Optical-Correlator Neural Network Based On Neocognitron

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin; Stoner, William W.

1994-01-01

Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.

Local interconnection neural networks

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

Zhang Jiajun; Zhang Li; Yan Dapen

1993-06-01

The idea of a local interconnection neural network (LINN) is presentd and compared with the globally interconnected Hopfield model. Under the storage limit requirement, LINN is shown to offer the same associative memory capability as the global interconnection neural network while having a much smaller interconnection matrix. LINN can be readily implemented optically using the currently available spatial light modulators. 15 refs.

Implementing neural nets with programmable logic

NASA Technical Reports Server (NTRS)

Vidal, Jacques J.

1988-01-01

Networks of Boolean programmable logic modules are presented as one purely digital class of artificial neural nets. The approach contrasts with the continuous analog framework usually suggested. Programmable logic networks are capable of handling many neural-net applications. They avoid some of the limitations of threshold logic networks and present distinct opportunities. The network nodes are called dynamically programmable logic modules. They can be implemented with digitally controlled demultiplexers. Each node performs a Boolean function of its inputs which can be dynamically assigned. The overall network is therefore a combinational circuit and its outputs are Boolean global functions of the network's input variables. The approach offers definite advantages for VLSI implementation, namely, a regular architecture with limited connectivity, simplicity of the control machinery, natural modularity, and the support of a mature technology.

The Emerging Role of Epigenetics in Stroke

PubMed Central

Qureshi, Irfan A.; Mehler, Mark F.

2013-01-01

The transplantation of exogenous stem cells and the activation of endogenous neural stem and progenitor cells (NSPCs) are promising treatments for stroke. These cells can modulate intrinsic responses to ischemic injury and may even integrate directly into damaged neural networks. However, the neuroprotective and neural regenerative effects that can be mediated by these cells are limited and may even be deleterious. Epigenetic reprogramming represents a novel strategy for enhancing the intrinsic potential of the brain to protect and repair itself by modulating pathologic neural gene expression and promoting the recapitulation of seminal neural developmental processes. In fact, recent evidence suggests that emerging epigenetic mechanisms are critical for orchestrating nearly every aspect of neural development and homeostasis, including brain patterning, neural stem cell maintenance, neurogenesis and gliogenesis, neural subtype specification, and synaptic and neural network connectivity and plasticity. In this review, we survey the therapeutic potential of exogenous stem cells and endogenous NSPCs and highlight innovative technological approaches for designing, developing, and delivering epigenetic therapies for targeted reprogramming of endogenous pools of NSPCs, neural cells at risk, and dysfunctional neural networks to rescue and restore neurologic function in the ischemic brain. PMID:21403016

Metastability and Inter-Band Frequency Modulation in Networks of Oscillating Spiking Neuron Populations

PubMed Central

Bhowmik, David; Shanahan, Murray

2013-01-01

Groups of neurons firing synchronously are hypothesized to underlie many cognitive functions such as attention, associative learning, memory, and sensory selection. Recent theories suggest that transient periods of synchronization and desynchronization provide a mechanism for dynamically integrating and forming coalitions of functionally related neural areas, and that at these times conditions are optimal for information transfer. Oscillating neural populations display a great amount of spectral complexity, with several rhythms temporally coexisting in different structures and interacting with each other. This paper explores inter-band frequency modulation between neural oscillators using models of quadratic integrate-and-fire neurons and Hodgkin-Huxley neurons. We vary the structural connectivity in a network of neural oscillators, assess the spectral complexity, and correlate the inter-band frequency modulation. We contrast this correlation against measures of metastable coalition entropy and synchrony. Our results show that oscillations in different neural populations modulate each other so as to change frequency, and that the interaction of these fluctuating frequencies in the network as a whole is able to drive different neural populations towards episodes of synchrony. Further to this, we locate an area in the connectivity space in which the system directs itself in this way so as to explore a large repertoire of synchronous coalitions. We suggest that such dynamics facilitate versatile exploration, integration, and communication between functionally related neural areas, and thereby supports sophisticated cognitive processing in the brain. PMID:23614040

Statistical process control using optimized neural networks: a case study.

PubMed

Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid

2014-09-01

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Spatial light modulators and applications III; Proceedings of the Meeting, San Diego, CA, Aug. 7, 8, 1989

NASA Astrophysics Data System (ADS)

Efron, Uzi

Recent advances in the technology and applications of spatial light modulators (SLMs) are discussed in review essays by leading experts. Topics addressed include materials for SLMs, SLM devices and device technology, applications to optical data processing, and applications to artificial neural networks. Particular attention is given to nonlinear optical polymers, liquid crystals, magnetooptic SLMs, multiple-quantum-well SLMs, deformable-mirror SLMs, three-dimensional optical memories, applications of photorefractive devices to optical computing, photonic neurocomputers and learning machines, holographic associative memories, SLMs as parallel memories for optoelectronic neural networks, and coherent-optics implementations of neural-network models.

Spatial light modulators and applications III; Proceedings of the Meeting, San Diego, CA, Aug. 7, 8, 1989

NASA Technical Reports Server (NTRS)

Efron, Uzi (Editor)

1990-01-01

Recent advances in the technology and applications of spatial light modulators (SLMs) are discussed in review essays by leading experts. Topics addressed include materials for SLMs, SLM devices and device technology, applications to optical data processing, and applications to artificial neural networks. Particular attention is given to nonlinear optical polymers, liquid crystals, magnetooptic SLMs, multiple-quantum-well SLMs, deformable-mirror SLMs, three-dimensional optical memories, applications of photorefractive devices to optical computing, photonic neurocomputers and learning machines, holographic associative memories, SLMs as parallel memories for optoelectronic neural networks, and coherent-optics implementations of neural-network models.

Pharmacological Tools to Study the Role of Astrocytes in Neural Network Functions.

PubMed

Peña-Ortega, Fernando; Rivera-Angulo, Ana Julia; Lorea-Hernández, Jonathan Julio

2016-01-01

Despite that astrocytes and microglia do not communicate by electrical impulses, they can efficiently communicate among them, with each other and with neurons, to participate in complex neural functions requiring broad cell-communication and long-lasting regulation of brain function. Glial cells express many receptors in common with neurons; secrete gliotransmitters as well as neurotrophic and neuroinflammatory factors, which allow them to modulate synaptic transmission and neural excitability. All these properties allow glial cells to influence the activity of neuronal networks. Thus, the incorporation of glial cell function into the understanding of nervous system dynamics will provide a more accurate view of brain function. Our current knowledge of glial cell biology is providing us with experimental tools to explore their participation in neural network modulation. In this chapter, we review some of the classical, as well as some recent, pharmacological tools developed for the study of astrocyte's influence in neural function. We also provide some examples of the use of these pharmacological agents to understand the role of astrocytes in neural network function and dysfunction.

Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks.

PubMed

Schmidt, Christoph; Piper, Diana; Pester, Britta; Mierau, Andreas; Witte, Herbert

2018-05-01

Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework's potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.

Discovering Multimodal Behavior in Ms. Pac-Man through Evolution of Modular Neural Networks.

PubMed

Schrum, Jacob; Miikkulainen, Risto

2016-03-12

Ms. Pac-Man is a challenging video game in which multiple modes of behavior are required: Ms. Pac-Man must escape ghosts when they are threats and catch them when they are edible, in addition to eating all pills in each level. Past approaches to learning behavior in Ms. Pac-Man have treated the game as a single task to be learned using monolithic policy representations. In contrast, this paper uses a framework called Modular Multi-objective NEAT (MM-NEAT) to evolve modular neural networks. Each module defines a separate behavior. The modules are used at different times according to a policy that can be human-designed (i.e. Multitask) or discovered automatically by evolution. The appropriate number of modules can be fixed or discovered using a genetic operator called Module Mutation. Several versions of Module Mutation are evaluated in this paper. Both fixed modular networks and Module Mutation networks outperform monolithic networks and Multitask networks. Interestingly, the best networks dedicate modules to critical behaviors (such as escaping when surrounded after luring ghosts near a power pill) that do not follow the customary division of the game into chasing edible and escaping threat ghosts. The results demonstrate that MM-NEAT can discover interesting and effective behavior for agents in challenging games.

Discovering Multimodal Behavior in Ms. Pac-Man through Evolution of Modular Neural Networks

PubMed Central

Schrum, Jacob; Miikkulainen, Risto

2015-01-01

Ms. Pac-Man is a challenging video game in which multiple modes of behavior are required: Ms. Pac-Man must escape ghosts when they are threats and catch them when they are edible, in addition to eating all pills in each level. Past approaches to learning behavior in Ms. Pac-Man have treated the game as a single task to be learned using monolithic policy representations. In contrast, this paper uses a framework called Modular Multi-objective NEAT (MM-NEAT) to evolve modular neural networks. Each module defines a separate behavior. The modules are used at different times according to a policy that can be human-designed (i.e. Multitask) or discovered automatically by evolution. The appropriate number of modules can be fixed or discovered using a genetic operator called Module Mutation. Several versions of Module Mutation are evaluated in this paper. Both fixed modular networks and Module Mutation networks outperform monolithic networks and Multitask networks. Interestingly, the best networks dedicate modules to critical behaviors (such as escaping when surrounded after luring ghosts near a power pill) that do not follow the customary division of the game into chasing edible and escaping threat ghosts. The results demonstrate that MM-NEAT can discover interesting and effective behavior for agents in challenging games. PMID:27030803

A Biophysical Neural Model To Describe Spatial Visual Attention

NASA Astrophysics Data System (ADS)

Hugues, Etienne; José, Jorge V.

2008-02-01

Visual scenes have enormous spatial and temporal information that are transduced into neural spike trains. Psychophysical experiments indicate that only a small portion of a spatial image is consciously accessible. Electrophysiological experiments in behaving monkeys have revealed a number of modulations of the neural activity in special visual area known as V4, when the animal is paying attention directly towards a particular stimulus location. The nature of the attentional input to V4, however, remains unknown as well as to the mechanisms responsible for these modulations. We use a biophysical neural network model of V4 to address these issues. We first constrain our model to reproduce the experimental results obtained for different external stimulus configurations and without paying attention. To reproduce the known neuronal response variability, we found that the neurons should receive about equal, or balanced, levels of excitatory and inhibitory inputs and whose levels are high as they are in in vivo conditions. Next we consider attentional inputs that can induce and reproduce the observed spiking modulations. We also elucidate the role played by the neural network to generate these modulations.

A Biophysical Neural Model To Describe Spatial Visual Attention

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

Hugues, Etienne; Jose, Jorge V.

2008-02-14

Visual scenes have enormous spatial and temporal information that are transduced into neural spike trains. Psychophysical experiments indicate that only a small portion of a spatial image is consciously accessible. Electrophysiological experiments in behaving monkeys have revealed a number of modulations of the neural activity in special visual area known as V4, when the animal is paying attention directly towards a particular stimulus location. The nature of the attentional input to V4, however, remains unknown as well as to the mechanisms responsible for these modulations. We use a biophysical neural network model of V4 to address these issues. We firstmore » constrain our model to reproduce the experimental results obtained for different external stimulus configurations and without paying attention. To reproduce the known neuronal response variability, we found that the neurons should receive about equal, or balanced, levels of excitatory and inhibitory inputs and whose levels are high as they are in in vivo conditions. Next we consider attentional inputs that can induce and reproduce the observed spiking modulations. We also elucidate the role played by the neural network to generate these modulations.« less

Phonological experience modulates voice discrimination: Evidence from functional brain networks analysis.

PubMed

Hu, Xueping; Wang, Xiangpeng; Gu, Yan; Luo, Pei; Yin, Shouhang; Wang, Lijun; Fu, Chao; Qiao, Lei; Du, Yi; Chen, Antao

2017-10-01

Numerous behavioral studies have found a modulation effect of phonological experience on voice discrimination. However, the neural substrates underpinning this phenomenon are poorly understood. Here we manipulated language familiarity to test the hypothesis that phonological experience affects voice discrimination via mediating the engagement of multiple perceptual and cognitive resources. The results showed that during voice discrimination, the activation of several prefrontal regions was modulated by language familiarity. More importantly, the same effect was observed concerning the functional connectivity from the fronto-parietal network to the voice-identity network (VIN), and from the default mode network to the VIN. Our findings indicate that phonological experience could bias the recruitment of cognitive control and information retrieval/comparison processes during voice discrimination. Therefore, the study unravels the neural substrates subserving the modulation effect of phonological experience on voice discrimination, and provides new insights into studying voice discrimination from the perspective of network interactions. Copyright © 2017. Published by Elsevier Inc.

The mechanics of state dependent neural correlations

PubMed Central

Doiron, Brent; Litwin-Kumar, Ashok; Rosenbaum, Robert; Ocker, Gabriel K.; Josić, Krešimir

2016-01-01

Simultaneous recordings from large neural populations are becoming increasingly common. An important feature of the population activity are the trial-to-trial correlated fluctuations of the spike train outputs of recorded neuron pairs. Like the firing rate of single neurons, correlated activity can be modulated by a number of factors, from changes in arousal and attentional state to learning and task engagement. However, the network mechanisms that underlie these changes are not fully understood. We review recent theoretical results that identify three separate biophysical mechanisms that modulate spike train correlations: changes in input correlations, internal fluctuations, and the transfer function of single neurons. We first examine these mechanisms in feedforward pathways, and then show how the same approach can explain the modulation of correlations in recurrent networks. Such mechanistic constraints on the modulation of population activity will be important in statistical analyses of high dimensional neural data. PMID:26906505

Modular neural networks: a survey.

PubMed

Auda, G; Kamel, M

1999-04-01

Modular Neural Networks (MNNs) is a rapidly growing field in artificial Neural Networks (NNs) research. This paper surveys the different motivations for creating MNNs: biological, psychological, hardware, and computational. Then, the general stages of MNN design are outlined and surveyed as well, viz., task decomposition techniques, learning schemes and multi-module decision-making strategies. Advantages and disadvantages of the surveyed methods are pointed out, and an assessment with respect to practical potential is provided. Finally, some general recommendations for future designs are presented.

Low-complexity nonlinear adaptive filter based on a pipelined bilinear recurrent neural network.

PubMed

Zhao, Haiquan; Zeng, Xiangping; He, Zhengyou

2011-09-01

To reduce the computational complexity of the bilinear recurrent neural network (BLRNN), a novel low-complexity nonlinear adaptive filter with a pipelined bilinear recurrent neural network (PBLRNN) is presented in this paper. The PBLRNN, inheriting the modular architectures of the pipelined RNN proposed by Haykin and Li, comprises a number of BLRNN modules that are cascaded in a chained form. Each module is implemented by a small-scale BLRNN with internal dynamics. Since those modules of the PBLRNN can be performed simultaneously in a pipelined parallelism fashion, it would result in a significant improvement of computational efficiency. Moreover, due to nesting module, the performance of the PBLRNN can be further improved. To suit for the modular architectures, a modified adaptive amplitude real-time recurrent learning algorithm is derived on the gradient descent approach. Extensive simulations are carried out to evaluate the performance of the PBLRNN on nonlinear system identification, nonlinear channel equalization, and chaotic time series prediction. Experimental results show that the PBLRNN provides considerably better performance compared to the single BLRNN and RNN models.

Deinterlacing using modular neural network

NASA Astrophysics Data System (ADS)

Woo, Dong H.; Eom, Il K.; Kim, Yoo S.

2004-05-01

Deinterlacing is the conversion process from the interlaced scan to progressive one. While many previous algorithms that are based on weighted-sum cause blurring in edge region, deinterlacing using neural network can reduce the blurring through recovering of high frequency component by learning process, and is found robust to noise. In proposed algorithm, input image is divided into edge and smooth region, and then, to each region, one neural network is assigned. Through this process, each neural network learns only patterns that are similar, therefore it makes learning more effective and estimation more accurate. But even within each region, there are various patterns such as long edge and texture in edge region. To solve this problem, modular neural network is proposed. In proposed modular neural network, two modules are combined in output node. One is for low frequency feature of local area of input image, and the other is for high frequency feature. With this structure, each modular neural network can learn different patterns with compensating for drawback of counterpart. Therefore it can adapt to various patterns within each region effectively. In simulation, the proposed algorithm shows better performance compared with conventional deinterlacing methods and single neural network method.

Functional neural networks underlying response inhibition in adolescents and adults.

PubMed

Stevens, Michael C; Kiehl, Kent A; Pearlson, Godfrey D; Calhoun, Vince D

2007-07-19

This study provides the first description of neural network dynamics associated with response inhibition in healthy adolescents and adults. Functional and effective connectivity analyses of whole brain hemodynamic activity elicited during performance of a Go/No-Go task were used to identify functionally integrated neural networks and characterize their causal interactions. Three response inhibition circuits formed a hierarchical, inter-dependent system wherein thalamic modulation of input to premotor cortex by fronto-striatal regions led to response suppression. Adolescents differed from adults in the degree of network engagement, regional fronto-striatal-thalamic connectivity, and network dynamics. We identify and characterize several age-related differences in the function of neural circuits that are associated with behavioral performance changes across adolescent development.

Functional neural networks underlying response inhibition in adolescents and adults

PubMed Central

Stevens, Michael C.; Kiehl, Kent A.; Pearlson, Godfrey D.; Calhoun, Vince D.

2008-01-01

This study provides the first description of neural network dynamics associated with response inhibition in healthy adolescents and adults. Functional and effective connectivity analyses of whole brain hemodynamic activity elicited during performance of a Go/No-Go task were used to identify functionally-integrated neural networks and characterize their causal interactions. Three response inhibition circuits formed a hierarchical, inter-dependent system wherein thalamic modulation of input to premotor cortex by frontostriatal regions led to response suppression. Adolescents differed from adults in the degree of network engagement, regional fronto-striatal-thalamic connectivity, and network dynamics. We identify and characterize several age-related differences in the function of neural circuits that are associated with behavioral performance changes across adolescent development. PMID:17467816

Reward-Modulated Hebbian Plasticity as Leverage for Partially Embodied Control in Compliant Robotics

PubMed Central

Burms, Jeroen; Caluwaerts, Ken; Dambre, Joni

2015-01-01

In embodied computation (or morphological computation), part of the complexity of motor control is offloaded to the body dynamics. We demonstrate that a simple Hebbian-like learning rule can be used to train systems with (partial) embodiment, and can be extended outside of the scope of traditional neural networks. To this end, we apply the learning rule to optimize the connection weights of recurrent neural networks with different topologies and for various tasks. We then apply this learning rule to a simulated compliant tensegrity robot by optimizing static feedback controllers that directly exploit the dynamics of the robot body. This leads to partially embodied controllers, i.e., hybrid controllers that naturally integrate the computations that are performed by the robot body into a neural network architecture. Our results demonstrate the universal applicability of reward-modulated Hebbian learning. Furthermore, they demonstrate the robustness of systems trained with the learning rule. This study strengthens our belief that compliant robots should or can be seen as computational units, instead of dumb hardware that needs a complex controller. This link between compliant robotics and neural networks is also the main reason for our search for simple universal learning rules for both neural networks and robotics. PMID:26347645

Compression of Flow Can Reveal Overlapping-Module Organization in Networks

NASA Astrophysics Data System (ADS)

Viamontes Esquivel, Alcides; Rosvall, Martin

2011-10-01

To better understand the organization of overlapping modules in large networks with respect to flow, we introduce the map equation for overlapping modules. In this information-theoretic framework, we use the correspondence between compression and regularity detection. The generalized map equation measures how well we can compress a description of flow in the network when we partition it into modules with possible overlaps. When we minimize the generalized map equation over overlapping network partitions, we detect modules that capture flow and determine which nodes at the boundaries between modules should be classified in multiple modules and to what degree. With a novel greedy-search algorithm, we find that some networks, for example, the neural network of the nematode Caenorhabditis elegans, are best described by modules dominated by hard boundaries, but that others, for example, the sparse European-roads network, have an organization of highly overlapping modules.

Detection of network attacks based on adaptive resonance theory

NASA Astrophysics Data System (ADS)

Bukhanov, D. G.; Polyakov, V. M.

2018-05-01

The paper considers an approach to intrusion detection systems using a neural network of adaptive resonant theory. It suggests the structure of an intrusion detection system consisting of two types of program modules. The first module manages connections of user applications by preventing the undesirable ones. The second analyzes the incoming network traffic parameters to check potential network attacks. After attack detection, it notifies the required stations using a secure transmission channel. The paper describes the experiment on the detection and recognition of network attacks using the test selection. It also compares the obtained results with similar experiments carried out by other authors. It gives findings and conclusions on the sufficiency of the proposed approach. The obtained information confirms the sufficiency of applying the neural networks of adaptive resonant theory to analyze network traffic within the intrusion detection system.

Oxytocin receptors modulate a social salience neural network in male prairie voles.

PubMed

Johnson, Zachary V; Walum, Hasse; Xiao, Yao; Riefkohl, Paula C; Young, Larry J

2017-01-01

Social behavior is regulated by conserved neural networks across vertebrates. Variation in the organization of neuropeptide systems across these networks is thought to contribute to individual and species diversity in network function during social contexts. For example, oxytocin (OT) is an ancient neuropeptide that binds to OT receptors (OTRs) in the brain and modulates social and reproductive behavior across vertebrate species, including humans. Central OTRs exhibit extraordinarily diverse expression patterns that are associated with individual and species differences in social behavior. In voles, OTR density in the nucleus accumbens (NAc)-a region important for social and reward learning-is associated with individual and species variation in social attachment behavior. Here we test whether OTRs in the NAc modulate a social salience network (SSN)-a network of interconnected brain nuclei thought to encode valence and incentive salience of sociosensory cues-during a social context in the socially monogamous male prairie vole. Using a selective OTR antagonist, we test whether activation of OTRs in the NAc during sociosexual interaction and mating modulates expression of the immediate early gene product Fos across nuclei of the SSN. We show that blockade of endogenous OTR signaling in the NAc during sociosexual interaction and mating does not strongly modulate levels of Fos expression in individual nodes of the network, but strongly modulates patterns of correlated Fos expression between the NAc and other SSN nuclei. Published by Elsevier Inc.

