects on neural circuitry mediating navigation.

  2. Design of an adaptive neural network based power system stabilizer.

    Science.gov (United States)

    Liu, Wenxin; Venayagamoorthy, Ganesh K; Wunsch, Donald C

    2003-01-01

    Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, this paper presents an indirect adaptive neural network based power system stabilizer (IDNC) design. The proposed IDNC consists of a neuro-controller, which is used to generate a supplementary control signal to the excitation system, and a neuro-identifier, which is used to model the dynamics of the power system and to adapt the neuro-controller parameters. The proposed method has the features of a simple structure, adaptivity and fast response. The proposed IDNC is evaluated on a single machine infinite bus power system under different operating conditions and disturbances to demonstrate its effectiveness and robustness.

  3. The concept of biologically motivated time-pulse information processing for design and construction of multifunctional devices of neural logic

    Science.gov (United States)

    Krasilenko, Vladimir G.; Nikolsky, Alexander I.; Lazarev, Alexander A.; Sholohov, V. I.

    2004-04-01

    On the basis of the analysis of advanced approaches and optoelectronic systems for realization of various logics: two-valued, multi-valued, neural, continuous and others the biologically motivated time-pulse conception for building of multifunctional reconfigurable universal elements with programmable tuning for neurobiologic is grounded. The concept consists in usage of preliminary conversion of multi-level or continuous optic 2D signals into durations of time intervals (the conversion to a temporal area) and further use of time-pulse two-level digital signals that allows to ensure fast tuning to a required function of two-valued, multi-valued and other logics. It is shown that optoelectronic pulse-phase and pulse-width modulators (PPM and PWM) are the base elements for that. Time-pulse coding universal elements for matrix two-valued and multi-valued logics and structural-functional design of universal time-pulse coding elements for neural (continuous) logic are considered in the article. PPMs realized on 1.5μm technology CMOS transistors are considered. The PPMs have parameters: the input photocurrent range is 10nA...10μA the conversion period is 10μs...1ms the conversion relative error is 0.1...1%; the conversion law is ramp; the supply voltage is 3V and the power consumption is 83μW. The small power consumption of such PPMs enables successfully their integration in 2Darray with size of 128x128 elements and more and productivity equals 1...10 Giga continuous logic operations per sec.

  4. Neural coding in the visual system of Drosophila melanogaster: How do small neural populations support visually guided behaviours?

    Science.gov (United States)

    Dewar, Alex D M; Wystrach, Antoine; Philippides, Andrew; Graham, Paul

    2017-10-01

    All organisms wishing to survive and reproduce must be able to respond adaptively to a complex, changing world. Yet the computational power available is constrained by biology and evolution, favouring mechanisms that are parsimonious yet robust. Here we investigate the information carried in small populations of visually responsive neurons in Drosophila melanogaster. These so-called 'ring neurons', projecting to the ellipsoid body of the central complex, are reported to be necessary for complex visual tasks such as pattern recognition and visual navigation. Recently the receptive fields of these neurons have been mapped, allowing us to investigate how well they can support such behaviours. For instance, in a simulation of classic pattern discrimination experiments, we show that the pattern of output from the ring neurons matches observed fly behaviour. However, performance of the neurons (as with flies) is not perfect and can be easily improved with the addition of extra neurons, suggesting the neurons' receptive fields are not optimised for recognising abstract shapes, a conclusion which casts doubt on cognitive explanations of fly behaviour in pattern recognition assays. Using artificial neural networks, we then assess how easy it is to decode more general information about stimulus shape from the ring neuron population codes. We show that these neurons are well suited for encoding information about size, position and orientation, which are more relevant behavioural parameters for a fly than abstract pattern properties. This leads us to suggest that in order to understand the properties of neural systems, one must consider how perceptual circuits put information at the service of behaviour.

  5. Systems biology of vaccination in the elderly.

    Science.gov (United States)

    Duraisingham, Sai S; Rouphael, Nadine; Cavanagh, Mary M; Nakaya, Helder I; Goronzy, Jorg J; Pulendran, Bali

    2013-01-01

    Aging population demographics, combined with suboptimal vaccine responses in the elderly, make the improvement of vaccination strategies in the elderly a developing public health issue. The immune system changes with age, with innate and adaptive cell components becoming increasingly dysfunctional. As such, vaccine responses in the elderly are impaired in ways that differ depending on the type of vaccine (e.g., live attenuated, polysaccharide, conjugate, or subunit) and the mediators of protection (e.g., antibody and/or T cell). The rapidly progressing field of systems biology has been shown to be useful in predicting immunogenicity and offering insights into potential mechanisms of protection in young adults. Future application of systems biology to vaccination in the elderly may help to identify gene signatures that predict suboptimal responses and help to identify more accurate correlates of protection. Moreover, the identification of specific defects may be used to target novel vaccination strategies that improve efficacy in elderly populations.

  6. A simple mechanical system for studying adaptive oscillatory neural networks

    DEFF Research Database (Denmark)

    Jouffroy, Guillaume; Jouffroy, Jerome

    that the network oscillates in a suitable way, this tuning being a non trivial task. It also appears that the link with the physical body that these oscillatory entities control has a fundamental importance, and it seems that most bodies used for experimental validation in the literature (walking robots, lamprey...... model, etc.) might be too complex to study. In this paper, we use a comparatively simple mechanical system, the nonholonomic vehicle referred to as the Roller-Racer, as a means towards testing different learning strategies for an Recurrent Neural Network-based (RNN) controller/guidance system. After...

  7. A simple mechanical system for studying adaptive oscillatory neural networks

    DEFF Research Database (Denmark)

    Jouffroy, Guillaume; Jouffroy, Jerome

    model, etc.) might be too complex to study. In this paper, we use a comparatively simple mechanical system, the nonholonomic vehicle referred to as the Roller-Racer, as a means towards testing different learning strategies for an Recurrent Neural Network-based (RNN) controller/guidance system. After...... a brief description of the Roller-Racer, we present as a preliminary study an RNN-based feed-forward controller whose parameters are obtained through the well-known teacher forcing learning algorithm, extended to learn signals with a continuous component....

  8. Measuring cell identity in noisy biological systems

    Science.gov (United States)

    Birnbaum, Kenneth D.; Kussell, Edo

    2011-01-01

    Global gene expression measurements are increasingly obtained as a function of cell type, spatial position within a tissue and other biologically meaningful coordinates. Such data should enable quantitative analysis of the cell-type specificity of gene expression, but such analyses can often be confounded by the presence of noise. We introduce a specificity measure Spec that quantifies the information in a gene's complete expression profile regarding any given cell type, and an uncertainty measure dSpec, which measures the effect of noise on specificity. Using global gene expression data from the mouse brain, plant root and human white blood cells, we show that Spec identifies genes with variable expression levels that are nonetheless highly specific of particular cell types. When samples from different individuals are used, dSpec measures genes’ transcriptional plasticity in each cell type. Our approach is broadly applicable to mapped gene expression measurements in stem cell biology, developmental biology, cancer biology and biomarker identification. As an example of such applications, we show that Spec identifies a new class of biomarkers, which exhibit variable expression without compromising specificity. The approach provides a unifying theoretical framework for quantifying specificity in the presence of noise, which is widely applicable across diverse biological systems. PMID:21803789

  9. Radial basis function (RBF) neural network control for mechanical systems design, analysis and Matlab simulation

    CERN Document Server

    Liu, Jinkun

    2013-01-01

    Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design.   This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...

  10. Neural network application to aircraft control system design

    Science.gov (United States)

    Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

    1991-01-01

    The feasibility of using artificial neural network as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research identified to enhance the practical applicability of neural networks to flight control design.

  11. Neural Computations in a Dynamical System with Multiple Time Scales

    Directory of Open Access Journals (Sweden)

    Yuanyuan Mi

    2016-09-01

    Full Text Available Neural systems display rich short-term dynamics at various levels, e.g., spike-frequencyadaptation (SFA at single neurons, and short-term facilitation (STF and depression (STDat neuronal synapses. These dynamical features typically covers a broad range of time scalesand exhibit large diversity in different brain regions. It remains unclear what the computationalbenefit for the brain to have such variability in short-term dynamics is. In this study, we proposethat the brain can exploit such dynamical features to implement multiple seemingly contradictorycomputations in a single neural circuit. To demonstrate this idea, we use continuous attractorneural network (CANN as a working model and include STF, SFA and STD with increasing timeconstants in their dynamics. Three computational tasks are considered, which are persistent activity,adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, andhence cannot be implemented by a single dynamical feature or any combination with similar timeconstants. However, with properly coordinated STF, SFA and STD, we show that the network isable to implement the three computational tasks concurrently. We hope this study will shed lighton the understanding of how the brain orchestrates its rich dynamics at various levels to realizediverse cognitive functions.

  12. Effects of Pesticides on Biological Systems

    Directory of Open Access Journals (Sweden)

    Ergul Belge Kurutas

    2003-06-01

    Full Text Available The use of pesticid both in Turkey and other contries is widespread in order to combat against many pests which cause economical damages. However, pesticides in human pass through skin, respiratory or digestive systems and is metabolized by monooxygenase system dependent upon cytocrome P450 in liver. They also give rise to severe decreases cytochrome P450 and amount of "hem" enzyme activites of glucose-6-phosphatase, pyrophosphatase by stimulating lipid peroxidation on hepatic microsomes. In this study effects of pesticides on biological systems will be presented in genaral terms. [Archives Medical Review Journal 2003; 12(3.000: 215-228

  13. Neural Network Target Identification System for False Alarm Reduction

    Science.gov (United States)

    Ye, David; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

    2009-01-01

    A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

  14. Optoelectronic system and apparatus for connection to biological systems

    Energy Technology Data Exchange (ETDEWEB)

    Okandan, Murat; Nielson, Gregory N.

    2018-03-06

    The present invention relates to a biological probe structure, as well as apparatuses, systems, and methods employing this structure. In particular embodiments, the structure includes a hermetically sealed unit configured to receive and transmit one or more optical signals. Furthermore, the structure can be implanted subcutaneously and interrogated externally. In this manner, a minimally invasive method can be employed to detect, treat, and/or assess the biological target. Additional methods and systems are also provided.

  15. Olfactory systems and neural circuits that modulate predator odor fear

    Directory of Open Access Journals (Sweden)

    Lorey K. Takahashi

    2014-03-01

    Full Text Available When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator odor fear. For instance, the main (MOS and accessory olfactory systems (AOS detect predator odors and different types of predator odors are sensed by specific receptors located in either the MOS or AOS. However, complex predator chemosignals may be processed by both the MOS and AOS, which complicate our understanding of the specific neural circuits connected directly and indirectly from the MOS and AOS to activate the physiological and behavioral components of unconditioned and conditioned fear. Studies indicate that brain structures including the dorsal periaqueductal gray, paraventricular nucleus of the hypothalamus, and the medial amygdala appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal stress hormone secretion. The medial amygdala also plays a key role in predator odor unconditioned fear behavior and retrieval of contextual fear memory associated with prior predator odor experiences. Other neural structures including the bed nucleus of the stria terminalis and the ventral hippocampus appear prominently involve in predator odor fear behavior. The basolateral amygdala, medial hypothalamic nuclei, and medial prefrontal cortex are also activated by some but not all predator odors. Future research that characterizes how distinct predator odors are uniquely processed in olfactory systems and neural circuits will provide significant insights into the differences of how diverse predator odors activate

  16. Olfactory systems and neural circuits that modulate predator odor fear.

    Science.gov (United States)

    Takahashi, Lorey K

    2014-01-01

    When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator odor fear. For instance, the main (MOS) and accessory olfactory systems (AOS) detect predator odors and different types of predator odors are sensed by specific receptors located in either the MOS or AOS. However, complex predator chemosignals may be processed by both the MOS and AOS, which complicate our understanding of the specific neural circuits connected directly and indirectly from the MOS and AOS to activate the physiological and behavioral components of unconditioned and conditioned fear. Studies indicate that brain structures including the dorsal periaqueductal gray (DPAG), paraventricular nucleus (PVN) of the hypothalamus, and the medial amygdala (MeA) appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal (HPA) stress hormone secretion. The MeA also plays a key role in predator odor unconditioned fear behavior and retrieval of contextual fear memory associated with prior predator odor experiences. Other neural structures including the bed nucleus of the stria terminalis and the ventral hippocampus (VHC) appear prominently involved in predator odor fear behavior. The basolateral amygdala (BLA), medial hypothalamic nuclei, and medial prefrontal cortex (mPFC) are also activated by some but not all predator odors. Future research that characterizes how distinct predator odors are uniquely processed in olfactory systems and neural circuits will provide significant insights into the differences of how diverse predator

  17. Set membership experimental design for biological systems

    Directory of Open Access Journals (Sweden)

    Marvel Skylar W

    2012-03-01

    Full Text Available Abstract Background Experimental design approaches for biological systems are needed to help conserve the limited resources that are allocated for performing experiments. The assumptions used when assigning probability density functions to characterize uncertainty in biological systems are unwarranted when only a small number of measurements can be obtained. In these situations, the uncertainty in biological systems is more appropriately characterized in a bounded-error context. Additionally, effort must be made to improve the connection between modelers and experimentalists by relating design metrics to biologically relevant information. Bounded-error experimental design approaches that can assess the impact of additional measurements on model uncertainty are needed to identify the most appropriate balance between the collection of data and the availability of resources. Results In this work we develop a bounded-error experimental design framework for nonlinear continuous-time systems when few data measurements are available. This approach leverages many of the recent advances in bounded-error parameter and state estimation methods that use interval analysis to generate parameter sets and state bounds consistent with uncertain data measurements. We devise a novel approach using set-based uncertainty propagation to estimate measurement ranges at candidate time points. We then use these estimated measurements at the candidate time points to evaluate which candidate measurements furthest reduce model uncertainty. A method for quickly combining multiple candidate time points is presented and allows for determining the effect of adding multiple measurements. Biologically relevant metrics are developed and used to predict when new data measurements should be acquired, which system components should be measured and how many additional measurements should be obtained. Conclusions The practicability of our approach is illustrated with a case study. This

  18. System identification of an unmanned quadcopter system using MRAN neural

    Science.gov (United States)

    Pairan, M. F.; Shamsudin, S. S.

    2017-12-01

    This project presents the performance analysis of the radial basis function neural network (RBF) trained with Minimal Resource Allocating Network (MRAN) algorithm for real-time identification of quadcopter. MRAN’s performance is compared with the RBF with Constant Trace algorithm for 2500 input-output pair data sampling. MRAN utilizes adding and pruning hidden neuron strategy to obtain optimum RBF structure, increase prediction accuracy and reduce training time. The results indicate that MRAN algorithm produces fast training time and more accurate prediction compared with standard RBF. The model proposed in this paper is capable of identifying and modelling a nonlinear representation of the quadcopter flight dynamics.

  19. Accurate Wavelet Neural Network for Efficient Controlling of an Active Magnetic Bearing System

    OpenAIRE

    Youssef Harkouss; Souhad Mcheik; Roger Achkar

    2010-01-01

    Problem statement: The synthesis of a command by the neural network has an excellent advantage over the classical one such as PID. This study presented a fast and accurate Wavelet Neural Network (WNN) approach for efficient controlling of an Active Magnetic Bearing (AMB) system. Approach: The proposed approach combined neural network with the wavelet theory. Wavelet theory may be exploited in deriving a good initialization for the neural network and thus improved conv...

  20. Autism and talent: the cognitive and neural basis of systemizing.

    Science.gov (United States)

    Baron-Cohen, Simon; Lombardo, Michael V

    2017-12-01

    In 2003, we proposed the hypersystemizing theory of autism. The theory proposes that the human mind possesses a systemizing mechanism (SM) that helps identify lawful regularities (often causal) that govern the input-operation-output workings of a system. The SM can be tuned to different levels, from low to high, with a normal distribution of individual differences in how strongly people search for such input-operation-out-put regularities in any data that is systemizable. Evidence suggests that people with autism are on average hypersystemizers, scoring higher than average on the systemizing quotient and on performance tests of systemizing. In this article, we consider the neural basis behind the SM, since there has been little consideration of the brain basis of systemizing. Finally, we discuss directions for future work in this field.

  1. A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks

    Directory of Open Access Journals (Sweden)

    Jim Harkin

    2009-01-01

    Full Text Available FPGA devices have emerged as a popular platform for the rapid prototyping of biological Spiking Neural Networks (SNNs applications, offering the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biologically plausible neuron and synaptic models of SNNs, and current FPGA routing structures cannot accommodate the high levels of interneuron connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing scalable SNNs on reconfigurable FPGAs. The paper proposes a novel field programmable neural network architecture (EMBRACE, incorporating low-power analogue spiking neurons, interconnected using a Network-on-Chip architecture. Results on the evaluation of the EMBRACE architecture using the XOR benchmark problem are presented, and the performance of the architecture is discussed. The paper also discusses the adaptability of the EMBRACE architecture in supporting fault tolerant computing.

  2. The Impact of Systems Biology on Bioprocessing

    DEFF Research Database (Denmark)

    Campbell, Kate; Xia, Jianye; Nielsen, Jens

    2017-01-01

    alongside mathematical modeling to characterize and predict cellular physiology. This approach can drive cycles of design, build, test, and learn implemented by metabolic engineers to optimize the cell factory performance. Streamlining of the design phase requires a clearer understanding of metabolism...... and its regulation, which can be achieved using quantitative and integrated omic characterization, alongside more advanced analytical methods. We discuss here the current impact of systems biology and challenges of closing the gap between bioprocessing and more traditional methods of chemical production....

  3. Nutritional systems biology of type 2 diabetes

    OpenAIRE

    Zhao, Y; Barrere-Cain, RE; Yang, X

    2015-01-01

    © 2015, The Author(s). Type 2 diabetes (T2D) has become an increasingly challenging health burden due to its high morbidity, mortality, and heightened prevalence worldwide. Although dietary and nutritional imbalances have long been recognized as key risk factors for T2D, the underlying mechanisms remain unclear. The advent of nutritional systems biology, a field that aims to elucidate the interactions between dietary nutrients and endogenous molecular entities in disease-related tissues, offe...

  4. Modelling and Inference Strategies for Biological Systems

    OpenAIRE

    Palmisano, Alida

    2010-01-01

    For many years, computers have played an important role in helping scientists to store, manipulate, and analyze data coming from many different disciplines. In recent years, however, new technological capabilities and new ways of thinking about the usefulness of computer science is extending the reach of computers from simple analysis of collected data to hypothesis generation. The aim of this work is to provide a contribution in the Computational Systems Biology field. The main purpose of...

  5. Systems biology solutions for biochemical production challenges

    DEFF Research Database (Denmark)

    Hansen, Anne Sofie Lærke; Lennen, Rebecca M; Sonnenschein, Nikolaus

    2017-01-01

    characterization, genome-scale metabolic modeling, and adaptive laboratory evolution can be readily deployed in metabolic engineering projects. However, high performance strains usually carry tens of genetic modifications and need to operate in challenging environmental conditions. This additional complexity......There is an urgent need to significantly accelerate the development of microbial cell factories to produce fuels and chemicals from renewable feedstocks in order to facilitate the transition to a biobased society. Methods commonly used within the field of systems biology including omics...

  6. The ctenophore genome and the evolutionary origins of neural systems.

    Science.gov (United States)

    Moroz, Leonid L; Kocot, Kevin M; Citarella, Mathew R; Dosung, Sohn; Norekian, Tigran P; Povolotskaya, Inna S; Grigorenko, Anastasia P; Dailey, Christopher; Berezikov, Eugene; Buckley, Katherine M; Ptitsyn, Andrey; Reshetov, Denis; Mukherjee, Krishanu; Moroz, Tatiana P; Bobkova, Yelena; Yu, Fahong; Kapitonov, Vladimir V; Jurka, Jerzy; Bobkov, Yuri V; Swore, Joshua J; Girardo, David O; Fodor, Alexander; Gusev, Fedor; Sanford, Rachel; Bruders, Rebecca; Kittler, Ellen; Mills, Claudia E; Rast, Jonathan P; Derelle, Romain; Solovyev, Victor V; Kondrashov, Fyodor A; Swalla, Billie J; Sweedler, Jonathan V; Rogaev, Evgeny I; Halanych, Kenneth M; Kohn, Andrea B

    2014-06-05

    The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores (comb jellies) have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here we present the draft genome of Pleurobrachia bachei, Pacific sea gooseberry, together with ten other ctenophore transcriptomes, and show that they are remarkably distinct from other animal genomes in their content of neurogenic, immune and developmental genes. Our integrative analyses place Ctenophora as the earliest lineage within Metazoa. This hypothesis is supported by comparative analysis of multiple gene families, including the apparent absence of HOX genes, canonical microRNA machinery, and reduced immune complement in ctenophores. Although two distinct nervous systems are well recognized in ctenophores, many bilaterian neuron-specific genes and genes of 'classical' neurotransmitter pathways either are absent or, if present, are not expressed in neurons. Our metabolomic and physiological data are consistent with the hypothesis that ctenophore neural systems, and possibly muscle specification, evolved independently from those in other animals.

  7. Phase transitions in glassy systems via convolutional neural networks

    Science.gov (United States)

    Fang, Chao

    Machine learning is a powerful approach commonplace in industry to tackle large data sets. Most recently, it has found its way into condensed matter physics, allowing for the first time the study of, e.g., topological phase transitions and strongly-correlated electron systems. The study of spin glasses is plagued by finite-size effects due to the long thermalization times needed. Here we use convolutional neural networks in an attempt to detect a phase transition in three-dimensional Ising spin glasses. Our results are compared to traditional approaches.

  8. Fault Tolerant Neural Network for ECG Signal Classification Systems

    Directory of Open Access Journals (Sweden)

    MERAH, M.

    2011-08-01

    Full Text Available The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT ? BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.

  9. Optimizing Markovian modeling of chaotic systems with recurrent neural networks

    International Nuclear Information System (INIS)

    Cechin, Adelmo L.; Pechmann, Denise R.; Oliveira, Luiz P.L. de

    2008-01-01

    In this paper, we propose a methodology for optimizing the modeling of an one-dimensional chaotic time series with a Markov Chain. The model is extracted from a recurrent neural network trained for the attractor reconstructed from the data set. Each state of the obtained Markov Chain is a region of the reconstructed state space where the dynamics is approximated by a specific piecewise linear map, obtained from the network. The Markov Chain represents the dynamics of the time series in its statistical essence. An application to a time series resulted from Lorenz system is included

  10. NEURAL NETWORK SYSTEM FOR DIAGNOSTICS OF AVIATION DESIGNATION PRODUCTS

    Directory of Open Access Journals (Sweden)

    В. Єременко

    2011-02-01

    Full Text Available In the article for solving the classification problem of the technical state of the  object, proposed to use a hybrid neural network with a Kohonen layer and multilayer perceptron. The information-measuring system can be used for standardless diagnostics, cluster analysis and to classify the products which made from composite materials. The advantage of this architecture is flexibility, high performance, ability to use different methods for collecting diagnostic information about unit under test, high reliability of information processing

  11. Central Neural Control of the Cardiovascular System: Current Perspectives

    Science.gov (United States)

    Dampney, Roger A. L.

    2016-01-01

    This brief review, which is based on a lecture presented at the American Physiological Society Teaching Refresher Course on the Brain and Systems Control as part of the Experimental Biology meeting in 2015, aims to summarize current concepts of the principal mechanisms in the brain that regulate the autonomic outflow to the cardiovascular system.…

  12. Examination of neural systems sub-serving facebook "addiction".

    Science.gov (United States)

    Turel, Ofir; He, Qinghua; Xue, Gui; Xiao, Lin; Bechara, Antoine

    2014-12-01

    Because addictive behaviors typically result from violated homeostasis of the impulsive (amygdala-striatal) and inhibitory (prefrontal cortex) brain systems, this study examined whether these systems sub-serve a specific case of technology-related addiction, namely Facebook "addiction." Using a go/no-go paradigm in functional MRI settings, the study examined how these brain systems in 20 Facebook users (M age = 20.3 yr., SD = 1.3, range = 18-23) who completed a Facebook addiction questionnaire, responded to Facebook and less potent (traffic sign) stimuli. The findings indicated that at least at the examined levels of addiction-like symptoms, technology-related "addictions" share some neural features with substance and gambling addictions, but more importantly they also differ from such addictions in their brain etiology and possibly pathogenesis, as related to abnormal functioning of the inhibitory-control brain system.