Neural net target-tracking system using structured laser patterns

NASA Astrophysics Data System (ADS)

Cho, Jae-Wan; Lee, Yong-Bum; Lee, Nam-Ho; Park, Soon-Yong; Lee, Jongmin; Choi, Gapchu; Baek, Sunghyun; Park, Dong-Sun

1996-06-01

In this paper, we describe a robot endeffector tracking system using sensory information from recently-announced structured pattern laser diodes, which can generate images with several different types of structured pattern. The neural network approach is employed to recognize the robot endeffector covering the situation of three types of motion: translation, scaling and rotation. Features for the neural network to detect the position of the endeffector are extracted from the preprocessed images. Artificial neural networks are used to store models and to match with unknown input features recognizing the position of the robot endeffector. Since a minimal number of samples are used for different directions of the robot endeffector in the system, an artificial neural network with the generalization capability can be utilized for unknown input features. A feedforward neural network with the generalization capability can be utilized for unknown input features. A feedforward neural network trained with the back propagation learning is used to detect the position of the robot endeffector. Another feedforward neural network module is used to estimate the motion from a sequence of images and to control movements of the robot endeffector. COmbining the tow neural networks for recognizing the robot endeffector and estimating the motion with the preprocessing stage, the whole system keeps tracking of the robot endeffector effectively.

Integration of Optical Manipulation and Electrophysiological Tools to Modulate and Record Activity in Neural Networks

NASA Astrophysics Data System (ADS)

Difato, F.; Schibalsky, L.; Benfenati, F.; Blau, A.

2011-07-01

We present an optical system that combines IR (1064 nm) holographic optical tweezers with a sub-nanosecond-pulsed UV (355 nm) laser microdissector for the optical manipulation of single neurons and entire networks both on transparent and non-transparent substrates in vitro. The phase-modulated laser beam can illuminate the sample concurrently or independently from above or below assuring compatibility with different types of microelectrode array and patch-clamp electrophysiology. By combining electrophysiological and optical tools, neural activity in response to localized stimuli or injury can be studied and quantified at sub-cellular, cellular, and network level.

Estimation of Energy Expenditure Using a Patch-Type Sensor Module with an Incremental Radial Basis Function Neural Network

PubMed Central

Li, Meina; Kwak, Keun-Chang; Kim, Youn Tae

2016-01-01

Conventionally, indirect calorimetry has been used to estimate oxygen consumption in an effort to accurately measure human body energy expenditure. However, calorimetry requires the subject to wear a mask that is neither convenient nor comfortable. The purpose of our study is to develop a patch-type sensor module with an embedded incremental radial basis function neural network (RBFNN) for estimating the energy expenditure. The sensor module contains one ECG electrode and a three-axis accelerometer, and can perform real-time heart rate (HR) and movement index (MI) monitoring. The embedded incremental network includes linear regression (LR) and RBFNN based on context-based fuzzy c-means (CFCM) clustering. This incremental network is constructed by building a collection of information granules through CFCM clustering that is guided by the distribution of error of the linear part of the LR model. PMID:27669249

Optical implementation of a feature-based neural network with application to automatic target recognition

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin; Stoner, William W.

1993-01-01

An optical neural network based on the neocognitron paradigm is introduced. A novel aspect of the architecture design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by feeding back the ouput of the feature correlator interatively to the input spatial light modulator and by updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intraclass fault tolerance and interclass discrimination is achieved. A detailed system description is provided. Experimental demonstrations of a two-layer neural network for space-object discrimination is also presented.

Automatic target recognition using a feature-based optical neural network

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin

1992-01-01

An optical neural network based upon the Neocognitron paradigm (K. Fukushima et al. 1983) is introduced. A novel aspect of the architectural design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by iteratively feeding back the output of the feature correlator to the input spatial light modulator and updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intra-class fault tolerance and inter-class discrimination is achieved. A detailed system description is provided. Experimental demonstration of a two-layer neural network for space objects discrimination is also presented.

Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks.

PubMed

Khan, Faisal Nadeem; Zhong, Kangping; Zhou, Xian; Al-Arashi, Waled Hussein; Yu, Changyuan; Lu, Chao; Lau, Alan Pak Tao

2017-07-24

We experimentally demonstrate the use of deep neural networks (DNNs) in combination with signals' amplitude histograms (AHs) for simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in digital coherent receivers. The proposed technique automatically extracts OSNR and modulation format dependent features of AHs, obtained after constant modulus algorithm (CMA) equalization, and exploits them for the joint estimation of these parameters. Experimental results for 112 Gbps polarization-multiplexed (PM) quadrature phase-shift keying (QPSK), 112 Gbps PM 16 quadrature amplitude modulation (16-QAM), and 240 Gbps PM 64-QAM signals demonstrate OSNR monitoring with mean estimation errors of 1.2 dB, 0.4 dB, and 1 dB, respectively. Similarly, the results for MFI show 100% identification accuracy for all three modulation formats. The proposed technique applies deep machine learning algorithms inside standard digital coherent receiver and does not require any additional hardware. Therefore, it is attractive for cost-effective multi-parameter estimation in next-generation elastic optical networks (EONs).

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

PubMed Central

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

2011-01-01

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

Intracranial spectral amplitude dynamics of perceptual suppression in fronto-insular, occipito-temporal, and primary visual cortex

PubMed Central

Vidal, Juan R.; Perrone-Bertolotti, Marcela; Kahane, Philippe; Lachaux, Jean-Philippe

2015-01-01

If conscious perception requires global information integration across active distant brain networks, how does the loss of conscious perception affect neural processing in these distant networks? Pioneering studies on perceptual suppression (PS) described specific local neural network responses in primary visual cortex, thalamus and lateral prefrontal cortex of the macaque brain. Yet the neural effects of PS have rarely been studied with intracerebral recordings outside these cortices and simultaneously across distant brain areas. Here, we combined (1) a novel experimental paradigm in which we produced a similar perceptual disappearance and also re-appearance by using visual adaptation with transient contrast changes, with (2) electrophysiological observations from human intracranial electrodes sampling wide brain areas. We focused on broadband high-frequency (50–150 Hz, i.e., gamma) and low-frequency (8–24 Hz) neural activity amplitude modulations related to target visibility and invisibility. We report that low-frequency amplitude modulations reflected stimulus visibility in a larger ensemble of recording sites as compared to broadband gamma responses, across distinct brain regions including occipital, temporal and frontal cortices. Moreover, the dynamics of the broadband gamma response distinguished stimulus visibility from stimulus invisibility earlier in anterior insula and inferior frontal gyrus than in temporal regions, suggesting a possible role of fronto-insular cortices in top–down processing for conscious perception. Finally, we report that in primary visual cortex only low-frequency amplitude modulations correlated directly with perceptual status. Interestingly, in this sensory area broadband gamma was not modulated during PS but became positively modulated after 300 ms when stimuli were rendered visible again, suggesting that local networks could be ignited by top–down influences during conscious perception. PMID:25642199

Feedback modulation of neural network synchrony and seizure susceptibility by Mdm2-p53-Nedd4-2 signaling.

PubMed

Jewett, Kathryn A; Christian, Catherine A; Bacos, Jonathan T; Lee, Kwan Young; Zhu, Jiuhe; Tsai, Nien-Pei

2016-03-22

Neural network synchrony is a critical factor in regulating information transmission through the nervous system. Improperly regulated neural network synchrony is implicated in pathophysiological conditions such as epilepsy. Despite the awareness of its importance, the molecular signaling underlying the regulation of neural network synchrony, especially after stimulation, remains largely unknown. In this study, we show that elevation of neuronal activity by the GABA(A) receptor antagonist, Picrotoxin, increases neural network synchrony in primary mouse cortical neuron cultures. The elevation of neuronal activity triggers Mdm2-dependent degradation of the tumor suppressor p53. We show here that blocking the degradation of p53 further enhances Picrotoxin-induced neural network synchrony, while promoting the inhibition of p53 with a p53 inhibitor reduces Picrotoxin-induced neural network synchrony. These data suggest that Mdm2-p53 signaling mediates a feedback mechanism to fine-tune neural network synchrony after activity stimulation. Furthermore, genetically reducing the expression of a direct target gene of p53, Nedd4-2, elevates neural network synchrony basally and occludes the effect of Picrotoxin. Finally, using a kainic acid-induced seizure model in mice, we show that alterations of Mdm2-p53-Nedd4-2 signaling affect seizure susceptibility. Together, our findings elucidate a critical role of Mdm2-p53-Nedd4-2 signaling underlying the regulation of neural network synchrony and seizure susceptibility and reveal potential therapeutic targets for hyperexcitability-associated neurological disorders.

Intranasal oxytocin modulates neural functional connectivity during human social interaction.

PubMed

Rilling, James K; Chen, Xiangchuan; Chen, Xu; Haroon, Ebrahim

2018-02-10

Oxytocin (OT) modulates social behavior in primates and many other vertebrate species. Studies in non-primate animals have demonstrated that, in addition to influencing activity within individual brain areas, OT influences functional connectivity across networks of areas involved in social behavior. Previously, we used fMRI to image brain function in human subjects during a dyadic social interaction task following administration of either intranasal oxytocin (INOT) or placebo, and analyzed the data with a standard general linear model. Here, we conduct an extensive re-analysis of these data to explore how OT modulates functional connectivity across a neural network that animal studies implicate in social behavior. OT induced widespread increases in functional connectivity in response to positive social interactions among men and widespread decreases in functional connectivity in response to negative social interactions among women. Nucleus basalis of Meynert, an important regulator of selective attention and motivation with a particularly high density of OT receptors, had the largest number of OT-modulated connections. Regions known to receive mesolimbic dopamine projections such as the nucleus accumbens and lateral septum were also hubs for OT effects on functional connectivity. Our results suggest that the neural mechanism by which OT influences primate social cognition may include changes in patterns of activity across neural networks that regulate social behavior in other animals. © 2018 Wiley Periodicals, Inc.

Artificial Neural Network applied to lightning flashes

NASA Astrophysics Data System (ADS)

Gin, R. B.; Guedes, D.; Bianchi, R.

2013-05-01

The development of video cameras enabled cientists to study lightning discharges comportment with more precision. The main goal of this project is to create a system able to detect images of lightning discharges stored in videos and classify them using an Artificial Neural Network (ANN)using C Language and OpenCV libraries. The developed system, can be split in two different modules: detection module and classification module. The detection module uses OpenCV`s computer vision libraries and image processing techniques to detect if there are significant differences between frames in a sequence, indicating that something, still not classified, occurred. Whenever there is a significant difference between two consecutive frames, two main algorithms are used to analyze the frame image: brightness and shape algorithms. These algorithms detect both shape and brightness of the event, removing irrelevant events like birds, as well as detecting the relevant events exact position, allowing the system to track it over time. The classification module uses a neural network to classify the relevant events as horizontal or vertical lightning, save the event`s images and calculates his number of discharges. The Neural Network was implemented using the backpropagation algorithm, and was trained with 42 training images , containing 57 lightning events (one image can have more than one lightning). TheANN was tested with one to five hidden layers, with up to 50 neurons each. The best configuration achieved a success rate of 95%, with one layer containing 20 neurons (33 test images with 42 events were used in this phase). This configuration was implemented in the developed system to analyze 20 video files, containing 63 lightning discharges previously manually detected. Results showed that all the lightning discharges were detected, many irrelevant events were unconsidered, and the event's number of discharges was correctly computed. The neural network used in this project achieved a success rate of 90%. The videos used in this experiment were acquired by seven video cameras installed in São Bernardo do Campo, Brazil, that continuously recorded lightning events during the summer. The cameras were disposed in a 360 loop, recording all data at a time resolution of 33ms. During this period, several convective storms were recorded.

Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement

PubMed Central

Negri, Lucas; Nied, Ademir; Kalinowski, Hypolito; Paterno, Aleksander

2011-01-01

This paper presents a benchmark for peak detection algorithms employed in fiber Bragg grating spectrometric interrogation systems. The accuracy, precision, and computational performance of currently used algorithms and those of a new proposed artificial neural network algorithm are compared. Centroid and gaussian fitting algorithms are shown to have the highest precision but produce systematic errors that depend on the FBG refractive index modulation profile. The proposed neural network displays relatively good precision with reduced systematic errors and improved computational performance when compared to other networks. Additionally, suitable algorithms may be chosen with the general guidelines presented. PMID:22163806

Application of artificial neural network to fMRI regression analysis.

PubMed

Misaki, Masaya; Miyauchi, Satoru

2006-01-15

We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.

A neural-network approach to robotic control

NASA Technical Reports Server (NTRS)

Graham, D. P. W.; Deleuterio, G. M. T.

1993-01-01

An artificial neural-network paradigm for the control of robotic systems is presented. The approach is based on the Cerebellar Model Articulation Controller created by James Albus and incorporates several extensions. First, recognizing the essential structure of multibody equations of motion, two parallel modules are used that directly reflect the dynamical characteristics of multibody systems. Second, the architecture of the proposed network is imbued with a self-organizational capability which improves efficiency and accuracy. Also, the networks can be arranged in hierarchical fashion with each subsequent network providing finer and finer resolution.

Structure and function of complex brain networks

PubMed Central

Sporns, Olaf

2013-01-01

An increasing number of theoretical and empirical studies approach the function of the human brain from a network perspective. The analysis of brain networks is made feasible by the development of new imaging acquisition methods as well as new tools from graph theory and dynamical systems. This review surveys some of these methodological advances and summarizes recent findings on the architecture of structural and functional brain networks. Studies of the structural connectome reveal several modules or network communities that are interlinked by hub regions mediating communication processes between modules. Recent network analyses have shown that network hubs form a densely linked collective called a “rich club,” centrally positioned for attracting and dispersing signal traffic. In parallel, recordings of resting and task-evoked neural activity have revealed distinct resting-state networks that contribute to functions in distinct cognitive domains. Network methods are increasingly applied in a clinical context, and their promise for elucidating neural substrates of brain and mental disorders is discussed. PMID:24174898

Cognitive emotion regulation in children: Reappraisal of emotional faces modulates neural source activity in a frontoparietal network.

PubMed

Wessing, Ida; Rehbein, Maimu A; Romer, Georg; Achtergarde, Sandra; Dobel, Christian; Zwitserlood, Pienie; Fürniss, Tilman; Junghöfer, Markus

2015-06-01

Emotion regulation has an important role in child development and psychopathology. Reappraisal as cognitive regulation technique can be used effectively by children. Moreover, an ERP component known to reflect emotional processing called late positive potential (LPP) can be modulated by children using reappraisal and this modulation is also related to children's emotional adjustment. The present study seeks to elucidate the neural generators of such LPP effects. To this end, children aged 8-14 years reappraised emotional faces, while neural activity in an LPP time window was estimated using magnetoencephalography-based source localization. Additionally, neural activity was correlated with two indexes of emotional adjustment and age. Reappraisal reduced activity in the left dorsolateral prefrontal cortex during down-regulation and enhanced activity in the right parietal cortex during up-regulation. Activity in the visual cortex decreased with increasing age, more adaptive emotion regulation and less anxiety. Results demonstrate that reappraisal changed activity within a frontoparietal network in children. Decreasing activity in the visual cortex with increasing age is suggested to reflect neural maturation. A similar decrease with adaptive emotion regulation and less anxiety implies that better emotional adjustment may be associated with an advance in neural maturation. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Neural network-based system for pattern recognition through a fiber optic bundle

NASA Astrophysics Data System (ADS)

Gamo-Aranda, Javier; Rodriguez-Horche, Paloma; Merchan-Palacios, Miguel; Rosales-Herrera, Pablo; Rodriguez, M.

2001-04-01

A neural network based system to identify images transmitted through a Coherent Fiber-optic Bundle (CFB) is presented. Patterns are generated in a computer, displayed on a Spatial Light Modulator, imaged onto the input face of the CFB, and recovered optically by a CCD sensor array for further processing. Input and output optical subsystems were designed and used to that end. The recognition step of the transmitted patterns is made by a powerful, widely-used, neural network simulator running on the control PC. A complete PC-based interface was developed to control the different tasks involved in the system. An optical analysis of the system capabilities was carried out prior to performing the recognition step. Several neural network topologies were tested, and the corresponding numerical results are also presented and discussed.

Multiprocessor Neural Network in Healthcare.

PubMed

Godó, Zoltán Attila; Kiss, Gábor; Kocsis, Dénes

2015-01-01

A possible way of creating a multiprocessor artificial neural network is by the use of microcontrollers. The RISC processors' high performance and the large number of I/O ports mean they are greatly suitable for creating such a system. During our research, we wanted to see if it is possible to efficiently create interaction between the artifical neural network and the natural nervous system. To achieve as much analogy to the living nervous system as possible, we created a frequency-modulated analog connection between the units. Our system is connected to the living nervous system through 128 microelectrodes. Two-way communication is provided through A/D transformation, which is even capable of testing psychopharmacons. The microcontroller-based analog artificial neural network can play a great role in medical singal processing, such as ECG, EEG etc.

Contextual Modulation is Related to Efficiency in a Spiking Network Model of Visual Cortex.

PubMed

Sharifian, Fariba; Heikkinen, Hanna; Vigário, Ricardo; Vanni, Simo

2015-01-01

In the visual cortex, stimuli outside the classical receptive field (CRF) modulate the neural firing rate, without driving the neuron by themselves. In the primary visual cortex (V1), such contextual modulation can be parametrized with an area summation function (ASF): increasing stimulus size causes first an increase and then a decrease of firing rate before reaching an asymptote. Earlier work has reported increase of sparseness when CRF stimulation is extended to its surroundings. However, there has been no clear connection between the ASF and network efficiency. Here we aimed to investigate possible link between ASF and network efficiency. In this study, we simulated the responses of a biomimetic spiking neural network model of the visual cortex to a set of natural images. We varied the network parameters, and compared the V1 excitatory neuron spike responses to the corresponding responses predicted from earlier single neuron data from primate visual cortex. The network efficiency was quantified with firing rate (which has direct association to neural energy consumption), entropy per spike and population sparseness. All three measures together provided a clear association between the network efficiency and the ASF. The association was clear when varying the horizontal connectivity within V1, which influenced both the efficiency and the distance to ASF, DAS. Given the limitations of our biophysical model, this association is qualitative, but nevertheless suggests that an ASF-like receptive field structure can cause efficient population response.

Disentangling the attention network test: behavioral, event related potentials, and neural source analyses

PubMed Central

Galvao-Carmona, Alejandro; González-Rosa, Javier J.; Hidalgo-Muñoz, Antonio R.; Páramo, Dolores; Benítez, María L.; Izquierdo, Guillermo; Vázquez-Marrufo, Manuel

2014-01-01

Background: The study of the attentional system remains a challenge for current neuroscience. The “Attention Network Test” (ANT) was designed to study simultaneously three different attentional networks (alerting, orienting, and executive) based in subtraction of different experimental conditions. However, some studies recommend caution with these calculations due to the interactions between the attentional networks. In particular, it is highly relevant that several interpretations about attentional impairment have arisen from these calculations in diverse pathologies. Event related potentials (ERPs) and neural source analysis can be applied to disentangle the relationships between these attentional networks not specifically shown by behavioral measures. Results: This study shows that there is a basic level of alerting (tonic alerting) in the no cue (NC) condition, represented by a slow negative trend in the ERP trace prior to the onset of the target stimuli. A progressive increase in the CNV amplitude related to the amount of information provided by the cue conditions is also shown. Neural source analysis reveals specific modulations of the CNV related to a task-related expectancy presented in the NC condition; a late modulation triggered by the central cue (CC) condition and probably representing a generic motor preparation; and an early and late modulation for spatial cue (SC) condition suggesting specific motor and sensory preactivation. Finally, the first component in the information processing of the target stimuli modulated by the interaction between orienting network and the executive system can be represented by N1. Conclusions: The ANT is useful as a paradigm to study specific attentional mechanisms and their interactions. However, calculation of network effects is based in subtractions with non-comparable experimental conditions, as evidenced by the present data, which can induce misinterpretations in the study of the attentional capacity in human subjects. PMID:25352800

Low frequency steady-state brain responses modulate large scale functional networks in a frequency-specific means.

PubMed

Wang, Yi-Feng; Long, Zhiliang; Cui, Qian; Liu, Feng; Jing, Xiu-Juan; Chen, Heng; Guo, Xiao-Nan; Yan, Jin H; Chen, Hua-Fu

2016-01-01

Neural oscillations are essential for brain functions. Research has suggested that the frequency of neural oscillations is lower for more integrative and remote communications. In this vein, some resting-state studies have suggested that large scale networks function in the very low frequency range (<1 Hz). However, it is difficult to determine the frequency characteristics of brain networks because both resting-state studies and conventional frequency tagging approaches cannot simultaneously capture multiple large scale networks in controllable cognitive activities. In this preliminary study, we aimed to examine whether large scale networks can be modulated by task-induced low frequency steady-state brain responses (lfSSBRs) in a frequency-specific pattern. In a revised attention network test, the lfSSBRs were evoked in the triple network system and sensory-motor system, indicating that large scale networks can be modulated in a frequency tagging way. Furthermore, the inter- and intranetwork synchronizations as well as coherence were increased at the fundamental frequency and the first harmonic rather than at other frequency bands, indicating a frequency-specific modulation of information communication. However, there was no difference among attention conditions, indicating that lfSSBRs modulate the general attention state much stronger than distinguishing attention conditions. This study provides insights into the advantage and mechanism of lfSSBRs. More importantly, it paves a new way to investigate frequency-specific large scale brain activities. © 2015 Wiley Periodicals, Inc.

Optical computing and neural networks; Proceedings of the Meeting, National Chiao Tung Univ., Hsinchu, Taiwan, Dec. 16, 17, 1992

NASA Technical Reports Server (NTRS)

Hsu, Ken-Yuh (Editor); Liu, Hua-Kuang (Editor)

1992-01-01

The present conference discusses optical neural networks, photorefractive nonlinear optics, optical pattern recognition, digital and analog processors, and holography and its applications. Attention is given to bifurcating optical information processing, neural structures in digital halftoning, an exemplar-based optical neural net classifier for color pattern recognition, volume storage in photorefractive disks, and microlaser-based compact optical neuroprocessors. Also treated are the optical implementation of a feature-enhanced optical interpattern-associative neural network model and its optical implementation, an optical pattern binary dual-rail logic gate module, a theoretical analysis for holographic associative memories, joint transform correlators, image addition and subtraction via the Talbot effect, and optical wavelet-matched filters. (No individual items are abstracted in this volume)

Optical computing and neural networks; Proceedings of the Meeting, National Chiao Tung Univ., Hsinchu, Taiwan, Dec. 16, 17, 1992

NASA Astrophysics Data System (ADS)

Hsu, Ken-Yuh; Liu, Hua-Kuang

The present conference discusses optical neural networks, photorefractive nonlinear optics, optical pattern recognition, digital and analog processors, and holography and its applications. Attention is given to bifurcating optical information processing, neural structures in digital halftoning, an exemplar-based optical neural net classifier for color pattern recognition, volume storage in photorefractive disks, and microlaser-based compact optical neuroprocessors. Also treated are the optical implementation of a feature-enhanced optical interpattern-associative neural network model and its optical implementation, an optical pattern binary dual-rail logic gate module, a theoretical analysis for holographic associative memories, joint transform correlators, image addition and subtraction via the Talbot effect, and optical wavelet-matched filters. (No individual items are abstracted in this volume)

A neural network controller for hydronic heating systems of solar buildings.

PubMed

Argiriou, Athanassios A; Bellas-Velidis, Ioannis; Kummert, Michaël; André, Philippe

2004-04-01

An artificial neural network (ANN)-based controller for hydronic heating plants of buildings is presented. The controller has forecasting capabilities: it includes a meteorological module, forecasting the ambient temperature and solar irradiance, an indoor temperature predictor module, a supply temperature predictor module and an optimizing module for the water supply temperature. All ANN modules are based on the Feed Forward Back Propagation (FFBP) model. The operation of the controller has been tested experimentally, on a real-scale office building during real operating conditions. The operation results were compared to those of a conventional controller. The performance was also assessed via numerical simulation. The detailed thermal simulation tool for solar systems and buildings TRNSYS was used. Both experimental and numerical results showed that the expected percentage of energy savings with respect to a conventional controller is of about 15% under North European weather conditions.

Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks

PubMed Central

2018-01-01

Much of the information the brain processes and stores is temporal in nature—a spoken word or a handwritten signature, for example, is defined by how it unfolds in time. However, it remains unclear how neural circuits encode complex time-varying patterns. We show that by tuning the weights of a recurrent neural network (RNN), it can recognize and then transcribe spoken digits. The model elucidates how neural dynamics in cortical networks may resolve three fundamental challenges: first, encode multiple time-varying sensory and motor patterns as stable neural trajectories; second, generalize across relevant spatial features; third, identify the same stimuli played at different speeds—we show that this temporal invariance emerges because the recurrent dynamics generate neural trajectories with appropriately modulated angular velocities. Together our results generate testable predictions as to how recurrent networks may use different mechanisms to generalize across the relevant spatial and temporal features of complex time-varying stimuli. PMID:29537963

Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks.

PubMed

Goudar, Vishwa; Buonomano, Dean V

2018-03-14

Much of the information the brain processes and stores is temporal in nature-a spoken word or a handwritten signature, for example, is defined by how it unfolds in time. However, it remains unclear how neural circuits encode complex time-varying patterns. We show that by tuning the weights of a recurrent neural network (RNN), it can recognize and then transcribe spoken digits. The model elucidates how neural dynamics in cortical networks may resolve three fundamental challenges: first, encode multiple time-varying sensory and motor patterns as stable neural trajectories; second, generalize across relevant spatial features; third, identify the same stimuli played at different speeds-we show that this temporal invariance emerges because the recurrent dynamics generate neural trajectories with appropriately modulated angular velocities. Together our results generate testable predictions as to how recurrent networks may use different mechanisms to generalize across the relevant spatial and temporal features of complex time-varying stimuli. © 2018, Goudar et al.

Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode.

PubMed

Ye, Tao; Wang, Baocheng; Song, Ping; Li, Juan

2018-06-12

Many accidents happen under shunting mode when the speed of a train is below 45 km/h. In this mode, train attendants observe the railway condition ahead using the traditional manual method and tell the observation results to the driver in order to avoid danger. To address this problem, an automatic object detection system based on convolutional neural network (CNN) is proposed to detect objects ahead in shunting mode, which is called Feature Fusion Refine neural network (FR-Net). It consists of three connected modules, i.e., the depthwise-pointwise convolution, the coarse detection module, and the object detection module. Depth-wise-pointwise convolutions are used to improve the detection in real time. The coarse detection module coarsely refine the locations and sizes of prior anchors to provide better initialization for the subsequent module and also reduces search space for the classification, whereas the object detection module aims to regress accurate object locations and predict the class labels for the prior anchors. The experimental results on the railway traffic dataset show that FR-Net achieves 0.8953 mAP with 72.3 FPS performance on a machine with a GeForce GTX1080Ti with the input size of 320 × 320 pixels. The results imply that FR-Net takes a good tradeoff both on effectiveness and real time performance. The proposed method can meet the needs of practical application in shunting mode.

Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs

NASA Astrophysics Data System (ADS)

Zamora-López, Gorka; Chen, Yuhan; Deco, Gustavo; Kringelbach, Morten L.; Zhou, Changsong

2016-12-01

The large-scale structural ingredients of the brain and neural connectomes have been identified in recent years. These are, similar to the features found in many other real networks: the arrangement of brain regions into modules and the presence of highly connected regions (hubs) forming rich-clubs. Here, we examine how modules and hubs shape the collective dynamics on networks and we find that both ingredients lead to the emergence of complex dynamics. Comparing the connectomes of C. elegans, cats, macaques and humans to surrogate networks in which either modules or hubs are destroyed, we find that functional complexity always decreases in the perturbed networks. A comparison between simulated and empirically obtained resting-state functional connectivity indicates that the human brain, at rest, lies in a dynamical state that reflects the largest complexity its anatomical connectome can host. Last, we generalise the topology of neural connectomes into a new hierarchical network model that successfully combines modular organisation with rich-club forming hubs. This is achieved by centralising the cross-modular connections through a preferential attachment rule. Our network model hosts more complex dynamics than other hierarchical models widely used as benchmarks.

Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs

PubMed Central

Zamora-López, Gorka; Chen, Yuhan; Deco, Gustavo; Kringelbach, Morten L.; Zhou, Changsong

2016-01-01

The large-scale structural ingredients of the brain and neural connectomes have been identified in recent years. These are, similar to the features found in many other real networks: the arrangement of brain regions into modules and the presence of highly connected regions (hubs) forming rich-clubs. Here, we examine how modules and hubs shape the collective dynamics on networks and we find that both ingredients lead to the emergence of complex dynamics. Comparing the connectomes of C. elegans, cats, macaques and humans to surrogate networks in which either modules or hubs are destroyed, we find that functional complexity always decreases in the perturbed networks. A comparison between simulated and empirically obtained resting-state functional connectivity indicates that the human brain, at rest, lies in a dynamical state that reflects the largest complexity its anatomical connectome can host. Last, we generalise the topology of neural connectomes into a new hierarchical network model that successfully combines modular organisation with rich-club forming hubs. This is achieved by centralising the cross-modular connections through a preferential attachment rule. Our network model hosts more complex dynamics than other hierarchical models widely used as benchmarks. PMID:27917958

Consciousness, cognition and brain networks: New perspectives.

PubMed

Aldana, E M; Valverde, J L; Fábregas, N

2016-10-01

A detailed analysis of the literature on consciousness and cognition mechanisms based on the neural networks theory is presented. The immune and inflammatory response to the anesthetic-surgical procedure induces modulation of neuronal plasticity by influencing higher cognitive functions. Anesthetic drugs can cause unconsciousness, producing a functional disruption of cortical and thalamic cortical integration complex. The external and internal perceptions are processed through an intricate network of neural connections, involving the higher nervous activity centers, especially the cerebral cortex. This requires an integrated model, formed by neural networks and their interactions with highly specialized regions, through large-scale networks, which are distributed throughout the brain collecting information flow of these perceptions. Functional and effective connectivity between large-scale networks, are essential for consciousness, unconsciousness and cognition. It is what is called the "human connectome" or map neural networks. Copyright © 2014 Sociedad Española de Anestesiología, Reanimación y Terapéutica del Dolor. Publicado por Elsevier España, S.L.U. All rights reserved.

Neurometaplasticity: Glucoallostasis control of plasticity of the neural networks of error commission, detection, and correction modulates neuroplasticity to influence task precision

NASA Astrophysics Data System (ADS)

Welcome, Menizibeya O.; Dane, Şenol; Mastorakis, Nikos E.; Pereverzev, Vladimir A.

2017-12-01

The term "metaplasticity" is a recent one, which means plasticity of synaptic plasticity. Correspondingly, neurometaplasticity simply means plasticity of neuroplasticity, indicating that a previous plastic event determines the current plasticity of neurons. Emerging studies suggest that neurometaplasticity underlie many neural activities and neurobehavioral disorders. In our previous work, we indicated that glucoallostasis is essential for the control of plasticity of the neural network that control error commission, detection and correction. Here we review recent works, which suggest that task precision depends on the modulatory effects of neuroplasticity on the neural networks of error commission, detection, and correction. Furthermore, we discuss neurometaplasticity and its role in error commission, detection, and correction.

Review on Neural Correlates of Emotion Regulation and Music: Implications for Emotion Dysregulation

PubMed Central

Hou, Jiancheng; Song, Bei; Chen, Andrew C. N.; Sun, Changan; Zhou, Jiaxian; Zhu, Haidong; Beauchaine, Theodore P.

2017-01-01

Previous studies have examined the neural correlates of emotion regulation and the neural changes that are evoked by music exposure. However, the link between music and emotion regulation is poorly understood. The objectives of this review are to (1) synthesize what is known about the neural correlates of emotion regulation and music-evoked emotions, and (2) consider the possibility of therapeutic effects of music on emotion dysregulation. Music-evoked emotions can modulate activities in both cortical and subcortical systems, and across cortical-subcortical networks. Functions within these networks are integral to generation and regulation of emotions. Since dysfunction in these networks are observed in numerous psychiatric disorders, a better understanding of neural correlates of music exposure may lead to more systematic and effective use of music therapy in emotion dysregulation. PMID:28421017

Dynamic security contingency screening and ranking using neural networks.

PubMed

Mansour, Y; Vaahedi, E; El-Sharkawi, M A

1997-01-01

This paper summarizes BC Hydro's experience in applying neural networks to dynamic security contingency screening and ranking. The idea is to use the information on the prevailing operating condition and directly provide contingency screening and ranking using a trained neural network. To train the two neural networks for the large scale systems of BC Hydro and Hydro Quebec, in total 1691 detailed transient stability simulation were conducted, 1158 for BC Hydro system and 533 for the Hydro Quebec system. The simulation program was equipped with the energy margin calculation module (second kick) to measure the energy margin in each run. The first set of results showed poor performance for the neural networks in assessing the dynamic security. However a number of corrective measures improved the results significantly. These corrective measures included: 1) the effectiveness of output; 2) the number of outputs; 3) the type of features (static versus dynamic); 4) the number of features; 5) system partitioning; and 6) the ratio of training samples to features. The final results obtained using the large scale systems of BC Hydro and Hydro Quebec demonstrates a good potential for neural network in dynamic security assessment contingency screening and ranking.

Computational properties of networks of synchronous groups of spiking neurons.

PubMed

Dayhoff, Judith E

2007-09-01

We demonstrate a model in which synchronously firing ensembles of neurons are networked to produce computational results. Each ensemble is a group of biological integrate-and-fire spiking neurons, with probabilistic interconnections between groups. An analogy is drawn in which each individual processing unit of an artificial neural network corresponds to a neuronal group in a biological model. The activation value of a unit in the artificial neural network corresponds to the fraction of active neurons, synchronously firing, in a biological neuronal group. Weights of the artificial neural network correspond to the product of the interconnection density between groups, the group size of the presynaptic group, and the postsynaptic potential heights in the synchronous group model. All three of these parameters can modulate connection strengths between neuronal groups in the synchronous group models. We give an example of nonlinear classification (XOR) and a function approximation example in which the capability of the artificial neural network can be captured by a neural network model with biological integrate-and-fire neurons configured as a network of synchronously firing ensembles of such neurons. We point out that the general function approximation capability proven for feedforward artificial neural networks appears to be approximated by networks of neuronal groups that fire in synchrony, where the groups comprise integrate-and-fire neurons. We discuss the advantages of this type of model for biological systems, its possible learning mechanisms, and the associated timing relationships.

Comparing success levels of different neural network structures in extracting discriminative information from the response patterns of a temperature-modulated resistive gas sensor

NASA Astrophysics Data System (ADS)

Hosseini-Golgoo, S. M.; Bozorgi, H.; Saberkari, A.

2015-06-01

Performances of three neural networks, consisting of a multi-layer perceptron, a radial basis function, and a neuro-fuzzy network with local linear model tree training algorithm, in modeling and extracting discriminative features from the response patterns of a temperature-modulated resistive gas sensor are quantitatively compared. For response pattern recording, a voltage staircase containing five steps each with a 20 s plateau is applied to the micro-heater of the sensor, when 12 different target gases, each at 11 concentration levels, are present. In each test, the hidden layer neuron weights are taken as the discriminatory feature vector of the target gas. These vectors are then mapped to a 3D feature space using linear discriminant analysis. The discriminative information content of the feature vectors are determined by the calculation of the Fisher’s discriminant ratio, affording quantitative comparison among the success rates achieved by the different neural network structures. The results demonstrate a superior discrimination ratio for features extracted from local linear neuro-fuzzy and radial-basis-function networks with recognition rates of 96.27% and 90.74%, respectively.

Intrinsic and Extrinsic Neuromodulation of Olfactory Processing.

PubMed

Lizbinski, Kristyn M; Dacks, Andrew M

2017-01-01

Neuromodulation is a ubiquitous feature of neural systems, allowing flexible, context specific control over network dynamics. Neuromodulation was first described in invertebrate motor systems and early work established a basic dichotomy for neuromodulation as having either an intrinsic origin (i.e., neurons that participate in network coding) or an extrinsic origin (i.e., neurons from independent networks). In this conceptual dichotomy, intrinsic sources of neuromodulation provide a "memory" by adjusting network dynamics based upon previous and ongoing activation of the network itself, while extrinsic neuromodulators provide the context of ongoing activity of other neural networks. Although this dichotomy has been thoroughly considered in motor systems, it has received far less attention in sensory systems. In this review, we discuss intrinsic and extrinsic modulation in the context of olfactory processing in invertebrate and vertebrate model systems. We begin by discussing presynaptic modulation of olfactory sensory neurons by local interneurons (LNs) as a mechanism for gain control based on ongoing network activation. We then discuss the cell-class specific effects of serotonergic centrifugal neurons on olfactory processing. Finally, we briefly discuss the integration of intrinsic and extrinsic neuromodulation (metamodulation) as an effective mechanism for exerting global control over olfactory network dynamics. The heterogeneous nature of neuromodulation is a recurring theme throughout this review as the effects of both intrinsic and extrinsic modulation are generally non-uniform.

Performing music can induce greater modulation of emotion-related psychophysiological responses than listening to music.

PubMed

Nakahara, Hidehiro; Furuya, Shinichi; Masuko, Tsutomu; Francis, Peter R; Kinoshita, Hiroshi

2011-09-01

The present study investigated the differential effects of music-induced emotion on heart rate (HR) and its variability (HRV) while playing music on the piano and listening to a recording of the same piece of music. Sixteen pianists were monitored during tasks involving emotional piano performance, non-emotional piano performance, emotional perception, and non-emotional perception. It was found that emotional induction during both perception and performance modulated HR and HRV, and that such modulations were significantly greater during musical performance than during perception. The results confirmed that musical performance was far more effective in modulating emotion-related autonomic nerve activity than musical perception in musicians. The findings suggest the presence of a neural network of reward-emotion-associated autonomic nerve activity for musical performance that is independent of a neural network for musical perception. Copyright © 2011 Elsevier B.V. All rights reserved.

The neural correlates of reciprocity are sensitive to prior experience of reciprocity.

PubMed

Cáceda, Ricardo; Prendes-Alvarez, Stefania; Hsu, Jung-Jiin; Tripathi, Shanti P; Kilts, Clint D; James, G Andrew

2017-08-14

Reciprocity is central to human relationships and is strongly influenced by multiple factors including the nature of social exchanges and their attendant emotional reactions. Despite recent advances in the field, the neural processes involved in this modulation of reciprocal behavior by ongoing social interaction are poorly understood. We hypothesized that activity within a discrete set of neural networks including a putative moral cognitive neural network is associated with reciprocity behavior. Nineteen healthy adults underwent functional magnetic resonance imaging scanning while playing the trustee role in the Trust Game. Personality traits and moral development were assessed. Independent component analysis was used to identify task-related functional brain networks and assess their relationship to behavior. The saliency network (insula and anterior cingulate) was positively correlated with reciprocity behavior. A consistent array of brain regions supports the engagement of emotional, self-referential and planning processes during social reciprocity behavior. Published by Elsevier B.V.

Neuromorphic photonic networks using silicon photonic weight banks.

PubMed

Tait, Alexander N; de Lima, Thomas Ferreira; Zhou, Ellen; Wu, Allie X; Nahmias, Mitchell A; Shastri, Bhavin J; Prucnal, Paul R

2017-08-07

Photonic systems for high-performance information processing have attracted renewed interest. Neuromorphic silicon photonics has the potential to integrate processing functions that vastly exceed the capabilities of electronics. We report first observations of a recurrent silicon photonic neural network, in which connections are configured by microring weight banks. A mathematical isomorphism between the silicon photonic circuit and a continuous neural network model is demonstrated through dynamical bifurcation analysis. Exploiting this isomorphism, a simulated 24-node silicon photonic neural network is programmed using "neural compiler" to solve a differential system emulation task. A 294-fold acceleration against a conventional benchmark is predicted. We also propose and derive power consumption analysis for modulator-class neurons that, as opposed to laser-class neurons, are compatible with silicon photonic platforms. At increased scale, Neuromorphic silicon photonics could access new regimes of ultrafast information processing for radio, control, and scientific computing.

Geometric Bioinspired Networks for Recognition of 2-D and 3-D Low-Level Structures and Transformations.

PubMed

Bayro-Corrochano, Eduardo; Vazquez-Santacruz, Eduardo; Moya-Sanchez, Eduardo; Castillo-Munis, Efrain

2016-10-01

This paper presents the design of radial basis function geometric bioinspired networks and their applications. Until now, the design of neural networks has been inspired by the biological models of neural networks but mostly using vector calculus and linear algebra. However, these designs have never shown the role of geometric computing. The question is how biological neural networks handle complex geometric representations involving Lie group operations like rotations. Even though the actual artificial neural networks are biologically inspired, they are just models which cannot reproduce a plausible biological process. Until now researchers have not shown how, using these models, one can incorporate them into the processing of geometric computing. Here, for the first time in the artificial neural networks domain, we address this issue by designing a kind of geometric RBF using the geometric algebra framework. As a result, using our artificial networks, we show how geometric computing can be carried out by the artificial neural networks. Such geometric neural networks have a great potential in robot vision. This is the most important aspect of this contribution to propose artificial geometric neural networks for challenging tasks in perception and action. In our experimental analysis, we show the applicability of our geometric designs, and present interesting experiments using 2-D data of real images and 3-D screw axis data. In general, our models should be used to process different types of inputs, such as visual cues, touch (texture, elasticity, temperature), taste, and sound. One important task of a perception-action system is to fuse a variety of cues coming from the environment and relate them via a sensor-motor manifold with motor modules to carry out diverse reasoned actions.

Motivation but not valence modulates neuroticism-dependent cingulate cortex and insula activity.