  13. Life: An Introduction to Complex Systems Biology

    CERN Document Server

    Kaneko, Kunihiko

    2006-01-01

    What is life? Has molecular biology given us a satisfactory answer to this question? And if not, why, and how to carry on from there? This book examines life not from the reductionist point of view, but rather asks the question: what are the universal properties of living systems and how can one construct from there a phenomenological theory of life that leads naturally to complex processes such as reproductive cellular systems, evolution and differentiation? The presentation has been deliberately kept fairly non-technical so as to address a broad spectrum of students and researchers from the natural sciences and informatics.

  14. Complex biological and bio-inspired systems

    Energy Technology Data Exchange (ETDEWEB)

    Ecke, Robert E [Los Alamos National Laboratory

    2009-01-01

    The understanding and characterization ofthe fundamental processes of the function of biological systems underpins many of the important challenges facing American society, from the pathology of infectious disease and the efficacy ofvaccines, to the development of materials that mimic biological functionality and deliver exceptional and novel structural and dynamic properties. These problems are fundamentally complex, involving many interacting components and poorly understood bio-chemical kinetics. We use the basic science of statistical physics, kinetic theory, cellular bio-chemistry, soft-matter physics, and information science to develop cell level models and explore the use ofbiomimetic materials. This project seeks to determine how cell level processes, such as response to mechanical stresses, chemical constituents and related gradients, and other cell signaling mechanisms, integrate and combine to create a functioning organism. The research focuses on the basic physical processes that take place at different levels ofthe biological organism: the basic role of molecular and chemical interactions are investigated, the dynamics of the DNA-molecule and its phylogenetic role are examined and the regulatory networks of complex biochemical processes are modeled. These efforts may lead to early warning algorithms ofpathogen outbreaks, new bio-sensors to detect hazards from pathomic viruses to chemical contaminants. Other potential applications include the development of efficient bio-fuel alternative-energy processes and the exploration ofnovel materials for energy usages. Finally, we use the notion of 'coarse-graining,' which is a method for averaging over less important degrees of freedom to develop computational models to predict cell function and systems-level response to disease, chemical stress, or biological pathomic agents. This project supports Energy Security, Threat Reduction, and the missions of the DOE Office of Science through its efforts to

  15. Improving neural network prediction of effluent from biological wastewater treatment plant of industrial park using fuzzy learning approach.

    Science.gov (United States)

    Pai, Tzu-Yi; Wang, S C; Chiang, C F; Su, H C; Yu, L F; Sung, P J; Lin, C Y; Hu, H C

    2009-10-01

    Three types of adaptive network-based fuzzy inference system (ANFIS) in which the online monitoring parameters served as the input variable were employed to predict suspended solids (SS(eff)), chemical oxygen demand (COD(eff)), and pH(eff) in the effluent from a biological wastewater treatment plant in industrial park. Artificial neural network (ANN) was also used for comparison. The results indicated that ANFIS statistically outperforms ANN in terms of effluent prediction. When predicting, the minimum mean absolute percentage errors of 2.90, 2.54 and 0.36% for SS(eff), COD(eff) and pH(eff) could be achieved using ANFIS. The maximum values of correlation coefficient for SS(eff), COD(eff), and pH(eff) were 0.97, 0.95, and 0.98, respectively. The minimum mean square errors of 0.21, 1.41 and 0.00, and the minimum root mean square errors of 0.46, 1.19 and 0.04 for SS(eff), COD(eff), and pH(eff) could also be achieved.

  16. GPSR: A Resource for Genomics Proteomics and Systems Biology

    Indian Academy of Sciences (India)

    GPSR: A Resource for Genomics Proteomics and Systems Biology · Simple Calculation Programs for Biology Immunological Methods · Simple Calculation Programs for Biology Methods in Molecular Biology · Simple Calculation Programs for Biology Other Methods · PowerPoint Presentation · Slide 6 · Slide 7 · Prediction of ...

  17. Biologically inspired collision avoidance system for unmanned vehicles

    Science.gov (United States)

    Ortiz, Fernando E.; Graham, Brett; Spagnoli, Kyle; Kelmelis, Eric J.

    2009-05-01

    In this project, we collaborate with researchers in the neuroscience department at the University of Delaware to develop an Field Programmable Gate Array (FPGA)-based embedded computer, inspired by the brains of small vertebrates (fish). The mechanisms of object detection and avoidance in fish have been extensively studied by our Delaware collaborators. The midbrain optic tectum is a biological multimodal navigation controller capable of processing input from all senses that convey spatial information, including vision, audition, touch, and lateral-line (water current sensing in fish). Unfortunately, computational complexity makes these models too slow for use in real-time applications. These simulations are run offline on state-of-the-art desktop computers, presenting a gap between the application and the target platform: a low-power embedded device. EM Photonics has expertise in developing of high-performance computers based on commodity platforms such as graphic cards (GPUs) and FPGAs. FPGAs offer (1) high computational power, low power consumption and small footprint (in line with typical autonomous vehicle constraints), and (2) the ability to implement massively-parallel computational architectures, which can be leveraged to closely emulate biological systems. Combining UD's brain modeling algorithms and the power of FPGAs, this computer enables autonomous navigation in complex environments, and further types of onboard neural processing in future applications.

  18. A New Approach to Fault Diagnosis of Power Systems Using Fuzzy Reasoning Spiking Neural P Systems

    OpenAIRE

    Xiong, Guojiang; Shi, Dongyuan; Zhu, Lin; Duan, Xianzhong

    2013-01-01

    Fault diagnosis of power systems is an important task in power system operation. In this paper, fuzzy reasoning spiking neural P systems (FRSN P systems) are implemented for fault diagnosis of power systems for the first time. As a graphical modeling tool, FRSN P systems are able to represent fuzzy knowledge and perform fuzzy reasoning well. When the cause-effect relationship between candidate faulted section and protective devices is represented by the FRSN P systems, the diagnostic conclusi...

  19. Neural systems underlying aversive conditioning in humans with primary and secondary reinforcers

    Directory of Open Access Journals (Sweden)

    Mauricio R Delgado

    2011-05-01

    Full Text Available Money is a secondary reinforcer commonly used across a range of disciplines in experimental paradigms investigating reward learning and decision-making. The effectiveness of monetary reinforcers during aversive learning and its neural basis, however, remains a topic of debate. Specifically, it is unclear if the initial acquisition of aversive representations of monetary losses depends on similar neural systems as more traditional aversive conditioning that involves primary reinforcers. This study contrasts the efficacy of a biologically defined primary reinforcer (shock and a socially defined secondary reinforcer (money during aversive learning and its associated neural circuitry. During a two-part experiment, participants first played a gambling game where wins and losses were based on performance to gain an experimental bank. Participants were then exposed to two separate aversive conditioning sessions. In one session, a primary reinforcer (mild shock served as an unconditioned stimulus (US and was paired with one of two colored squares, the conditioned stimuli (CS+ and CS-, respectively. In another session, a secondary reinforcer (loss of money served as the US and was paired with one of two different CS. Skin conductance responses were greater for CS+ compared to CS- trials irrespective of type of reinforcer. Neuroimaging results revealed that the striatum, a region typically linked with reward-related processing, was found to be involved in the acquisition of aversive conditioned response irrespective of reinforcer type. In contrast, the amygdala was involved during aversive conditioning with primary reinforcers, as suggested by both an exploratory fMRI analysis and a follow-up case study with a patient with bilateral amygdala damage. Taken together, these results suggest that learning about potential monetary losses may depend on reinforcement learning related systems, rather than on typical structures involved in more biologically based

  20. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  1. Engineering biological systems toward a sustainable bioeconomy.

    Science.gov (United States)

    Lopes, Mateus Schreiner Garcez

    2015-06-01

    The nature of our major global risks calls for sustainable innovations to decouple economic growth from greenhouse gases emission. The development of sustainable technologies has been negatively impacted by several factors including sugar production costs, production scale, economic crises, hydraulic fracking development and the market inability to capture externality costs. However, advances in engineering of biological systems allow bridging the gap between exponential growth of knowledge about biology and the creation of sustainable value chains for a broad range of economic sectors. Additionally, industrial symbiosis of different biobased technologies can increase competitiveness and sustainability, leading to the development of eco-industrial parks. Reliable policies for carbon pricing and revenue reinvestments in disruptive technologies and in the deployment of eco-industrial parks could boost the welfare while addressing our major global risks toward the transition from a fossil to a biobased economy.

  2. Biological Therapy in Systemic Lupus Erythematosus

    Directory of Open Access Journals (Sweden)

    Mariana Postal

    2012-01-01

    Full Text Available Systemic lupus erythematosus (SLE is a prototypic inflammatory autoimmune disorder characterized by multisystem involvement and fluctuating disease activity. Symptoms range from rather mild manifestations such as rash or arthritis to life-threatening end-organ manifestations. Despite new and improved therapy having positively impacted the prognosis of SLE, a subgroup of patients do not respond to conventional therapy. Moreover, the risk of fatal outcomes and the damaging side effects of immunosuppressive therapies in SLE call for an improvement in the current therapeutic management. New therapeutic approaches are focused on B-cell targets, T-cell downregulation and costimulatory blockade, cytokine inhibition, and the modulation of complement. Several biological agents have been developed, but this encouraging news is associated with several disappointments in trials and provide a timely moment to reflect on biologic therapy in SLE.

  3. Recurrent Neural Network for Single Machine Power System Stabilizer

    Directory of Open Access Journals (Sweden)

    Widi Aribowo

    2010-04-01

    Full Text Available In this paper, recurrent neural network (RNN is used to design power system stabilizer (PSS due to its advantage on the dependence not only on present input but also on past condition. A RNN-PSS is able to capture the dynamic response of a system without any delays caused by external feedback, primarily by the internal feedback loop in recurrent neuron. In this paper, RNNPSS consists of a RNN-identifier and a RNN-controller. The RNN-Identifier functions as the tracker of dynamics characteristics of the plant, while the RNN-controller is used to damp the system’s low frequency oscillations. Simulation results using MATLAB demonstrate that the RNNPSS can successfully damp out oscillation and improve the performance of the system.

  4. A Gamma Memory Neural Network for System Identification

    Science.gov (United States)

    Motter, Mark A.; Principe, Jose C.

    1992-01-01

    A gamma neural network topology is investigated for a system identification application. A discrete gamma memory structure is used in the input layer, providing delayed values of both the control inputs and the network output to the input layer. The discrete gamma memory structure implements a tapped dispersive delay line, with the amount of dispersion regulated by a single, adaptable parameter. The network is trained using static back propagation, but captures significant features of the system dynamics. The system dynamics identified with the network are the Mach number dynamics of the 16 Foot Transonic Tunnel at NASA Langley Research Center, Hampton, Virginia. The training data spans an operating range of Mach numbers from 0.4 to 1.3.

  5. A Chinese Named Entity Recognition System with Neural Networks

    Directory of Open Access Journals (Sweden)

    Yi Hui-Kang

    2017-01-01

    Full Text Available Named entity recognition (NER is a typical sequential labeling problem that plays an important role in natural language processing (NLP systems. In this paper, we discussed the details of applying a comprehensive model aggregating neural networks and conditional random field (CRF on Chinese NER tasks, and how to discovery character level features when implement a NER system in word level. We compared the difference between Chinese and English when modeling the character embeddings. We developed a NER system based on our analysis, it works well on the ACE 2004 and SIGHAN bakeoff 2006 MSRA dataset, and doesn’t rely on any gazetteers or handcraft features. We obtained F1 score of 82.3% on MSRA 2006.

  6. Adaptable data management for systems biology investigations

    Directory of Open Access Journals (Sweden)

    Burdick David

    2009-03-01

    Full Text Available Abstract Background Within research each experiment is different, the focus changes and the data is generated from a continually evolving barrage of technologies. There is a continual introduction of new techniques whose usage ranges from in-house protocols through to high-throughput instrumentation. To support these requirements data management systems are needed that can be rapidly built and readily adapted for new usage. Results The adaptable data management system discussed is designed to support the seamless mining and analysis of biological experiment data that is commonly used in systems biology (e.g. ChIP-chip, gene expression, proteomics, imaging, flow cytometry. We use different content graphs to represent different views upon the data. These views are designed for different roles: equipment specific views are used to gather instrumentation information; data processing oriented views are provided to enable the rapid development of analysis applications; and research project specific views are used to organize information for individual research experiments. This management system allows for both the rapid introduction of new types of information and the evolution of the knowledge it represents. Conclusion Data management is an important aspect of any research enterprise. It is the foundation on which most applications are built, and must be easily extended to serve new functionality for new scientific areas. We have found that adopting a three-tier architecture for data management, built around distributed standardized content repositories, allows us to rapidly develop new applications to support a diverse user community.

  7. Adaptable data management for systems biology investigations

    Science.gov (United States)

    Boyle, John; Rovira, Hector; Cavnor, Chris; Burdick, David; Killcoyne, Sarah; Shmulevich, Ilya

    2009-01-01

    Background Within research each experiment is different, the focus changes and the data is generated from a continually evolving barrage of technologies. There is a continual introduction of new techniques whose usage ranges from in-house protocols through to high-throughput instrumentation. To support these requirements data management systems are needed that can be rapidly built and readily adapted for new usage. Results The adaptable data management system discussed is designed to support the seamless mining and analysis of biological experiment data that is commonly used in systems biology (e.g. ChIP-chip, gene expression, proteomics, imaging, flow cytometry). We use different content graphs to represent different views upon the data. These views are designed for different roles: equipment specific views are used to gather instrumentation information; data processing oriented views are provided to enable the rapid development of analysis applications; and research project specific views are used to organize information for individual research experiments. This management system allows for both the rapid introduction of new types of information and the evolution of the knowledge it represents. Conclusion Data management is an important aspect of any research enterprise. It is the foundation on which most applications are built, and must be easily extended to serve new functionality for new scientific areas. We have found that adopting a three-tier architecture for data management, built around distributed standardized content repositories, allows us to rapidly develop new applications to support a diverse user community. PMID:19265554

  8. Speaker diarization system using HXLPS and deep neural network

    Directory of Open Access Journals (Sweden)

    V. Subba Ramaiah

    2018-03-01

    Full Text Available In general, speaker diarization is defined as the process of segmenting the input speech signal and grouped the homogenous regions with regard to the speaker identity. The main idea behind this system is that it is able to discriminate the speaker signal by assigning the label of the each speaker signal. Due to rapid growth of broadcasting and meeting, the speaker diarization is burdensome to enhance the readability of the speech transcription. In order to solve this issue, Holoentropy with the eXtended Linear Prediction using autocorrelation Snapshot (HXLPS and deep neural network (DNN is proposed for the speaker diarization system. The HXLPS extraction method is newly developed by incorporating the Holoentropy with the XLPS. Once we attain the features, the speech and non-speech signals are detected by the Voice Activity Detection (VAD method. Then, i-vector representation of every segmented signal is obtained using Universal Background Model (UBM model. Consequently, DNN is utilized to assign the label for the speaker signal which is then clustered according to the speaker label. The performance is analysed using the evaluation metrics, such as tracking distance, false alarm rate and diarization error rate. The outcome of the proposed method ensures the better diarization performance by achieving the lower DER of 1.36% based on lambda value and DER of 2.23% depends on the frame length. Keywords: Speaker diarization, HXLPS feature extraction, Voice activity detection, Deep neural network, Speaker clustering, Diarization Error Rate (DER

  9. It's the System, Stupid: How Systems Biology Is Transforming.

    Science.gov (United States)

    2010-01-01

    So far, little is known about systems biology and its potential for changing how we diagnose and treat disease. That will change soon, say the systems experts, who advise payers to begin learning now about how it could make healthcare efficient.

  10. Neural Network Enhanced Structure Determination of Osteoporosis, Immune System, and Radiation Repair Proteins, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed innovation will utilize self learning neural network technology to determine the structure of osteoporosis, immune system disease, and excess radiation...

  11. Systems Biology: Impressions from a Newcomer Graduate Student in 2016

    Science.gov (United States)

    Simpson, Melanie Rae

    2016-01-01

    As a newcomer, the philosophical basis of systems biology seems intuitive and appealing, the underlying philosophy being that the whole of a living system cannot be completely understood by the study of its individual parts. Yet answers to the questions "What is systems biology?" and "What constitutes a systems biology approach in…

  12. Hybrid fault diagnosis of nonlinear systems using neural parameter estimators.

    Science.gov (United States)

    Sobhani-Tehrani, E; Talebi, H A; Khorasani, K

    2014-02-01

    This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems taking advantage of both the system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPEs) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FPs) that are indicators of faults in the system. Two NPE structures, series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. In contrast, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the two NPEs that originally assumes full state measurements for systems that have only partial state measurements. The proposed FTO is a neural state estimator that can estimate unmeasured states even in the presence of faults. The estimated and the measured states then comprise the inputs to the two proposed FDII schemes. Simulation results for FDII of reaction wheels of a three-axis stabilized satellite in the presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solutions under partial state measurements. Copyright © 2013 Elsevier Ltd. All rights reserved.

  13. Biological diversity in the patent system.

    Directory of Open Access Journals (Sweden)

    Paul Oldham

    Full Text Available Biological diversity in the patent system is an enduring focus of controversy but empirical analysis of the presence of biodiversity in the patent system has been limited. To address this problem we text mined 11 million patent documents for 6 million Latin species names from the Global Names Index (GNI established by the Global Biodiversity Information Facility (GBIF and Encyclopedia of Life (EOL. We identified 76,274 full Latin species names from 23,882 genera in 767,955 patent documents. 25,595 species appeared in the claims section of 136,880 patent documents. This reveals that human innovative activity involving biodiversity in the patent system focuses on approximately 4% of taxonomically described species and between 0.8-1% of predicted global species. In this article we identify the major features of the patent landscape for biological diversity by focusing on key areas including pharmaceuticals, neglected diseases, traditional medicines, genetic engineering, foods, biocides, marine genetic resources and Antarctica. We conclude that the narrow focus of human innovative activity and ownership of genetic resources is unlikely to be in the long term interest of humanity. We argue that a broader spectrum of biodiversity needs to be opened up to research and development based on the principles of equitable benefit-sharing, respect for the objectives of the Convention on Biological Diversity, human rights and ethics. Finally, we argue that alternative models of innovation, such as open source and commons models, are required to open up biodiversity for research that addresses actual and neglected areas of human need. The research aims to inform the implementation of the 2010 Nagoya Protocol on Access to Genetic Resources and the Equitable Sharing of Benefits Arising from their Utilization and international debates directed to the governance of genetic resources. Our research also aims to inform debates under the Intergovernmental Committee on

  14. Biological Diversity in the Patent System

    Science.gov (United States)

    Oldham, Paul; Hall, Stephen; Forero, Oscar

    2013-01-01

    Biological diversity in the patent system is an enduring focus of controversy but empirical analysis of the presence of biodiversity in the patent system has been limited. To address this problem we text mined 11 million patent documents for 6 million Latin species names from the Global Names Index (GNI) established by the Global Biodiversity Information Facility (GBIF) and Encyclopedia of Life (EOL). We identified 76,274 full Latin species names from 23,882 genera in 767,955 patent documents. 25,595 species appeared in the claims section of 136,880 patent documents. This reveals that human innovative activity involving biodiversity in the patent system focuses on approximately 4% of taxonomically described species and between 0.8–1% of predicted global species. In this article we identify the major features of the patent landscape for biological diversity by focusing on key areas including pharmaceuticals, neglected diseases, traditional medicines, genetic engineering, foods, biocides, marine genetic resources and Antarctica. We conclude that the narrow focus of human innovative activity and ownership of genetic resources is unlikely to be in the long term interest of humanity. We argue that a broader spectrum of biodiversity needs to be opened up to research and development based on the principles of equitable benefit-sharing, respect for the objectives of the Convention on Biological Diversity, human rights and ethics. Finally, we argue that alternative models of innovation, such as open source and commons models, are required to open up biodiversity for research that addresses actual and neglected areas of human need. The research aims to inform the implementation of the 2010 Nagoya Protocol on Access to Genetic Resources and the Equitable Sharing of Benefits Arising from their Utilization and international debates directed to the governance of genetic resources. Our research also aims to inform debates under the Intergovernmental Committee on Intellectual

  15. System for determining sizes of biological macromolecules

    International Nuclear Information System (INIS)

    Nelson, R.M.; Danby, P.C.

    1987-01-01

    An electrophoresis system for determining the sizes of radiolabelled biological macromolecules is described. It comprises a cell containing an electrophoresis gel and having at least one lane, a voltage source connected across the gel for effecting the movement of macromolecules in the lane, a detector fixed relative to the moving molecules for generating electrical pulses responsive to signals emitted by the radiolabelled molecules; a pulse processor for counting the pulse rate, and a computational device for comparing the pulse rate to a predetermined value. (author)

  16. Integrative Systems Biology Applied to Toxicology

    DEFF Research Database (Denmark)

    Kongsbak, Kristine Grønning

    associated with combined exposure to multiple chemicals. Testing all possible combinations of the tens of thousands environmental chemicals is impractical. This PhD project was launched to apply existing computational systems biology methods to toxicological research. In this thesis, I present in three...... projects three different approaches to using computational toxicology to aid classical toxicological investigations. In project I, we predicted human health effects of five pesticides using publicly available data. We obtained a grouping of the chemical according to their potential human health effects...

  17. Hybrid case-neural network (CNN) diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

    recently, the mobile health care has a great attention for the researcher and people all over the world. Case based reasoning (CBR) systems have proved their performance as world wide web (WWW) medical diagnostic systems. They were preferred rather than different reasoning approaches due to their high performance and results' explanation. But, their operations require a complex knowledge acquisition and management processes. On the other hand, it is found that, artificial neural network (ANN) has a great acceptance as a classifier methodology using a little amount of knowledge. But, ANN lacks of an explanation capability .The present research introduces a new web-based hybrid diagnostic system that can use the ANN inside the CBR , cycle.It can provide higher performance for the web diagnostic systems. Besides, the proposed system can be used as a web diagnostic system. It can be applied for diagnosis different types of systems in several domains. It has been applied in diagnosis of the cancer diseases that has a great spreading in recent years as a case of study . However, the suggested system has proved its acceptance in the manner.

  18. Neural map formation and sensory coding in the vomeronasal system.

    Science.gov (United States)

    Brignall, Alexandra C; Cloutier, Jean-François

    2015-12-01

    Sensory systems enable us to encode a clear representation of our environment in the nervous system by spatially organizing sensory stimuli being received. The organization of neural circuitry to form a map of sensory activation is critical for the interpretation of these sensory stimuli. In rodents, social communication relies strongly on the detection of chemosignals by the vomeronasal system, which regulates a wide array of behaviours, including mate recognition, reproduction, and aggression. The binding of these chemosignals to receptors on vomeronasal sensory neurons leads to activation of second-order neurons within glomeruli of the accessory olfactory bulb. Here, vomeronasal receptor activation by a stimulus is organized into maps of glomerular activation that represent phenotypic qualities of the stimuli detected. Genetic, electrophysiological and imaging studies have shed light on the principles underlying cell connectivity and sensory map formation in the vomeronasal system, and have revealed important differences in sensory coding between the vomeronasal and main olfactory system. In this review, we summarize the key factors and mechanisms that dictate circuit formation and sensory coding logic in the vomeronasal system, emphasizing differences with the main olfactory system. Furthermore, we discuss how detection of chemosignals by the vomeronasal system regulates social behaviour in mice, specifically aggression.

  19. Stability Analysis of Neural Networks-Based System Identification

    Directory of Open Access Journals (Sweden)

    Talel Korkobi

    2008-01-01

    Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

  20. Neural crest and placode interaction during the development of the cranial sensory system.

    Science.gov (United States)

    Steventon, Ben; Mayor, Roberto; Streit, Andrea

    2014-05-01

    In the vertebrate head, the peripheral components of the sensory nervous system are derived from two embryonic cell populations, the neural crest and cranial sensory placodes. Both arise in close proximity to each other at the border of the neural plate: neural crest precursors abut the future central nervous system, while placodes originate in a common preplacodal region slightly more lateral. During head morphogenesis, complex events organise these precursors into functional sensory structures, raising the question of how their development is coordinated. Here we review the evidence that neural crest and placode cells remain in close proximity throughout their development and interact repeatedly in a reciprocal manner. We also review recent controversies about the relative contribution of the neural crest and placodes to the otic and olfactory systems. We propose that a sequence of mutual interactions between the neural crest and placodes drives the coordinated morphogenesis that generates functional sensory systems within the head. Copyright © 2014 Elsevier Inc. All rights reserved.

  1. Neural networks as a possible architecture for the distributed control of space systems

    Science.gov (United States)

    Fiesler, E.; Choudry, A.

    1987-01-01

    Researchers attempted to identify the features essential for large, complex, multi-modular multi-functional systems possessing a high level of interconnectivity. These features were studied in the context of neural networks with the aim of arriving at a possible architecture of the distributed control system-specific features of the neural networks and their applicability in space systems.

  2. Introduction to Concepts in Artificial Neural Networks

    Science.gov (United States)

    Niebur, Dagmar

    1995-01-01

    This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

  3. Stellar Image Interpretation System Using Artificial Neural Networks:

    Directory of Open Access Journals (Sweden)

    A. El-Bassuny Alawy

    2004-01-01

    Full Text Available A supervised Artificial Neural Network (ANN based system is being developed employing the Bi-polar function for identifying stellar images in CCD frames. It is based on feed-forward artificial neural networks with error back-propagation learning. It has been coded in C language. The learning process was performed on a 341 input pattern set, while a similar set was used for testing. The present approach has been applied on a CCD frame of the open star cluster M67. The results obtained have been discussed and compared with those derived in our previous work employing the Uni-polar function and by a package known in the astronomical community (DAOPHOT-II. Full agreement was found between the present approach, that of Elnagahy et al, and the standard astronomical data for the cluster. It has been shown that the developed technique resembles that of the Uni-Polar function, possessing a simple, much faster yet reliable approach. Moreover, neither prior knowledge on, nor initial data from, the frame to be analysed is required, as it is for DAOPHOT-II. 

  4. Redefining plant systems biology: from cell to ecosystem

    NARCIS (Netherlands)

    Keurentjes, J.J.B.; Angenent, G.C.; Dicke, M.; Martins Dos Santos, V.A.P.; Molenaar, J.; Van der Putten, W.H.; de Ruiter, P.C.; Struik, P.C.; Thomma, B.P.H.J.

    2011-01-01

    Molecular biologists typically restrict systems biology to cellular levels. By contrast, ecologists define biological systems as communities of interacting individuals at different trophic levels that process energy, nutrient and information flows. Modern plant breeding needs to increase

  5. AN INTEGRATED BIOLOGICAL CONTROL SYSTEM AT HANFORD

    Energy Technology Data Exchange (ETDEWEB)

    JOHNSON AR; CAUDILL JG; GIDDINGS RF; RODRIGUEZ JM; ROOS RC; WILDE JW

    2010-02-11

    In 1999 an integrated biological control system was instituted at the U.S. Department of Energy's Hanford Site. Successes and changes to the program needed to be communicated to a large and diverse mix of organizations and individuals. Efforts at communication are directed toward the following: Hanford Contractors (Liquid or Tank Waste, Solid Waste, Environmental Restoration, Science and Technology, Site Infrastructure), General Hanford Employees, and Hanford Advisory Board (Native American Tribes, Environmental Groups, Local Citizens, Washington State and Oregon State regulatory agencies). Communication was done through direct interface meetings, individual communication, where appropriate, and broadly sharing program reports. The objectives of the communication efforts was to have the program well coordinated with Hanford contractors, and to have the program understood well enough that all stakeholders would have confidence in the work performed by the program to reduce or elimated spread of radioactive contamination by biotic vectors. Communication of successes and changes to an integrated biological control system instituted in 1999 at the Department of Energy's Hanford Site have required regular interfaces with not only a diverse group of Hanford contractors (i.e., those responsible for liquid or tank waste, solid wastes, environmental restoration, science and technology, and site infrastructure), and general Hanford employees, but also with a consortium of designated stake holders organized as the Hanford Advisory Board (i.e., Native American tribes, various environmental groups, local citizens, Washington state and Oregon regulatory agencies, etc.). Direct interface meetings, individual communication where appropriate, and transparency of the biological control program were the methods and outcome of this effort.

  6. An Integrated Biological Control System At Hanford

    International Nuclear Information System (INIS)

    Johnson, A.R.; Caudill, J.G.; Giddings, R.F.; Rodriguez, J.M.; Roos, R.C.; Wilde, J.W.

    2010-01-01

    In 1999 an integrated biological control system was instituted at the U.S. Department of Energy's Hanford Site. Successes and changes to the program needed to be communicated to a large and diverse mix of organizations and individuals. Efforts at communication are directed toward the following: Hanford Contractors (Liquid or Tank Waste, Solid Waste, Environmental Restoration, Science and Technology, Site Infrastructure), General Hanford Employees, and Hanford Advisory Board (Native American Tribes, Environmental Groups, Local Citizens, Washington State and Oregon State regulatory agencies). Communication was done through direct interface meetings, individual communication, where appropriate, and broadly sharing program reports. The objectives of the communication efforts was to have the program well coordinated with Hanford contractors, and to have the program understood well enough that all stakeholders would have confidence in the work performed by the program to reduce or elimate spread of radioactive contamination by biotic vectors. Communication of successes and changes to an integrated biological control system instituted in 1999 at the Department of Energy's Hanford Site have required regular interfaces with not only a diverse group of Hanford contractors (i.e., those responsible for liquid or tank waste, solid wastes, environmental restoration, science and technology, and site infrastructure), and general Hanford employees, but also with a consortium of designated stake holders organized as the Hanford Advisory Board (i.e., Native American tribes, various environmental groups, local citizens, Washington state and Oregon regulatory agencies, etc.). Direct interface meetings, individual communication where appropriate, and transparency of the biological control program were the methods and outcome of this effort.

  7. Application of neural networks to connectional expert system for identification of transients in nuclear power plants

    International Nuclear Information System (INIS)

    Cheon, Se Woo; Kim, Wan Joo; Chang, Soon Heung; Roh, Myung Sub

    1991-01-01

    The Back-propagation Neural Network (BPN) algorithm is applied to connectionist expert system for the identification of BWR transients. Several powerful features of neural network-based expert systems over traditional rule-based expert systems are described. The general mapping capability of the neural networks enables to identify transients easily. A number of case studies were performed with emphasis on the applicability of the neural networks to the diagnostic domain. It is revealed that the BPN algorithm can identify transients properly, even when incomplete or untrained symptoms are given. It is also shown that multiple transients are easily identified

  8. Identification of the non-linear systems using internal recurrent neural networks

    Directory of Open Access Journals (Sweden)

    Bogdan CODRES

    2006-12-01

    Full Text Available In the past years utilization of neural networks took a distinct ampleness because of the following properties: distributed representation of information, capacity of generalization in case of uncontained situation in training data set, tolerance to noise, resistance to partial destruction, parallel processing. Another major advantage of neural networks is that they allow us to obtain the model of the investigated system, systems that is not necessarily to be linear. In fact, the true value of neural networks is seen in the case of identification and control of nonlinear systems. In this paper there are presented some identification techniques using neural networks.

  9. Stochastic transport processes in discrete biological systems

    CERN Document Server

    Frehland, Eckart

    1982-01-01

    These notes are in part based on a course for advanced students in the applications of stochastic processes held in 1978 at the University of Konstanz. These notes contain the results of re­ cent studies on the stochastic description of ion transport through biological membranes. In particular, they serve as an introduction to an unified theory of fluctuations in complex biological transport systems. We emphasize that the subject of this volume is not to introduce the mathematics of stochastic processes but to present a field of theoretical biophysics in which stochastic methods are important. In the last years the study of membrane noise has become an important method in biophysics. Valuable information on the ion transport mechanisms in membranes can be obtained from noise analysis. A number of different processes such as the opening and closing of ion channels have been shown to be sources of the measured current or voltage fluctuations. Bio­ logical 'transport systems can be complex. For example, the tr...

  10. Advancing metabolic engineering through systems biology of industrial microorganisms

    DEFF Research Database (Denmark)

    Dai, Zongjie; Nielsen, Jens

    2015-01-01

    resources. The objective of systems biology is to gain a comprehensive and quantitative understanding of living cells and can hereby enhance our ability to characterize and predict cellular behavior. Systems biology of industrial microorganisms is therefore valuable for metabolic engineering. Here we review...... the application of systems biology tools for the identification of metabolic engineering targets which may lead to reduced development time for efficient cell factories. Finally, we present some perspectives of systems biology for advancing metabolic engineering further....

  11. Biological Systems for Hydrogen Photoproduction (Presentation)

    Energy Technology Data Exchange (ETDEWEB)

    Ghirardi, M. L.

    2012-05-01

    This presentation summarizes NREL biological systems for hydrogen photoproduction work for the DOE Hydrogen and Fuel Cells Program Annual Merit Review and Peer Evaluation Meeting, May 14-18, 2012. General goal is develop photobiological systems for large-scale, low cost and efficient H{sub 2} production from water (barriers AH, AI and AJ). Specific tasks are: (1) Address the O{sub 2} sensitivity of hydrogenases that prevent continuity of H{sub 2} photoproduction under aerobic, high solar-to-hydrogen (STH) light conversion efficiency conditions; and (2) Utilize a limited STH H{sub 2}-producing method (sulfur deprivation) as a platform to address or test other factors limiting commercial algal H{sub 2} photoproduction, including low rates due to biochemical and engineering mechanisms.

  12. Language Based Techniques for Systems Biology

    DEFF Research Database (Denmark)

    Pilegaard, Henrik

    Process calculus is the common denominator for a class of compact, idealised, domain-specific formalisms normally associated with the study of reactive concurrent systems within Computer Science. With the rise of the interactioncentred science of Systems Biology a number of bio-inspired process......), is context insensitive, while the other, a poly-variant analysis (2CFA), is context-sensitive. These analyses compute safe approximations to the set of spatial configurations that are reachable according to a given model. This is useful in the qualitative study of cellular self-organisation and, e.......g., the effects of receptor defects or drug delivery mechanisms. The property of sequential realisability. which is closely related to the function of biochemical pathways, is addressed by a variant of traditional Data Flow Analysis (DFA). This so-called ‘Pathway Analysis’ computes safe approximations to the set...

  13. Spatial Structures and Regulation in Biological Systems

    DEFF Research Database (Denmark)

    Yde, Pernille

    , and the other is the spatial regulation of biological systems, here related to different aspects of the inflammatory response. All systems are studied using computational modelling and mathematical analysis. The first part of the thesis explores different protein aggregation scenarios. In Chapter 1, we consider...... a previously studied and very general aggregation model describing frangible linear filaments. This model is especially relevant for the growth of amyloid fibres, that have been related to a number of serious human diseases, and which are known to grow in an accelerated self-enhanced manner.We derive...... model of the tissue and show how coupled cells are able to function as an excitable medium and propagate waves of high cytokine concentration through the tissue. If the internal regulation in the cells is over-productive, the model predicts a continuous amplification of cytokines, which spans the entire...

  14. Microbial stress tolerance for biofuels. Systems biology

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Zonglin Lewis (ed.) [National Center for Agricultural Utilization Research, USDA-ARS, Peoria, IL (United States)

    2012-07-01

    The development of sustainable and renewable biofuels is attracting growing interest. It is vital to develop robust microbial strains for biocatalysts that are able to function under multiple stress conditions. This Microbiology Monograph provides an overview of methods for studying microbial stress tolerance for biofuels applications using a systems biology approach. Topics covered range from mechanisms to methodology for yeast and bacteria, including the genomics of yeast tolerance and detoxification; genetics and regulation of glycogen and trehalose metabolism; programmed cell death; high gravity fermentations; ethanol tolerance; improving biomass sugar utilization by engineered Saccharomyces; the genomics on tolerance of Zymomonas mobilis; microbial solvent tolerance; control of stress tolerance in bacterial host organisms; metabolomics for ethanologenic yeast; automated proteomics work cell systems for strain improvement; and unification of gene expression data for comparable analyses under stress conditions. (orig.)

  15. Neural mechanism of facilitation system during physical fatigue.

    Directory of Open Access Journals (Sweden)

    Masaaki Tanaka

    Full Text Available An enhanced facilitation system caused by motivational input plays an important role in supporting performance during physical fatigue. We tried to clarify the neural mechanisms of the facilitation system during physical fatigue using magnetoencephalography (MEG and a classical conditioning technique. Twelve right-handed volunteers participated in this study. Participants underwent MEG recording during the imagery of maximum grips of the right hand guided by metronome sounds for 10 min. Thereafter, fatigue-inducing maximum handgrip trials were performed for 10 min; the metronome sounds were started 5 min after the beginning of the handgrip trials. The metronome sounds were used as conditioned stimuli and maximum handgrip trials as unconditioned stimuli. The next day, they were randomly assigned to two groups in a single-blinded, two-crossover fashion to undergo two types of MEG recordings, that is, for the control and motivation sessions, during the imagery of maximum grips of the right hand guided by metronome sounds for 10 min. The alpha-band event-related desynchronizations (ERDs of the motivation session relative to the control session within the time windows of 500 to 700 and 800 to 900 ms after the onset of handgrip cue sounds were identified in the sensorimotor areas. In addition, the alpha-band ERD within the time window of 400 to 500 ms was identified in the right dorsolateral prefrontal cortex (Brodmann's area 46. The ERD level in the right dorsolateral prefrontal cortex was positively associated with that in the sensorimotor areas within the time window of 500 to 700 ms. These results suggest that the right dorsolateral prefrontal cortex is involved in the neural substrates of the facilitation system and activates the sensorimotor areas during physical fatigue.

  16. Optimal Workflow Scheduling in Critical Infrastructure Systems with Neural Networks

    Directory of Open Access Journals (Sweden)

    S. Vukmirović

    2012-04-01

    Full Text Available Critical infrastructure systems (CISs, such as power grids, transportation systems, communication networks and water systems are the backbone of a country’s national security and industrial prosperity. These CISs execute large numbers of workflows with very high resource requirements that can span through different systems and last for a long time. The proper functioning and synchronization of these workflows is essential since humanity’s well-being is connected to it. Because of this, the challenge of ensuring availability and reliability of these services in the face of a broad range of operating conditions is very complicated. This paper proposes an architecture which dynamically executes a scheduling algorithm using feedback about the current status of CIS nodes. Different artificial neural networks (ANNs were created in order to solve the scheduling problem. Their performances were compared and as the main result of this paper, an optimal ANN architecture for workflow scheduling in CISs is proposed. A case study is shown for a meter data management system with measurements from a power distribution management system in Serbia. Performance tests show that significant improvement of the overall execution time can be achieved by ANNs.

  17. Network Analyses in Systems Biology: New Strategies for Dealing with Biological Complexity

    DEFF Research Database (Denmark)

    Green, Sara; Serban, Maria; Scholl, Raphael

    2018-01-01

    The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research...

  18. Systems biology approaches and pathway tools for investigating cardiovascular disease

    NARCIS (Netherlands)

    Wheelock, C.E.; Wheelock, A.M.; Kawashima, S.; Diez, D.; Kanehisa, M.; Erk, M. van; Kleemann, R.; Haeggström, J.Z.; Goto, S.

    2009-01-01

    Systems biology aims to understand the nonlinear interactions of multiple biomolecular components that characterize a living organism. One important aspect of systems biology approaches is to identify the biological pathways or networks that connect the differing elements of a system, and examine

  19. Promoting Systems Thinking through Biology Lessons

    Science.gov (United States)

    Riess, Werner; Mischo, Christoph

    2010-04-01

    This study's goal was to analyze various teaching approaches within the context of natural science lessons, especially in biology. The main focus of the paper lies on the effectiveness of different teaching methods in promoting systems thinking in the field of Education for Sustainable Development. The following methods were incorporated into the study: special lessons designed to promote systems thinking, a computer-simulated scenario on the topic "ecosystem forest," and a combination of both special lessons and the computer simulation. These groups were then compared to a control group. A questionnaire was used to assess systems thinking skills of 424 sixth-grade students of secondary schools in Germany. The assessment differentiated between a conceptual understanding (measured as achievement score) and a reflexive justification (measured as justification score) of systems thinking. The following control variables were used: logical thinking, grades in school, memory span, and motivational goal orientation. Based on the pretest-posttest control group design, only those students who received both special instruction and worked with the computer simulation showed a significant increase in their achievement scores. The justification score increased in the computer simulation condition as well as in the combination of computer simulation and lesson condition. The possibilities and limits of promoting various forms of systems thinking by using realistic computer simulations are discussed.

  20. Molecular profiles to biology and pathways: a systems biology approach.

    Science.gov (United States)

    Van Laere, Steven; Dirix, Luc; Vermeulen, Peter

    2016-06-16

    Interpreting molecular profiles in a biological context requires specialized analysis strategies. Initially, lists of relevant genes were screened to identify enriched concepts associated with pathways or specific molecular processes. However, the shortcoming of interpreting gene lists by using predefined sets of genes has resulted in the development of novel methods that heavily rely on network-based concepts. These algorithms have the advantage that they allow a more holistic view of the signaling properties of the condition under study as well as that they are suitable for integrating different data types like gene expression, gene mutation, and even histological parameters.

  1. Systems Biology, Neuroimaging, Neuropsychology, Neuroconnectivity and Traumatic Brain Injury.

    Science.gov (United States)

    Bigler, Erin D

    2016-01-01

    The patient who sustains a traumatic brain injury (TBI) typically undergoes neuroimaging studies, usually in the form of computed tomography (CT) and magnetic resonance imaging (MRI). In most cases the neuroimaging findings are clinically assessed with descriptive statements that provide qualitative information about the presence/absence of visually identifiable abnormalities; though little if any of the potential information in a scan is analyzed in any quantitative manner, except in research settings. Fortunately, major advances have been made, especially during the last decade, in regards to image quantification techniques, especially those that involve automated image analysis methods. This review argues that a systems biology approach to understanding quantitative neuroimaging findings in TBI provides an appropriate framework for better utilizing the information derived from quantitative neuroimaging and its relation with neuropsychological outcome. Different image analysis methods are reviewed in an attempt to integrate quantitative neuroimaging methods with neuropsychological outcome measures and to illustrate how different neuroimaging techniques tap different aspects of TBI-related neuropathology. Likewise, how different neuropathologies may relate to neuropsychological outcome is explored by examining how damage influences brain connectivity and neural networks. Emphasis is placed on the dynamic changes that occur following TBI and how best to capture those pathologies via different neuroimaging methods. However, traditional clinical neuropsychological techniques are not well suited for interpretation based on contemporary and advanced neuroimaging methods and network analyses. Significant improvements need to be made in the cognitive and behavioral assessment of the brain injured individual to better interface with advances in neuroimaging-based network analyses. By viewing both neuroimaging and neuropsychological processes within a systems biology

  2. Neural systems language: a formal modeling language for the systematic description, unambiguous communication, and automated digital curation of neural connectivity.

    Science.gov (United States)

    Brown, Ramsay A; Swanson, Larry W

    2013-09-01

    Systematic description and the unambiguous communication of findings and models remain among the unresolved fundamental challenges in systems neuroscience. No common descriptive frameworks exist to describe systematically the connective architecture of the nervous system, even at the grossest level of observation. Furthermore, the accelerating volume of novel data generated on neural connectivity outpaces the rate at which this data is curated into neuroinformatics databases to synthesize digitally systems-level insights from disjointed reports and observations. To help address these challenges, we propose the Neural Systems Language (NSyL). NSyL is a modeling language to be used by investigators to encode and communicate systematically reports of neural connectivity from neuroanatomy and brain imaging. NSyL engenders systematic description and communication of connectivity irrespective of the animal taxon described, experimental or observational technique implemented, or nomenclature referenced. As a language, NSyL is internally consistent, concise, and comprehensible to both humans and computers. NSyL is a promising development for systematizing the representation of neural architecture, effectively managing the increasing volume of data on neural connectivity and streamlining systems neuroscience research. Here we present similar precedent systems, how NSyL extends existing frameworks, and the reasoning behind NSyL's development. We explore NSyL's potential for balancing robustness and consistency in representation by encoding previously reported assertions of connectivity from the literature as examples. Finally, we propose and discuss the implications of a framework for how NSyL will be digitally implemented in the future to streamline curation of experimental results and bridge the gaps among anatomists, imagers, and neuroinformatics databases. Copyright © 2013 Wiley Periodicals, Inc.

  3. 3S - Systematic, systemic, and systems biology and toxicology.

    Science.gov (United States)

    Smirnova, Lena; Kleinstreuer, Nicole; Corvi, Raffaella; Levchenko, Andre; Fitzpatrick, Suzanne C; Hartung, Thomas

    2018-01-01

    A biological system is more than the sum of its parts - it accomplishes many functions via synergy. Deconstructing the system down to the molecular mechanism level necessitates the complement of reconstructing functions on all levels, i.e., in our conceptualization of biology and its perturbations, our experimental models and computer modelling. Toxicology contains the somewhat arbitrary subclass "systemic toxicities"; however, there is no relevant toxic insult or general disease that is not systemic. At least inflammation and repair are involved that require coordinated signaling mechanisms across the organism. However, the more body components involved, the greater the challenge to reca-pitulate such toxicities using non-animal models. Here, the shortcomings of current systemic testing and the development of alternative approaches are summarized. We argue that we need a systematic approach to integrating existing knowledge as exemplified by systematic reviews and other evidence-based approaches. Such knowledge can guide us in modelling these systems using bioengineering and virtual computer models, i.e., via systems biology or systems toxicology approaches. Experimental multi-organ-on-chip and microphysiological systems (MPS) provide a more physiological view of the organism, facilitating more comprehensive coverage of systemic toxicities, i.e., the perturbation on organism level, without using substitute organisms (animals). The next challenge is to establish disease models, i.e., micropathophysiological systems (MPPS), to expand their utility to encompass biomedicine. Combining computational and experimental systems approaches and the chal-lenges of validating them are discussed. The suggested 3S approach promises to leverage 21st century technology and systematic thinking to achieve a paradigm change in studying systemic effects.

  4. Stochastic Neural Field Theory and the System-Size Expansion

    KAUST Repository

    Bressloff, Paul C.

    2010-01-01

    We analyze a master equation formulation of stochastic neurodynamics for a network of synaptically coupled homogeneous neuronal populations each consisting of N identical neurons. The state of the network is specified by the fraction of active or spiking neurons in each population, and transition rates are chosen so that in the thermodynamic or deterministic limit (N → ∞) we recover standard activity-based or voltage-based rate models. We derive the lowest order corrections to these rate equations for large but finite N using two different approximation schemes, one based on the Van Kampen system-size expansion and the other based on path integral methods. Both methods yield the same series expansion of the moment equations, which at O(1/N) can be truncated to form a closed system of equations for the first-and second-order moments. Taking a continuum limit of the moment equations while keeping the system size N fixed generates a system of integrodifferential equations for the mean and covariance of the corresponding stochastic neural field model. We also show how the path integral approach can be used to study large deviation or rare event statistics underlying escape from the basin of attraction of a stable fixed point of the mean-field dynamics; such an analysis is not possible using the system-size expansion since the latter cannot accurately determine exponentially small transitions. © by SIAM.

  5. A neural network architecture for implementation of expert systems for real time monitoring

    Science.gov (United States)

    Ramamoorthy, P. A.

    1991-01-01

    Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.

  6. Hydrogen production from biomass by biological systems

    International Nuclear Information System (INIS)

    Sharifan, H.R.; Qader, S.