PubMed

Deng, Yaling; Li, Shijia; Zhou, Renlai; Walter, Martin

2018-04-01

Neuroticism has been found to specifically modulate amygdala activations during differential processing of valence and motivation while other brain networks yet are unexplored for associated effects. The main purpose of this study was to investigate whether neural mechanisms processing valence or motivation are prone to neuroticism in the salience network (SN), a network that is anchored in the anterior cingulate cortex (ACC) and the anterior insula. This study used functional magnetic resonance imaging (fMRI) and an approach/avoid emotional pictures task to investigate brain activations modulated by pictures' valence or motivational status between high and low neurotic individuals. We found that neuroticism-dependent SN and the parahippocampal-fusiform area activations were modulated by motivation but not valence. Valence in contrast interacted with neuroticism in the lateral orbitofrontal cortex. We suggested that neuroticism modulated valence and motivation processing, however, under the influence of the two distinct networks. Neuroticism modulated the motivation through the SN while it modulated the valence through the orbitofrontal networks. © 2018 Wiley Periodicals, Inc.

Neural Networks For Demodulation Of Phase-Modulated Signals

NASA Technical Reports Server (NTRS)

Altes, Richard A.

1995-01-01

Hopfield neural networks proposed for demodulating quadrature phase-shift-keyed (QPSK) signals carrying digital information. Networks solve nonlinear integral equations prior demodulation circuits cannot solve. Consists of set of N operational amplifiers connected in parallel, with weighted feedback from output terminal of each amplifier to input terminals of other amplifiers. Used to solve signal processing problems. Implemented as analog very-large-scale integrated circuit that achieves rapid convergence. Alternatively, implemented as digital simulation of such circuit. Also used to improve phase estimation performance over that of phase-locked loop.

Tweaked residual convolutional network for face alignment

NASA Astrophysics Data System (ADS)

Du, Wenchao; Li, Ke; Zhao, Qijun; Zhang, Yi; Chen, Hu

2017-08-01

We propose a novel Tweaked Residual Convolutional Network approach for face alignment with two-level convolutional networks architecture. Specifically, the first-level Tweaked Convolutional Network (TCN) module predicts the landmark quickly but accurately enough as a preliminary, by taking low-resolution version of the detected face holistically as the input. The following Residual Convolutional Networks (RCN) module progressively refines the landmark by taking as input the local patch extracted around the predicted landmark, particularly, which allows the Convolutional Neural Network (CNN) to extract local shape-indexed features to fine tune landmark position. Extensive evaluations show that the proposed Tweaked Residual Convolutional Network approach outperforms existing methods.

Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

PubMed

Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

2016-01-01

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

Structure-function relationships during segregated and integrated network states of human brain functional connectivity.

PubMed

Fukushima, Makoto; Betzel, Richard F; He, Ye; van den Heuvel, Martijn P; Zuo, Xi-Nian; Sporns, Olaf

2018-04-01

Structural white matter connections are thought to facilitate integration of neural information across functionally segregated systems. Recent studies have demonstrated that changes in the balance between segregation and integration in brain networks can be tracked by time-resolved functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data and that fluctuations between segregated and integrated network states are related to human behavior. However, how these network states relate to structural connectivity is largely unknown. To obtain a better understanding of structural substrates for these network states, we investigated how the relationship between structural connectivity, derived from diffusion tractography, and functional connectivity, as measured by rs-fMRI, changes with fluctuations between segregated and integrated states in the human brain. We found that the similarity of edge weights between structural and functional connectivity was greater in the integrated state, especially at edges connecting the default mode and the dorsal attention networks. We also demonstrated that the similarity of network partitions, evaluated between structural and functional connectivity, increased and the density of direct structural connections within modules in functional networks was elevated during the integrated state. These results suggest that, when functional connectivity exhibited an integrated network topology, structural connectivity and functional connectivity were more closely linked to each other and direct structural connections mediated a larger proportion of neural communication within functional modules. Our findings point out the possibility of significant contributions of structural connections to integrative neural processes underlying human behavior.

Intrinsic and Extrinsic Neuromodulation of Olfactory Processing

PubMed Central

Lizbinski, Kristyn M.; Dacks, Andrew M.

2018-01-01

Neuromodulation is a ubiquitous feature of neural systems, allowing flexible, context specific control over network dynamics. Neuromodulation was first described in invertebrate motor systems and early work established a basic dichotomy for neuromodulation as having either an intrinsic origin (i.e., neurons that participate in network coding) or an extrinsic origin (i.e., neurons from independent networks). In this conceptual dichotomy, intrinsic sources of neuromodulation provide a “memory” by adjusting network dynamics based upon previous and ongoing activation of the network itself, while extrinsic neuromodulators provide the context of ongoing activity of other neural networks. Although this dichotomy has been thoroughly considered in motor systems, it has received far less attention in sensory systems. In this review, we discuss intrinsic and extrinsic modulation in the context of olfactory processing in invertebrate and vertebrate model systems. We begin by discussing presynaptic modulation of olfactory sensory neurons by local interneurons (LNs) as a mechanism for gain control based on ongoing network activation. We then discuss the cell-class specific effects of serotonergic centrifugal neurons on olfactory processing. Finally, we briefly discuss the integration of intrinsic and extrinsic neuromodulation (metamodulation) as an effective mechanism for exerting global control over olfactory network dynamics. The heterogeneous nature of neuromodulation is a recurring theme throughout this review as the effects of both intrinsic and extrinsic modulation are generally non-uniform. PMID:29375314

Hypothalamic neural systems controlling the female reproductive life cycle: Gonadotropin-releasing hormone, glutamate, and GABA

PubMed Central

Maffucci, Jacqueline A.; Gore, Andrea C.

2009-01-01

The hypothalamic-pituitary-gonadal (HPG) axis undergoes a number of changes throughout the reproductive life cycle that are responsible for the development, puberty, adulthood, and senescence of reproductive systems. This natural progression is dictated by the neural network controlling the hypothalamus including the cells that synthesize and release gonadotropin-releasing hormone (GnRH) and their regulatory neurotransmitters. Glutamate and GABA are the primary excitatory and inhibitory neurotransmitters in the central nervous system, and as such contribute a great deal to modulating this axis throughout the lifetime via their actions on receptors in the hypothalamus, both directly on GnRH neurons as well as indirectly though other hypothalamic neural networks. Interactions among GnRH neurons, glutamate, and GABA, including the regulation of GnRH gene and protein expression, hormone release, and modulation by estrogen, are critical to age-appropriate changes in reproductive function. Here, we present evidence for the modulation of GnRH neurosecretory cells by the balance of glutamate and GABA in the hypothalamus, and the functional consequences of these interactions on reproductive physiology across the life cycle. PMID:19349036

Auto-programmable impulse neural circuits

NASA Technical Reports Server (NTRS)

Watula, D.; Meador, J.

1990-01-01

Impulse neural networks use pulse trains to communicate neuron activation levels. Impulse neural circuits emulate natural neurons at a more detailed level than that typically employed by contemporary neural network implementation methods. An impulse neural circuit which realizes short term memory dynamics is presented. The operation of that circuit is then characterized in terms of pulse frequency modulated signals. Both fixed and programmable synapse circuits for realizing long term memory are also described. The implementation of a simple and useful unsupervised learning law is then presented. The implementation of a differential Hebbian learning rule for a specific mean-frequency signal interpretation is shown to have a straightforward implementation using digital combinational logic with a variation of a previously developed programmable synapse circuit. This circuit is expected to be exploited for simple and straightforward implementation of future auto-adaptive neural circuits.

Dynamic decomposition of spatiotemporal neural signals

PubMed Central

2017-01-01

Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals. PMID:28558039

The effects of dynamical synapses on firing rate activity: a spiking neural network model.

PubMed

Khalil, Radwa; Moftah, Marie Z; Moustafa, Ahmed A

2017-11-01

Accumulating evidence relates the fine-tuning of synaptic maturation and regulation of neural network activity to several key factors, including GABA A signaling and a lateral spread length between neighboring neurons (i.e., local connectivity). Furthermore, a number of studies consider short-term synaptic plasticity (STP) as an essential element in the instant modification of synaptic efficacy in the neuronal network and in modulating responses to sustained ranges of external Poisson input frequency (IF). Nevertheless, evaluating the firing activity in response to the dynamical interaction between STP (triggered by ranges of IF) and these key parameters in vitro remains elusive. Therefore, we designed a spiking neural network (SNN) model in which we incorporated the following parameters: local density of arbor essences and a lateral spread length between neighboring neurons. We also created several network scenarios based on these key parameters. Then, we implemented two classes of STP: (1) short-term synaptic depression (STD) and (2) short-term synaptic facilitation (STF). Each class has two differential forms based on the parametric value of its synaptic time constant (either for depressing or facilitating synapses). Lastly, we compared the neural firing responses before and after the treatment with STP. We found that dynamical synapses (STP) have a critical differential role on evaluating and modulating the firing rate activity in each network scenario. Moreover, we investigated the impact of changing the balance between excitation (E) and inhibition (I) on stabilizing this firing activity. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Quantum generalisation of feedforward neural networks

NASA Astrophysics Data System (ADS)

Wan, Kwok Ho; Dahlsten, Oscar; Kristjánsson, Hlér; Gardner, Robert; Kim, M. S.

2017-09-01

We propose a quantum generalisation of a classical neural network. The classical neurons are firstly rendered reversible by adding ancillary bits. Then they are generalised to being quantum reversible, i.e., unitary (the classical networks we generalise are called feedforward, and have step-function activation functions). The quantum network can be trained efficiently using gradient descent on a cost function to perform quantum generalisations of classical tasks. We demonstrate numerically that it can: (i) compress quantum states onto a minimal number of qubits, creating a quantum autoencoder, and (ii) discover quantum communication protocols such as teleportation. Our general recipe is theoretical and implementation-independent. The quantum neuron module can naturally be implemented photonically.

Neural network for photoplethysmographic respiratory rate monitoring

NASA Astrophysics Data System (ADS)

Johansson, Anders

2001-10-01

The photoplethysmographic signal (PPG) includes respiratory components seen as frequency modulation of the heart rate (respiratory sinus arrhythmia, RSA), amplitude modulation of the cardiac pulse, and respiratory induced intensity variations (RIIV) in the PPG baseline. The aim of this study was to evaluate the accuracy of these components in determining respiratory rate, and to combine the components in a neural network for improved accuracy. The primary goal is to design a PPG ventilation monitoring system. PPG signals were recorded from 15 healthy subjects. From these signals, the systolic waveform, diastolic waveform, respiratory sinus arrhythmia, pulse amplitude and RIIV were extracted. By using simple algorithms, the rates of false positive and false negative detection of breaths were calculated for each of the five components in a separate analysis. Furthermore, a simple neural network (NN) was tried out in a combined pattern recognition approach. In the separate analysis, the error rates (sum of false positives and false negatives) ranged from 9.7% (pulse amplitude) to 14.5% (systolic waveform). The corresponding value of the NN analysis was 9.5-9.6%.

Reservoir characterization using core, well log, and seismic data and intelligent software

NASA Astrophysics Data System (ADS)

Soto Becerra, Rodolfo

We have developed intelligent software, Oilfield Intelligence (OI), as an engineering tool to improve the characterization of oil and gas reservoirs. OI integrates neural networks and multivariate statistical analysis. It is composed of five main subsystems: data input, preprocessing, architecture design, graphics design, and inference engine modules. More than 1,200 lines of programming code as M-files using the language MATLAB been written. The degree of success of many oil and gas drilling, completion, and production activities depends upon the accuracy of the models used in a reservoir description. Neural networks have been applied for identification of nonlinear systems in almost all scientific fields of humankind. Solving reservoir characterization problems is no exception. Neural networks have a number of attractive features that can help to extract and recognize underlying patterns, structures, and relationships among data. However, before developing a neural network model, we must solve the problem of dimensionality such as determining dominant and irrelevant variables. We can apply principal components and factor analysis to reduce the dimensionality and help the neural networks formulate more realistic models. We validated OI by obtaining confident models in three different oil field problems: (1) A neural network in-situ stress model using lithology and gamma ray logs for the Travis Peak formation of east Texas, (2) A neural network permeability model using porosity and gamma ray and a neural network pseudo-gamma ray log model using 3D seismic attributes for the reservoir VLE 196 Lamar field located in Block V of south-central Lake Maracaibo (Venezuela), and (3) Neural network primary ultimate oil recovery (PRUR), initial waterflooding ultimate oil recovery (IWUR), and infill drilling ultimate oil recovery (IDUR) models using reservoir parameters for San Andres and Clearfork carbonate formations in west Texas. In all cases, we compared the results from the neural network models with the results from regression statistical and non-parametric approach models. The results show that it is possible to obtain the highest cross-correlation coefficient between predicted and actual target variables, and the lowest average absolute errors using the integrated techniques of multivariate statistical analysis and neural networks in our intelligent software.

Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment

PubMed Central

Yamashita, Yuichi; Tani, Jun

2008-01-01

It is generally thought that skilled behavior in human beings results from a functional hierarchy of the motor control system, within which reusable motor primitives are flexibly integrated into various sensori-motor sequence patterns. The underlying neural mechanisms governing the way in which continuous sensori-motor flows are segmented into primitives and the way in which series of primitives are integrated into various behavior sequences have, however, not yet been clarified. In earlier studies, this functional hierarchy has been realized through the use of explicit hierarchical structure, with local modules representing motor primitives in the lower level and a higher module representing sequences of primitives switched via additional mechanisms such as gate-selecting. When sequences contain similarities and overlap, however, a conflict arises in such earlier models between generalization and segmentation, induced by this separated modular structure. To address this issue, we propose a different type of neural network model. The current model neither makes use of separate local modules to represent primitives nor introduces explicit hierarchical structure. Rather than forcing architectural hierarchy onto the system, functional hierarchy emerges through a form of self-organization that is based on two distinct types of neurons, each with different time properties (“multiple timescales”). Through the introduction of multiple timescales, continuous sequences of behavior are segmented into reusable primitives, and the primitives, in turn, are flexibly integrated into novel sequences. In experiments, the proposed network model, coordinating the physical body of a humanoid robot through high-dimensional sensori-motor control, also successfully situated itself within a physical environment. Our results suggest that it is not only the spatial connections between neurons but also the timescales of neural activity that act as important mechanisms leading to functional hierarchy in neural systems. PMID:18989398

Gain Modulation by an Urgency Signal Controls the Speed–Accuracy Trade-Off in a Network Model of a Cortical Decision Circuit

PubMed Central

Standage, Dominic; You, Hongzhi; Wang, Da-Hui; Dorris, Michael C.

2011-01-01

The speed–accuracy trade-off (SAT) is ubiquitous in decision tasks. While the neural mechanisms underlying decisions are generally well characterized, the application of decision-theoretic methods to the SAT has been difficult to reconcile with experimental data suggesting that decision thresholds are inflexible. Using a network model of a cortical decision circuit, we demonstrate the SAT in a manner consistent with neural and behavioral data and with mathematical models that optimize speed and accuracy with respect to one another. In simulations of a reaction time task, we modulate the gain of the network with a signal encoding the urgency to respond. As the urgency signal builds up, the network progresses through a series of processing stages supporting noise filtering, integration of evidence, amplification of integrated evidence, and choice selection. Analysis of the network's dynamics formally characterizes this progression. Slower buildup of urgency increases accuracy by slowing down the progression. Faster buildup has the opposite effect. Because the network always progresses through the same stages, decision-selective firing rates are stereotyped at decision time. PMID:21415911

Gain modulation by an urgency signal controls the speed-accuracy trade-off in a network model of a cortical decision circuit.

PubMed

Standage, Dominic; You, Hongzhi; Wang, Da-Hui; Dorris, Michael C

2011-01-01

The speed-accuracy trade-off (SAT) is ubiquitous in decision tasks. While the neural mechanisms underlying decisions are generally well characterized, the application of decision-theoretic methods to the SAT has been difficult to reconcile with experimental data suggesting that decision thresholds are inflexible. Using a network model of a cortical decision circuit, we demonstrate the SAT in a manner consistent with neural and behavioral data and with mathematical models that optimize speed and accuracy with respect to one another. In simulations of a reaction time task, we modulate the gain of the network with a signal encoding the urgency to respond. As the urgency signal builds up, the network progresses through a series of processing stages supporting noise filtering, integration of evidence, amplification of integrated evidence, and choice selection. Analysis of the network's dynamics formally characterizes this progression. Slower buildup of urgency increases accuracy by slowing down the progression. Faster buildup has the opposite effect. Because the network always progresses through the same stages, decision-selective firing rates are stereotyped at decision time.

A New Measure for Neural Compensation Is Positively Correlated With Working Memory and Gait Speed.

PubMed

Ji, Lanxin; Pearlson, Godfrey D; Hawkins, Keith A; Steffens, David C; Guo, Hua; Wang, Lihong

2018-01-01

Neuroimaging studies suggest that older adults may compensate for declines in brain function and cognition through reorganization of neural resources. A limitation of prior research is reliance on between-group comparisons of neural activation (e.g., younger vs. older), which cannot be used to assess compensatory ability quantitatively. It is also unclear about the relationship between compensatory ability with cognitive function or how other factors such as physical exercise modulates compensatory ability. Here, we proposed a data-driven method to semi-quantitatively measure neural compensation under a challenging cognitive task, and we then explored connections between neural compensation to cognitive engagement and cognitive reserve (CR). Functional and structural magnetic resonance imaging scans were acquired for 26 healthy older adults during a face-name memory task. Spatial independent component analysis (ICA) identified visual, attentional and left executive as core networks. Results show that the smaller the volumes of the gray matter (GM) structures within core networks, the more networks were needed to conduct the task ( r = -0.408, p = 0.035). Therefore, the number of task-activated networks controlling for the GM volume within core networks was defined as a measure of neural compensatory ability. We found that compensatory ability correlated with working memory performance ( r = 0.528, p = 0.035). Among subjects with good memory task performance, those with higher CR used fewer networks than subjects with lower CR. Among poor-performance subjects, those using more networks had higher CR. Our results indicated that using a high cognitive-demanding task to measure the number of activated neural networks could be a useful and sensitive measure of neural compensation in older adults.

Self-Tuning Fully-Connected PID Neural Network System for Distributed Temperature Sensing and Control of Instrument with Multi-Modules.

PubMed

Zhang, Zhen; Ma, Cheng; Zhu, Rong

2016-10-14

High integration of multi-functional instruments raises a critical issue in temperature control that is challenging due to its spatial-temporal complexity. This paper presents a multi-input multi-output (MIMO) self-tuning temperature sensing and control system for efficiently modulating the temperature environment within a multi-module instrument. The smart system ensures that the internal temperature of the instrument converges to a target without the need of a system model, thus making the control robust. The system consists of a fully-connected proportional-integral-derivative (PID) neural network (FCPIDNN) and an on-line self-tuning module. The experimental results show that the presented system can effectively control the internal temperature under various mission scenarios, in particular, it is able to self-reconfigure upon actuator failure. The system provides a new scheme for a complex and time-variant MIMO control system which can be widely applied for the distributed measurement and control of the environment in instruments, integration electronics, and house constructions.

Higher serum cholesterol is associated with intensified age-related neural network decoupling and cognitive decline in early- to mid-life.

PubMed

Spielberg, Jeffrey M; Sadeh, Naomi; Leritz, Elizabeth C; McGlinchey, Regina E; Milberg, William P; Hayes, Jasmeet P; Salat, David H

2017-06-01

Mounting evidence indicates that serum cholesterol and other risk factors for cardiovascular disease intensify normative trajectories of age-related cognitive decline. However, the neural mechanisms by which this occurs remain largely unknown. To understand the impact of cholesterol on brain networks, we applied graph theory to resting-state fMRI in a large sample of early- to mid-life Veterans (N = 206, Mean age  = 32). A network emerged (centered on the banks of the superior temporal sulcus) that evidenced age-related decoupling (i.e., decreased network connectivity with age), but only in participants with clinically-elevated total cholesterol (≥180 mg/dL). Crucially, decoupling in this network corresponded to greater day-to-day disability and mediated age-related declines in psychomotor speed. Finally, examination of network organization revealed a pattern of age-related dedifferentiation for the banks of the superior temporal sulcus, again present only with higher cholesterol. More specifically, age was related to decreasing within-module communication (indexed by Within-Module Degree Z-Score) and increasing between-module communication (indexed by Participation Coefficient), but only in participants with clinically-elevated cholesterol. Follow-up analyses indicated that all findings were driven by low-density lipoprotein (LDL) levels, rather than high-density lipoprotein (HDL) or triglycerides, which is interesting as LDL levels have been linked to increased risk for cardiovascular disease, whereas HDL levels appear inversely related to such disease. These findings provide novel insight into the deleterious effects of cholesterol on brain health and suggest that cholesterol accelerates the impact of age on neural trajectories by disrupting connectivity in circuits implicated in integrative processes and behavioral control. Hum Brain Mapp 38:3249-3261, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Neuronal and behavioural modulations by pathway-selective optogenetic stimulation of the primate oculomotor system

PubMed Central

Inoue, Ken-ichi; Takada, Masahiko; Matsumoto, Masayuki

2015-01-01

Optogenetics enables temporally and spatially precise control of neuronal activity in vivo. One of the key advantages of optogenetics is that it can be used to control the activity of targeted neural pathways that connect specific brain regions. While such pathway-selective optogenetic control is a popular tool in rodents, attempts at modulating behaviour using pathway-selective optogenetics have not yet been successful in primates. Here we develop a methodology for pathway-selective optogenetics in macaque monkeys, focusing on the pathway from the frontal eye field (FEF) to the superior colliculus (SC), part of the complex oculomotor network. We find that the optogenetic stimulation of FEF projections to the SC modulates SC neuron activity and is sufficient to evoke saccadic eye movements towards the response field corresponding to the stimulation site. Thus, our results demonstrate the feasibility of using pathway-selective optogenetics to elucidate neural network function in primates. PMID:26387804

Neuronal and behavioural modulations by pathway-selective optogenetic stimulation of the primate oculomotor system.