    2009-01-01

    Hydrogen gas is seen as a future energy carrier, not involved in 'greenhouse' gas and its released energy in combustion can be converted to electric power. Biological system with low energy can produce hydrogen compared to electrochemical hydrogen production via solar battery-based water splitting which requires the use of solar batteries with high energy requirements. The biological hydrogen production occurs in microalgae and cyanobacteria by photosynthesis. They consume biochemical energy to produce molecular hydrogen. Hydrogen in some algae is an anaerobic production in the absence of light. In cyanobacteria the hydrogen production simultaneously happens with nitrogen fixation, and also catalyzed by nitrogenase as a side reaction. Hydrogen production by photosynthetic bacteria is mediated by nitrogenase activity, although hydrogenases may be active for both hydrogen production and hydrogen uptake under some conditions. Genetic studies on photosynthetic microorganisms have markedly increased in recent times, relatively few genetic engineering studies have focused on altering the characteristics of these microorganisms, particularly with respect to enhancing the hydrogen-producing capabilities of photosynthetic bacteria and cyanobacteria. (author)

  7. A Parallel Supercomputer Implementation of a Biological Inspired Neural Network and its use for Pattern Recognition

    International Nuclear Information System (INIS)

    De Ladurantaye, Vincent; Lavoie, Jean; Bergeron, Jocelyn; Parenteau, Maxime; Lu Huizhong; Pichevar, Ramin; Rouat, Jean

    2012-01-01

    A parallel implementation of a large spiking neural network is proposed and evaluated. The neural network implements the binding by synchrony process using the Oscillatory Dynamic Link Matcher (ODLM). Scalability, speed and performance are compared for 2 implementations: Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA) running on clusters of multicore supercomputers and NVIDIA graphical processing units respectively. A global spiking list that represents at each instant the state of the neural network is described. This list indexes each neuron that fires during the current simulation time so that the influence of their spikes are simultaneously processed on all computing units. Our implementation shows a good scalability for very large networks. A complex and large spiking neural network has been implemented in parallel with success, thus paving the road towards real-life applications based on networks of spiking neurons. MPI offers a better scalability than CUDA, while the CUDA implementation on a GeForce GTX 285 gives the best cost to performance ratio. When running the neural network on the GTX 285, the processing speed is comparable to the MPI implementation on RQCHP's Mammouth parallel with 64 notes (128 cores).

  8. Systems Biology of Metabolism: Annual Review of Biochemistry

    DEFF Research Database (Denmark)

    Nielsen, Jens

    2017-01-01

    Metabolism is highly complex and involves thousands of different connected reactions; it is therefore necessary to use mathematical models for holistic studies. The use of mathematical models in biology is referred to as systems biology. In this review, the principles of systems biology......, the application of systems biology for analyzing global regulatory structures, engineering the metabolism of cell factories, and analyzing human diseases is discussed....

  9. Computational Modeling of Biological Systems From Molecules to Pathways

    CERN Document Server

    2012-01-01

    Computational modeling is emerging as a powerful new approach for studying and manipulating biological systems. Many diverse methods have been developed to model, visualize, and rationally alter these systems at various length scales, from atomic resolution to the level of cellular pathways. Processes taking place at larger time and length scales, such as molecular evolution, have also greatly benefited from new breeds of computational approaches. Computational Modeling of Biological Systems: From Molecules to Pathways provides an overview of established computational methods for the modeling of biologically and medically relevant systems. It is suitable for researchers and professionals working in the fields of biophysics, computational biology, systems biology, and molecular medicine.

  10. Integrative Systems Biology: Elucidating Complex Traits

    DEFF Research Database (Denmark)

    Pers, Tune Hannes

    Risk-phenotypes and diseases are oen caused by perturbed cellular networks, as biological processes depend on an overwhelming number of heavily intertwined components. e impact of a genetically altered gene may ripple through its molecular neighborhood instead of being confined to the gene...... product itself. My doctoral studies have been focused on the development of integrative approaches to identify systemic risk-modifying and disease-causing patterns. ey have been rooted in the hypothesis that data integration of complementary data sets may yield additional etiologic insights compared...... to analyses conducted within a single type of data. e first line of research presented here outlines two integrative methodologies designed to identify etiological pathways and susceptibility genes. In Paper I, my coworkers and I present an integrative approach that interrogates protein complexes...

  11. A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

    Science.gov (United States)

    Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh

    2017-12-19

    Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

  12. Systems Biology and Ecology of Streamlined Bacterioplankton

    Science.gov (United States)

    Giovannoni, S. J.

    2014-12-01

    complex questions hinge on translating gene frequencies into trait based ecological models that reflect the systems biology of cells.

  13. Multiple neural systems controlling food intake and body weight.

    Science.gov (United States)

    Berthoud, Hans-Rudolf

    2002-06-01

    Discovery of the leptin receptor and its downstream peptidergic pathways has reconfirmed the crucial role of the hypothalamus in the regulation of food intake and energy balance. Strategically located in the midst of the mammalian neuraxis, the hypothalamus receives at least three distinct types of relevant information via direct or indirect neural connections as well as hormone receptors and substrate sensors bestowed on hypothalamic neurons. First, the medial and to a lesser extent the lateral hypothalamus receive a rich mix of information pertaining to the internal state of relative energy repletion/depletion. Second, specific hypothalamic nuclei receive information about the behavioral state, such as diurnal clock, physical activity-level, reproductive cycle, developmental stage, as well as imminent (e.g. fight and flight) and chronic (e.g. infection) stressors, that can potentially impact on short-term availability of fuels and long-term energy balance. Third, the hypothalamus, particularly its lateral aspects, receives information from areas in the forebrain involved in the acquisition, storage, and retrieval of sensory representations of the external food space and internal food experience, as well as from the executive forebrain involved in behavior selection and initiation. In addition, rich intrahypothalamic connections facilitate further distribution of incoming information to various hypothalamic nuclei. On the other hand, the hypothalamus has widespread neural projections to the same cortical areas it receives inputs, and many hypothalamic neurons are one synapse away from most endocrine systems and from both sympathetic and parasympathetic effector organs involved in the flux, storage, mobilization, and utilization of fuels. It is argued that processing within cortico-limbic areas and communication with hypothalamic areas are particularly important in human food intake control that is more and more guided by cognitive rather than metabolic aspects in

  14. NNETS - NEURAL NETWORK ENVIRONMENT ON A TRANSPUTER SYSTEM

    Science.gov (United States)

    Villarreal, J.

    1994-01-01

    The primary purpose of NNETS (Neural Network Environment on a Transputer System) is to provide users a high degree of flexibility in creating and manipulating a wide variety of neural network topologies at processing speeds not found in conventional computing environments. To accomplish this purpose, NNETS supports back propagation and back propagation related algorithms. The back propagation algorithm used is an implementation of Rumelhart's Generalized Delta Rule. NNETS was developed on the INMOS Transputer. NNETS predefines a Back Propagation Network, a Jordan Network, and a Reinforcement Network to assist users in learning and defining their own networks. The program also allows users to configure other neural network paradigms from the NNETS basic architecture. The Jordan network is basically a feed forward network that has the outputs connected to a pseudo input layer. The state of the network is dependent on the inputs from the environment plus the state of the network. The Reinforcement network learns via a scalar feedback signal called reinforcement. The network propagates forward randomly. The environment looks at the outputs of the network to produce a reinforcement signal that is fed back to the network. NNETS was written for the INMOS C compiler D711B version 1.3 or later (MS-DOS version). A small portion of the software was written in the OCCAM language to perform the communications routing between processors. NNETS is configured to operate on a 4 X 10 array of Transputers in sequence with a Transputer based graphics processor controlled by a master IBM PC 286 (or better) Transputer. A RGB monitor is required which must be capable of 512 X 512 resolution. It must be able to receive red, green, and blue signals via BNC connectors. NNETS is meant for experienced Transputer users only. The program is distributed on 5.25 inch 1.2Mb MS-DOS format diskettes. NNETS was developed in 1991. Transputer and OCCAM are registered trademarks of Inmos Corporation. MS

  15. Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Ruliang Wang

    2012-01-01

    Full Text Available This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

  16. Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms

    DEFF Research Database (Denmark)

    Vinther, Kasper; Green, Torben; Østergaard, Søren

    2017-01-01

    This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additio...... space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model....

  17. Three-dimensional hydrogel cell culture systems for modeling neural tissue

    Science.gov (United States)

    Frampton, John

    Two-dimensional (2-D) neural cell culture systems have served as physiological models for understanding the cellular and molecular events that underlie responses to physical and chemical stimuli, control sensory and motor function, and lead to the development of neurological diseases. However, the development of three-dimensional (3-D) cell culture systems will be essential for the advancement of experimental research in a variety of fields including tissue engineering, chemical transport and delivery, cell growth, and cell-cell communication. In 3-D cell culture, cells are provided with an environment similar to tissue, in which they are surrounded on all sides by other cells, structural molecules and adhesion ligands. Cells grown in 3-D culture systems display morphologies and functions more similar to those observed in vivo, and can be cultured in such a way as to recapitulate the structural organization and biological properties of tissue. This thesis describes a hydrogel-based culture system, capable of supporting the growth and function of several neural cell types in 3-D. Alginate hydrogels were characterized in terms of their biomechanical and biochemical properties and were functionalized by covalent attachment of whole proteins and peptide epitopes. Methods were developed for rapid cross-linking of alginate hydrogels, thus permitting the incorporation of cells into 3-D scaffolds without adversely affecting cell viability or function. A variety of neural cell types were tested including astrocytes, microglia, and neurons. Cells remained viable and functional for longer than two weeks in culture and displayed process outgrowth in 3-D. Cell constructs were created that varied in cell density, type and organization, providing experimental flexibility for studying cell interactions and behavior. In one set of experiments, 3-D glial-endothelial cell co-cultures were used to model blood-brain barrier (BBB) structure and function. This co-culture system was

  18. Algebraic and adaptive learning in neural control systems

    Science.gov (United States)

    Ferrari, Silvia

    A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.

  19. Systems biology in practice: concepts, implementation and application

    National Research Council Canada - National Science Library

    Klipp, E

    2005-01-01

    ... approaching systems biology from a different discipline. We see the origin and the methodological foundations for systems biology (1) in the accumulation of detailed biological knowledge with the prospect of utilization in biotechnology and health care, (2) in the emergence of new experimental techniques in genomics and proteomics, (3) in the traditio...

  20. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...

  1. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...... choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview...

  2. A SYSTEMIC VISION OF BIOLOGY: OVERCOMING LINEARITY

    Directory of Open Access Journals (Sweden)

    M. Mayer

    2005-07-01

    Full Text Available Many  authors have proposed  that contextualization of reality  is necessary  to teach  Biology, empha- sizing students´ social and  economic realities.   However, contextualization means  more than  this;  it is related  to working with  different kinds of phenomena  and/or objects  which enable  the  expression of scientific concepts.  Thus,  contextualization allows the integration of different contents.  Under this perspective,  the  objectives  of this  work were to articulate different  biology concepts  in order  to de- velop a systemic vision of biology; to establish  relationships with other areas of knowledge and to make concrete the  cell molecular  structure and organization as well as their  implications  on living beings´ environment, using  contextualization.  The  methodology  adopted  in this  work  was based  on three aspects:  interdisciplinarity, contextualization and development of competences,  using energy:  its flux and transformations as a thematic axis and  an approach  which allowed the  interconnection between different situations involving  these  concepts.   The  activities developed  were:  1.   dialectic exercise, involving a movement around  micro and macroscopic aspects,  by using questions  and activities,  sup- ported  by the use of alternative material  (as springs, candles on the energy, its forms, transformations and  implications  in the  biological way (microscopic  concepts;  2, Construction of molecular  models, approaching the concepts of atom,  chemical bonds and bond energy in molecules; 3. Observations de- veloped in Manguezal¨(mangrove swamp  ecosystem (Itapissuma, PE  were used to work macroscopic concepts  (as  diversity  and  classification  of plants  and  animals,  concerning  to  energy  flow through food chains and webs. A photograph register of all activities  along the course plus texts

  3. Robust Adaptive Control for Nonlinear Uncertain Systems Using Type-2 Fuzzy Neural Network System

    Directory of Open Access Journals (Sweden)

    Ching-Hung Lee

    2011-01-01

    Full Text Available This paper proposes a novel intelligent control scheme using type-2 fuzzy neural network (type-2 FNN system. The control scheme is developed using a type-2 FNN controller and an adaptive compensator. The type-2 FNN combines the type-2 fuzzy logic system (FLS, neural network, and its learning algorithm using the optimal learning algorithm. The properties of type-1 FNN system parallel computation scheme and parameter convergence are easily extended to type-2 FNN systems. In addition, a robust adaptive control scheme which combines the adaptive type-2 FNN controller and compensated controller is proposed for nonlinear uncertain systems. Simulation results are presented to illustrate the effectiveness of our approach.

  4. Programmable temperature control system for biological materials

    Science.gov (United States)

    Anselmo, V. J.; Harrison, R. G.; Rinfret, A. P.

    1982-01-01

    A system was constructed which allows programmable temperature-time control for a 5 cu cm sample volume of arbitrary biological material. The system also measures the parameters necessary for the determination of the sample volume specific heat and thermal conductivity as a function of temperature, and provides a detailed measurement of the temperature during phase change and a means of calculating the heat of the phase change. Steady-state and dynamic temperature control is obtained by supplying heat to the sample volume through resistive elements constructed as an integral part of the sample container. For cooling purposes, this container is totally immersed into a cold heat sink. Using a mixture of dry ice and alcohol at 79 C, the sample volume can be controlled from +40 to -60 C at rates from steady state to + or - 65 C/min. Steady-state temperature precision is better than 0.2 C, while the dynamic capability depends on the temperature rate of change as well as the mass of both the sample and the container.

  5. Drawing inspiration from biological optical systems

    Science.gov (United States)

    Wolpert, H. D.

    2009-08-01

    Bio-Mimicking/Bio-Inspiration: How can we not be inspired by Nature? Life has evolved on earth over the last 3.5 to 4 billion years. Materials formed during this time were not toxic; they were created at low temperatures and low pressures unlike many of the materials developed today. The natural materials formed are self-assembled, multifunctional, nonlinear, complex, adaptive, self-repairing and biodegradable. The designs that failed are fossils. Those that survived are the success stories. Natural materials are mostly formed from organics, inorganic crystals and amorphous phases. The materials make economic sense by optimizing the design of the structures or systems to meet multiple needs. We constantly "see" many similar strategies in approaches, between man and nature, but we seldom look at the details of natures approaches. The power of image processing, in many of natures creatures, is a detail that is often overlooked. Seldon does the engineer interact with the biologist and learn what nature has to teach us. The variety and complexity of biological materials and the optical systems formed should inspire us.

  6. ANOMALY NETWORK INTRUSION DETECTION SYSTEM BASED ON DISTRIBUTED TIME-DELAY NEURAL NETWORK (DTDNN

    Directory of Open Access Journals (Sweden)

    LAHEEB MOHAMMAD IBRAHIM

    2010-12-01

    Full Text Available In this research, a hierarchical off-line anomaly network intrusion detection system based on Distributed Time-Delay Artificial Neural Network is introduced. This research aims to solve a hierarchical multi class problem in which the type of attack (DoS, U2R, R2L and Probe attack detected by dynamic neural network. The results indicate that dynamic neural nets (Distributed Time-Delay Artificial Neural Network can achieve a high detection rate, where the overall accuracy classification rate average is equal to 97.24%.

  7. Complexity, Analysis and Control of Singular Biological Systems

    CERN Document Server

    Zhang, Qingling; Zhang, Xue

    2012-01-01

    Complexity, Analysis and Control of Singular Biological Systems follows the control of real-world biological systems at both ecological and phyisological levels concentrating on the application of now-extensively-investigated singular system theory. Much effort has recently been dedicated to the modelling and analysis of developing bioeconomic systems and the text establishes singular examples of these, showing how proper control can help to maintain sustainable economic development of biological resources. The book begins from the essentials of singular systems theory and bifurcations before tackling  the use of various forms of control in singular biological systems using examples including predator-prey relationships and viral vaccination and quarantine control. Researchers and graduate students studying the control of complex biological systems are shown how a variety of methods can be brought to bear and practitioners working with the economics of biological systems and their control will also find the ...

  8. Hybrid energy system evaluation in water supply system energy production: neural network approach

    Energy Technology Data Exchange (ETDEWEB)

    Goncalves, Fabio V.; Ramos, Helena M. [Civil Engineering Department, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon (Portugal); Reis, Luisa Fernanda R. [Universidade de Sao Paulo, EESC/USP, Departamento de Hidraulica e Saneamento., Avenida do Trabalhador Saocarlense, 400, Sao Carlos-SP (Brazil)

    2010-07-01

    Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator - CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with hydraulic and power model simulator - HPS. The results show that this computational model is useful as advanced decision support system in the design of configurations of hybrid power systems applied to water supply systems, improving the solutions in the development of its global energy efficiency.

  9. Network Reconstruction of Dynamic Biological Systems

    OpenAIRE

    Asadi, Behrang

    2013-01-01

    Inference of network topology from experimental data is a central endeavor in biology, since knowledge of the underlying signaling mechanisms a requirement for understanding biological phenomena. As one of the most important tools in bioinformatics area, development of methods to reconstruct biological networks has attracted remarkable attention in the current decade. Integration of different data types can lead to remarkable improvements in our ability to identify the connectivity of differe...

  10. An alternative respiratory sounds classification system utilizing artificial neural networks

    Directory of Open Access Journals (Sweden)

    Rami J Oweis

    2015-04-01

    Full Text Available Background: Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. Methods: This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs and adaptive neuro-fuzzy inference systems (ANFIS toolboxes. The methods have been applied to 10 different respiratory sounds for classification. Results: The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. Conclusions: The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.

  11. An alternative respiratory sounds classification system utilizing artificial neural networks.

    Science.gov (United States)

    Oweis, Rami J; Abdulhay, Enas W; Khayal, Amer; Awad, Areen

    2015-01-01

    Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) toolboxes. The methods have been applied to 10 different respiratory sounds for classification. The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.

  12. Systems biology of microbial exopolysaccharides production

    Directory of Open Access Journals (Sweden)

    Ozlem eAtes

    2015-12-01

    Full Text Available Exopolysaccharides (EPS produced by diverse group of microbial systems are rapidly emerging as new and industrially important biomaterials. Due to their unique and complex chemical structures and many interesting physicochemical and rheological properties with novel functionality, the microbial EPSs find wide range of commercial applications in various fields of the economy such as food, feed, packaging, chemical, textile, cosmetics and pharmaceutical industry, agriculture and medicine. EPSs are mainly associated with high-value applications and they have received considerable research attention over recent decades with their biocompatibility, biodegradability, and both environmental and human compatibility. However only a few microbial EPSs have achieved to be used commercially due to their high production costs. The emerging need to overcome economic hurdles and the increasing significance of microbial EPSs in industrial and medical biotechnology call for the elucidation of the interrelations between metabolic pathways and EPS biosynthesis mechanism in order to control and hence enhance its microbial productivity. Moreover a better understanding of biosynthesis mechanism is a significant issue for improvement of product quality and properties and also for the design of novel strains. Therefore a systems-based approach constitutes an important step towards understanding the interplay between metabolism and EPS biosynthesis and further enhances its metabolic performance for industrial application. In this review, primarily the microbial EPSs, their biosynthesis mechanism and important factors for their production will be discussed. After this brief introduction, recent literature on the application of omics technologies and systems biology tools for the improvement of production yields will be critically evaluated. Special focus will be given to EPSs with high market value such as xanthan, levan, pullulan and dextran.

  13. Systems Biology of Microbial Exopolysaccharides Production.

    Science.gov (United States)

    Ates, Ozlem

    2015-01-01

    Exopolysaccharides (EPSs) produced by diverse group of microbial systems are rapidly emerging as new and industrially important biomaterials. Due to their unique and complex chemical structures and many interesting physicochemical and rheological properties with novel functionality, the microbial EPSs find wide range of commercial applications in various fields of the economy such as food, feed, packaging, chemical, textile, cosmetics and pharmaceutical industry, agriculture, and medicine. EPSs are mainly associated with high-value applications, and they have received considerable research attention over recent decades with their biocompatibility, biodegradability, and both environmental and human compatibility. However, only a few microbial EPSs have achieved to be used commercially due to their high production costs. The emerging need to overcome economic hurdles and the increasing significance of microbial EPSs in industrial and medical biotechnology call for the elucidation of the interrelations between metabolic pathways and EPS biosynthesis mechanism in order to control and hence enhance its microbial productivity. Moreover, a better understanding of biosynthesis mechanism is a significant issue for improvement of product quality and properties and also for the design of novel strains. Therefore, a systems-based approach constitutes an important step toward understanding the interplay between metabolism and EPS biosynthesis and further enhances its metabolic performance for industrial application. In this review, primarily the microbial EPSs, their biosynthesis mechanism, and important factors for their production will be discussed. After this brief introduction, recent literature on the application of omics technologies and systems biology tools for the improvement of production yields will be critically evaluated. Special focus will be given to EPSs with high market value such as xanthan, levan, pullulan, and dextran.

  14. Modeling and Simulation Tools: From Systems Biology to Systems Medicine.

    Science.gov (United States)

    Olivier, Brett G; Swat, Maciej J; Moné, Martijn J

    2016-01-01

    Modeling is an integral component of modern biology. In this chapter we look into the role of the model, as it pertains to Systems Medicine, and the software that is required to instantiate and run it. We do this by comparing the development, implementation, and characteristics of tools that have been developed to work with two divergent methodologies: Systems Biology and Pharmacometrics. From the Systems Biology perspective we consider the concept of "Software as a Medical Device" and what this may imply for the migration of research-oriented, simulation software into the domain of human health.In our second perspective, we see how in practice hundreds of computational tools already accompany drug discovery and development at every stage of the process. Standardized exchange formats are required to streamline the model exchange between tools, which would minimize translation errors and reduce the required time. With the emergence, almost 15 years ago, of the SBML standard, a large part of the domain of interest is already covered and models can be shared and passed from software to software without recoding them. Until recently the last stage of the process, the pharmacometric analysis used in clinical studies carried out on subject populations, lacked such an exchange medium. We describe a new emerging exchange format in Pharmacometrics which covers the non-linear mixed effects models, the standard statistical model type used in this area. By interfacing these two formats the entire domain can be covered by complementary standards and subsequently the according tools.

  15. PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET

    Directory of Open Access Journals (Sweden)

    S. Devaraju

    2014-04-01

    Full Text Available Intrusion Detection Systems are challenging task for finding the user as normal user or attack user in any organizational information systems or IT Industry. The Intrusion Detection System is an effective method to deal with the kinds of problem in networks. Different classifiers are used to detect the different kinds of attacks in networks. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the four types of classifiers used are Feed Forward Neural Network (FFNN, Generalized Regression Neural Network (GRNN, Probabilistic Neural Network (PNN and Radial Basis Neural Network (RBNN. The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency and False Alarm Rate is measured. It is proved that the reduced dataset is performing better than the full featured dataset.

  16. Neural Control System in Obstacle Avoidance in Mobile Robots Using Ultrasonic Sensors

    Directory of Open Access Journals (Sweden)

    A. Medina-Santiago

    2014-02-01

    Full Text Available This paper presents the development and implementation of neural control systems in mobile robots in obstacle avoidance in real time using ultrasonic sensors with complex strategies of decision-making in development (Matlab and Processing. An Arduino embedded platform is used to implement the neural control for field results.

  17. A neural network method for solving a system of linear variational inequalities

    International Nuclear Information System (INIS)

    Lan Hengyou; Cui Yishun

    2009-01-01

    In this paper, we transmute the solution for a new system of linear variational inequalities to an equilibrium point of neural networks, and by using analytic technique, some sufficient conditions are presented. Further, the estimation of the exponential convergence rates of the neural networks is investigated. The new and useful results obtained in this paper generalize and improve the corresponding results of recent works.

  18. Review: the role of neural crest cells in the endocrine system.

    Science.gov (United States)

    Adams, Meghan Sara; Bronner-Fraser, Marianne

    2009-01-01

    The neural crest is a pluripotent population of cells that arises at the junction of the neural tube and the dorsal ectoderm. These highly migratory cells form diverse derivatives including neurons and glia of the sensory, sympathetic, and enteric nervous systems, melanocytes, and the bones, cartilage, and connective tissues of the face. The neural crest has long been associated with the endocrine system, although not always correctly. According to current understanding, neural crest cells give rise to the chromaffin cells of the adrenal medulla, chief cells of the extra-adrenal paraganglia, and thyroid C cells. The endocrine tumors that correspond to these cell types are pheochromocytomas, extra-adrenal paragangliomas, and medullary thyroid carcinomas. Although controversies concerning embryological origin appear to have mostly been resolved, questions persist concerning the pathobiology of each tumor type and its basis in neural crest embryology. Here we present a brief history of the work on neural crest development, both in general and in application to the endocrine system. In particular, we present findings related to the plasticity and pluripotency of neural crest cells as well as a discussion of several different neural crest tumors in the endocrine system.

  19. Modeling of the height control system using artificial neural networks

    Directory of Open Access Journals (Sweden)

    A. R Tahavvor

    2016-09-01

    Full Text Available Introduction Automation of agricultural and machinery construction has generally been enhanced by intelligent control systems due to utility and efficiency rising, ease of use, profitability and upgrading according to market demand. A broad variety of industrial merchandise are now supplied with computerized control systems of earth moving processes to be performed by construction and agriculture field vehicle such as grader, backhoe, tractor and scraper machines. A height control machine which is used in measuring base thickness is consisted of two mechanical and electronic parts. The mechanical part is consisted of conveyor belt, main body, electrical engine and invertors while the electronic part is consisted of ultrasonic, wave transmitter and receiver sensor, electronic board, control set, and microcontroller. The main job of these controlling devices consists of the topographic surveying, cutting and filling of elevated and spotted low area, and these actions fundamentally dependent onthe machine's ability in elevation and thickness measurement and control. In this study, machine was first tested and then some experiments were conducted for data collection. Study of system modeling in artificial neural networks (ANN was done for measuring, controlling the height for bases by input variable input vectors such as sampling time, probe speed, conveyer speed, sound wave speed and speed sensor are finally the maximum and minimum probe output vector on various conditions. The result reveals the capability of this procedure for experimental recognition of sensors' behavior and improvement of field machine control systems. Inspection, calibration and response, diagnosis of the elevation control system in combination with machine function can also be evaluated by some extra development of this system. Materials and Methods Designing and manufacture of the planned apparatus classified in three dissimilar, mechanical and electronic module, courses of

  20. Toxicity of silver nanoparticles in biological systems: Does the complexity of biological systems matter?

    Science.gov (United States)

    Vazquez-Muñoz, Roberto; Borrego, Belen; Juárez-Moreno, Karla; García-García, Maritza; Mota Morales, Josué D; Bogdanchikova, Nina; Huerta-Saquero, Alejandro

    2017-07-05

    Currently, nanomaterials are more frequently in our daily life, specifically in biomedicine, electronics, food, textiles and catalysis just to name a few. Although nanomaterials provide many benefits, recently their toxicity profiles have begun to be explored. In this work, the toxic effects of silver nanoparticles (35nm-average diameter and Polyvinyl-Pyrrolidone-coated) on biological systems of different levels of complexity was assessed in a comprehensive and comparatively way, through a variety of viability and toxicological assays. The studied organisms included viruses, bacteria, microalgae, fungi, animal and human cells (including cancer cell lines). It was found that biological systems of different taxonomical groups are inhibited at concentrations of silver nanoparticles within the same order of magnitude. Thus, the toxicity of nanomaterials on biological/living systems, constrained by their complexity, e.g. taxonomic groups, resulted contrary to the expected. The fact that cells and virus are inhibited with a concentration of silver nanoparticles within the same order of magnitude could be explained considering that silver nanoparticles affects very primitive cellular mechanisms by interacting with fundamental structures for cells and virus alike. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. FGF Signaling Transforms Non-neural Ectoderm into Neural Crest

    OpenAIRE

    Yardley, Nathan; García-Castro, Martín I.

    2012-01-01

    The neural crest arises at the border between the neural plate and the adjacent non-neural ectoderm. It has been suggested that both neural and non-neural ectoderm can contribute to the neural crest. Several studies have examined the molecular mechanisms that regulate neural crest induction in neuralized tissues or the neural plate border. Here, using the chick as a model system, we address the molecular mechanisms by which non-neural ectoderm generates neural crest. We report that in respons...

  2. Systems biology: properties of reconstructed networks

    National Research Council Canada - National Science Library

    Palsson, Bernhard

    2006-01-01

    ... between the mathematical ideas and biological processes are made clear, the book reflects the irreversible trend of increasing mathematical content in biology education. Therefore to assist both teacher and student, Palsson provides problem sets, projects, and PowerPoint slides in an associated web site and keeps the presentation in the book concrete with illustrat...

  3. Stochastic modelling of dynamical systems in biology

    NARCIS (Netherlands)

    Pellin, Danilo

    2017-01-01

    In this thesis two relevant biological problems will be addressed from a statistical modelling perspective. The first regards the study of hematopoiesis, a still not well understood biological process rarely observable in humans due to technical and ethical reasons. Hematopoiesis is responsible for

  4. Biologically inspired intelligent decision making: a commentary on the use of artificial neural networks in bioinformatics.

    Science.gov (United States)

    Manning, Timmy; Sleator, Roy D; Walsh, Paul

    2014-01-01

    Artificial neural networks (ANNs) are a class of powerful machine learning models for classification and function approximation which have analogs in nature. An ANN learns to map stimuli to responses through repeated evaluation of exemplars of the mapping. This learning approach results in networks which are recognized for their noise tolerance and ability to generalize meaningful responses for novel stimuli. It is these properties of ANNs which make them appealing for applications to bioinformatics problems where interpretation of data may not always be obvious, and where the domain knowledge required for deductive techniques is incomplete or can cause a combinatorial explosion of rules. In this paper, we provide an introduction to artificial neural network theory and review some interesting recent applications to bioinformatics problems.

  5. Isotopic fractionation of tritium in biological systems.

    Science.gov (United States)

    Le Goff, Pierre; Fromm, Michel; Vichot, Laurent; Badot, Pierre-Marie; Guétat, Philippe

    2014-04-01

    Isotopic fractionation of tritium is a highly relevant issue in radiation protection and requires certain radioecological considerations. Sound evaluation of this factor is indeed necessary to determine whether environmental compartments are enriched/depleted in tritium or if tritium is, on the contrary, isotopically well-distributed in a given system. The ubiquity of tritium and the standard analytical methods used to assay it may induce biases in both the measurement and the signification that is accorded to the so-called fractionation: based on an exhaustive review of the literature, we show how, sometimes large deviations may appear. It is shown that when comparing the non-exchangeable fraction of organically bound tritium (neOBT) to another fraction of tritium (e.g. tritiated water) the preparation of samples and the measurement of neOBT reported frequently led to underestimation of the ratio of tritium to hydrogen (T/H) in the non-exchangeable compartment by a factor of 5% to 50%. In the present study, corrections are proposed for most of the biological matrices studied so far. Nevertheless, the values of isotopic fractionation reported in the literature remain difficult to compare with each other, especially since the physical quantities and units often vary between authors. Some improvements are proposed to better define what should encompass the concepts of exchangeable and non-exchangeable fractions. Copyright © 2014 Elsevier Ltd. All rights reserved.

  6. Cardiovascular Biology of the Incretin System

    Science.gov (United States)

    Ussher, John R.; Drucker, Daniel J.

    2012-01-01

    Glucagon-like peptide-1 (GLP-1) is an incretin hormone that enhances glucose-stimulated insulin secretion and exerts direct and indirect actions on the cardiovascular system. GLP-1 and its related incretin hormone, glucose-dependent insulinotropic polypeptide (GIP), are rapidly inactivated by the enzyme dipeptidyl peptidase 4 (DPP-4), a key determinant of incretin bioactivity. Two classes of medications that enhance incretin action, GLP-1R agonists and DPP-4 inhibitors, are used for the treatment of type 2 diabetes mellitus (T2DM). We review herein the cardiovascular biology of GLP-1R agonists and DPP-4 inhibitors, including direct and indirect effects on cardiomyocytes, blood vessels, adipocytes, the control of blood pressure and postprandial lipoprotein secretion. Both GLP-1R activation and DPP-4 inhibition exert multiple cardioprotective actions in preclinical models of cardiovascular dysfunction, and short term studies in human subjects appear to demonstrate modest yet beneficial actions on cardiac function in subjects with ischemic heart disease. Incretin-based agents control body weight, improve glycemic control with a low risk of hypoglycemia, decrease blood pressure, inhibit the secretion of intestinal chylomicrons, and reduce inflammation in preclinical studies. Nevertheless, there is limited information on the cardiovascular actions of these agents in patients with diabetes and established cardiovascular disease. Hence, a more complete understanding of the cardiovascular risk:benefit ratio of incretin-based therapies will require completion of long term cardiovascular outcome studies currently underway in patients with T2DM. PMID:22323472

  7. Toward mechanical systems biology in bone.

    Science.gov (United States)

    Trüssel, Andreas; Müller, Ralph; Webster, Duncan

    2012-11-01

    Cyclic mechanical loading is perhaps the most important physiological factor regulating bone mass and shape in a way which balances optimal strength with minimal weight. This bone adaptation process spans multiple length and time scales. Forces resulting from physiological exercise at the organ scale are sensed at the cellular scale by osteocytes, which reside inside the bone matrix. Via biochemical pathways, osteocytes orchestrate the local remodeling action of osteoblasts (bone formation) and osteoclasts (bone resorption). Together these local adaptive remodeling activities sum up to strengthen bone globally at the organ scale. To resolve the underlying mechanisms it is required to identify and quantify both cause and effect across the different scales. Progress has been made at the different scales experimentally. Computational models of bone adaptation have been developed to piece together various experimental observations at the different scales into coherent and plausible mechanisms. However additional quantitative experimental validation is still required to build upon the insights which have already been achieved. In this review we discuss emerging as well as state of the art experimental and computational techniques and how they might be used in a mechanical systems biology approach to further our understanding of the mechanisms governing load induced bone adaptation, i.e., ways are outlined in which experimental and computational approaches could be coupled, in a quantitative manner to create more reliable multiscale models of bone.

  8. Adult neurogenesis and neurodegenerative diseases: A systems biology perspective.

    Science.gov (United States)

    Horgusluoglu, Emrin; Nudelman, Kelly; Nho, Kwangsik; Saykin, Andrew J

    2017-01-01

    New neurons are generated throughout adulthood in two regions of the brain, the olfactory bulb and dentate gyrus of the hippocampus, and are incorporated into the hippocampal network circuitry; disruption of this process has been postulated to contribute to neurodegenerative diseases including Alzheimer's disease and Parkinson's disease. Known modulators of adult neurogenesis include signal transduction pathways, the vascular and immune systems, metabolic factors, and epigenetic regulation. Multiple intrinsic and extrinsic factors such as neurotrophic factors, transcription factors, and cell cycle regulators control neural stem cell proliferation, maintenance in the adult neurogenic niche, and differentiation into mature neurons; these factors act in networks of signaling molecules that influence each other during construction and maintenance of neural circuits, and in turn contribute to learning and memory. The immune system and vascular system are necessary for neuronal formation and neural stem cell fate determination. Inflammatory cytokines regulate adult neurogenesis in response to immune system activation, whereas the vasculature regulates the neural stem cell niche. Vasculature, immune/support cell populations (microglia/astrocytes), adhesion molecules, growth factors, and the extracellular matrix also provide a homing environment for neural stem cells. Epigenetic changes during hippocampal neurogenesis also impact memory and learning. Some genetic variations in neurogenesis related genes may play important roles in the alteration of neural stem cells differentiation into new born neurons during adult neurogenesis, with important therapeutic implications. In this review, we discuss mechanisms of and interactions between these modulators of adult neurogenesis, as well as implications for neurodegenerative disease and current therapeutic research. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  9. Adult Neurogenesis and Neurodegenerative Diseases: A Systems Biology Perspective

    Science.gov (United States)

    Horgusluoglu, Emrin; Nudelman, Kelly; Nho, Kwangsik; Saykin, Andrew J.

    2016-01-01

    New neurons are generated throughout adulthood in two regions of the brain, the olfactory bulb and dentate gyrus of the hippocampus, and are incorporated into the hippocampal network circuitry; disruption of this process has been postulated to contribute to neurodegenerative diseases including Alzheimer’s disease and Parkinson’s disease. Known modulators of adult neurogenesis include signal transduction pathways, the vascular and immune systems, metabolic factors, and epigenetic regulation. Multiple intrinsic and extrinsic factors such as neurotrophic factors, transcription factors, and cell cycle regulators control neural stem cell proliferation, maintenance in the adult neurogenic niche, and differentiation into mature neurons; these factors act in networks of signaling molecules that influence each other during construction and maintenance of neural circuits, and in turn contribute to learning and memory. The immune system and vascular system are necessary for neuronal formation and neural stem cell fate determination. Inflammatory cytokines regulate adult neurogenesis in response to immune system activation, whereas the vasculature regulates the neural stem cell niche. Vasculature, immune/support cell populations (microglia/astrocytes), adhesion molecules, growth factors, and the extracellular matrix also provide a homing environment for neural stem cells. Epigenetic changes during hippocampal neurogenesis also impact memory and learning. Some genetic variations in neurogenesis related genes may play important roles in the alteration of neural stem cells differentiation into new born neurons during adult neurogenesis, with important therapeutic implications. In this review, we discuss mechanisms of and interactions between these modulators of adult neurogenesis, as well as implications for neurodegenerative disease and current therapeutic research. PMID:26879907

  10. Use of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate

    International Nuclear Information System (INIS)

    Gulliford, Sarah L.; Webb, Steve; Rowbottom, Carl G.; Corne, David W.; Dearnaley, David P.

    2004-01-01

    Background and purpose: This paper discusses the application of artificial neural networks (ANN) in predicting biological outcomes following prostate radiotherapy. A number of model-based methods have been developed to correlate the dose distributions calculated for a patient receiving radiotherapy and the radiobiological effect this will produce. Most widely used are the normal tissue complication probability and tumour control probability models. An alternative method for predicting specific examples of tumour control and normal tissue complications is to use an ANN. One of the advantages of this method is that there is no need for a priori information regarding the relationship between the data being correlated. Patients and methods: A set of retrospective clinical data from patients who received radical prostate radiotherapy was used to train ANNs to predict specific biological outcomes by learning the relationship between the treatment plan prescription, dose distribution and the corresponding biological effect. The dose and volume were included as a differential dose-volume histogram in order to provide a holistic description of the available data. Results: It was shown that the ANNs were able to predict biochemical control and specific bladder and rectum complications with sensitivity and specificity of above 55% when the outcomes were dichotomised. It was also possible to analyse information from the ANNs to investigate the effect of individual treatment parameters on the outcome. Conclusion: ANNs have been shown to learn something of the complex relationship between treatment parameters and outcome which, if developed further, may prove to be a useful tool in predicting biological outcomes

  11. Multi-scale, multi-modal analysis uncovers complex relationship at the brain tissue-implant neural interface: New Emphasis on the Biological Interface.

    Science.gov (United States)

    Michelson, Nicholas J; Vazquez, Alberto L; Eles, James R; Salatino, Joseph William; Purcell, Erin K; Williams, Jordan John; Cui, Tracy; Kozai, Takashi D Yoshida

    2017-11-28

    Implantable neural electrode devices are important tools for neuroscience research and have an increasing range of clinical applications. However, the intricacies of the biological response after implantation, and their ultimate impact on recording performance, remain challenging to elucidate. Establishing a relationship between the neurobiology and chronic recording performance is confounded by technical challenges related to traditional electrophysiological, material, and histological limitations. This can greatly impact the interpretations of results pertaining to device performance and tissue health surrounding the implant. Approach: In this work, electrophysiological activity and immunohistological analysis are compared after controlling for motion artifacts, quiescent neuronal activity, and material failure of devices in order to better understand the relationship between histology and electrophysiological outcomes. Results: Even after carefully accounting for these factors, the presence of viable neurons and lack or glial scarring does not convey single unit recording performance. Significance: To better understand the biological factors influencing neural activity, detailed cellular and molecular tissue responses were examined. Decreases in neural activity and blood oxygenation in the tissue surrounding the implant, shift in expression levels of vesicular transporter proteins and ion channels, axon and myelin injury, and interrupted blood flow in nearby capillaries can impact neural activity around implanted neural interfaces. Combined, these tissue changes highlight the need for more comprehensive, basic science research to elucidate the relationship between biology and chronic electrophysiology performance in order to advance neural technologies. © 2017 IOP Publishing Ltd.

  12. Noise-tolerant inverse analysis models for nondestructive evaluation of transportation infrastructure systems using neural networks

    Science.gov (United States)

    Ceylan, Halil; Gopalakrishnan, Kasthurirangan; Birkan Bayrak, Mustafa; Guclu, Alper

    2013-09-01

    The need to rapidly and cost-effectively evaluate the present condition of pavement infrastructure is a critical issue concerning the deterioration of ageing transportation infrastructure all around the world. Nondestructive testing (NDT) and evaluation methods are well-suited for characterising materials and determining structural integrity of pavement systems. The falling weight deflectometer (FWD) is a NDT equipment used to assess the structural condition of highway and airfield pavement systems and to determine the moduli of pavement layers. This involves static or dynamic inverse analysis (referred to as backcalculation) of FWD deflection profiles in the pavement surface under a simulated truck load. The main objective of this study was to employ biologically inspired computational systems to develop robust pavement layer moduli backcalculation algorithms that can tolerate noise or inaccuracies in the FWD deflection data collected in the field. Artificial neural systems, also known as artificial neural networks (ANNs), are valuable computational intelligence tools that are increasingly being used to solve resource-intensive complex engineering problems. Unlike the linear elastic layered theory commonly used in pavement layer backcalculation, non-linear unbound aggregate base and subgrade soil response models were used in an axisymmetric finite element structural analysis programme to generate synthetic database for training and testing the ANN models. In order to develop more robust networks that can tolerate the noisy or inaccurate pavement deflection patterns in the NDT data, several network architectures were trained with varying levels of noise in them. The trained ANN models were capable of rapidly predicting the pavement layer moduli and critical pavement responses (tensile strains at the bottom of the asphalt concrete layer, compressive strains on top of the subgrade layer and the deviator stresses on top of the subgrade layer), and also pavement

  13. The prokineticin system: an interface between neural inflammation and pain.

    Science.gov (United States)

    Franchi, Silvia; Sacerdote, Paola; Panerai, Alberto

    2017-05-01

    Prokineticins (PK) 1 and 2 belong to a new family of chemokines capable to interact with two different G coupled receptors: Prokineticin receptor (PKR)1 and 2. Both prokineticins and their receptors are widely distributed in different tissues and regulate several biological functions. In particular, a role of the PK system in inflammation and nociception has been established. PKRs are expressed in regions of the nervous system associated with pain and in primary sensitive neurons they colocalize with transient potential receptor vanilloid-TRPV1 providing an anatomical interaction in nociceptor sensitization. Moreover, PKs are strongly upregulated in immune and glial cells and sustain a proinflammatory loop in inflamed tissues. Recent evidences indicate that the block of the PK system represents a promising strategy to contrast inflammation and pain.

  14. Radionuclide Imaging Technologies for Biological Systems

    Energy Technology Data Exchange (ETDEWEB)

    Howell, Calvin R. [Duke Univ., Durham, NC (United States); Reid, Chantal D. [Duke Univ., Durham, NC (United States); Weisenberger, Andrew G. [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)

    2014-05-14

    The main objective of this project is to develop technologies and experimental techniques for studying the dynamics of physiological responses of plants to changes in their interface with the local environment and to educate a new generation of scientists in an interdisciplinary environment of biology, physics and engineering. Also an important goal is to perform measurements to demonstrate the new data that can be produced and made available to the plant-biology community using the imaging technologies and experimental techniques developed in this project. The study of the plant-environment interface includes a wide range of topics in plant physiology, e.g., the root-soil interface, resource availability, impact of herbivores, influence of microbes on root surface, and responses to toxins in the air and soil. The initial scientific motivation for our work is to improve understanding of the mechanisms for physiological responses to abrupt changes in the local environment, in particular, the responses that result in short-term adjustments in resource (e.g., sugars, nutrients and water) allocations. Data of time-dependent responses of plants to environmental changes are essential in developing mechanistic models for substance intake and resource allocation. Our approach is to use radioisotope tracing techniques to study whole-plant and plant organ (e.g., leaves, stems, roots) dynamical responses to abrupt changes in environmental conditions such as concentration of CO2 in the atmosphere, nutrient availability and lighting. To this aim we are collaborating with the Radiation Detector and Imaging Group at the Thomas Jefferson National Laboratory Facility (JLab) to develop gamma-ray and beta particle imaging systems optimized for plant studies. The radioisotope tracing measurements are conducted at the Phytotron facility at Duke University. The Phytotron is a controlled environment plant research facility with a variety of plant growth chambers. One chamber

  15. Honey characterization using computer vision system and artificial neural networks.

    Science.gov (United States)

    Shafiee, Sahameh; Minaei, Saeid; Moghaddam-Charkari, Nasrollah; Barzegar, Mohsen

    2014-09-15

    This paper reports the development of a computer vision system (CVS) for non-destructive characterization of honey based on colour and its correlated chemical attributes including ash content (AC), antioxidant activity (AA), and total phenolic content (TPC). Artificial neural network (ANN) models were applied to transform RGB values of images to CIE L*a*b* colourimetric measurements and to predict AC, TPC and AA from colour features of images. The developed ANN models were able to convert RGB values to CIE L*a*b* colourimetric parameters with low generalization error of 1.01±0.99. In addition, the developed models for prediction of AC, TPC and AA showed high performance based on colour parameters of honey images, as the R(2) values for prediction were 0.99, 0.98, and 0.87, for AC, AA and TPC, respectively. The experimental results show the effectiveness and possibility of applying CVS for non-destructive honey characterization by the industry. Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. Artificial Neural Network-Based System for PET Volume Segmentation

    Directory of Open Access Journals (Sweden)

    Mhd Saeed Sharif

    2010-01-01

    Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  17. Artificial Neural Network-Based System for PET Volume Segmentation.

    Science.gov (United States)

    Sharif, Mhd Saeed; Abbod, Maysam; Amira, Abbes; Zaidi, Habib

    2010-01-01

    Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI) approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs), as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  18. Radial basis function neural network for power system load-flow

    International Nuclear Information System (INIS)

    Karami, A.; Mohammadi, M.S.

    2008-01-01

    This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)

  19. Compensating for Channel Fading in DS-CDMA Communication Systems Employing ICA Neural Network Detectors

    Directory of Open Access Journals (Sweden)

    David Overbye

    2005-06-01

    Full Text Available In this paper we examine the impact of channel fading on the bit error rate of a DS-CDMA communication system. The system employs detectors that incorporate neural networks effecting methods of independent component analysis (ICA, subspace estimation of channel noise, and Hopfield type neural networks. The Rayleigh fading channel model is used. When employed in a Rayleigh fading environment, the ICA neural network detectors that give superior performance in a flat fading channel did not retain this superior performance. We then present a new method of compensating for channel fading based on the incorporation of priors in the ICA neural network learning algorithms. When the ICA neural network detectors were compensated using the incorporation of priors, they give significantly better performance than the traditional detectors and the uncompensated ICA detectors. Keywords: CDMA, Multi-user Detection, Rayleigh Fading, Multipath Detection, Independent Component Analysis, Prior Probability Hebbian Learning, Natural Gradient

  20. Impact of systems biology on metabolic engineering of Saccharomyces cerevisiae

    DEFF Research Database (Denmark)

    Nielsen, Jens; Jewett, Michael Christopher

    2008-01-01

    in the industrial application of this yeast. Developments in genomics and high-throughput systems biology tools are enhancing one's ability to rapidly characterize cellular behaviour, which is valuable in the field of metabolic engineering where strain characterization is often the bottleneck in strain development...... programmes. Here, the impact of systems biology on metabolic engineering is reviewed and perspectives on the role of systems biology in the design of cell factories are given....

  1. Interactive analysis of systems biology molecular expression data

    OpenAIRE

    Prabhakar Sunil; Salt David E; Kane Michael D; Stephenson Alan; Ouyang Qi; Zhang Mingwu; Burgner John; Buck Charles; Zhang Xiang

    2008-01-01

    Abstract Background Systems biology aims to understand biological systems on a comprehensive scale, such that the components that make up the whole are connected to one another and work through dependent interactions. Molecular correlations and comparative studies of molecular expression are crucial to establishing interdependent connections in systems biology. The existing software packages provide limited data mining capability. The user must first generate visualization data with a preferr...