PubMed

Inoue, Ken-ichi; Takada, Masahiko; Matsumoto, Masayuki

2015-09-21

Optogenetics enables temporally and spatially precise control of neuronal activity in vivo. One of the key advantages of optogenetics is that it can be used to control the activity of targeted neural pathways that connect specific brain regions. While such pathway-selective optogenetic control is a popular tool in rodents, attempts at modulating behaviour using pathway-selective optogenetics have not yet been successful in primates. Here we develop a methodology for pathway-selective optogenetics in macaque monkeys, focusing on the pathway from the frontal eye field (FEF) to the superior colliculus (SC), part of the complex oculomotor network. We find that the optogenetic stimulation of FEF projections to the SC modulates SC neuron activity and is sufficient to evoke saccadic eye movements towards the response field corresponding to the stimulation site. Thus, our results demonstrate the feasibility of using pathway-selective optogenetics to elucidate neural network function in primates.

Functional model of biological neural networks.

PubMed

Lo, James Ting-Ho

2010-12-01

A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks

PubMed Central

Cavallari, Stefano; Panzeri, Stefano; Mazzoni, Alberto

2014-01-01

Models of networks of Leaky Integrate-and-Fire (LIF) neurons are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single neuron and neural population dynamics of conductance-based networks (COBNs) and current-based networks (CUBNs) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-modulated in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, the network activity of COBN showed stronger synchronization in the gamma band, and spectral information about the input higher and spread over a broader range of frequencies. These results suggest that the second order statistics of network dynamics depend strongly on the choice of synaptic model. PMID:24634645

Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks.

PubMed

Cavallari, Stefano; Panzeri, Stefano; Mazzoni, Alberto

2014-01-01

Models of networks of Leaky Integrate-and-Fire (LIF) neurons are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single neuron and neural population dynamics of conductance-based networks (COBNs) and current-based networks (CUBNs) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-modulated in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, the network activity of COBN showed stronger synchronization in the gamma band, and spectral information about the input higher and spread over a broader range of frequencies. These results suggest that the second order statistics of network dynamics depend strongly on the choice of synaptic model.

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

NASA Technical Reports Server (NTRS)

Smith, Aaron; Evans, Michael; Downey, Joseph

2017-01-01

National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

Remote Synchronization Reveals Network Symmetries and Functional Modules

NASA Astrophysics Data System (ADS)

Nicosia, Vincenzo; Valencia, Miguel; Chavez, Mario; Díaz-Guilera, Albert; Latora, Vito

2013-04-01

We study a Kuramoto model in which the oscillators are associated with the nodes of a complex network and the interactions include a phase frustration, thus preventing full synchronization. The system organizes into a regime of remote synchronization where pairs of nodes with the same network symmetry are fully synchronized, despite their distance on the graph. We provide analytical arguments to explain this result, and we show how the frustration parameter affects the distribution of phases. An application to brain networks suggests that anatomical symmetry plays a role in neural synchronization by determining correlated functional modules across distant locations.

Distinct sets of locomotor modules control the speed and modes of human locomotion

PubMed Central

Yokoyama, Hikaru; Ogawa, Tetsuya; Kawashima, Noritaka; Shinya, Masahiro; Nakazawa, Kimitaka

2016-01-01

Although recent vertebrate studies have revealed that different spinal networks are recruited in locomotor mode- and speed-dependent manners, it is unknown whether humans share similar neural mechanisms. Here, we tested whether speed- and mode-dependence in the recruitment of human locomotor networks exists or not by statistically extracting locomotor networks. From electromyographic activity during walking and running over a wide speed range, locomotor modules generating basic patterns of muscle activities were extracted using non-negative matrix factorization. The results showed that the number of modules changed depending on the modes and speeds. Different combinations of modules were extracted during walking and running, and at different speeds even during the same locomotor mode. These results strongly suggest that, in humans, different spinal locomotor networks are recruited while walking and running, and even in the same locomotor mode different networks are probably recruited at different speeds. PMID:27805015

Evidence of Rentian Scaling of Functional Modules in Diverse Biological Networks.

PubMed

How, Javier J; Navlakha, Saket

2018-06-12

Biological networks have long been known to be modular, containing sets of nodes that are highly connected internally. Less emphasis, however, has been placed on understanding how intermodule connections are distributed within a network. Here, we borrow ideas from engineered circuit design and study Rentian scaling, which states that the number of external connections between nodes in different modules is related to the number of nodes inside the modules by a power-law relationship. We tested this property in a broad class of molecular networks, including protein interaction networks for six species and gene regulatory networks for 41 human and 25 mouse cell types. Using evolutionarily defined modules corresponding to known biological processes in the cell, we found that all networks displayed Rentian scaling with a broad range of exponents. We also found evidence for Rentian scaling in functional modules in the Caenorhabditis elegans neural network, but, interestingly, not in three different social networks, suggesting that this property does not inevitably emerge. To understand how such scaling may have arisen evolutionarily, we derived a new graph model that can generate Rentian networks given a target Rent exponent and a module decomposition as inputs. Overall, our work uncovers a new principle shared by engineered circuits and biological networks.

MLP based LOGSIG transfer function for solar generation monitoring

NASA Astrophysics Data System (ADS)

Hashim, Fakroul Ridzuan; Din, Muhammad Faiz Md; Ahmad, Shahril; Arif, Farah Khairunnisa; Rizman, Zairi Ismael

2018-02-01

Solar panel is one of the renewable energy that can reduce the environmental pollution and have a wide potential of application. The exact solar prediction model will give a big impact on the management of solar power plants and the design of solar energy systems. This paper attempts to use Multilayer Perceptron (MLP) neural network based transfer function. The MLP network can be used to calculate the temperature module (TM) in Malaysia. This can be done by simulating the collected data of four weather variables which are the ambient temperature (TA), local wind speed (VW), solar radiation flux (GT) and the relative humidity (RH) as the input into the neural network. The transfer function will be applied to the 14 types of training. Finally, an equation from the best training algorithm will be deduced to calculate the temperature module based on the input of weather variables in Malaysia.

Invariant-feature-based adaptive automatic target recognition in obscured 3D point clouds

NASA Astrophysics Data System (ADS)

Khuon, Timothy; Kershner, Charles; Mattei, Enrico; Alverio, Arnel; Rand, Robert

2014-06-01

Target recognition and classification in a 3D point cloud is a non-trivial process due to the nature of the data collected from a sensor system. The signal can be corrupted by noise from the environment, electronic system, A/D converter, etc. Therefore, an adaptive system with a desired tolerance is required to perform classification and recognition optimally. The feature-based pattern recognition algorithm architecture as described below is particularly devised for solving a single-sensor classification non-parametrically. Feature set is extracted from an input point cloud, normalized, and classifier a neural network classifier. For instance, automatic target recognition in an urban area would require different feature sets from one in a dense foliage area. The figure above (see manuscript) illustrates the architecture of the feature based adaptive signature extraction of 3D point cloud including LIDAR, RADAR, and electro-optical data. This network takes a 3D cluster and classifies it into a specific class. The algorithm is a supervised and adaptive classifier with two modes: the training mode and the performing mode. For the training mode, a number of novel patterns are selected from actual or artificial data. A particular 3D cluster is input to the network as shown above for the decision class output. The network consists of three sequential functional modules. The first module is for feature extraction that extracts the input cluster into a set of singular value features or feature vector. Then the feature vector is input into the feature normalization module to normalize and balance it before being fed to the neural net classifier for the classification. The neural net can be trained by actual or artificial novel data until each trained output reaches the declared output within the defined tolerance. In case new novel data is added after the neural net has been learned, the training is then resumed until the neural net has incrementally learned with the new novel data. The associative memory capability of the neural net enables the incremental learning. The back propagation algorithm or support vector machine can be utilized for the classification and recognition.

From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation.

PubMed

Soltoggio, Andrea; Stanley, Kenneth O

2012-10-01

Synaptic plasticity is a major mechanism for adaptation, learning, and memory. Yet current models struggle to link local synaptic changes to the acquisition of behaviors. The aim of this paper is to demonstrate a computational relationship between local Hebbian plasticity and behavior learning by exploiting two traditionally unwanted features: neural noise and synaptic weight saturation. A modulation signal is employed to arbitrate the sign of plasticity: when the modulation is positive, the synaptic weights saturate to express exploitative behavior; when it is negative, the weights converge to average values, and neural noise reconfigures the network's functionality. This process is demonstrated through simulating neural dynamics in the autonomous emergence of fearful and aggressive navigating behaviors and in the solution to reward-based problems. The neural model learns, memorizes, and modifies different behaviors that lead to positive modulation in a variety of settings. The algorithm establishes a simple relationship between local plasticity and behavior learning by demonstrating the utility of noise and weight saturation. Moreover, it provides a new tool to simulate adaptive behavior, and contributes to bridging the gap between synaptic changes and behavior in neural computation. Copyright © 2012 Elsevier Ltd. All rights reserved.

Epigenetic Principles and Mechanisms Underlying Nervous System Functions in Health and Disease

PubMed Central

Mehler, Mark F.

2009-01-01

Epigenetics and epigenomic medicine encompass a new science of brain and behavior that are already providing unique insights into the mechanisms underlying brain development, evolution, neuronal and network plasticity and homeostasis, senescence, the etiology of diverse neurological diseases and neural regenerative processes. Epigenetic mechanisms include DNA methylation, histone modifications, nucleosome repositioning, higher-order chromatin remodeling, non-coding RNAs, and RNA and DNA editing. RNA is centrally involved in directing these processes, implying that the transcriptional state of the cell is the primary determinant of epigenetic memory. This transcriptional state can be modified by internal and external cues affecting gene expression and post-transcriptional processing, but also by RNA and DNA editing through activity-dependent intracellular transport and modulation of RNAs and RNA regulatory supercomplexes, and through trans-neuronal and systemic trafficking of functional RNA subclasses. These integrated processes promote dynamic reorganization of nuclear architecture and the genomic landscape to modulate functional gene and neural networks with complex temporal and spatial trajectories. Epigenetics represents the long sought after molecular interface mediating gene-environmental interactions during critical periods throughout the lifecycle. The discipline of environmental epigenomics has begun to identify combinatorial profiles of environmental stressors modulating the latency, initiation and progression of specific neurological disorders, and more selective disease biomarkers and graded molecular responses to emerging therapeutic interventions. Pharmacoepigenomic therapies will promote accelerated recovery of impaired and seemingly irrevocably lost cognitive, behavioral, sensorimotor functions through epigenetic reprogramming of endogenous regional neural stem cell fate decisions, targeted tissue remodeling and restoration of neural network integrity, plasticity and connectivity. PMID:18940229

Neural Network for Image-to-Image Control of Optical Tweezers

NASA Technical Reports Server (NTRS)

Decker, Arthur J.; Anderson, Robert C.; Weiland, Kenneth E.; Wrbanek, Susan Y.

2004-01-01

A method is discussed for using neural networks to control optical tweezers. Neural-net outputs are combined with scaling and tiling to generate 480 by 480-pixel control patterns for a spatial light modulator (SLM). The SLM can be combined in various ways with a microscope to create movable tweezers traps with controllable profiles. The neural nets are intended to respond to scattered light from carbon and silicon carbide nanotube sensors. The nanotube sensors are to be held by the traps for manipulation and calibration. Scaling and tiling allow the 100 by 100-pixel maximum resolution of the neural-net software to be applied in stages to exploit the full 480 by 480-pixel resolution of the SLM. One of these stages is intended to create sensitive null detectors for detecting variations in the scattered light from the nanotube sensors.

Neural constraints on learning.

PubMed

Sadtler, Patrick T; Quick, Kristin M; Golub, Matthew D; Chase, Steven M; Ryu, Stephen I; Tyler-Kabara, Elizabeth C; Yu, Byron M; Batista, Aaron P

2014-08-28

Learning, whether motor, sensory or cognitive, requires networks of neurons to generate new activity patterns. As some behaviours are easier to learn than others, we asked if some neural activity patterns are easier to generate than others. Here we investigate whether an existing network constrains the patterns that a subset of its neurons is capable of exhibiting, and if so, what principles define this constraint. We employed a closed-loop intracortical brain-computer interface learning paradigm in which Rhesus macaques (Macaca mulatta) controlled a computer cursor by modulating neural activity patterns in the primary motor cortex. Using the brain-computer interface paradigm, we could specify and alter how neural activity mapped to cursor velocity. At the start of each session, we observed the characteristic activity patterns of the recorded neural population. The activity of a neural population can be represented in a high-dimensional space (termed the neural space), wherein each dimension corresponds to the activity of one neuron. These characteristic activity patterns comprise a low-dimensional subspace (termed the intrinsic manifold) within the neural space. The intrinsic manifold presumably reflects constraints imposed by the underlying neural circuitry. Here we show that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the intrinsic manifold. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the intrinsic manifold. These results suggest that the existing structure of a network can shape learning. On a timescale of hours, it seems to be difficult to learn to generate neural activity patterns that are not consistent with the existing network structure. These findings offer a network-level explanation for the observation that we are more readily able to learn new skills when they are related to the skills that we already possess.

Action Video Game Experience Related to Altered Large-Scale White Matter Networks.

PubMed

Gong, Diankun; Ma, Weiyi; Gong, Jinnan; He, Hui; Dong, Li; Zhang, Dan; Li, Jianfu; Luo, Cheng; Yao, Dezhong

2017-01-01

With action video games (AVGs) becoming increasingly popular worldwide, the cognitive benefits of AVG experience have attracted continuous research attention over the past two decades. Research has repeatedly shown that AVG experience can causally enhance cognitive ability and is related to neural plasticity in gray matter and functional networks in the brain. However, the relation between AVG experience and the plasticity of white matter (WM) network still remains unclear. WM network modulates the distribution of action potentials, coordinating the communication between brain regions and acting as the framework of neural networks. And various types of cognitive deficits are usually accompanied by impairments of WM networks. Thus, understanding this relation is essential in assessing the influence of AVG experience on neural plasticity and using AVG experience as an interventional tool for impairments of WM networks. Using graph theory, this study analyzed WM networks in AVG experts and amateurs. Results showed that AVG experience is related to altered WM networks in prefrontal networks, limbic system, and sensorimotor networks, which are related to cognitive control and sensorimotor functions. These results shed new light on the influence of AVG experience on the plasticity of WM networks and suggested the clinical applicability of AVG experience.

A neural network controller for automated composite manufacturing

NASA Technical Reports Server (NTRS)

Lichtenwalner, Peter F.

1994-01-01

At McDonnell Douglas Aerospace (MDA), an artificial neural network based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns an approximate inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional plus integral (PI) controller. However after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A Cerebellar Model Articulation Controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM compatible 386 PC with an A/D board interface to the machine.

Neuroscience of drug craving for addiction medicine: From circuits to therapies.

PubMed

Ekhtiari, Hamed; Nasseri, Padideh; Yavari, Fatemeh; Mokri, Azarkhsh; Monterosso, John

2016-01-01

Drug craving is a dynamic neurocognitive emotional-motivational response to a wide range of cues, from internal to external environments and from drug-related to stressful or affective events. The subjective feeling of craving, as an appetitive or compulsive state, could be considered a part of this multidimensional process, with modules in different levels of consciousness and embodiment. The neural correspondence of this dynamic and complex phenomenon may be productively investigated in relation to regional, small-scale networks, large-scale networks, and brain states. Within cognitive neuroscience, this approach has provided a long list of neural and cognitive targets for craving modulations with different cognitive, electrical, or pharmacological interventions. There are new opportunities to integrate different approaches for carving management from environmental, behavioral, psychosocial, cognitive, and neural perspectives. By using cognitive neuroscience models that treat drug craving as a dynamic and multidimensional process, these approaches may yield more effective interventions for addiction medicine. © 2016 Elsevier B.V. All rights reserved.

Development of module for neural network identification of attacks on applications and services in multi-cloud platforms

NASA Astrophysics Data System (ADS)

Parfenov, D. I.; Bolodurina, I. P.

2018-05-01

The article presents the results of developing an approach to detecting and protecting against network attacks on the corporate infrastructure deployed on the multi-cloud platform. The proposed approach is based on the combination of two technologies: a softwareconfigurable network and virtualization of network functions. The approach for searching for anomalous traffic is to use a hybrid neural network consisting of a self-organizing Kohonen network and a multilayer perceptron. The study of the work of the prototype of the system for detecting attacks, the method of forming a learning sample, and the course of experiments are described. The study showed that using the proposed approach makes it possible to increase the effectiveness of the obfuscation of various types of attacks and at the same time does not reduce the performance of the network

[Neuronal and synaptic properties: fundamentals of network plasticity].

PubMed

Le Masson, G

2000-02-01

Neurons, within the nervous system, are organized in different neural networks through synaptic connections. Two fundamental components are dynamically interacting in these functional units. The first one are the neurons themselves, and far from being simple action potential generators, they are capable of complex electrical integrative properties due to various types, number, distribution and modulation of voltage-gated ionic channels. The second elements are the synapses where a similar complexity and plasticity is found. Identifying both cellular and synaptic intrinsic properties is necessary to understand the links between neural networks behavior and physiological function, and is a useful step towards a better control of neurological diseases.

Development of efficiency module of organization of Arctic sea cargo transportation with application of neural network technologies

NASA Astrophysics Data System (ADS)

Sobolevskaya, E. Yu; Glushkov, S. V.; Levchenko, N. G.; Orlov, A. P.

2018-05-01

The analysis of software intended for organizing and managing the processes of sea cargo transportation has been carried out. The shortcomings of information resources are presented, for the organization of work in the Arctic and Subarctic regions of the Far East: the lack of decision support systems, the lack of factor analysis to calculate the time and cost of delivery. The architecture of the module for calculating the effectiveness of the organization of sea cargo transportation has been developed. The simulation process has been considered, which is based on the neural network. The main classification factors with their weighting coefficients have been identified. The architecture of the neural network has been developed to calculate the efficiency of the organization of sea cargo transportation in Arctic conditions. The architecture of the intellectual system of organization of sea cargo transportation has been developed, taking into account the difficult navigation conditions in the Arctic. Its implementation will allow one to provide the management of the shipping company with predictive analytics; to support decision-making; to calculate the most efficient delivery route; to provide on demand online transportation forecast, to minimize the shipping cost, delays in transit, and risks to cargo safety.

Simulator for neural networks and action potentials.

PubMed

Baxter, Douglas A; Byrne, John H

2007-01-01

A key challenge for neuroinformatics is to devise methods for representing, accessing, and integrating vast amounts of diverse and complex data. A useful approach to represent and integrate complex data sets is to develop mathematical models [Arbib (The Handbook of Brain Theory and Neural Networks, pp. 741-745, 2003); Arbib and Grethe (Computing the Brain: A Guide to Neuroinformatics, 2001); Ascoli (Computational Neuroanatomy: Principles and Methods, 2002); Bower and Bolouri (Computational Modeling of Genetic and Biochemical Networks, 2001); Hines et al. (J. Comput. Neurosci. 17, 7-11, 2004); Shepherd et al. (Trends Neurosci. 21, 460-468, 1998); Sivakumaran et al. (Bioinformatics 19, 408-415, 2003); Smolen et al. (Neuron 26, 567-580, 2000); Vadigepalli et al. (OMICS 7, 235-252, 2003)]. Models of neural systems provide quantitative and modifiable frameworks for representing data and analyzing neural function. These models can be developed and solved using neurosimulators. One such neurosimulator is simulator for neural networks and action potentials (SNNAP) [Ziv (J. Neurophysiol. 71, 294-308, 1994)]. SNNAP is a versatile and user-friendly tool for developing and simulating models of neurons and neural networks. SNNAP simulates many features of neuronal function, including ionic currents and their modulation by intracellular ions and/or second messengers, and synaptic transmission and synaptic plasticity. SNNAP is written in Java and runs on most computers. Moreover, SNNAP provides a graphical user interface (GUI) and does not require programming skills. This chapter describes several capabilities of SNNAP and illustrates methods for simulating neurons and neural networks. SNNAP is available at http://snnap.uth.tmc.edu .

Density-based clustering: A 'landscape view' of multi-channel neural data for inference and dynamic complexity analysis.

PubMed

Baglietto, Gabriel; Gigante, Guido; Del Giudice, Paolo

2017-01-01

Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the 'mean-shift' algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters' centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network's state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.

Neural basis of social status hierarchy across species.

PubMed

Chiao, Joan Y

2010-12-01

Social status hierarchy is a ubiquitous principle of social organization across the animal kingdom. Recent findings in social neuroscience reveal distinct neural networks associated with the recognition and experience of social hierarchy in humans, as well as modulation of these networks by personality and culture. Additionally, allelic variation in the serotonin transporter gene is associated with prevalence of social hierarchy across species and cultures, suggesting the importance of the study of genetic factors underlying social hierarchy. Future studies are needed to determine how genetic and environmental factors shape neural systems involved in the production and maintenance of social hierarchy across ontogeny and phylogeny. Copyright © 2010 Elsevier Ltd. All rights reserved.

Synchrony between sensory and cognitive networks is associated with subclinical variation in autistic traits

PubMed Central

Young, Jacob S.; Smith, David V.; Coutlee, Christopher G.; Huettel, Scott A.