  2. Adapting to Biology: Maintaining Container-Closure System Compatibility with the Therapeutic Biologic Revolution.

    Science.gov (United States)

    Degrazio, Dominick

    Many pharmaceutical companies are transitioning their research and development drug product pipeline from traditional small-molecule injectables to the dimension of evolving therapeutic biologics. Important concerns associated with this changeover are becoming forefront, as challenges develop of varying complexity uncommon with the synthesis and production of traditional drugs. Therefore, alternative measures must be established that aim to preserve the efficacy and functionality of a biologic that might not be implemented for small molecules. Conserving protein stability is relative to perpetuating a net equilibrium of both intrinsic and extrinsic factors. Key to sustaining this balance is the ability of container-closure systems to maintain their compatibility with the ever-changing dynamics of therapeutic biologics. Failure to recognize and adjust the material properties of packaging components to support compatibility with therapeutic biologics can compromise patient safety, drug productivity, and biological stability. This review will examine the differences between small-molecule drugs and therapeutic biologics, lay a basic foundation for understanding the stability of therapeutic biologics, and demonstrate potential sources of container-closure systems' incompatibilities with therapeutic biologics at a mechanistic level. Many pharmaceutical companies are transitioning their research and development drug product pipeline from traditional small-molecule injectables to recombinantly derived therapeutic biologics. Concerns associated with this transformation are becoming prominent, as therapeutic biologics are uncharacteristic to small-molecule drugs. Maintaining the stability of a therapeutic biologic is a combination of balancing intrinsic factors and external elements within the biologic's microenvironment. An important aspect of this balance is relegated to the overall compatibility of primary, parenteral container-closure systems with therapeutic biologics

  3. Development of the disable software reporting system on the basis of the neural network

    Science.gov (United States)

    Gavrylenko, S.; Babenko, O.; Ignatova, E.

    2018-04-01

    The PE structure of malicious and secure software is analyzed, features are highlighted, binary sign vectors are obtained and used as inputs for training the neural network. A software model for detecting malware based on the ART-1 neural network was developed, optimal similarity coefficients were found, and testing was performed. The obtained research results showed the possibility of using the developed system of identifying malicious software in computer systems protection systems

  4. Integrating Artificial Immune, Neural and Endrocine Systems in Autonomous Sailing Robots

    Science.gov (United States)

    2010-09-24

    was to develop a combined neural-immune-endocrine system, but after staffing issues in the first year of the project, work was refocussed to focus on...system - Development of an adaptive hormone system capable of changing operation and control of the neural network depending on changing enviromental ...wide variety of differnt types of hormome, focussing on wider power management issues than was tackled with our previous work. For these experiments

  5. Research on architecture of intelligent design platform for artificial neural network expert system

    Science.gov (United States)

    Gu, Honghong

    2017-09-01

    Based on the review of the development and current situation of CAD technology, the necessity of combination of artificial neural network and expert system, and then present an intelligent design system based on artificial neural network. Moreover, it discussed the feasibility of realization of a design-oriented expert system development tools on the basis of above combination. In addition, knowledge representation strategy and method and the solving process are given in this paper.

  6. Neural Network Expert System in the Application of Tower Fault Diagnosis

    Science.gov (United States)

    Liu, Xiaoyang; Xia, Zhongwu; Tao, Zhiyong; Zhao, Zhenlian

    For the corresponding fuzzy relationship between the fault symptoms and the fault causes in the process of tower crane operation, this paper puts forward a kind of rapid new method of fast detection and diagnosis for common fault based on neural network expert system. This paper makes full use of expert system and neural network advantages, and briefly introduces the structure, function, algorithm and realization of the adopted system. Results show that the new algorithm is feasible and can achieve rapid faults diagnosis.

  7. Seasonal allergic rhinitis and systems biology-oriented biomarker discovery

    NARCIS (Netherlands)

    Baars, E.W.; Nierop, A.F.M.; Savelkoul, H.F.J.

    2015-01-01

    There is an increasing interest in science and medicine in the systems approach. Instead of the reductionist approach that focuses on the physical and chemical properties of the individual components, systems biology aims to describe, understand, and explain from the complex biological systems

  8. Breeding system and pollination biology of the semidomesticated ...

    African Journals Online (AJOL)

    Breeding system and pollination biology of the semidomesticated fruit tree, Tamarindus indica L. (Leguminosae: Caesalpinioideae ): Implications for fruit production, selective breeding, and conservation of genetic resources.

  9. A guide to numerical modelling in systems biology

    CERN Document Server

    Deuflhard, Peter

    2015-01-01

    This book is intended for students of computational systems biology with only a limited background in mathematics. Typical books on systems biology merely mention algorithmic approaches, but without offering a deeper understanding. On the other hand, mathematical books are typically unreadable for computational biologists. The authors of the present book have worked hard to fill this gap. The result is not a book on systems biology, but on computational methods in systems biology. This book originated from courses taught by the authors at Freie Universität Berlin. The guiding idea of the courses was to convey those mathematical insights that are indispensable for systems biology, teaching the necessary mathematical prerequisites by means of many illustrative examples and without any theorems. The three chapters cover the mathematical modelling of biochemical and physiological processes, numerical simulation of the dynamics of biological networks, and identification of model parameters by means of comparisons...

  10. Statistical mechanics of complex neural systems and high dimensional data

    International Nuclear Information System (INIS)

    Advani, Madhu; Lahiri, Subhaneil; Ganguli, Surya

    2013-01-01

    Recent experimental advances in neuroscience have opened new vistas into the immense complexity of neuronal networks. This proliferation of data challenges us on two parallel fronts. First, how can we form adequate theoretical frameworks for understanding how dynamical network processes cooperate across widely disparate spatiotemporal scales to solve important computational problems? Second, how can we extract meaningful models of neuronal systems from high dimensional datasets? To aid in these challenges, we give a pedagogical review of a collection of ideas and theoretical methods arising at the intersection of statistical physics, computer science and neurobiology. We introduce the interrelated replica and cavity methods, which originated in statistical physics as powerful ways to quantitatively analyze large highly heterogeneous systems of many interacting degrees of freedom. We also introduce the closely related notion of message passing in graphical models, which originated in computer science as a distributed algorithm capable of solving large inference and optimization problems involving many coupled variables. We then show how both the statistical physics and computer science perspectives can be applied in a wide diversity of contexts to problems arising in theoretical neuroscience and data analysis. Along the way we discuss spin glasses, learning theory, illusions of structure in noise, random matrices, dimensionality reduction and compressed sensing, all within the unified formalism of the replica method. Moreover, we review recent conceptual connections between message passing in graphical models, and neural computation and learning. Overall, these ideas illustrate how statistical physics and computer science might provide a lens through which we can uncover emergent computational functions buried deep within the dynamical complexities of neuronal networks. (paper)

  11. Transcriptional Profiling of Hypoxic Neural Stem Cells Identifies Calcineurin-NFATc4 Signaling as a Major Regulator of Neural Stem Cell Biology

    Science.gov (United States)

    Moreno, Marta; Fernández, Virginia; Monllau, Josep M.; Borrell, Víctor; Lerin, Carles; de la Iglesia, Núria

    2015-01-01

    Summary Neural stem cells (NSCs) reside in a hypoxic microenvironment within the brain. However, the crucial transcription factors (TFs) that regulate NSC biology under physiologic hypoxia are poorly understood. Here we have performed gene set enrichment analysis (GSEA) of microarray datasets from hypoxic versus normoxic NSCs with the aim of identifying pathways and TFs that are activated under oxygen concentrations mimicking normal brain tissue microenvironment. Integration of TF target (TFT) and pathway enrichment analysis identified the calcium-regulated TF NFATc4 as a major candidate to regulate hypoxic NSC functions. Nfatc4 expression was coordinately upregulated by top hypoxia-activated TFs, while NFATc4 target genes were enriched in hypoxic NSCs. Loss-of-function analyses further revealed that the calcineurin-NFATc4 signaling axis acts as a major regulator of NSC self-renewal and proliferation in vitro and in vivo by promoting the expression of TFs, including Id2, that contribute to the maintenance of the NSC state. PMID:26235896

  12. Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R. [UAZ, Av. Ramon Lopez Velarde No. 801, 98000 Zacatecas (Mexico)

    2006-07-01

    An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

  13. Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

    International Nuclear Information System (INIS)

    Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R.

    2006-01-01

    An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

  14. A Magnetic Sensor System for Biological Detection

    KAUST Repository

    Li, Fuquan

    2015-05-01

    Magnetic biosensors detect biological targets through sensing the stray field of magnetic beads which label the targets. Commonly, magnetic biosensors employ the “sandwich” method to immobilize biological targets, i.e., the targets are sandwiched between a bio-functionalized sensor surface and bio-functionalized magnetic beads. This method has been used very successfully in different application, but its execution requires a rather elaborate procedure including several washing and incubation steps. This dissertation investigates a new magnetic biosensor concept, which enables a simple and effective detection of biological targets. The biosensor takes advantage of the size difference between bare magnetic beads and compounds of magnetic beads and biological targets. First, the detection of super-paramagnetic beads via magnetic tunnel junction (MTJ) sensors is implemented. Frequency modulation is used to enhance the signal-to-noise ratio, enabling the detection of a single magnetic bead. Second, the concept of the magnetic biosensor is investigated theoretically. The biosensor consists of an MTJ sensor, which detects the stray field of magnetic beads inside of a trap on top of the MTJ. A microwire between the trap and the MTJ is used to attract magnetic beads to the trapping well by applying a current to it. The MTJ sensor’s output depends on the number of beads inside the trap. If biological targets are in the sample solution, the beads will form bead compounds consisting of beads linked to the biological targets. Since bead compounds are larger than bare beads, the number of beads inside the trapping well will depend on the presence of biological targets. Hence, the output of the MTJ sensor will depend on the biological targets. The dependences of sensor signals on the sizes of the MTJ sensor, magnetic beads and biological targets are studied to find the optimum constellations for the detection of specific biological targets. The optimization is demonstrated

  15. A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics

    OpenAIRE

    Somvanshi, Pramod Rajaram; Venkatesh, K. V.

    2013-01-01

    Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level respo...

  16. Identification of Complex Dynamical Systems with Neural Networks (2/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  17. Identification of Complex Dynamical Systems with Neural Networks (1/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  18. Computational neural network regression model for Host based Intrusion Detection System

    Directory of Open Access Journals (Sweden)

    Sunil Kumar Gautam

    2016-09-01

    Full Text Available The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN model and Multilayer Perceptron Neural Network (MPNN model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM significantly improved the detection accuracy while retaining minimum false alarm rate.

  19. Simulation of Missile Autopilot with Two-Rate Hybrid Neural Network System

    Directory of Open Access Journals (Sweden)

    ASTROV, I.

    2007-04-01

    Full Text Available This paper proposes a two-rate hybrid neural network system, which consists of two artificial neural network subsystems. These neural network subsystems are used as the dynamic subsystems controllers.1 This is because such neuromorphic controllers are especially suitable to control complex systems. An illustrative example - two-rate neural network hybrid control of decomposed stochastic model of a rigid guided missile over different operating conditions - was carried out using the proposed two-rate state-space decomposition technique. This example demonstrates that this research technique results in simplified low-order autonomous control subsystems with various speeds of actuation, and shows the quality of the proposed technique. The obtained results show that the control tasks for the autonomous subsystems can be solved more qualitatively than for the original system. The simulation and animation results with use of software package Simulink demonstrate that this research technique would work for real-time stochastic systems.

  20. Neural Network Autopilot System for a Mathematical Model of the Boeing 747

    National Research Council Canada - National Science Library

    Cottrill, Gerald

    1998-01-01

    .... Five on-line learning neural network autopilot systems, trained with the Standard and Extended Back-Progagation algorithms, were applied to a six degree-of-freedom non-linear simulation of a Boeing 747-200...

  1. Adaptive Neural Control of Uncertain MIMO Nonlinear Systems With State and Input Constraints.

    Science.gov (United States)

    Chen, Ziting; Li, Zhijun; Chen, C L Philip

    2017-06-01

    An adaptive neural control strategy for multiple input multiple output nonlinear systems with various constraints is presented in this paper. To deal with the nonsymmetric input nonlinearity and the constrained states, the proposed adaptive neural control is combined with the backstepping method, radial basis function neural network, barrier Lyapunov function (BLF), and disturbance observer. By ensuring the boundedness of the BLF of the closed-loop system, it is demonstrated that the output tracking is achieved with all states remaining in the constraint sets and the general assumption on nonsingularity of unknown control coefficient matrices has been eliminated. The constructed adaptive neural control has been rigorously proved that it can guarantee the semiglobally uniformly ultimate boundedness of all signals in the closed-loop system. Finally, the simulation studies on a 2-DOF robotic manipulator system indicate that the designed adaptive control is effective.

  2. A review of imaging techniques for systems biology

    Directory of Open Access Journals (Sweden)

    Po Ming J

    2008-08-01

    Full Text Available Abstract This paper presents a review of imaging techniques and of their utility in system biology. During the last decade systems biology has matured into a distinct field and imaging has been increasingly used to enable the interplay of experimental and theoretical biology. In this review, we describe and compare the roles of microscopy, ultrasound, CT (Computed Tomography, MRI (Magnetic Resonance Imaging, PET (Positron Emission Tomography, and molecular probes such as quantum dots and nanoshells in systems biology. As a unified application area among these different imaging techniques, examples in cancer targeting are highlighted.

  3. Neural sensitivity to statistical regularities as a fundamental biological process that underlies auditory learning: the role of musical practice.

    Science.gov (United States)

    François, Clément; Schön, Daniele

    2014-02-01

    There is increasing evidence that humans and other nonhuman mammals are sensitive to the statistical structure of auditory input. Indeed, neural sensitivity to statistical regularities seems to be a fundamental biological property underlying auditory learning. In the case of speech, statistical regularities play a crucial role in the acquisition of several linguistic features, from phonotactic to more complex rules such as morphosyntactic rules. Interestingly, a similar sensitivity has been shown with non-speech streams: sequences of sounds changing in frequency or timbre can be segmented on the sole basis of conditional probabilities between adjacent sounds. We recently ran a set of cross-sectional and longitudinal experiments showing that merging music and speech information in song facilitates stream segmentation and, further, that musical practice enhances sensitivity to statistical regularities in speech at both neural and behavioral levels. Based on recent findings showing the involvement of a fronto-temporal network in speech segmentation, we defend the idea that enhanced auditory learning observed in musicians originates via at least three distinct pathways: enhanced low-level auditory processing, enhanced phono-articulatory mapping via the left Inferior Frontal Gyrus and Pre-Motor cortex and increased functional connectivity within the audio-motor network. Finally, we discuss how these data predict a beneficial use of music for optimizing speech acquisition in both normal and impaired populations. Copyright © 2013 Elsevier B.V. All rights reserved.

  4. Recent advances in the involvement of long non-coding RNAs in neural stem cell biology and brain pathophysiology

    Directory of Open Access Journals (Sweden)

    Daphne eAntoniou

    2014-04-01

    Full Text Available Exploration of non-coding genome has recently uncovered a growing list of formerly unknown regulatory long non-coding RNAs (lncRNAs with important functions in stem cell pluripotency, development and homeostasis of several tissues. Although thousands of lncRNAs are expressed in mammalian brain in a highly patterned manner, their roles in brain development have just begun to emerge. Recent data suggest key roles for these molecules in gene regulatory networks controlling neuronal and glial cell differentiation. Analysis of the genomic distribution of genes encoding for lncRNAs indicates a physical association of these regulatory RNAs with transcription factors (TFs with well-established roles in neural differentiation, suggesting that lncRNAs and TFs may form coherent regulatory networks with important functions in neural stem cells (NSCs. Additionally, many studies show that lncRNAs are involved in the pathophysiology of brain-related diseases/disorders. Here we discuss these observations and investigate the links between lncRNAs, brain development and brain-related diseases. Understanding the functions of lncRNAs in NSCs and brain organogenesis could revolutionize the basic principles of developmental biology and neuroscience.

  5. Morphogenesis and pattern formation in biological systems experiments and models

    CERN Document Server

    Noji, Sumihare; Ueno, Naoto; Maini, Philip

    2003-01-01

    A central goal of current biology is to decode the mechanisms that underlie the processes of morphogenesis and pattern formation. Concerned with the analysis of those phenomena, this book covers a broad range of research fields, including developmental biology, molecular biology, plant morphogenesis, ecology, epidemiology, medicine, paleontology, evolutionary biology, mathematical biology, and computational biology. In Morphogenesis and Pattern Formation in Biological Systems: Experiments and Models, experimental and theoretical aspects of biology are integrated for the construction and investigation of models of complex processes. This collection of articles on the latest advances by leading researchers not only brings together work from a wide spectrum of disciplines, but also provides a stepping-stone to the creation of new areas of discovery.

  6. SWANN: The Snow Water Artificial Neural Network Modelling System

    Science.gov (United States)

    Broxton, P. D.; van Leeuwen, W.; Biederman, J. A.

    2017-12-01

    Snowmelt from mountain forests is important for water supply and ecosystem health. Along Arizona's Mogollon Rim, snowmelt contributes to rivers and streams that provide a significant water supply for hydro-electric power generation, agriculture, and human consumption in central Arizona. In this project, we are building a snow monitoring system for the Salt River Project (SRP), which supplies water and power to millions of customers in the Phoenix metropolitan area. We are using process-based hydrological models and artificial neural networks (ANNs) to generate information about both snow water equivalent (SWE) and snow cover. The snow-cover data is generated with ANNs that are applied to Landsat and MODIS satellite reflectance data. The SWE data is generated using a combination of gridded SWE estimates generated by process-based snow models and ANNs that account for variations in topography, forest cover, and solar radiation. The models are trained and evaluated with snow data from SNOTEL stations as well as from aerial LiDAR and field data that we collected this past winter in northern Arizona, as well as with similar data from other sites in the Southwest US. These snow data are produced in near-real time, and we have built a prototype decision support tool to deliver them to SRP. This tool is designed to provide daily-to annual operational monitoring of spatial and temporal changes in SWE and snow cover conditions over the entire Salt River Watershed (covering 17,000 km2), and features advanced web mapping capabilities and watershed analytics displayed as graphical data.

  7. Biological oceanography of the red oceanic system

    Science.gov (United States)

    Theil, Hjalmar; Weikert, Horst

    1. In 1977, 1979 and 1980-81, investigations were carried out which aimed at evaluating the potential risks from mining metalliferous muds precipating in the Atlantis II Deep of the central Red Sea. This environmental research was initiated by the Saudi Sudanese Red Sea Joint Commission in order to avoid any danger for the Red Sea ecosystem. The broad environmental research programme coherent studies in physical, chemical, biological, and geological oceanography as well as toxicological investigations in the oceanic and in reef zones. We summarise the results from our biological fiels studies in the open sea. 2. The biological investigations were concentrated on the area of the Atlantis II Deep. Benthos was sampled between 700-2000m. For comparison a few samples were also taken further north in the central Red Sea, and to east and west along the flanking deep terraces (500-1000m). Plankton studies covered the total water column above the Deep, and were extended along the axial through to north and south. 3. Benthos sampling was carried out using a heavy closing trawl, a large box grab (box size 50 × 50 cm), Van Veen grabs and traps; photographic surveys were made a phototrap and a photosled. Community respiration was measured with a ship-board method using grab subsamples. Nutrient concentrations, seston and phytoplankton standing stocks as well as in situ primary production were determined from hydrocast samples. Data on zooplankton and micronekton composition and standing stock were obtained from samples collected using different multiple opening-and-closing nets equipped with 100 μm, 300 μm, and 1000 μm mesh sizes. Daily and ontogenetical vertical migration patterns were studied by comparisons of data from midday and midnight tows. 4. Throughout the whole area the sediment is a pteropod ooze containing low contentrations of organic matter; measured organic carbon and nitrogen contents were 0.5 and 0.05% respectively, and chloroplastic pigment equivalents

  8. Identification and control of non-linear time-varying dynamical systems using artificial neural networks

    OpenAIRE

    Dror, Shahar

    1992-01-01

    Approved for public release; distribution is unlimited Identification and control of non-linear dynamical systems is a very complex task which requires new methods of approaching. This research addresses the problem of emulation and control via the use of distributed parallel processing, namely artificial neural networks. Four models for describing non-linear MIMO dynamical systems are presented. Based on these models a combined feedforward and recurrent neural networks are structured t...

  9. Programming Morphogenesis through Systems and Synthetic Biology.

    Science.gov (United States)

    Velazquez, Jeremy J; Su, Emily; Cahan, Patrick; Ebrahimkhani, Mo R

    2018-04-01

    Mammalian tissue development is an intricate, spatiotemporal process of self-organization that emerges from gene regulatory networks of differentiating stem cells. A major goal in stem cell biology is to gain a sufficient understanding of gene regulatory networks and cell-cell interactions to enable the reliable and robust engineering of morphogenesis. Here, we review advances in synthetic biology, single cell genomics, and multiscale modeling, which, when synthesized, provide a framework to achieve the ambitious goal of programming morphogenesis in complex tissues and organoids. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Role of Amyloid Precursor Protein (APP) and Its Derivatives in the Biology and Cell Fate Specification of Neural Stem Cells.

    Science.gov (United States)

    Coronel, Raquel; Bernabeu-Zornoza, Adela; Palmer, Charlotte; Muñiz-Moreno, Mar; Zambrano, Alberto; Cano, Eva; Liste, Isabel

    2018-01-30

    Amyloid precursor protein (APP) is a member of the APP family of proteins, and different enzymatic processing leads to the production of several derivatives that are shown to have distinct biological functions. APP is involved in the pathology of Alzheimer's disease (AD), the most common neurodegenerative disorder causing dementia. Furthermore, it is believed that individuals with Down syndrome (DS) have increased APP expression, due to an extra copy of chromosome 21 (Hsa21), that contains the gene for APP. Nevertheless, the physiological function of APP remains unclear. It is known that APP plays an important role in neural growth and maturation during brain development, possibly by influencing proliferation, cell fate specification and neurogenesis of neural stem cells (NSCs). Proteolytic cleavage of APP occurs mainly via two mutually exclusive pathways, the non-amyloidogenic pathway or the amyloidogenic pathway. Other alternative pathways (η-secretase, δ-secretase and meprin pathways) have also been described for the physiological processing of APP. The different metabolites generated from these pathways, including soluble APPα (sAPPα), soluble APPβ (sAPPβ), β-amyloid (Aβ) peptides and the APP intracellular domain (AICD), have different functions determined by their structural differences, equilibrium and concentration with respect to other fragments derived from APP. This review discusses recent observations regarding possible functions of APP and its proteolytic derivatives in the biology and phenotypic specification of NSCs. This can be important for a better understanding of the pathogenesis and the development of future therapeutic applications for AD and/or DS, diseases in which alterations in neurogenesis have been described.

  11. Revolution of Alzheimer Precision Neurology Passageway of Systems Biology and Neurophysiology.

    Science.gov (United States)

    Hampel, Harald; Toschi, Nicola; Babiloni, Claudio; Baldacci, Filippo; Black, Keith L; Bokde, Arun L W; Bun, René S; Cacciola, Francesco; Cavedo, Enrica; Chiesa, Patrizia A; Colliot, Olivier; Coman, Cristina-Maria; Dubois, Bruno; Duggento, Andrea; Durrleman, Stanley; Ferretti, Maria-Teresa; George, Nathalie; Genthon, Remy; Habert, Marie-Odile; Herholz, Karl; Koronyo, Yosef; Koronyo-Hamaoui, Maya; Lamari, Foudil; Langevin, Todd; Lehéricy, Stéphane; Lorenceau, Jean; Neri, Christian; Nisticò, Robert; Nyasse-Messene, Francis; Ritchie, Craig; Rossi, Simone; Santarnecchi, Emiliano; Sporns, Olaf; Verdooner, Steven R; Vergallo, Andrea; Villain, Nicolas; Younesi, Erfan; Garaci, Francesco; Lista, Simone

    2018-03-16

    The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer's disease. The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group "Alzheimer Precision Medicine" (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development toward breakthrough innovation based on the investigation of the

  12. NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems

    Directory of Open Access Journals (Sweden)

    Caglayan Ozan

    2017-10-01

    Full Text Available In this paper, we present nmtpy, a flexible Python toolkit based on Theano for training Neural Machine Translation and other neural sequence-to-sequence architectures. nmtpy decouples the specification of a network from the training and inference utilities to simplify the addition of a new architecture and reduce the amount of boilerplate code to be written. nmtpy has been used for LIUM’s top-ranked submissions to WMT Multimodal Machine Translation and News Translation tasks in 2016 and 2017.