2015-01-01

Individuals with autistic spectrum disorders exhibit distinct personality traits linked to attentional, social, and affective functions, and those traits are expressed with varying levels of severity in the neurotypical and subclinical population. Variation in autistic traits has been linked to reduced functional and structural connectivity (i.e., underconnectivity, or reduced synchrony) with neural networks modulated by attentional, social, and affective functions. Yet, it remains unclear whether reduced synchrony between these neural networks contributes to autistic traits. To investigate this issue, we used functional magnetic resonance imaging to record brain activation while neurotypical participants who varied in their subclinical scores on the Autism-Spectrum Quotient (AQ) viewed alternating blocks of social and nonsocial stimuli (i.e., images of faces and of landscape scenes). We used independent component analysis (ICA) combined with a spatiotemporal regression to quantify synchrony between neural networks. Our results indicated that decreased synchrony between the executive control network (ECN) and a face-scene network (FSN) predicted higher scores on the AQ. This relationship was not explained by individual differences in head motion, preferences for faces, or personality variables related to social cognition. Our findings build on clinical reports by demonstrating that reduced synchrony between distinct neural networks contributes to a range of subclinical autistic traits. PMID:25852527

Evidence of soft bound behaviour in analogue memristive devices for neuromorphic computing.

PubMed

Frascaroli, Jacopo; Brivio, Stefano; Covi, Erika; Spiga, Sabina

2018-05-08

The development of devices that can modulate their conductance under the application of electrical stimuli constitutes a fundamental step towards the realization of synaptic connectivity in neural networks. Optimization of synaptic functionality requires the understanding of the analogue conductance update under different programming conditions. Moreover, properties of physical devices such as bounded conductance values and state-dependent modulation should be considered as they affect storage capacity and performance of the network. This work provides a study of the conductance dynamics produced by identical pulses as a function of the programming parameters in an HfO 2 memristive device. The application of a phenomenological model that considers a soft approach to the conductance boundaries allows the identification of different operation regimes and to quantify conductance modulation in the analogue region. Device non-linear switching kinetics is recognized as the physical origin of the transition between different dynamics and motivates the crucial trade-off between degree of analog modulation and memory window. Different kinetics for the processes of conductance increase and decrease account for device programming asymmetry. The identification of programming trade-off together with an evaluation of device variations provide a guideline for the optimization of the analogue programming in view of hardware implementation of neural networks.

Organization of Anti-Phase Synchronization Pattern in Neural Networks: What are the Key Factors?

PubMed Central

Li, Dong; Zhou, Changsong

2011-01-01

Anti-phase oscillation has been widely observed in cortical neural network. Elucidating the mechanism underlying the organization of anti-phase pattern is of significance for better understanding more complicated pattern formations in brain networks. In dynamical systems theory, the organization of anti-phase oscillation pattern has usually been considered to relate to time delay in coupling. This is consistent to conduction delays in real neural networks in the brain due to finite propagation velocity of action potentials. However, other structural factors in cortical neural network, such as modular organization (connection density) and the coupling types (excitatory or inhibitory), could also play an important role. In this work, we investigate the anti-phase oscillation pattern organized on a two-module network of either neuronal cell model or neural mass model, and analyze the impact of the conduction delay times, the connection densities, and coupling types. Our results show that delay times and coupling types can play key roles in this organization. The connection densities may have an influence on the stability if an anti-phase pattern exists due to the other factors. Furthermore, we show that anti-phase synchronization of slow oscillations can be achieved with small delay times if there is interaction between slow and fast oscillations. These results are significant for further understanding more realistic spatiotemporal dynamics of cortico-cortical communications. PMID:22232576

Construction of multi-agent mobile robots control system in the problem of persecution with using a modified reinforcement learning method based on neural networks

NASA Astrophysics Data System (ADS)

Patkin, M. L.; Rogachev, G. N.

2018-02-01

A method for constructing a multi-agent control system for mobile robots based on training with reinforcement using deep neural networks is considered. Synthesis of the management system is proposed to be carried out with reinforcement training and the modified Actor-Critic method, in which the Actor module is divided into Action Actor and Communication Actor in order to simultaneously manage mobile robots and communicate with partners. Communication is carried out by sending partners at each step a vector of real numbers that are added to the observation vector and affect the behaviour. Functions of Actors and Critic are approximated by deep neural networks. The Critics value function is trained by using the TD-error method and the Actor’s function by using DDPG. The Communication Actor’s neural network is trained through gradients received from partner agents. An environment in which a cooperative multi-agent interaction is present was developed, computer simulation of the application of this method in the control problem of two robots pursuing two goals was carried out.

Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network

PubMed Central

Qu, Xiaobo; He, Yifan

2018-01-01

Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods. PMID:29509666

Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network.

PubMed

Du, Xiaofeng; Qu, Xiaobo; He, Yifan; Guo, Di

2018-03-06

Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods.

Matrix stiffness modulates formation and activity of neuronal networks of controlled architectures.

PubMed

Lantoine, Joséphine; Grevesse, Thomas; Villers, Agnès; Delhaye, Geoffrey; Mestdagh, Camille; Versaevel, Marie; Mohammed, Danahe; Bruyère, Céline; Alaimo, Laura; Lacour, Stéphanie P; Ris, Laurence; Gabriele, Sylvain

2016-05-01

The ability to construct easily in vitro networks of primary neurons organized with imposed topologies is required for neural tissue engineering as well as for the development of neuronal interfaces with desirable characteristics. However, accumulating evidence suggests that the mechanical properties of the culture matrix can modulate important neuronal functions such as growth, extension, branching and activity. Here we designed robust and reproducible laminin-polylysine grid micropatterns on cell culture substrates that have similar biochemical properties but a 100-fold difference in Young's modulus to investigate the role of the matrix rigidity on the formation and activity of cortical neuronal networks. We found that cell bodies of primary cortical neurons gradually accumulate in circular islands, whereas axonal extensions spread on linear tracks to connect circular islands. Our findings indicate that migration of cortical neurons is enhanced on soft substrates, leading to a faster formation of neuronal networks. Furthermore, the pre-synaptic density was two times higher on stiff substrates and consistently the number of action potentials and miniature synaptic currents was enhanced on stiff substrates. Taken together, our results provide compelling evidence to indicate that matrix stiffness is a key parameter to modulate the growth dynamics, synaptic density and electrophysiological activity of cortical neuronal networks, thus providing useful information on scaffold design for neural tissue engineering. Copyright © 2016 Elsevier Ltd. All rights reserved.

The spatio-temporal dynamics of deviance and target detection in the passive and active auditory oddball paradigm: a sLORETA study.

PubMed

Justen, Christoph; Herbert, Cornelia

2018-04-19

Numerous studies have investigated the neural underpinnings of passive and active deviance and target detection in the well-known auditory oddball paradigm by means of event-related potentials (ERPs) or functional magnetic resonance imaging (fMRI). The present auditory oddball study investigates the spatio-temporal dynamics of passive versus active deviance and target detection by analyzing amplitude modulations of early and late ERPs while at the same time exploring the neural sources underling this modulation with standardized low-resolution brain electromagnetic tomography (sLORETA) . A 64-channel EEG was recorded from twelve healthy right-handed participants while listening to 'standards' and 'deviants' (500 vs. 1000 Hz pure tones) during a passive (block 1) and an active (block 2) listening condition. During passive listening, participants had to simply listen to the tones. During active listening they had to attend and press a key in response to the deviant tones. Passive and active listening elicited an N1 component, a mismatch negativity (MMN) as difference potential (whose amplitudes were temporally overlapping with the N1) and a P3 component. N1/MMN and P3 amplitudes were significantly more pronounced for deviants as compared to standards during both listening conditions. Active listening augmented P3 modulation to deviants significantly compared to passive listening, whereas deviance detection as indexed by N1/MMN modulation was unaffected by the task. During passive listening, sLORETA contrasts (deviants > standards) revealed significant activations in the right superior temporal gyrus (STG) and the lingual gyri bilaterally (N1/MMN) as well as in the left and right insulae (P3). During active listening, significant activations were found for the N1/MMN in the right inferior parietal lobule (IPL) and for the P3 in multiple cortical regions (e.g., precuneus). The results provide evidence for the hypothesis that passive as well as active deviance and target detection elicit cortical activations in spatially distributed brain regions and neural networks including the ventral attention network (VAN), dorsal attention network (DAN) and salience network (SN). Based on the temporal activation of the neural sources underlying ERP modulations, a neurophysiological model of passive and active deviance and target detection is proposed which can be tested in future studies.

Emotional Intent Modulates The Neural Substrates Of Creativity: An fMRI Study of Emotionally Targeted Improvisation in Jazz Musicians.

PubMed

McPherson, Malinda J; Barrett, Frederick S; Lopez-Gonzalez, Monica; Jiradejvong, Patpong; Limb, Charles J

2016-01-04

Emotion is a primary motivator for creative behaviors, yet the interaction between the neural systems involved in creativity and those involved in emotion has not been studied. In the current study, we addressed this gap by using fMRI to examine piano improvisation in response to emotional cues. We showed twelve professional jazz pianists photographs of an actress representing a positive, negative or ambiguous emotion. Using a non-ferromagnetic thirty-five key keyboard, the pianists improvised music that they felt represented the emotion expressed in the photographs. Here we show that activity in prefrontal and other brain networks involved in creativity is highly modulated by emotional context. Furthermore, emotional intent directly modulated functional connectivity of limbic and paralimbic areas such as the amygdala and insula. These findings suggest that emotion and creativity are tightly linked, and that the neural mechanisms underlying creativity may depend on emotional state.

An architectural approach to create self organizing control systems for practical autonomous robots

NASA Technical Reports Server (NTRS)

Greiner, Helen

1991-01-01

For practical industrial applications, the development of trainable robots is an important and immediate objective. Therefore, the developing of flexible intelligence directly applicable to training is emphasized. It is generally agreed upon by the AI community that the fusion of expert systems, neural networks, and conventionally programmed modules (e.g., a trajectory generator) is promising in the quest for autonomous robotic intelligence. Autonomous robot development is hindered by integration and architectural problems. Some obstacles towards the construction of more general robot control systems are as follows: (1) Growth problem; (2) Software generation; (3) Interaction with environment; (4) Reliability; and (5) Resource limitation. Neural networks can be successfully applied to some of these problems. However, current implementations of neural networks are hampered by the resource limitation problem and must be trained extensively to produce computationally accurate output. A generalization of conventional neural nets is proposed, and an architecture is offered in an attempt to address the above problems.

Neural Network Model For Fast Learning And Retrieval

NASA Astrophysics Data System (ADS)

Arsenault, Henri H.; Macukow, Bohdan

1989-05-01

An approach to learning in a multilayer neural network is presented. The proposed network learns by creating interconnections between the input layer and the intermediate layer. In one of the new storage prescriptions proposed, interconnections are excitatory (positive) only and the weights depend on the stored patterns. In the intermediate layer each mother cell is responsible for one stored pattern. Mutually interconnected neurons in the intermediate layer perform a winner-take-all operation, taking into account correlations between stored vectors. The performance of networks using this interconnection prescription is compared with two previously proposed schemes, one using inhibitory connections at the output and one using all-or-nothing interconnections. The network can be used as a content-addressable memory or as a symbolic substitution system that yields an arbitrarily defined output for any input. The training of a model to perform Boolean logical operations is also described. Computer simulations using the network as an autoassociative content-addressable memory show the model to be efficient. Content-addressable associative memories and neural logic modules can be combined to perform logic operations on highly corrupted data.

Auditory neural networks involved in attention modulation prefer biologically significant sounds and exhibit sexual dimorphism in anurans.

PubMed

Xue, Fei; Yue, Xizi; Fan, Yanzhu; Cui, Jianguo; Brauth, Steven E; Tang, Yezhong; Fang, Guangzhan

2018-03-09

Allocating attention to biologically relevant stimuli in a complex environment is critically important for survival and reproductive success. In humans, attention modulation is regulated by the frontal cortex, and is often reflected by changes in specific components of the event-related potential (ERP). Although brain networks for attention modulation have been widely studied in primates and avian species, little is known about attention modulation in amphibians. The present study aimed to investigate the attention modulation networks in an anuran species, the Emei music frog ( Babina daunchina ). Male music frogs produce advertisement calls from within underground nest burrows that modify the acoustic features of the calls, and both males and females prefer calls produced from inside burrows. We broadcast call stimuli to male and female music frogs while simultaneously recording electroencephalographic (EEG) signals from the telencephalon and mesencephalon. Granger causal connectivity analysis was used to elucidate functional brain networks within the time window of ERP components. The results show that calls produced from inside nests which are highly sexually attractive result in the strongest brain connections; both ascending and descending connections involving the left telencephalon were stronger in males while those in females were stronger with the right telencephalon. Our findings indicate that the frog brain allocates neural attention resources to highly attractive sounds within the window of early components of ERP, and that such processing is sexually dimorphic, presumably reflecting the different reproductive strategies of males and females. © 2018. Published by The Company of Biologists Ltd.

Performance evaluation of MLP and RBF feed forward neural network for the recognition of off-line handwritten characters

NASA Astrophysics Data System (ADS)

Rishi, Rahul; Choudhary, Amit; Singh, Ravinder; Dhaka, Vijaypal Singh; Ahlawat, Savita; Rao, Mukta

2010-02-01

In this paper we propose a system for classification problem of handwritten text. The system is composed of preprocessing module, supervised learning module and recognition module on a very broad level. The preprocessing module digitizes the documents and extracts features (tangent values) for each character. The radial basis function network is used in the learning and recognition modules. The objective is to analyze and improve the performance of Multi Layer Perceptron (MLP) using RBF transfer functions over Logarithmic Sigmoid Function. The results of 35 experiments indicate that the Feed Forward MLP performs accurately and exhaustively with RBF. With the change in weight update mechanism and feature-drawn preprocessing module, the proposed system is competent with good recognition show.

Modulation of attention network activation under antidepressant agents in healthy subjects.

PubMed

Graf, Heiko; Abler, Birgit; Hartmann, Antonie; Metzger, Coraline D; Walter, Martin

2013-07-01

While antidepressants are supposed to exert similar effects on mood and drive via various mechanisms of action, diverging effects are observed regarding side-effects and accordingly on neural correlates of motivation, emotion, reward and salient stimuli processing as a function of the drugs impact on neurotransmission. In the context of erotic stimulation, a unidirectional modulation of attentional functioning despite opposite effects on sexual arousal has been suggested for the selective serotonin reuptake-inhibitor (SSRI) paroxetine and the selective dopamine and noradrenaline reuptake-inhibitor (SDNRI) bupropion. To further elucidate the effects of antidepressant-related alterations of neural attention networks, we investigated 18 healthy males under subchronic administration (7 d) of paroxetine (20 mg), bupropion (150 mg) and placebo within a randomized placebo-controlled cross-over double-blind functional magnetic resonance imaging (fMRI) design during an established preceding attention task. Neuropsychological effects beyond the fMRI-paradigm were assessed by measuring alertness and divided attention. Comparing preceding attention periods of salient vs. neutral pictures, we revealed congruent effects of both drugs vs. placebo within the anterior midcingulate cortex, dorsolateral prefrontal cortex, anterior prefrontal cortex, superior temporal gyrus, anterior insula and the thalamus. Relatively decreased activation in this network was paralleled by slower reaction times in the divided attention task in both verum conditions compared to placebo. Our results suggest similar effects of antidepressant treatments on behavioural and neural attentional functioning by diverging neurochemical pathways. Concurrent alterations of brain regions within a fronto-parietal and cingulo-opercular attention network for top-down control could point to basic neural mechanisms of antidepressant action irrespective of receptor profiles.

Oxytocin and vasopressin neural networks: implications for social behavioral diversity and translational neuroscience

PubMed Central

Johnson, Zachary V.; Young, Larry J.

2017-01-01

Oxytocin- and vasopressin-related systems are present in invertebrate and vertebrate bilaterian animals, including humans, and exhibit conserved neuroanatomical and functional properties. In vertebrates, these systems innervate conserved neural networks that regulate social learning and behavior, including conspecific recognition, social attachment, and parental behavior. Individual and species-level variation in central organization of oxytocin and vasopressin systems has been linked to individual and species variation in social learning and behavior. In humans, genetic polymorphisms in the genes encoding oxytocin and vasopressin peptides and/or their respective target receptors have been associated with individual variation in social recognition, social attachment phenotypes, parental behavior, and psychiatric phenotypes such as autism. Here we describe both conserved and variable features of central oxytocin and vasopressin systems in the context of social behavioral diversity, with a particular focus on neural networks that modulate social learning, behavior, and salience of sociosensory stimuli during species-typical social contexts. PMID:28434591

Application of fuzzy neural network technologies in management of transport and logistics processes in Arctic

NASA Astrophysics Data System (ADS)

Levchenko, N. G.; Glushkov, S. V.; Sobolevskaya, E. Yu; Orlov, A. P.

2018-05-01

The method of modeling the transport and logistics process using fuzzy neural network technologies has been considered. The analysis of the implemented fuzzy neural network model of the information management system of transnational multimodal transportation of the process showed the expediency of applying this method to the management of transport and logistics processes in the Arctic and Subarctic conditions. The modular architecture of this model can be expanded by incorporating additional modules, since the working conditions in the Arctic and the subarctic themselves will present more and more realistic tasks. The architecture allows increasing the information management system, without affecting the system or the method itself. The model has a wide range of application possibilities, including: analysis of the situation and behavior of interacting elements; dynamic monitoring and diagnostics of management processes; simulation of real events and processes; prediction and prevention of critical situations.

Reduced Pain Sensation and Reduced BOLD Signal in Parietofrontal Networks during Religious Prayer

PubMed Central

Elmholdt, Else-Marie; Skewes, Joshua; Dietz, Martin; Møller, Arne; Jensen, Martin S.; Roepstorff, Andreas; Wiech, Katja; Jensen, Troels S.

2017-01-01

Previous studies suggest that religious prayer can alter the experience of pain via expectation mechanisms. While brain processes related to other types of top-down modulation of pain have been studied extensively, no research has been conducted on the potential effects of active religious coping. Here, we aimed at investigating the neural mechanisms during pain modulation by prayer and their dependency on the opioidergic system. Twenty-eight devout Protestants performed religious prayer and a secular contrast prayer during painful electrical stimulation in two fMRI sessions. Naloxone or saline was administered prior to scanning. Results show that pain intensity was reduced by 11% and pain unpleasantness by 26% during religious prayer compared to secular prayer. Expectancy predicted large amounts (70–89%) of the variance in pain intensity. Neuroimaging results revealed reduced neural activity during religious prayer in a large parietofrontal network relative to the secular condition. Naloxone had no significant effect on ratings or neural activity. Our results thus indicate that, under these conditions, pain modulation by prayer is not opioid-dependent. Further studies should employ an optimized design to explore whether reduced engagement of the frontoparietal system could indicate that prayer may attenuate pain through a reduction in processing of pain stimulus saliency and prefrontal control rather than through known descending pain inhibitory systems. PMID:28701940

Reduced Pain Sensation and Reduced BOLD Signal in Parietofrontal Networks during Religious Prayer.

PubMed

Elmholdt, Else-Marie; Skewes, Joshua; Dietz, Martin; Møller, Arne; Jensen, Martin S; Roepstorff, Andreas; Wiech, Katja; Jensen, Troels S

2017-01-01

Previous studies suggest that religious prayer can alter the experience of pain via expectation mechanisms. While brain processes related to other types of top-down modulation of pain have been studied extensively, no research has been conducted on the potential effects of active religious coping. Here, we aimed at investigating the neural mechanisms during pain modulation by prayer and their dependency on the opioidergic system. Twenty-eight devout Protestants performed religious prayer and a secular contrast prayer during painful electrical stimulation in two fMRI sessions. Naloxone or saline was administered prior to scanning. Results show that pain intensity was reduced by 11% and pain unpleasantness by 26% during religious prayer compared to secular prayer. Expectancy predicted large amounts (70-89%) of the variance in pain intensity. Neuroimaging results revealed reduced neural activity during religious prayer in a large parietofrontal network relative to the secular condition. Naloxone had no significant effect on ratings or neural activity. Our results thus indicate that, under these conditions, pain modulation by prayer is not opioid-dependent. Further studies should employ an optimized design to explore whether reduced engagement of the frontoparietal system could indicate that prayer may attenuate pain through a reduction in processing of pain stimulus saliency and prefrontal control rather than through known descending pain inhibitory systems.

Onset age of L2 acquisition influences language network in early and late Cantonese-Mandarin bilinguals.

PubMed

Liu, Xiaojin; Tu, Liu; Wang, Junjing; Jiang, Bo; Gao, Wei; Pan, Ximin; Li, Meng; Zhong, Miao; Zhu, Zhenzhen; Niu, Meiqi; Li, Yanyan; Zhao, Ling; Chen, Xiaoxi; Liu, Chang; Lu, Zhi; Huang, Ruiwang

2017-11-01

Early second language (L2) experience influences the neural organization of L2 in neuro-plastic terms. Previous studies tried to reveal these plastic effects of age of second language acquisition (AoA-L2) and proficiency-level in L2 (PL-L2) on the neural basis of language processing in bilinguals. Although different activation patterns have been observed during language processing in early and late bilinguals by task-fMRI, few studies reported the effect of AoA-L2 and high PL-L2 on language network at resting state. In this study, we acquired resting-state fMRI (R-fMRI) data from 10 Cantonese (L1)-Mandarin (L2) early bilinguals (acquired L2: 3years old) and 11 late bilinguals (acquired L2: 6years old), and analyzed their topological properties of language networks after controlling the language daily exposure and usage as well as PL in L1 and L2. We found that early bilinguals had significantly a higher clustering coefficient, global and local efficiency, but significantly lower characteristic path length compared to late bilinguals. Modular analysis indicated that compared to late bilinguals, early bilinguals showed significantly stronger intra-modular functional connectivity in the semantic and phonetic modules, stronger inter-modular functional connectivity between the semantic and phonetic modules as well as between the phonetic and syntactic modules. Differences in global and local parameters may reflect different patterns of neuro-plasticity respectively for early and late bilinguals. These results suggested that different L2 experience influences topological properties of language network, even if late bilinguals achieve high PL-L2. Our findings may provide a new perspective of neural mechanisms related to early and late bilinguals. Copyright © 2017 Elsevier Inc. All rights reserved.