  13. Network science of biological systems at different scales: A review

    Science.gov (United States)

    Gosak, Marko; Markovič, Rene; Dolenšek, Jurij; Slak Rupnik, Marjan; Marhl, Marko; Stožer, Andraž; Perc, Matjaž

    2018-03-01

    Network science is today established as a backbone for description of structure and function of various physical, chemical, biological, technological, and social systems. Here we review recent advances in the study of complex biological systems that were inspired and enabled by methods of network science. First, we present

  14. Automated implementation of rule-based expert systems with neural networks for time-critical applications

    Science.gov (United States)

    Ramamoorthy, P. A.; Huang, Song; Govind, Girish

    1991-01-01

    In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

  15. Comparison of Fuzzy System with Neural Aggregation FSNA with Classical TSK Fuzzy System in Anti-Collision Problem of USV

    Directory of Open Access Journals (Sweden)

    Szymak Piotr

    2017-09-01

    Full Text Available The paper presents the research whose the main goal was to compare a new Fuzzy System with Neural Aggregation of fuzzy rules FSNA with a classical Takagi-Sugeno-Kanga TSK fuzzy system in an anti-collision problem of Unmanned Surface Vehicle USV. Both systems the FSNA and the TSK were learned by means of Cooperative Co-evolutionary Genetic Algorithm with Indirect Neural Encoding CCGA-INE.

  16. Optical Biosensors to Explore Biological Systems

    DEFF Research Database (Denmark)

    Palanco, Marta Espina; Mogensen, Klaus Bo; Andersen, Nils H. Skovgaard

    2016-01-01

    protein may be used as an efficient sensor in an organic environment via a biomimetic membrane model. The combination of both biomimetic membranes and protein membranes as a signal transduction medium has interesting applications in biology and medicine. It is crucial that the matrix where a protein...

  17. A Systems Approach to Biology (SAB).

    Science.gov (United States)

    Bush, Kenneth H.; And Others

    This pupil's study guide is intended to be used with audio-taped biology modules. Each of the units (on laboratory techniques, plant and animal diversity, chemistry, cells, energy, microbiology, genetics, and development) contains an abstract providing an overview of the unit, the rationale and performance objectives for each module, questions to…

  18. Yeast systems biology to unravel the network of life

    DEFF Research Database (Denmark)

    Mustacchi, Roberta; Hohmann, S; Nielsen, Jens

    2006-01-01

    Systems biology focuses on obtaining a quantitative description of complete biological systems, even complete cellular function. In this way, it will be possible to perform computer-guided design of novel drugs, advanced therapies for treatment of complex diseases, and to perform in silico design....... Furthermore, it serves as an industrial workhorse for production of a wide range of chemicals and pharmaceuticals. Systems biology involves the combination of novel experimental techniques from different disciplines as well as functional genomics, bioinformatics and mathematical modelling, and hence no single...... laboratory has access to all the necessary competences. For this reason the Yeast Systems Biology Network (YSBN) has been established. YSBN will coordinate research efforts, in yeast systems biology and, through the recently obtained EU funding for a Coordination Action, it will be possible to set...

  19. Tracing organizing principles: Learning from the history of systems biology

    DEFF Research Database (Denmark)

    Green, Sara; Wolkenhauer, Olaf

    2014-01-01

    With the emergence of systems biology, the identification of organizing principles is being highlighted as a key research aim. Researchers attempt to “reverse engineer” the functional organization of biological systems using methodologies from mathematics, engineering and computer science while...... taking advantage of data produced by new experimental techniques. While systems biology is a relatively new approach, the quest for general principles of biological organization dates back to systems theoretic approaches in early and mid-twentieth century. The aim of this paper is to draw...... on this historical background in order to increase the understanding of the motivation behind the search for general principles and to clarify different epistemic aims within systems biology. We pinpoint key aspects of earlier approaches that also underlie the current practice. These are i) the focus on relational...

  20. Fundamentals of computational intelligence neural networks, fuzzy systems, and evolutionary computation

    CERN Document Server

    Keller, James M; Fogel, David B

    2016-01-01

    This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...

  1. Modeling and simulation of biological systems using SPICE language

    Science.gov (United States)

    Lallement, Christophe; Haiech, Jacques

    2017-01-01

    The article deals with BB-SPICE (SPICE for Biochemical and Biological Systems), an extension of the famous Simulation Program with Integrated Circuit Emphasis (SPICE). BB-SPICE environment is composed of three modules: a new textual and compact description formalism for biological systems, a converter that handles this description and generates the SPICE netlist of the equivalent electronic circuit and NGSPICE which is an open-source SPICE simulator. In addition, the environment provides back and forth interfaces with SBML (System Biology Markup Language), a very common description language used in systems biology. BB-SPICE has been developed in order to bridge the gap between the simulation of biological systems on the one hand and electronics circuits on the other hand. Thus, it is suitable for applications at the interface between both domains, such as development of design tools for synthetic biology and for the virtual prototyping of biosensors and lab-on-chip. Simulation results obtained with BB-SPICE and COPASI (an open-source software used for the simulation of biochemical systems) have been compared on a benchmark of models commonly used in systems biology. Results are in accordance from a quantitative viewpoint but BB-SPICE outclasses COPASI by 1 to 3 orders of magnitude regarding the computation time. Moreover, as our software is based on NGSPICE, it could take profit of incoming updates such as the GPU implementation, of the coupling with powerful analysis and verification tools or of the integration in design automation tools (synthetic biology). PMID:28787027

  2. [From bioinformatics to systems biology: account of the 12th international conference on intelligent systems in molecular biology].

    Science.gov (United States)

    Ivakhno, S S

    2004-01-01

    The paper reviews the 12th International Conference on Intelligent Systems for Molecular Biology/Third European Conference on Computational Biology 2004 that was held in Glasgow, UK, during July 31-August 4. A number of talks, papers and software demos from the conference in bioinformatics, genomics, proteomics, transcriptomics and systems biology are described. Recent applications of liquid chromatography - tandem mass spectrometry, comparative genomics and DNA microarrays are given along with the discussion of bioinformatics curricular in higher education.

  3. Neural network based system for script identification in Indian ...

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    With the recent emergence and widespread application of multimedia technologies, there is increasing demand to create a paperless ... implicit assumption that the language or script of the document to be processed is known beforehand. ... In order to take advantage of the learning and generalization abilities of the neural ...

  4. Computer simulation system of neural PID control on nuclear reactor

    International Nuclear Information System (INIS)

    Chen Yuzhong; Yang Kaijun; Shen Yongping

    2001-01-01

    Neural network proportional integral differential (PID) controller on nuclear reactor is designed, and the control process is simulated by computer. The simulation result show that neutral network PID controller can automatically adjust its parameter to ideal state, and good control result can be gotten in reactor control process

  5. Software implementation of artificial neural networks in automated intelligent systems

    Directory of Open Access Journals (Sweden)

    В.П. Харченко

    2009-02-01

    Full Text Available  Application of neural networks technologies effectively decides the task of synthesis of origin of accident risk and gives out the vector of managing signals of network on incomplete and distorted information about the phenomena, events and processes which influence on safety flights.

  6. Integrating resource defence theory with a neural nonapeptide pathway to explain territory-based mating systems.

    Science.gov (United States)

    Oldfield, Ronald G; Harris, Rayna M; Hofmann, Hans A

    2015-01-01

    The ultimate-level factors that drive the evolution of mating systems have been well studied, but an evolutionarily conserved neural mechanism involved in shaping behaviour and social organization across species has remained elusive. Here, we review studies that have investigated the role of neural arginine vasopressin (AVP), vasotocin (AVT), and their receptor V1a in mediating variation in territorial behaviour. First, we discuss how aggression and territoriality are a function of population density in an inverted-U relationship according to resource defence theory, and how territoriality influences some mating systems. Next, we find that neural AVP, AVT, and V1a expression, especially in one particular neural circuit involving the lateral septum of the forebrain, are associated with territorial behaviour in males of diverse species, most likely due to their role in enhancing social cognition. Then we review studies that examined multiple species and find that neural AVP, AVT, and V1a expression is associated with territory size in mammals and fishes. Because territoriality plays an important role in shaping mating systems in many species, we present the idea that neural AVP, AVT, and V1a expression that is selected to mediate territory size may also influence the evolution of different mating systems. Future research that interprets proximate-level neuro-molecular mechanisms in the context of ultimate-level ecological theory may provide deep insight into the brain-behaviour relationships that underlie the diversity of social organization and mating systems seen across the animal kingdom.

  7. A Biological-Realtime Neuromorphic System in 28 nm CMOS Using Low-Leakage Switched Capacitor Circuits.

    Science.gov (United States)

    Mayr, Christian; Partzsch, Johannes; Noack, Marko; Hänzsche, Stefan; Scholze, Stefan; Höppner, Sebastian; Ellguth, Georg; Schüffny, Rene

    2016-02-01

    A switched-capacitor (SC) neuromorphic system for closed-loop neural coupling in 28 nm CMOS is presented, occupying 600 um by 600 um. It offers 128 input channels (i.e., presynaptic terminals), 8192 synapses and 64 output channels (i.e., neurons). Biologically realistic neuron and synapse dynamics are achieved via a faithful translation of the behavioural equations to SC circuits. As leakage currents significantly affect circuit behaviour at this technology node, dedicated compensation techniques are employed to achieve biological-realtime operation, with faithful reproduction of time constants of several 100 ms at room temperature. Power draw of the overall system is 1.9 mW.

  8. Network biology: Describing biological systems by complex networks. Comment on "Network science of biological systems at different scales: A review" by M. Gosak et al.

    Science.gov (United States)

    Jalili, Mahdi

    2018-03-01

    I enjoyed reading Gosak et al. review on analysing biological systems from network science perspective [1]. Network science, first started within Physics community, is now a mature multidisciplinary field of science with many applications ranging from Ecology to biology, medicine, social sciences, engineering and computer science. Gosak et al. discussed how biological systems can be modelled and described by complex network theory which is an important application of network science. Although there has been considerable progress in network biology over the past two decades, this is just the beginning and network science has a great deal to offer to biology and medical sciences.

  9. Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering

    NARCIS (Netherlands)

    He, F.; Murabito, E.; Westerhoff, H.V.

    2016-01-01

    Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out throughin silicotheoretical studies with the aim to guide and complement furtherin vitroandin vivoexperimental

  10. Biological Detection System Technologies Technology and Industrial Base Study. A Primer on Biological Detection Technologies

    National Research Council Canada - National Science Library

    2001-01-01

    .... and Canadian military personnel. In light of these concerns both defense departments have increased efforts to develop and field biological agent detection systems to help protect their military forces and fixed assets...

  11. Scaling for Dynamical Systems in Biology.

    Science.gov (United States)

    Ledder, Glenn

    2017-11-01

    Asymptotic methods can greatly simplify the analysis of all but the simplest mathematical models and should therefore be commonplace in such biological areas as ecology and epidemiology. One essential difficulty that limits their use is that they can only be applied to a suitably scaled dimensionless version of the original dimensional model. Many books discuss nondimensionalization, but with little attention given to the problem of choosing the right scales and dimensionless parameters. In this paper, we illustrate the value of using asymptotics on a properly scaled dimensionless model, develop a set of guidelines that can be used to make good scaling choices, and offer advice for teaching these topics in differential equations or mathematical biology courses.

  12. Magnetic biosensor system to detect biological targets

    KAUST Repository

    Li, Fuquan

    2012-09-01

    Magneto-resistive sensors in combination with magnetic beads provide sensing platforms, which are small in size and highly sensitive. These platforms can be fully integrated with microchannels and electronics to enable devices capable of performing complex tasks. Commonly, a sandwich method is used that requires a specific coating of the sensor\\'s surface to immobilize magnetic beads and biological targets on top of the sensor. This paper concerns a micro device to detect biological targets using magnetic concentration, magnetic as well as mechanical trapping and magnetic sensing. Target detection is based on the size difference between bare magnetic beads and magnetic beads with targets attached. This method remedies the need for a coating layer and reduces the number of steps required to run an experiment. © 2012 IEEE.

  13. An adaptive fuzzy neural network for MIMO system model approximation in high-dimensional spaces.

    Science.gov (United States)

    Chak, C K; Feng, G; Ma, J

    1998-01-01

    An adaptive fuzzy system implemented within the framework of neural network is proposed. The integration of the fuzzy system into a neural network enables the new fuzzy system to have learning and adaptive capabilities. The proposed fuzzy neural network can locate its rules and optimize its membership functions by competitive learning, Kalman filter algorithm and extended Kalman filter algorithms. A key feature of the new architecture is that a high dimensional fuzzy system can be implemented with fewer number of rules than the Takagi-Sugeno fuzzy systems. A number of simulations are presented to demonstrate the performance of the proposed system including modeling nonlinear function, operator's control of chemical plant, stock prices and bioreactor (multioutput dynamical system).

  14. Redefining plant systems biology: from cell to ecosystem.

    Science.gov (United States)

    Keurentjes, Joost J B; Angenent, Gerco C; Dicke, Marcel; Dos Santos, Vítor A P Martins; Molenaar, Jaap; van der Putten, Wim H; de Ruiter, Peter C; Struik, Paul C; Thomma, Bart P H J

    2011-04-01

    Molecular biologists typically restrict systems biology to cellular levels. By contrast, ecologists define biological systems as communities of interacting individuals at different trophic levels that process energy, nutrient and information flows. Modern plant breeding needs to increase agricultural productivity while decreasing the ecological footprint. This requires a holistic systems biology approach that couples different aggregation levels while considering the variables that affect these biological systems from cell to community. The challenge is to generate accurate experimental data that can be used together with modelling concepts and techniques that allow experimentally verifying in silico predictions. The coupling of aggregation levels in plant sciences, termed Integral Quantification of Biological Organization (IQ(BiO)), might enhance our abilities to generate new desired plant phenotypes. Copyright © 2010 Elsevier Ltd. All rights reserved.

  15. Biodiversity Meets Neuroscience: From the Sequencing Ship (Ship-Seq) to Deciphering Parallel Evolution of Neural Systems in Omic's Era.

    Science.gov (United States)

    Moroz, Leonid L

    2015-12-01

    The origins of neural systems and centralized brains are one of the major transitions in evolution. These events might occur more than once over 570-600 million years. The convergent evolution of neural circuits is evident from a diversity of unique adaptive strategies implemented by ctenophores, cnidarians, acoels, molluscs, and basal deuterostomes. But, further integration of biodiversity research and neuroscience is required to decipher critical events leading to development of complex integrative and cognitive functions. Here, we outline reference species and interdisciplinary approaches in reconstructing the evolution of nervous systems. In the "omic" era, it is now possible to establish fully functional genomics laboratories aboard of oceanic ships and perform sequencing and real-time analyses of data at any oceanic location (named here as Ship-Seq). In doing so, fragile, rare, cryptic, and planktonic organisms, or even entire marine ecosystems, are becoming accessible directly to experimental and physiological analyses by modern analytical tools. Thus, we are now in a position to take full advantages from countless "experiments" Nature performed for us in the course of 3.5 billion years of biological evolution. Together with progress in computational and comparative genomics, evolutionary neuroscience, proteomic and developmental biology, a new surprising picture is emerging that reveals many ways of how nervous systems evolved. As a result, this symposium provides a unique opportunity to revisit old questions about the origins of biological complexity. © The Author 2015. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

  16. Synthetic Biology: Engineering Living Systems from Biophysical Principles.

    Science.gov (United States)

    Bartley, Bryan A; Kim, Kyung; Medley, J Kyle; Sauro, Herbert M

    2017-03-28

    Synthetic biology was founded as a biophysical discipline that sought explanations for the origins of life from chemical and physical first principles. Modern synthetic biology has been reinvented as an engineering discipline to design new organisms as well as to better understand fundamental biological mechanisms. However, success is still largely limited to the laboratory and transformative applications of synthetic biology are still in their infancy. Here, we review six principles of living systems and how they compare and contrast with engineered systems. We cite specific examples from the synthetic biology literature that illustrate these principles and speculate on their implications for further study. To fully realize the promise of synthetic biology, we must be aware of life's unique properties. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  17. Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization

    CERN Document Server

    Castillo, Oscar; Kacprzyk, Janusz

    2015-01-01

    This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents div...

  18. Inductive differentiation of two neural lineages reconstituted in a microculture system from Xenopus early gastrula cells.

    Science.gov (United States)

    Mitani, S; Okamoto, H

    1991-05-01

    Neural induction of ectoderm cells has been reconstituted and examined in a microculture system derived from dissociated early gastrula cells of Xenopus laevis. We have used monoclonal antibodies as specific markers to monitor cellular differentiation from three distinct ectoderm lineages in culture (N1 for CNS neurons from neural tube, Me1 for melanophores from neural crest and E3 for skin epidermal cells from epidermal lineages). CNS neurons and melanophores differentiate when deep layer cells of the ventral ectoderm (VE, prospective epidermis region; 150 cells/culture) and an appropriate region of the marginal zone (MZ, prospective mesoderm region; 5-150 cells/culture) are co-cultured, but not in cultures of either cell type on their own; VE cells cultured alone yield epidermal cells as we have previously reported. The extent of inductive neural differentiation in the co-culture system strongly depends on the origin and number of MZ cells initially added to culture wells. The potency to induce CNS neurons is highest for dorsal MZ cells and sharply decreases as more ventrally located cells are used. The same dorsoventral distribution of potency is seen in the ability of MZ cells to inhibit epidermal differentiation. In contrast, the ability of MZ cells to induce melanophores shows the reverse polarity, ventral to dorsal. These data indicate that separate developmental mechanisms are used for the induction of neural tube and neural crest lineages. Co-differentiation of CNS neurons or melanophores with epidermal cells can be obtained in a single well of co-cultures of VE cells (150) and a wide range of numbers of MZ cells (5 to 100). Further, reproducible differentiation of both neural lineages requires intimate association between cells from the two gastrula regions; virtually no differentiation is obtained when cells from the VE and MZ are separated in a culture well. These results indicate that the inducing signals from MZ cells for both neural tube and neural

  19. Developing and using expert systems and neural networks in medicine: a review on benefits and challenges.

    Science.gov (United States)

    Sheikhtaheri, Abbas; Sadoughi, Farahnaz; Hashemi Dehaghi, Zahra

    2014-09-01

    Complicacy of clinical decisions justifies utilization of information systems such as artificial intelligence (e.g. expert systems and neural networks) to achieve better decisions, however, application of these systems in the medical domain faces some challenges. We aimed at to review the applications of these systems in the medical domain and discuss about such challenges. Following a brief introduction of expert systems and neural networks by representing few examples, the challenges of these systems in the medical domain are discussed. We found that the applications of expert systems and artificial neural networks have been increased in the medical domain. These systems have shown many advantages such as utilization of experts' knowledge, gaining rare knowledge, more time for assessment of the decision, more consistent decisions, and shorter decision-making process. In spite of all these advantages, there are challenges ahead of developing and using such systems including maintenance, required experts, inputting patients' data into the system, problems for knowledge acquisition, problems in modeling medical knowledge, evaluation and validation of system performance, wrong recommendations and responsibility, limited domains of such systems and necessity of integrating such systems into the routine work flows. We concluded that expert systems and neural networks can be successfully used in medicine; however, there are many concerns and questions to be answered through future studies and discussions.

  20. SYSTEM DESIGN AND BEHAVIOR OF PATTERN RECOGNITION OF CONTROL CHART USING NEURAL NETWORK

    OpenAIRE

    Justin Yulius Halim

    2003-01-01

    Recognition of unnatural patterns in control charts is important to get more understanding about the process problem. Shewhart control chart can recognize shift pattern only, while the others cannot be detected by this chart. Neural networks were proposed by many researchers to achieve the better recognition of the patterns. A system of neural networks needs to be fitted to this problem by realizing the behaviors of control charts. Each behavior will affect the development of each part of the...

  1. A systems biology approach to study systemic inflammation.

    Science.gov (United States)

    Chen, Bor-Sen; Wu, Chia-Chou

    2014-01-01

    Systemic inflammation needs a precise control on the sequence and magnitude of occurring events. The high throughput data on the host-pathogen interactions gives us an opportunity to have a glimpse on the systemic inflammation. In this article, a dynamic Candida albicans-zebrafish interactive infectious network is built as an example to demonstrate how systems biology approach can be used to study systematic inflammation. In particular, based on microarray data of C. albicans and zebrafish during infection, the hyphal growth, zebrafish, and host-pathogen intercellular PPI networks were combined to form an integrated infectious PPI network that helps us understand the systematic mechanisms underlying the pathogenicity of C. albicans and the immune response of the host. The signaling pathways for morphogenesis and hyphal growth of C. albicans were 2 significant interactions found in the intercellular PPI network. Two cellular networks were also developed corresponding to the different infection stages (adhesion and invasion), and then compared with each other to identify proteins to gain more insight into the pathogenic role of hyphal development in the C. albicans infection process. Important defense-related proteins in zebrafish were predicted using the same approach. This integrated network consisting of intercellular invasion and cellular defense processes during infection can improve medical therapies and facilitate development of new antifungal drugs.

  2. Reverse allostasis in biological systems: Minimal conditions and implications.

    Science.gov (United States)

    Rezaei-Ghaleh, Nasrollah; Bakhtiari, Davood; Rashidi, Armin

    2017-08-07

    Biological control systems regulate the behavior of biological systems in a constantly changing environment. Homeostasis is the most widely studied outcome of biological control systems. Homeostatic systems maintain the system in its desired state despite variations in system parameters or the externally-determined input rates of their constituents, i.e. they have zero or near zero steady state error. On the other hand, allostatic systems are not resistant against environmental changes and the steady state level of their controlled variables responds positively to the changes in their input rates. Little is known, however, on the existence and frequency of reverse allostatic systems, where the steady state value of the controlled variable correlates negatively with the input rate of that variable. In the present study, we derive the minimal conditions for the existence and local stability of reverse allostatic systems, and demonstrate in examples of metabolic, pharmacological, pathophysiological and ecological systems that the reverse allostasis requirements are relatively non-stringent and may be satisfied in biological systems more commonly than usually thought. The possible existence of reverse allostatic systems in nature and their counter-intuitive implications in physiological systems, drug treatment, ecosystem management, and biological control are explored and testable predictions are made. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Development of a stimuli-responsive polymer nanocomposite toward biologically optimized, MEMS-based neural probes

    International Nuclear Information System (INIS)

    Hess, A E; Zorman, C A; Capadona, J R; Tyler, D J; Shanmuganathan, K; Hsu, L; Rowan, S J; Weder, C

    2011-01-01

    This paper reports the development of micromachining processes and mechanical evaluation of a stimuli-responsive, mechanically dynamic polymer nanocomposite for biomedical microsystems. This nanocomposite consists of a cellulose nanofiber network encased in a polyvinyl acetate matrix. Micromachined tensile testing structures fabricated from the nanocomposite displayed a reversible and switchable stiffness comparable to bulk samples, with a Young's modulus of 3420 MPa when dry, reducing to ∼20 MPa when wet, and a stiff-to-flexible transition time of ∼300 s. This mechanically dynamic behavior is particularly attractive for the development of adaptive intracortical probes that are sufficiently stiff to insert into the brain without buckling, but become highly compliant upon insertion. Along these lines, a micromachined neural probe incorporating parylene insulating/moisture barrier layers and Ti/Au electrodes was fabricated from the nanocomposite using a fabrication process designed specifically for this chemical- and temperature-sensitive material. It was found that the parylene layers only slightly increased the stiffness of the probe in the wet state in spite of its much higher Young's modulus. Furthermore, the Ti/Au electrodes exhibited impedance comparable to Au electrodes on conventional substrates. Swelling of the nanocomposite was highly anisotropic favoring the thickness dimension by a factor of 8 to 12, leading to excellent adhesion between the nanocomposite and parylene layers and no discernable deformation of the probes when deployed in deionized water

  4. Behaviors of tritium in terrestrial biological system

    International Nuclear Information System (INIS)

    Inomata, Tsuyako

    1983-01-01

    The in vivo behaviors of HTO- 3 H in food chain models in experimental animals were described. Of pregnant mice that had ingested HTO and drinking water alone for 19 days, the total 3 H content in the tissue/wet weight was greater by 20% in fetuses and newborns than in mothers, and the proportion of tissue-bound 3 H was 8-24% in mothers and 3% in fetuses. The mean 3 H concentration in the free water in tissues was about 36% of ingested HTO. When only 3 H foods were ingested for 18 days, the total 3 H content in the tissue/wet weight showed no marked difference among the mother, fetuses and newborns, nor did the bound 3 H level show great differences. With respect to the tissue distribution of 3 H, only the incorporation rate by the mother's brain from HTO was satisfactory, whereas in other organs, the mother, fetuses and newborns showed higher incorporation rates from 3 H foods. The ratio of specific radioactivity of soft tissue 3 H in mothers to HTO in drinking water exceeded 1 only for the spleen, but other tissues showed no biological concentration. Again, no biological concentration was observed with 3 H foods. Environmental HTO did not result in biological concentration of 3 H in mother mice that had ingested 3 H foods, but 3 H was rather diluted. Tissues other than the spleen showed similar values of 3 H ingestion from environmental HTO through all routes. However, the proportion of bound 3 H in the total 3 H in the soft tissue was about 1.4-1.6 times that on ingestion of HTO alone. (Chiba, N.)