A network approach for modulating memory processes via direct and indirect brain stimulation: Toward a causal approach for the neural basis of memory.

PubMed

Kim, Kamin; Ekstrom, Arne D; Tandon, Nitin

2016-10-01

Electrical stimulation of the brain is a unique tool to perturb endogenous neural signals, allowing us to evaluate the necessity of given neural processes to cognitive processing. An important issue, gaining increasing interest in the literature, is whether and how stimulation can be employed to selectively improve or disrupt declarative memory processes. Here, we provide a comprehensive review of both invasive and non-invasive stimulation studies aimed at modulating memory performance. The majority of past studies suggest that invasive stimulation of the hippocampus impairs memory performance; similarly, most non-invasive studies show that disrupting frontal or parietal regions also impairs memory performance, suggesting that these regions also play necessary roles in declarative memory. On the other hand, a handful of both invasive and non-invasive studies have also suggested modest improvements in memory performance following stimulation. These studies typically target brain regions connected to the hippocampus or other memory "hubs," which may affect endogenous activity in connected areas like the hippocampus, suggesting that to augment declarative memory, altering the broader endogenous memory network activity is critical. Together, studies reporting memory improvements/impairments are consistent with the idea that a network of distinct brain "hubs" may be crucial for successful memory encoding and retrieval rather than a single primary hub such as the hippocampus. Thus, it is important to consider neurostimulation from the network perspective, rather than from a purely localizationalist viewpoint. We conclude by proposing a novel approach to neurostimulation for declarative memory modulation that aims to facilitate interactions between multiple brain "nodes" underlying memory rather than considering individual brain regions in isolation. Copyright © 2016. Published by Elsevier Inc.

High definition-transcranial direct current stimulation changes older adults' subjective sleep and corresponding resting-state functional connectivity.

PubMed

Sheng, Jing; Xie, Chao; Fan, Dong-Qiong; Lei, Xu; Yu, Jing

2018-07-01

With advanced age, older adults show functional deterioration in sleep. Transcranial direct current stimulation (tDCS), a noninvasive brain stimulation, modulates individuals' behavioral performance in various cognitive domains. However, the modulation effect and neural mechanisms of tDCS on sleep, especially for the elderly population are not clear. Here, we aimed to investigate whether high-definition transcranial direct current stimulation (HD-tDCS) could modulate community-dwelling older adults' subjective sleep and whether these potential improvements are associated with the large-scale brain activity alterations recorded by functional magnetic resonance imaging. Thirty-one older adults were randomly allocated to the HD-tDCS group and the control group. HD-tDCS was applied for 25 min at 1.5 mA per day for two weeks. The anode electrode was placed over the left dorsolateral prefrontal cortex, surrounded by 4 cathodes at 7 cm radius. All participants completed sleep neuropsychological assessments and fMRI scans individually before and after intervention. Behaviorally, we observed a HD-tDCS-induced enhancement of older adults' sleep duration. On the aspect of the corresponding neural alterations, we observed that HD-tDCS decreased the functional connectivity between the default mode network (DMN) and subcortical network. More importantly, the decoupling connectivity of the DMN-subcortical network was correlated with the improvements of subjective sleep in the HD-tDCS group. Our findings add novel behavioral and neural evidences about tDCS-induced sleep improvement in community-dwelling older adults. With further development, tDCS may be used as an alternative treatment for sleep disorders and alleviate the dysfunction of brain networks induced by aging. Copyright © 2018 Elsevier B.V. All rights reserved.

Synaptic convergence regulates synchronization-dependent spike transfer in feedforward neural networks.

PubMed

Sailamul, Pachaya; Jang, Jaeson; Paik, Se-Bum

2017-12-01

Correlated neural activities such as synchronizations can significantly alter the characteristics of spike transfer between neural layers. However, it is not clear how this synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. To address this question, we implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uniform-Constant (UC) and Uniform-Exponential (UE) models, the connection probability density is a uniform constant within a certain range, but the connection strength is set as a constant value or an exponentially decaying function, respectively. Then we examined how the spike transfer function is modulated under these conditions, while static or synchronized input patterns were introduced to simulate different levels of feedforward spike synchronization. We observed that the synchronization-dependent modulation of the transfer function appeared noticeably different for each convergence condition. The modulation of the spike transfer function was largest in the UC model, and smallest in the UE model. Our analysis showed that this difference was induced by the different spike weight distributions that was generated from convergent synapses in each model. Our results suggest that, the structure of the feedforward convergence is a crucial factor for correlation-dependent spike control, thus must be considered important to understand the mechanism of information transfer in the brain.

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding.

PubMed

Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

2017-01-01

The recent "deep learning revolution" in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems.

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding

PubMed Central

Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

2017-01-01

The recent “deep learning revolution” in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems. PMID:28377709

Pubface: Celebrity face identification based on deep learning

NASA Astrophysics Data System (ADS)

Ouanan, H.; Ouanan, M.; Aksasse, B.

2018-05-01

In this paper, we describe a new real time application called PubFace, which allows to recognize celebrities in public spaces by employs a new pose invariant face recognition deep neural network algorithm with an extremely low error rate. To build this application, we make the following contributions: firstly, we build a novel dataset with over five million faces labelled. Secondly, we fine tuning the deep convolutional neural network (CNN) VGG-16 architecture on our new dataset that we have built. Finally, we deploy this model on the Raspberry Pi 3 model B using the OpenCv dnn module (OpenCV 3.3).

Interference Path Loss Prediction in A319/320 Airplanes Using Modulated Fuzzy Logic and Neural Networks

NASA Technical Reports Server (NTRS)

Jafri, Madiha J.; Ely, Jay J.; Vahala, Linda L.

2007-01-01

In this paper, neural network (NN) modeling is combined with fuzzy logic to estimate Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data. Combining fuzzy logic and NN modeling is shown to improve estimates of measured data over estimates obtained with NN alone. A plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft.

Modulation of cultured neural networks using neurotrophin release from hydrogel-coated microelectrode arrays

NASA Astrophysics Data System (ADS)

Jun, Sang Beom; Hynd, Matthew R.; Dowell-Mesfin, Natalie M.; Al-Kofahi, Yousef; Roysam, Badrinath; Shain, William; Kim, Sung June

2008-06-01

Polyacrylamide and poly(ethylene glycol) diacrylate hydrogels were synthesized and characterized for use as drug release and substrates for neuron cell culture. Protein release kinetics was determined by incorporating bovine serum albumin (BSA) into hydrogels during polymerization. To determine if hydrogel incorporation and release affect bioactivity, alkaline phosphatase was incorporated into hydrogels and a released enzyme activity determined using the fluorescence-based ELF-97 assay. Hydrogels were then used to deliver a brain-derived neurotrophic factor (BDNF) from hydrogels polymerized over planar microelectrode arrays (MEAs). Primary hippocampal neurons were cultured on both control and neurotrophin-containing hydrogel-coated MEAs. The effect of released BDNF on neurite length and process arborization was investigated using automated image analysis. An increased spontaneous activity as a response to the released BDNF was recorded from the neurons cultured on the top of hydrogel layers. These results demonstrate that proteins of biological interest can be incorporated into hydrogels to modulate development and function of cultured neural networks. These results also set the stage for development of hydrogel-coated neural prosthetic devices for local delivery of various biologically active molecules.

Stabilization of burn conditions in a thermonuclear reactor using artificial neural networks

NASA Astrophysics Data System (ADS)

Vitela, Javier E.; Martinell, Julio J.

1998-02-01

In this work we develop an artificial neural network (ANN) for the feedback stabilization of a thermonuclear reactor at nearly ignited burn conditions. A volume-averaged zero-dimensional nonlinear model is used to represent the time evolution of the electron density, the relative density of alpha particles and the temperature of the plasma, where a particular scaling law for the energy confinement time previously used by other authors, was adopted. The control actions include the concurrent modulation of the D-T refuelling rate, the injection of a neutral He-4 beam and an auxiliary heating power modulation, which are constrained to take values within a maximum and minimum levels. For this purpose a feedforward multilayer artificial neural network with sigmoidal activation function is trained using a back-propagation through-time technique. Numerical examples are used to illustrate the behaviour of the resulting ANN-dynamical system configuration. It is concluded that the resulting ANN can successfully stabilize the nonlinear model of the thermonuclear reactor at nearly ignited conditions for temperature and density departures significantly far from their nominal operating values. The NN-dynamical system configuration is shown to be robust with respect to the thermalization time of the alpha particles for perturbations within the region used to train the NN.

Causal relationship between effective connectivity within the default mode network and mind-wandering regulation and facilitation.

PubMed

Kajimura, Shogo; Kochiyama, Takanori; Nakai, Ryusuke; Abe, Nobuhito; Nomura, Michio

2016-06-01

Transcranial direct current stimulation (tDCS) can modulate mind wandering, which is a shift in the contents of thought away from an ongoing task and/or from events in the external environment to self-generated thoughts and feelings. Although modulation of the mind-wandering propensity is thought to be associated with neural alterations of the lateral prefrontal cortex (LPFC) and regions in the default mode network (DMN), the precise neural mechanisms remain unknown. Using functional magnetic resonance imaging (fMRI), we investigated the causal relationships among tDCS (one electrode placed over the right IPL, which is a core region of the DMN, and another placed over the left LPFC), stimulation-induced directed connection alterations within the DMN, and modulation of the mind-wandering propensity. At the behavioral level, anodal tDCS on the right IPL (with cathodal tDCS on the left LPFC) reduced mind wandering compared to the reversed stimulation. At the neural level, the anodal tDCS on the right IPL decreased the afferent connections of the posterior cingulate cortex (PCC) from the right IPL and the medial prefrontal cortex (mPFC). Furthermore, mediation analysis revealed that the changes in the connections from the right IPL and mPFC correlated with the facilitation and inhibition of mind wandering, respectively. These effects are the result of the heterogeneous function of effective connectivity: the connection from the right IPL to the PCC inhibits mind wandering, whereas the connection from the mPFC to the PCC facilitates mind wandering. The present study is the first to demonstrate the neural mechanisms underlying tDCS modulation of mind-wandering propensity. Copyright © 2016 Elsevier Inc. All rights reserved.

Structural Covariance of the Prefrontal-Amygdala Pathways Associated with Heart Rate Variability.

PubMed

Wei, Luqing; Chen, Hong; Wu, Guo-Rong

2018-01-01

The neurovisceral integration model has shown a key role of the amygdala in neural circuits underlying heart rate variability (HRV) modulation, and suggested that reciprocal connections from amygdala to brain regions centered on the central autonomic network (CAN) are associated with HRV. To provide neuroanatomical evidence for these theoretical perspectives, the current study used covariance analysis of MRI-based gray matter volume (GMV) to map structural covariance network of the amygdala, and then determined whether the interregional structural correlations related to individual differences in HRV. The results showed that covariance patterns of the amygdala encompassed large portions of cortical (e.g., prefrontal, cingulate, and insula) and subcortical (e.g., striatum, hippocampus, and midbrain) regions, lending evidence from structural covariance analysis to the notion that the amygdala was a pivotal node in neural pathways for HRV modulation. Importantly, participants with higher resting HRV showed increased covariance of amygdala to dorsal medial prefrontal cortex and anterior cingulate cortex (dmPFC/dACC) extending into adjacent medial motor regions [i.e., pre-supplementary motor area (pre-SMA)/SMA], demonstrating structural covariance of the prefrontal-amygdala pathways implicated in HRV, and also implying that resting HRV may reflect the function of neural circuits underlying cognitive regulation of emotion as well as facilitation of adaptive behaviors to emotion. Our results, thus, provide anatomical substrates for the neurovisceral integration model that resting HRV may index an integrative neural network which effectively organizes emotional, cognitive, physiological and behavioral responses in the service of goal-directed behavior and adaptability.

[Modulation of Metacognition with Decoded Neurofeedback].

PubMed

Koizumi, Ai; Cortese, Aurelio; Amano, Kaoru; Kawato, Mitsuo; Lau, Hakwan

2017-12-01

Humans often assess their confidence in their own perception, e.g., feeling "confident" or "certain" of having seen a friend, or feeling "uncertain" about whether the phone rang. The neural mechanism underlying the metacognitive function that reflects subjective perception still remains under debate. We have previously used decoded neurofeedback (DecNef) to demonstrate that manipulating the multivoxel activation patterns in the frontoparietal network modulates perceptual confidence without affecting perceptual performance. The results provided clear evidence for a dissociation between perceptual confidence and performance and suggested a distinct role of the frontoparietal network in metacognition.

Implementing Signature Neural Networks with Spiking Neurons

PubMed Central

Carrillo-Medina, José Luis; Latorre, Roberto

2016-01-01

Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm—i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data—to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. In view of these results, we argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks. PMID:28066221

Implementing Signature Neural Networks with Spiking Neurons.

PubMed

Carrillo-Medina, José Luis; Latorre, Roberto

2016-01-01

Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. In view of these results, we argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks.

Long-range functional interactions of anterior insula and medial frontal cortex are differently modulated by visuospatial and inductive reasoning tasks.

PubMed

Ebisch, Sjoerd J H; Mantini, Dante; Romanelli, Roberta; Tommasi, Marco; Perrucci, Mauro G; Romani, Gian Luca; Colom, Roberto; Saggino, Aristide

2013-09-01

The brain is organized into functionally specific networks as characterized by intrinsic functional relationships within discrete sets of brain regions. However, it is poorly understood whether such functional networks are dynamically organized according to specific task-states. The anterior insular cortex (aIC)-dorsal anterior cingulate cortex (dACC)/medial frontal cortex (mFC) network has been proposed to play a central role in human cognitive abilities. The present functional magnetic resonance imaging (fMRI) study aimed at testing whether functional interactions of the aIC-dACC/mFC network in terms of temporally correlated patterns of neural activity across brain regions are dynamically modulated by transitory, ongoing task demands. For this purpose, functional interactions of the aIC-dACC/mFC network are compared during two distinguishable fluid reasoning tasks, Visualization and Induction. The results show an increased functional coupling of bilateral aIC with visual cortices in the occipital lobe during the Visualization task, whereas coupling of mFC with right anterior frontal cortex was enhanced during the Induction task. These task-specific modulations of functional interactions likely reflect ability related neural processing. Furthermore, functional connectivity strength between right aIC and right dACC/mFC reliably predicts general task performance. The findings suggest that the analysis of long-range functional interactions may provide complementary information about brain-behavior relationships. On the basis of our results, it is proposed that the aIC-dACC/mFC network contributes to the integration of task-common and task-specific information based on its within-network as well as its between-network dynamic functional interactions. Copyright © 2013 Elsevier Inc. All rights reserved.

A generalized locomotion CPG architecture based on oscillatory building blocks.

PubMed

Yang, Zhijun; França, Felipe M G

2003-07-01

Neural oscillation is one of the most extensively investigated topics of artificial neural networks. Scientific approaches to the functionalities of both natural and artificial intelligences are strongly related to mechanisms underlying oscillatory activities. This paper concerns itself with the assumption of the existence of central pattern generators (CPGs), which are the plausible neural architectures with oscillatory capabilities, and presents a discrete and generalized approach to the functionality of locomotor CPGs of legged animals. Based on scheduling by multiple edge reversal (SMER), a primitive and deterministic distributed algorithm, it is shown how oscillatory building block (OBB) modules can be created and, hence, how OBB-based networks can be formulated as asymmetric Hopfield-like neural networks for the generation of complex coordinated rhythmic patterns observed among pairs of biological motor neurons working during different gait patterns. It is also shown that the resulting Hopfield-like network possesses the property of reproducing the whole spectrum of different gaits intrinsic to the target locomotor CPGs. Although the new approach is not restricted to the understanding of the neurolocomotor system of any particular animal, hexapodal and quadrupedal gait patterns are chosen as illustrations given the wide interest expressed by the ongoing research in the area.

Real-space mapping of topological invariants using artificial neural networks

NASA Astrophysics Data System (ADS)

Carvalho, D.; García-Martínez, N. A.; Lado, J. L.; Fernández-Rossier, J.

2018-03-01

Topological invariants allow one to characterize Hamiltonians, predicting the existence of topologically protected in-gap modes. Those invariants can be computed by tracing the evolution of the occupied wave functions under twisted boundary conditions. However, those procedures do not allow one to calculate a topological invariant by evaluating the system locally, and thus require information about the wave functions in the whole system. Here we show that artificial neural networks can be trained to identify the topological order by evaluating a local projection of the density matrix. We demonstrate this for two different models, a one-dimensional topological superconductor and a two-dimensional quantum anomalous Hall state, both with spatially modulated parameters. Our neural network correctly identifies the different topological domains in real space, predicting the location of in-gap states. By combining a neural network with a calculation of the electronic states that uses the kernel polynomial method, we show that the local evaluation of the invariant can be carried out by evaluating a local quantity, in particular for systems without translational symmetry consisting of tens of thousands of atoms. Our results show that supervised learning is an efficient methodology to characterize the local topology of a system.

A neural circuitry that emphasizes spinal feedback generates diverse behaviours of human locomotion

PubMed Central

Song, Seungmoon; Geyer, Hartmut

2015-01-01

Neural networks along the spinal cord contribute substantially to generating locomotion behaviours in humans and other legged animals. However, the neural circuitry involved in this spinal control remains unclear. We here propose a specific circuitry that emphasizes feedback integration over central pattern generation. The circuitry is based on neurophysiologically plausible muscle-reflex pathways that are organized in 10 spinal modules realizing limb functions essential to legged systems in stance and swing. These modules are combined with a supraspinal control layer that adjusts the desired foot placements and selects the leg that is to transition into swing control during double support. Using physics-based simulation, we test the proposed circuitry in a neuromuscular human model that includes neural transmission delays, musculotendon dynamics and compliant foot–ground contacts. We find that the control network is sufficient to compose steady and transitional 3-D locomotion behaviours including walking and running, acceleration and deceleration, slope and stair negotiation, turning, and deliberate obstacle avoidance. The results suggest feedback integration to be functionally more important than central pattern generation in human locomotion across behaviours. In addition, the proposed control architecture may serve as a guide in the search for the neurophysiological origin and circuitry of spinal control in humans. PMID:25920414

Noise influence on spike activation in a Hindmarsh-Rose small-world neural network

NASA Astrophysics Data System (ADS)

Zhe, Sun; Micheletto, Ruggero

2016-07-01

We studied the role of noise in neural networks, especially focusing on its relation to the propagation of spike activity in a small sized system. We set up a source of information using a single neuron that is constantly spiking. This element called initiator x o feeds spikes to the rest of the network that is initially quiescent and subsequently reacts with vigorous spiking after a transitional period of time. We found that noise quickly suppresses the initiator’s influence and favors spontaneous spike activity and, using a decibel representation of noise intensity, we established a linear relationship between noise amplitude and the interval from the initiator’s first spike and the rest of the network activation. We studied the same process with networks of different sizes (number of neurons) and found that the initiator x o has a measurable influence on small networks, but as the network grows in size, spontaneous spiking emerges disrupting its effects on networks of more than about N = 100 neurons. This suggests that the mechanism of internal noise generation allows information transmission within a small neural neighborhood, but decays for bigger network domains. We also analyzed the Fourier spectrum of the whole network membrane potential and verified that noise provokes the reduction of main θ and α peaks before transitioning into chaotic spiking. However, network size does not reproduce a similar phenomena; instead we recorded a reduction in peaks’ amplitude, a better sharpness and definition of Fourier peaks, but not the evident degeneration to chaos observed with increasing external noise. This work aims to contribute to the understanding of the fundamental mechanisms of propagation of spontaneous spiking in neural networks and gives a quantitative assessment of how noise can be used to control and modulate this phenomenon in Hindmarsh-Rose (H-R) neural networks.

Trade-off between Multiple Constraints Enables Simultaneous Formation of Modules and Hubs in Neural Systems

PubMed Central

Chen, Yuhan; Wang, Shengjun; Hilgetag, Claus C.; Zhou, Changsong

2013-01-01

The formation of the complex network architecture of neural systems is subject to multiple structural and functional constraints. Two obvious but apparently contradictory constraints are low wiring cost and high processing efficiency, characterized by short overall wiring length and a small average number of processing steps, respectively. Growing evidence shows that neural networks are results from a trade-off between physical cost and functional value of the topology. However, the relationship between these competing constraints and complex topology is not well understood quantitatively. We explored this relationship systematically by reconstructing two known neural networks, Macaque cortical connectivity and C. elegans neuronal connections, from combinatory optimization of wiring cost and processing efficiency constraints, using a control parameter , and comparing the reconstructed networks to the real networks. We found that in both neural systems, the reconstructed networks derived from the two constraints can reveal some important relations between the spatial layout of nodes and the topological connectivity, and match several properties of the real networks. The reconstructed and real networks had a similar modular organization in a broad range of , resulting from spatial clustering of network nodes. Hubs emerged due to the competition of the two constraints, and their positions were close to, and partly coincided, with the real hubs in a range of values. The degree of nodes was correlated with the density of nodes in their spatial neighborhood in both reconstructed and real networks. Generally, the rebuilt network matched a significant portion of real links, especially short-distant ones. These findings provide clear evidence to support the hypothesis of trade-off between multiple constraints on brain networks. The two constraints of wiring cost and processing efficiency, however, cannot explain all salient features in the real networks. The discrepancy suggests that there are further relevant factors that are not yet captured here. PMID:23505352

Dynamic Photorefractive Memory and its Application for Opto-Electronic Neural Networks.