  5. Soft computing integrating evolutionary, neural, and fuzzy systems

    CERN Document Server

    Tettamanzi, Andrea

    2001-01-01

    Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as

  6. Neural systems for the expression of hypoalgesia during nonassociative fear.

    Science.gov (United States)

    Bellgowan, P S; Helmstetter, F J

    1996-08-01

    A single brief exposure to moderately intense while noise is sufficient to produce opioid-mediated antinociception in rats. This form of stress-induced hypoalgesia represents a response to unconditional fear or anxiety. Three experiments compared the neural circuits responsible for learned versus unlearned fear responses. Male rats received lesions of the medial geniculate nucleus, lateral or central nuclei of the amygdala, or the ventral, dorsal lateral, or dorsal medial periaqueductal gray (PAG). Controls showed a pronounced elevation in tail-flick latency following presentation of 90-dB white noise. All lesions, with the exception of dorsolateral and dorsomedial PAG, significantly blocked this response. These results support the idea that hypoalgesia produced by aversive auditory stimuli uses a common neural circuit regardless of whether the response is a product of associative learning or unconditional fear/anxiety.

  7. Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

    Science.gov (United States)

    Peng, Jinzhu; Dubay, Rickey

    2011-10-01

    In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  8. The common ground of genomics and systems biology

    Science.gov (United States)

    2014-01-01

    The rise of systems biology is intertwined with that of genomics, yet their primordial relationship to one another is ill-defined. We discuss how the growth of genomics provided a critical boost to the popularity of systems biology. We describe the parts of genomics that share common areas of interest with systems biology today in the areas of gene expression, network inference, chromatin state analysis, pathway analysis, personalized medicine, and upcoming areas of synergy as genomics continues to expand its scope across all biomedical fields. PMID:25033072

  9. Noninvasive biological sensor system for detection of drunk driving.

    Science.gov (United States)

    Murata, Kohji; Fujita, Etsunori; Kojima, Shigeyuki; Maeda, Shinitirou; Ogura, Yumi; Kamei, Tsutomu; Tsuji, Toshio; Kaneko, Shigehiko; Yoshizumi, Masao; Suzuki, Nobutaka

    2011-01-01

    Systems capable of monitoring the biological condition of a driver and issuing warnings during instances of drowsiness have recently been studied. Moreover, many researchers have reported that biological signals, such as brain waves, pulsation waves, and heart rate, are different between people who have and have not consumed alcohol. Currently, we are developing a noninvasive system to detect individuals driving under the influence of alcohol by measuring biological signals. We used the frequency time series analysis to attempt to distinguish between normal and intoxicated states of a person as the basis of the sensing system.

  10. In vivo robotics: the automation of neuroscience and other intact-system biological fields.

    Science.gov (United States)

    Kodandaramaiah, Suhasa B; Boyden, Edward S; Forest, Craig R

    2013-12-01

    Robotic and automation technologies have played a huge role in in vitro biological science, having proved critical for scientific endeavors such as genome sequencing and high-throughput screening. Robotic and automation strategies are beginning to play a greater role in in vivo and in situ sciences, especially when it comes to the difficult in vivo experiments required for understanding the neural mechanisms of behavior and disease. In this perspective, we discuss the prospects for robotics and automation to influence neuroscientific and intact-system biology fields. We discuss how robotic innovations might be created to open up new frontiers in basic and applied neuroscience and present a concrete example with our recent automation of in vivo whole-cell patch clamp electrophysiology of neurons in the living mouse brain. © 2013 New York Academy of Sciences.

  11. Physical methods for investigating structural colours in biological systems

    NARCIS (Netherlands)

    Vukusic, P.; Stavenga, D. G.

    2009-01-01

    Many biological systems are known to use structural colour effects to generate aspects of their appearance and visibility. The study of these phenomena has informed an eclectic group of fields ranging, for example, from evolutionary processes in behavioural biology to micro-optical devices in

  12. Biochemical Space: A Framework for Systemic Annotation of Biological Models

    Czech Academy of Sciences Publication Activity Database

    Klement, M.; Děd, T.; Šafránek, D.; Červený, Jan; Müller, Stefan; Steuer, Ralf

    2014-01-01

    Roč. 306, JUL (2014), s. 31-44 ISSN 1571-0661 R&D Projects: GA MŠk(CZ) EE2.3.20.0256 Institutional support: RVO:67179843 Keywords : biological models * model annotation * systems biology * cyanobacteria Subject RIV: EH - Ecology, Behaviour

  13. Review of "Stochastic Modelling for Systems Biology" by Darren Wilkinson

    Directory of Open Access Journals (Sweden)

    Bullinger Eric

    2006-12-01

    Full Text Available Abstract "Stochastic Modelling for Systems Biology" by Darren Wilkinson introduces the peculiarities of stochastic modelling in biology. This book is particularly suited to as a textbook or for self-study, and for readers with a theoretical background.

  14. Interdisciplinary problem-solving: emerging modes in integrative systems biology

    NARCIS (Netherlands)

    MacLeod, Miles Alexander James; Nersessian, Nancy J.

    2016-01-01

    Integrative systems biology is an emerging field that attempts to integrate computation, applied mathematics, engineering concepts and methods, and biological experimentation in order to model large-scale complex biochemical networks. The field is thus an important contemporary instance of an

  15. GPSR: A Resource for Genomics Proteomics and Systems Biology

    Indian Academy of Sciences (India)

    Simple Calculation Programs for Biology Other Methods · PowerPoint Presentation · Slide 6 · Slide 7 · Prediction of B-Cell Epitopes · Slide 9 · Slide 10. Slide 12 · Slide 13 · Limitations of existing web services · GPSR: A Resource for Genomics Proteomics and Systems Biology · Important Information in Manual for Develpers.

  16. Defecting and Fitting System for Multi-pattern Functions using Neural Network

    International Nuclear Information System (INIS)

    Sakuda, Takeshi; Katsuki, Satoshi; Nagadori, Nobuyuki; Fukawa, Gakuto

    2002-01-01

    This paper proposes a new neural network system which differentiates the multi-pattern mixed data into multiple pattern fitting functions, and its application for structural monitoring system to detect the change of structural characteristics. All parameters dominating cause of output monitoring signals are not necessarily monitored in real monitoring system. In such case, the relationship between output and input monitoring data are seemed to be relating to multiple functions controlled by hidden parameter. The proposed neural network systems can differentiate those data into multiple pattern-fitting functions, and can detect the appearance of new pattern of input-output relationship of structural monitoring data caused by a damage or deterioration

  17. Genome modularity and synthetic biology: Engineering systems.

    Science.gov (United States)

    Mol, Milsee; Kabra, Ritika; Singh, Shailza

    2018-01-01

    Whole genome sequencing projects running in various laboratories around the world has generated immense data. A systematic phylogenetic analysis of this data shows that genome complexity goes on decreasing as it evolves, due to its modular nature. This modularity can be harnessed to minimize the genome further to reduce it with the bare minimum essential genes. A reduced modular genome, can fuel progress in the area of synthetic biology by providing a ready to use plug and play chassis. Advances in gene editing technology such as the use of tailor made synthetic transcription factors will further enhance the availability of synthetic devices to be applied in the fields of environment, agriculture and health. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. A Comparative Study of Neural Networks and Fuzzy Systems in Modeling of a Nonlinear Dynamic System

    Directory of Open Access Journals (Sweden)

    Metin Demirtas

    2011-07-01

    Full Text Available The aim of this paper is to compare the neural networks and fuzzy modeling approaches on a nonlinear system. We have taken Permanent Magnet Brushless Direct Current (PMBDC motor data and have generated models using both approaches. The predictive performance of both methods was compared on the data set for model configurations. The paper describes the results of these tests and discusses the effects of changing model parameters on predictive and practical performance. Modeling sensitivity was used to compare for two methods.

  19. Sensitivity analysis approaches applied to systems biology models.

    Science.gov (United States)

    Zi, Z

    2011-11-01

    With the rising application of systems biology, sensitivity analysis methods have been widely applied to study the biological systems, including metabolic networks, signalling pathways and genetic circuits. Sensitivity analysis can provide valuable insights about how robust the biological responses are with respect to the changes of biological parameters and which model inputs are the key factors that affect the model outputs. In addition, sensitivity analysis is valuable for guiding experimental analysis, model reduction and parameter estimation. Local and global sensitivity analysis approaches are the two types of sensitivity analysis that are commonly applied in systems biology. Local sensitivity analysis is a classic method that studies the impact of small perturbations on the model outputs. On the other hand, global sensitivity analysis approaches have been applied to understand how the model outputs are affected by large variations of the model input parameters. In this review, the author introduces the basic concepts of sensitivity analysis approaches applied to systems biology models. Moreover, the author discusses the advantages and disadvantages of different sensitivity analysis methods, how to choose a proper sensitivity analysis approach, the available sensitivity analysis tools for systems biology models and the caveats in the interpretation of sensitivity analysis results.

  20. Fault detection and classification in electrical power transmission system using artificial neural network.

    Science.gov (United States)

    Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

    2015-01-01

    This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

  1. Integrated Markov-neural reliability computation method: A case for multiple automated guided vehicle system

    International Nuclear Information System (INIS)

    Fazlollahtabar, Hamed; Saidi-Mehrabad, Mohammad; Balakrishnan, Jaydeep

    2015-01-01

    This paper proposes an integrated Markovian and back propagation neural network approaches to compute reliability of a system. While states of failure occurrences are significant elements for accurate reliability computation, Markovian based reliability assessment method is designed. Due to drawbacks shown by Markovian model for steady state reliability computations and neural network for initial training pattern, integration being called Markov-neural is developed and evaluated. To show efficiency of the proposed approach comparative analyses are performed. Also, for managerial implication purpose an application case for multiple automated guided vehicles (AGVs) in manufacturing networks is conducted. - Highlights: • Integrated Markovian and back propagation neural network approach to compute reliability. • Markovian based reliability assessment method. • Managerial implication is shown in an application case for multiple automated guided vehicles (AGVs) in manufacturing networks

  2. Generating Systems Biology Markup Language Models from the Synthetic Biology Open Language.

    Science.gov (United States)

    Roehner, Nicholas; Zhang, Zhen; Nguyen, Tramy; Myers, Chris J

    2015-08-21

    In the context of synthetic biology, model generation is the automated process of constructing biochemical models based on genetic designs. This paper discusses the use cases for model generation in genetic design automation (GDA) software tools and introduces the foundational concepts of standards and model annotation that make this process useful. Finally, this paper presents an implementation of model generation in the GDA software tool iBioSim and provides an example of generating a Systems Biology Markup Language (SBML) model from a design of a 4-input AND sensor written in the Synthetic Biology Open Language (SBOL).

  3. Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks

    Directory of Open Access Journals (Sweden)

    Xiao-Li Li

    2014-01-01

    Full Text Available As a kind of novel feedforward neural network with single hidden layer, ELM (extreme learning machine neural networks are studied for the identification and control of nonlinear dynamic systems. The property of simple structure and fast convergence of ELM can be shown clearly. In this paper, we are interested in adaptive control of nonlinear dynamic plants by using OS-ELM (online sequential extreme learning machine neural networks. Based on data scope division, the problem that training process of ELM neural network is sensitive to the initial training data is also solved. According to the output range of the controlled plant, the data corresponding to this range will be used to initialize ELM. Furthermore, due to the drawback of conventional adaptive control, when the OS-ELM neural network is used for adaptive control of the system with jumping parameters, the topological structure of the neural network can be adjusted dynamically by using multiple model switching strategy, and an MMAC (multiple model adaptive control will be used to improve the control performance. Simulation results are included to complement the theoretical results.

  4. Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems.

    Directory of Open Access Journals (Sweden)

    Marcus Kaiser

    2006-07-01

    Full Text Available It has been suggested that neural systems across several scales of organization show optimal component placement, in which any spatial rearrangement of the components would lead to an increase of total wiring. Using extensive connectivity datasets for diverse neural networks combined with spatial coordinates for network nodes, we applied an optimization algorithm to the network layouts, in order to search for wire-saving component rearrangements. We found that optimized component rearrangements could substantially reduce total wiring length in all tested neural networks. Specifically, total wiring among 95 primate (Macaque cortical areas could be decreased by 32%, and wiring of neuronal networks in the nematode Caenorhabditis elegans could be reduced by 48% on the global level, and by 49% for neurons within frontal ganglia. Wiring length reductions were possible due to the existence of long-distance projections in neural networks. We explored the role of these projections by comparing the original networks with minimally rewired networks of the same size, which possessed only the shortest possible connections. In the minimally rewired networks, the number of processing steps along the shortest paths between components was significantly increased compared to the original networks. Additional benchmark comparisons also indicated that neural networks are more similar to network layouts that minimize the length of processing paths, rather than wiring length. These findings suggest that neural systems are not exclusively optimized for minimal global wiring, but for a variety of factors including the minimization of processing steps.

  5. Neural Networks and other Techniques for Fault Identification and Isolation of Aircraft Systems

    Science.gov (United States)

    Innocenti, M.; Napolitano, M.

    2003-01-01

    Fault identification, isolation, and accomodation have become critical issues in the overall performance of advanced aircraft systems. Neural Networks have shown to be a very attractive alternative to classic adaptation methods for identification and control of non-linear dynamic systems. The purpose of this paper is to show the improvements in neural network applications achievable through the use of learning algorithms more efficient than the classic Back-Propagation, and through the implementation of the neural schemes in parallel hardware. The results of the analysis of a scheme for Sensor Failure, Detection, Identification and Accommodation (SFDIA) using experimental flight data of a research aircraft model are presented. Conventional approaches to the problem are based on observers and Kalman Filters while more recent methods are based on neural approximators. The work described in this paper is based on the use of neural networks (NNs) as on-line learning non-linear approximators. The performances of two different neural architectures were compared. The first architecture is based on a Multi Layer Perceptron (MLP) NN trained with the Extended Back Propagation algorithm (EBPA). The second architecture is based on a Radial Basis Function (RBF) NN trained with the Extended-MRAN (EMRAN) algorithms. In addition, alternative methods for communications links fault detection and accomodation are presented, relative to multiple unmanned aircraft applications.

  6. Catheter-manometer system damped blood pressures detected by neural nets.

    Science.gov (United States)

    Prentza, A; Wesseling, K H

    1995-07-01

    Degraded catheter-manometer systems cause distortion of blood pressure waveforms, often leading to erroneously resonant or damped waveforms, requiring waveforms quality control. We have tried multilayer perceptron back-propagation trained neural nets of varying architecture to detect damping on sets of normal and artificially damped brachial arterial pressure waves. A second-order digital simulation of a catheter-manometer system is used to cause waveform distortion. Each beat in the waveforms is represented by an 11 parameter input vector. From a group of normotensive or (borderline) hypertensive subjects, pressure waves are used to statistically test and train the neural nets. For each patient and category 5-10 waves are available. The best neural nets correctly classify about 75-85% of the individual beats as either adequate or damped. Using a single majority vote classification per subject per damped or adequate situation, the best neural nets correctly classify at least 16 of the 18 situations in nine test subjects (binomial P = 0.001). More importantly, these neural nets can always detect damping before clinically relevant parameters such as systolic pressure and computed stroke volume are reduced by more than 2%. Neural nets seem remarkably well adapted to solving such subtle problems as detecting a slight damping of arterial pressure waves before it affects waveforms to a clinically relevant degree.

  7. How do biological systems escape 'chaotic' state?

    Indian Academy of Sciences (India)

    B J Rao

    2018-02-13

    Feb 13, 2018 ... forward and feedback regulations among several interdependent components of the system, such that the system stays 'quasi- stable' at the expense of constant energy inputs. Homeostasis is revealed in a chair-shaped graphical relationship between environment or genotype (independent variable) and ...

  8. Systems biology approaches to the study of cardiovascular drugs

    NARCIS (Netherlands)

    Nikolsky, Y.; Kleemann, R.

    2010-01-01

    Atherogenic lipids and chronic inflammation drive the development of cardiovascular disorders such as atherosclerosis. Many cardiovascular drugs target the liver which is involved in the formation of lipid and inflammatory risk factors. With robust systems biology tools and comprehensive

  9. Insights from Systems Biology in Physiological Studies: Learning from Context

    Directory of Open Access Journals (Sweden)

    Pedro Henrique Imenez Silva

    2017-06-01

    Full Text Available Systems biology presents an integrated view of biological systems, focusing on the relations between elements, whether functional or evolutionary, and providing a rich framework for the comprehension of life. At the same time, many low-throughput experimental studies are performed without influence from this integrated view, whilst high-throughput experiments use low-throughput results in their validation and interpretation. We propose an inversion in this logic, and ask which benefits could be obtained from a holistic view coming from high-throughput studies―and systems biology in particular―in interpreting and designing low-throughput experiments. By exploring some key examples from the renal and adrenal physiology, we try to show that network and modularity theory, along with observed patterns of association between elements in a biological system, can have profound effects on our ability to draw meaningful conclusions from experiments.

  10. Advancing metabolic engineering through systems biology of industrial microorganisms.

    Science.gov (United States)

    Dai, Zongjie; Nielsen, Jens

    2015-12-01

    Development of sustainable processes to produce bio-based compounds is necessary due to the severe environmental problems caused by the use of fossil resources. Metabolic engineering can facilitate the development of highly efficient cell factories to produce these compounds from renewable resources. The objective of systems biology is to gain a comprehensive and quantitative understanding of living cells and can hereby enhance our ability to characterize and predict cellular behavior. Systems biology of industrial microorganisms is therefore valuable for metabolic engineering. Here we review the application of systems biology tools for the identification of metabolic engineering targets which may lead to reduced development time for efficient cell factories. Finally, we present some perspectives of systems biology for advancing metabolic engineering further. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. A Toxicity Evaluation and Predictive System Based on Neural Networks and Wavelets

    Energy Technology Data Exchange (ETDEWEB)

    Piotrowski, Pamela L [ORNL; Sumpter, Bobby G [ORNL; Malling, Heinrich [YAHSGS LLC, Richland, WA; Wassom, John [YAHSGS LLC, Richland, WA; Lu, Po-Yung [ORNL; Bothers, Robin [YAHSGS LLC, Richland, WA; Sega, Gary [YAHSGS LLC, Richland, WA; Martin, Sheryl A [ORNL; Parang, Morey [YAHSGS LLC, Richland, WA

    2007-01-01

    A computational approach has been developed for performing efficient and reasonably accurate toxicity evaluation and prediction. The approach is based on computational neural networks linked to modern computational chemistry and wavelet methods. In this paper we present details of this approach and results demonstrating its accuracy and flexibility for predicting diverse biological endpoints including metabolic processes, mode of action, and hepato- and neurotoxicity. The approach also can be used for automatic processing of microarray data to predict modes of action.

  12. Artificial frame filling using adaptive neural fuzzy inference system for particle image velocimetry dataset

    Science.gov (United States)

    Akdemir, Bayram; Doǧan, Sercan; Aksoy, Muharrem H.; Canli, Eyüp; Özgören, Muammer

    2015-03-01

    Liquid behaviors are very important for many areas especially for Mechanical Engineering. Fast camera is a way to observe and search the liquid behaviors. Camera traces the dust or colored markers travelling in the liquid and takes many pictures in a second as possible as. Every image has large data structure due to resolution. For fast liquid velocity, there is not easy to evaluate or make a fluent frame after the taken images. Artificial intelligence has much popularity in science to solve the nonlinear problems. Adaptive neural fuzzy inference system is a common artificial intelligence in literature. Any particle velocity in a liquid has two dimension speed and its derivatives. Adaptive Neural Fuzzy Inference System has been used to create an artificial frame between previous and post frames as offline. Adaptive neural fuzzy inference system uses velocities and vorticities to create a crossing point vector between previous and post points. In this study, Adaptive Neural Fuzzy Inference System has been used to fill virtual frames among the real frames in order to improve image continuity. So this evaluation makes the images much understandable at chaotic or vorticity points. After executed adaptive neural fuzzy inference system, the image dataset increase two times and has a sequence as virtual and real, respectively. The obtained success is evaluated using R2 testing and mean squared error. R2 testing has a statistical importance about similarity and 0.82, 0.81, 0.85 and 0.8 were obtained for velocities and derivatives, respectively.

  13. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    Directory of Open Access Journals (Sweden)

    Agustín Flores

    2014-01-01

    Full Text Available This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

  14. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    Science.gov (United States)

    Flores, Agustín; Morant, Francisco

    2014-01-01

    This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system. PMID:25610897

  15. A modular neural network scheme applied to fault diagnosis in electric power systems.

    Science.gov (United States)

    Flores, Agustín; Quiles, Eduardo; García, Emilio; Morant, Francisco; Correcher, Antonio

    2014-01-01

    This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

  16. SYNTHESIS OF NEURAL NETWORK MODEL REFERENCE CONTROLLER FOR AIMING AND STABILIZING SYSTEM

    Directory of Open Access Journals (Sweden)

    B.I. Kuznetsov

    2015-11-01

    Full Text Available The aim of this work is the synthesis of neural network reference model controller. The synthesis is performed in MATLAB for the problem of control of the aiming and stabilization system for the special equipment of moving objects. This paper presents the synthesis of the neural network reference model controller to meet the given performance characteristics of operation for the aiming and stabilization system for the special equipment of moving objects. Simulink tool in MATLAB is used to build the block diagram of double-loop neural network system of aiming and stabilization, where the reference model controller is put in the velocity loop and P-regulator is put in the position loop, with feedforward velocity control. Presented the method of synthesis of the neural network reference model controller that is implemented in the Neural Network Toolbox in MATLAB. System tests with the broad range of parameter values determined the key parameters defining the control quality. Optimal values of the key parameters were found to provide the highest control performance. System simulation and analysis of the obtained results is given.

  17. Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

    Science.gov (United States)

    Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza

    2015-11-01

    In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Stochastic chemical kinetics theory and (mostly) systems biological applications

    CERN Document Server

    Érdi, Péter; Lente, Gabor

    2014-01-01

    This volume reviews the theory and simulation methods of stochastic kinetics by integrating historical and recent perspectives, presents applications, mostly in the context of systems biology and also in combustion theory. In recent years, due to the development in experimental techniques, such as optical imaging, single cell analysis, and fluorescence spectroscopy, biochemical kinetic data inside single living cells have increasingly been available. The emergence of systems biology brought renaissance in the application of stochastic kinetic methods.

  19. Systems biology of neutrophil differentiation and immune response

    DEFF Research Database (Denmark)

    Theilgaard-Mönch, Kim; Porse, Bo T; Borregaard, Niels

    2005-01-01

    Systems biology has emerged as a new scientific field, which aims at investigating biological processes at the genomic and proteomic levels. Recent studies have unravelled aspects of neutrophil differentiation and immune responses at the systems level using high-throughput technologies....... These studies have identified a plethora of novel effector proteins stored in the granules of neutrophils. In addition, these studies provide evidence that neutrophil differentiation and immune response are governed by a highly coordinated transcriptional programme that regulates cellular fate and function...

  20. PID Control of Miniature Unmanned Helicopter Yaw System Based on RBF Neural Network

    Science.gov (United States)

    Pan, Yue; Song, Ping; Li, Kejie

    The yaw dynamics of a miniature unmanned helicopter exhibits a complex, nonlinear, time-varying and coupling dynamic behavior. In this paper, simplified yaw dynamics model of MUH in hovering or low-velocity flight mode is established. The SISO model of yaw dynamics is obtained by mechanism modeling and system identification modeling method. PID control based on RBF neural network method combines the advantages of traditional PID controller and neural network controller. It has fast response, good robustness and self-adapting ability. It is suitable to control the yaw system of MUH. Simulation results show that the control system works well with quick response, good robustness and self adaptation.