NASA Astrophysics Data System (ADS)

Sasaki, Hironori

This dissertation describes the analysis of the photorefractive crystal dynamics and its application for opto-electronic neural network systems. The realization of the dynamic photorefractive memory is investigated in terms of the following aspects: fast memory update, uniform grating multiplexing schedules and the prevention of the partial erasure of existing gratings. The fast memory update is realized by the selective erasure process that superimposes a new grating on the original one with an appropriate phase shift. The dynamics of the selective erasure process is analyzed using the first-order photorefractive material equations and experimentally confirmed. The effects of beam coupling and fringe bending on the selective erasure dynamics are also analyzed by numerically solving a combination of coupled wave equations and the photorefractive material equation. Incremental recording technique is proposed as a uniform grating multiplexing schedule and compared with the conventional scheduled recording technique in terms of phase distribution in the presence of an external dc electric field, as well as the image gray scale dependence. The theoretical analysis and experimental results proved the superiority of the incremental recording technique over the scheduled recording. Novel recirculating information memory architecture is proposed and experimentally demonstrated to prevent partial degradation of the existing gratings by accessing the memory. Gratings are circulated through a memory feed back loop based on the incremental recording dynamics and demonstrate robust read/write/erase capabilities. The dynamic photorefractive memory is applied to opto-electronic neural network systems. Module architecture based on the page-oriented dynamic photorefractive memory is proposed. This module architecture can implement two complementary interconnection organizations, fan-in and fan-out. The module system scalability and the learning capabilities are theoretically investigated using the photorefractive dynamics described in previous chapters of the dissertation. The implementation of the feed-forward image compression network with 900 input and 9 output neurons with 6-bit interconnection accuracy is experimentally demonstrated. Learning of the Perceptron network that determines sex based on input face images of 900 pixels is also successfully demonstrated.

Elimination of spiral waves in a locally connected chaotic neural network by a dynamic phase space constraint.

PubMed

Li, Yang; Oku, Makito; He, Guoguang; Aihara, Kazuyuki

2017-04-01

In this study, a method is proposed that eliminates spiral waves in a locally connected chaotic neural network (CNN) under some simplified conditions, using a dynamic phase space constraint (DPSC) as a control method. In this method, a control signal is constructed from the feedback internal states of the neurons to detect phase singularities based on their amplitude reduction, before modulating a threshold value to truncate the refractory internal states of the neurons and terminate the spirals. Simulations showed that with appropriate parameter settings, the network was directed from a spiral wave state into either a plane wave (PW) state or a synchronized oscillation (SO) state, where the control vanished automatically and left the original CNN model unaltered. Each type of state had a characteristic oscillation frequency, where spiral wave states had the highest, and the intra-control dynamics was dominated by low-frequency components, thereby indicating slow adjustments to the state variables. In addition, the PW-inducing and SO-inducing control processes were distinct, where the former generally had longer durations but smaller average proportions of affected neurons in the network. Furthermore, variations in the control parameter allowed partial selectivity of the control results, which were accompanied by modulation of the control processes. The results of this study broaden the applicability of DPSC to chaos control and they may also facilitate the utilization of locally connected CNNs in memory retrieval and the exploration of traveling wave dynamics in biological neural networks. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hybrid discrete-time neural networks.

PubMed

Cao, Hongjun; Ibarz, Borja

2010-11-13

Hybrid dynamical systems combine evolution equations with state transitions. When the evolution equations are discrete-time (also called map-based), the result is a hybrid discrete-time system. A class of biological neural network models that has recently received some attention falls within this category: map-based neuron models connected by means of fast threshold modulation (FTM). FTM is a connection scheme that aims to mimic the switching dynamics of a neuron subject to synaptic inputs. The dynamic equations of the neuron adopt different forms according to the state (either firing or not firing) and type (excitatory or inhibitory) of their presynaptic neighbours. Therefore, the mathematical model of one such network is a combination of discrete-time evolution equations with transitions between states, constituting a hybrid discrete-time (map-based) neural network. In this paper, we review previous work within the context of these models, exemplifying useful techniques to analyse them. Typical map-based neuron models are low-dimensional and amenable to phase-plane analysis. In bursting models, fast-slow decomposition can be used to reduce dimensionality further, so that the dynamics of a pair of connected neurons can be easily understood. We also discuss a model that includes electrical synapses in addition to chemical synapses with FTM. Furthermore, we describe how master stability functions can predict the stability of synchronized states in these networks. The main results are extended to larger map-based neural networks.

Modularity Induced Gating and Delays in Neuronal Networks

PubMed Central

Shein-Idelson, Mark; Cohen, Gilad; Hanein, Yael

2016-01-01

Neural networks, despite their highly interconnected nature, exhibit distinctly localized and gated activation. Modularity, a distinctive feature of neural networks, has been recently proposed as an important parameter determining the manner by which networks support activity propagation. Here we use an engineered biological model, consisting of engineered rat cortical neurons, to study the role of modular topology in gating the activity between cell populations. We show that pairs of connected modules support conditional propagation (transmitting stronger bursts with higher probability), long delays and propagation asymmetry. Moreover, large modular networks manifest diverse patterns of both local and global activation. Blocking inhibition decreased activity diversity and replaced it with highly consistent transmission patterns. By independently controlling modularity and disinhibition, experimentally and in a model, we pose that modular topology is an important parameter affecting activation localization and is instrumental for population-level gating by disinhibition. PMID:27104350

Adaptive Plasticity in the Healthy Language Network: Implications for Language Recovery after Stroke

PubMed Central

2016-01-01

Across the last three decades, the application of noninvasive brain stimulation (NIBS) has substantially increased the current knowledge of the brain's potential to undergo rapid short-term reorganization on the systems level. A large number of studies applied transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) in the healthy brain to probe the functional relevance and interaction of specific areas for different cognitive processes. NIBS is also increasingly being used to induce adaptive plasticity in motor and cognitive networks and shape cognitive functions. Recently, NIBS has been combined with electrophysiological techniques to modulate neural oscillations of specific cortical networks. In this review, we will discuss recent advances in the use of NIBS to modulate neural activity and effective connectivity in the healthy language network, with a special focus on the combination of NIBS and neuroimaging or electrophysiological approaches. Moreover, we outline how these results can be transferred to the lesioned brain to unravel the dynamics of reorganization processes in poststroke aphasia. We conclude with a critical discussion on the potential of NIBS to facilitate language recovery after stroke and propose a phase-specific model for the application of NIBS in language rehabilitation. PMID:27830094

A neural network based artificial vision system for licence plate recognition.

PubMed

Draghici, S

1997-02-01

This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solutions used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%.

Emotional Intent Modulates The Neural Substrates Of Creativity: An fMRI Study of Emotionally Targeted Improvisation in Jazz Musicians

PubMed Central

McPherson, Malinda J.; Barrett, Frederick S.; Lopez-Gonzalez, Monica; Jiradejvong, Patpong; Limb, Charles J.

2016-01-01

Emotion is a primary motivator for creative behaviors, yet the interaction between the neural systems involved in creativity and those involved in emotion has not been studied. In the current study, we addressed this gap by using fMRI to examine piano improvisation in response to emotional cues. We showed twelve professional jazz pianists photographs of an actress representing a positive, negative or ambiguous emotion. Using a non-ferromagnetic thirty-five key keyboard, the pianists improvised music that they felt represented the emotion expressed in the photographs. Here we show that activity in prefrontal and other brain networks involved in creativity is highly modulated by emotional context. Furthermore, emotional intent directly modulated functional connectivity of limbic and paralimbic areas such as the amygdala and insula. These findings suggest that emotion and creativity are tightly linked, and that the neural mechanisms underlying creativity may depend on emotional state. PMID:26725925

Effect of Heterogeneity on Decorrelation Mechanisms in Spiking Neural Networks: A Neuromorphic-Hardware Study

NASA Astrophysics Data System (ADS)

Pfeil, Thomas; Jordan, Jakob; Tetzlaff, Tom; Grübl, Andreas; Schemmel, Johannes; Diesmann, Markus; Meier, Karlheinz

2016-04-01

High-level brain function, such as memory, classification, or reasoning, can be realized by means of recurrent networks of simplified model neurons. Analog neuromorphic hardware constitutes a fast and energy-efficient substrate for the implementation of such neural computing architectures in technical applications and neuroscientific research. The functional performance of neural networks is often critically dependent on the level of correlations in the neural activity. In finite networks, correlations are typically inevitable due to shared presynaptic input. Recent theoretical studies have shown that inhibitory feedback, abundant in biological neural networks, can actively suppress these shared-input correlations and thereby enable neurons to fire nearly independently. For networks of spiking neurons, the decorrelating effect of inhibitory feedback has so far been explicitly demonstrated only for homogeneous networks of neurons with linear subthreshold dynamics. Theory, however, suggests that the effect is a general phenomenon, present in any system with sufficient inhibitory feedback, irrespective of the details of the network structure or the neuronal and synaptic properties. Here, we investigate the effect of network heterogeneity on correlations in sparse, random networks of inhibitory neurons with nonlinear, conductance-based synapses. Emulations of these networks on the analog neuromorphic-hardware system Spikey allow us to test the efficiency of decorrelation by inhibitory feedback in the presence of hardware-specific heterogeneities. The configurability of the hardware substrate enables us to modulate the extent of heterogeneity in a systematic manner. We selectively study the effects of shared input and recurrent connections on correlations in membrane potentials and spike trains. Our results confirm that shared-input correlations are actively suppressed by inhibitory feedback also in highly heterogeneous networks exhibiting broad, heavy-tailed firing-rate distributions. In line with former studies, cell heterogeneities reduce shared-input correlations. Overall, however, correlations in the recurrent system can increase with the level of heterogeneity as a consequence of diminished effective negative feedback.

An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture.

PubMed

Doulamis, A; Doulamis, N; Ntalianis, K; Kollias, S

2003-01-01

In this paper, an unsupervised video object (VO) segmentation and tracking algorithm is proposed based on an adaptable neural-network architecture. The proposed scheme comprises: 1) a VO tracking module and 2) an initial VO estimation module. Object tracking is handled as a classification problem and implemented through an adaptive network classifier, which provides better results compared to conventional motion-based tracking algorithms. Network adaptation is accomplished through an efficient and cost effective weight updating algorithm, providing a minimum degradation of the previous network knowledge and taking into account the current content conditions. A retraining set is constructed and used for this purpose based on initial VO estimation results. Two different scenarios are investigated. The first concerns extraction of human entities in video conferencing applications, while the second exploits depth information to identify generic VOs in stereoscopic video sequences. Human face/ body detection based on Gaussian distributions is accomplished in the first scenario, while segmentation fusion is obtained using color and depth information in the second scenario. A decision mechanism is also incorporated to detect time instances for weight updating. Experimental results and comparisons indicate the good performance of the proposed scheme even in sequences with complicated content (object bending, occlusion).

Traffic sign classification with dataset augmentation and convolutional neural network

NASA Astrophysics Data System (ADS)

Tang, Qing; Kurnianggoro, Laksono; Jo, Kang-Hyun

2018-04-01

This paper presents a method for traffic sign classification using a convolutional neural network (CNN). In this method, firstly we transfer a color image into grayscale, and then normalize it in the range (-1,1) as the preprocessing step. To increase robustness of classification model, we apply a dataset augmentation algorithm and create new images to train the model. To avoid overfitting, we utilize a dropout module before the last fully connection layer. To assess the performance of the proposed method, the German traffic sign recognition benchmark (GTSRB) dataset is utilized. Experimental results show that the method is effective in classifying traffic signs.

FMRI connectivity analysis of acupuncture effects on an amygdala-associated brain network

PubMed Central

Qin, Wei; Tian, Jie; Bai, Lijun; Pan, Xiaohong; Yang, Lin; Chen, Peng; Dai, Jianping; Ai, Lin; Zhao, Baixiao; Gong, Qiyong; Wang, Wei; von Deneen, Karen M; Liu, Yijun

2008-01-01

Background Recently, increasing evidence has indicated that the primary acupuncture effects are mediated by the central nervous system. However, specific brain networks underpinning these effects remain unclear. Results In the present study using fMRI, we employed a within-condition interregional covariance analysis method to investigate functional connectivity of brain networks involved in acupuncture. The fMRI experiment was performed before, during and after acupuncture manipulations on healthy volunteers at an acupuncture point, which was previously implicated in a neural pathway for pain modulation. We first identified significant fMRI signal changes during acupuncture stimulation in the left amygdala, which was subsequently selected as a functional reference for connectivity analyses. Our results have demonstrated that there is a brain network associated with the amygdala during a resting condition. This network encompasses the brain structures that are implicated in both pain sensation and pain modulation. We also found that such a pain-related network could be modulated by both verum acupuncture and sham acupuncture. Furthermore, compared with a sham acupuncture, the verum acupuncture induced a higher level of correlations among the amygdala-associated network. Conclusion Our findings indicate that acupuncture may change this amygdala-specific brain network into a functional state that underlies pain perception and pain modulation. PMID:19014532

Adult Neurogenesis and Neurodegenerative Diseases: A Systems Biology Perspective

PubMed Central

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

Exploring the limits of learning: Segregation of information integration and response selection is required for learning a serial reversal task

PubMed Central

Zanutto, B. Silvano

2017-01-01

Animals are proposed to learn the latent rules governing their environment in order to maximize their chances of survival. However, rules may change without notice, forcing animals to keep a memory of which one is currently at work. Rule switching can lead to situations in which the same stimulus/response pairing is positively and negatively rewarded in the long run, depending on variables that are not accessible to the animal. This fact raises questions on how neural systems are capable of reinforcement learning in environments where the reinforcement is inconsistent. Here we address this issue by asking about which aspects of connectivity, neural excitability and synaptic plasticity are key for a very general, stochastic spiking neural network model to solve a task in which rules change without being cued, taking the serial reversal task (SRT) as paradigm. Contrary to what could be expected, we found strong limitations for biologically plausible networks to solve the SRT. Especially, we proved that no network of neurons can learn a SRT if it is a single neural population that integrates stimuli information and at the same time is responsible of choosing the behavioural response. This limitation is independent of the number of neurons, neuronal dynamics or plasticity rules, and arises from the fact that plasticity is locally computed at each synapse, and that synaptic changes and neuronal activity are mutually dependent processes. We propose and characterize a spiking neural network model that solves the SRT, which relies on separating the functions of stimuli integration and response selection. The model suggests that experimental efforts to understand neural function should focus on the characterization of neural circuits according to their connectivity, neural dynamics, and the degree of modulation of synaptic plasticity with reward. PMID:29077735

Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification.

PubMed

Rueckauer, Bodo; Lungu, Iulia-Alexandra; Hu, Yuhuang; Pfeiffer, Michael; Liu, Shih-Chii

2017-01-01

Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference because the neurons in the networks are sparsely activated and computations are event-driven. Previous work showed that simple continuous-valued deep Convolutional Neural Networks (CNNs) can be converted into accurate spiking equivalents. These networks did not include certain common operations such as max-pooling, softmax, batch-normalization and Inception-modules. This paper presents spiking equivalents of these operations therefore allowing conversion of nearly arbitrary CNN architectures. We show conversion of popular CNN architectures, including VGG-16 and Inception-v3, into SNNs that produce the best results reported to date on MNIST, CIFAR-10 and the challenging ImageNet dataset. SNNs can trade off classification error rate against the number of available operations whereas deep continuous-valued neural networks require a fixed number of operations to achieve their classification error rate. From the examples of LeNet for MNIST and BinaryNet for CIFAR-10, we show that with an increase in error rate of a few percentage points, the SNNs can achieve more than 2x reductions in operations compared to the original CNNs. This highlights the potential of SNNs in particular when deployed on power-efficient neuromorphic spiking neuron chips, for use in embedded applications.

Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification

PubMed Central

Rueckauer, Bodo; Lungu, Iulia-Alexandra; Hu, Yuhuang; Pfeiffer, Michael; Liu, Shih-Chii

2017-01-01

Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference because the neurons in the networks are sparsely activated and computations are event-driven. Previous work showed that simple continuous-valued deep Convolutional Neural Networks (CNNs) can be converted into accurate spiking equivalents. These networks did not include certain common operations such as max-pooling, softmax, batch-normalization and Inception-modules. This paper presents spiking equivalents of these operations therefore allowing conversion of nearly arbitrary CNN architectures. We show conversion of popular CNN architectures, including VGG-16 and Inception-v3, into SNNs that produce the best results reported to date on MNIST, CIFAR-10 and the challenging ImageNet dataset. SNNs can trade off classification error rate against the number of available operations whereas deep continuous-valued neural networks require a fixed number of operations to achieve their classification error rate. From the examples of LeNet for MNIST and BinaryNet for CIFAR-10, we show that with an increase in error rate of a few percentage points, the SNNs can achieve more than 2x reductions in operations compared to the original CNNs. This highlights the potential of SNNs in particular when deployed on power-efficient neuromorphic spiking neuron chips, for use in embedded applications. PMID:29375284

Design and implementation of a random neural network routing engine.

PubMed

Kocak, T; Seeber, J; Terzioglu, H

2003-01-01

Random neural network (RNN) is an analytically tractable spiked neural network model that has been implemented in software for a wide range of applications for over a decade. This paper presents the hardware implementation of the RNN model. Recently, cognitive packet networks (CPN) is proposed as an alternative packet network architecture where there is no routing table, instead the RNN based reinforcement learning is used to route packets. Particularly, we describe implementation details for the RNN based routing engine of a CPN network processor chip: the smart packet processor (SPP). The SPP is a dual port device that stores, modifies, and interprets the defining characteristics of multiple RNN models. In addition to hardware design improvements over the software implementation such as the dual access memory, output calculation step, and reduced output calculation module, this paper introduces a major modification to the reinforcement learning algorithm used in the original CPN specification such that the number of weight terms are reduced from 2n/sup 2/ to 2n. This not only yields significant memory savings, but it also simplifies the calculations for the steady state probabilities (neuron outputs in RNN). Simulations have been conducted to confirm the proper functionality for the isolated SPP design as well as for the multiple SPP's in a networked environment.

Reversible large–scale modification of cortical networks during neuroprosthetic control

PubMed Central

Ganguly, Karunesh; Wallis, Jonathan D.

2012-01-01

Brain-Machine Interfaces (BMI) provide a framework to study cortical dynamics and the neural correlates of learning. Neuroprosthetic control has been associated with tuning changes in specific neurons directly projecting to the BMI (hereafter ‘direct neurons’). However, little is known about the larger network dynamics. By monitoring ensembles of neurons that were either causally linked to BMI control or indirectly involved, here we show that proficient neuroprosthetic control is associated with large-scale modifications to the cortical network in macaque monkeys. Specifically, there were changes in the preferred direction of both direct and indirect neurons. Interestingly, with learning, there was a relative decrease in the net modulation of indirect neural activity in comparison to the direct activity. These widespread differential changes in the direct and indirect population activity were remarkably stable from one day to the next and readily coexisted with the long-standing cortical network for upper limb control. Thus, the process of learning BMI control is associated with differential modification of neural populations based on their specific relation to movement control. PMID:21499255

Reversible large-scale modification of cortical networks during neuroprosthetic control.

PubMed

Ganguly, Karunesh; Dimitrov, Dragan F; Wallis, Jonathan D; Carmena, Jose M

2011-05-01

Brain-machine interfaces (BMIs) provide a framework for studying cortical dynamics and the neural correlates of learning. Neuroprosthetic control has been associated with tuning changes in specific neurons directly projecting to the BMI (hereafter referred to as direct neurons). However, little is known about the larger network dynamics. By monitoring ensembles of neurons that were either causally linked to BMI control or indirectly involved, we found that proficient neuroprosthetic control is associated with large-scale modifications to the cortical network in macaque monkeys. Specifically, there were changes in the preferred direction of both direct and indirect neurons. Notably, with learning, there was a relative decrease in the net modulation of indirect neural activity in comparison with direct activity. These widespread differential changes in the direct and indirect population activity were markedly stable from one day to the next and readily coexisted with the long-standing cortical network for upper limb control. Thus, the process of learning BMI control is associated with differential modification of neural populations based on their specific relation to movement control.

Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis

PubMed Central

Dordek, Yedidyah; Soudry, Daniel; Meir, Ron; Derdikman, Dori

2016-01-01

Many recent models study the downstream projection from grid cells to place cells, while recent data have pointed out the importance of the feedback projection. We thus asked how grid cells are affected by the nature of the input from the place cells. We propose a single-layer neural network with feedforward weights connecting place-like input cells to grid cell outputs. Place-to-grid weights are learned via a generalized Hebbian rule. The architecture of this network highly resembles neural networks used to perform Principal Component Analysis (PCA). Both numerical results and analytic considerations indicate that if the components of the feedforward neural network are non-negative, the output converges to a hexagonal lattice. Without the non-negativity constraint, the output converges to a square lattice. Consistent with experiments, grid spacing ratio between the first two consecutive modules is −1.4. Our results express a possible linkage between place cell to grid cell interactions and PCA. DOI: http://dx.doi.org/10.7554/eLife.10094.001 PMID:26952211

Structural Covariance of the Prefrontal-Amygdala Pathways Associated with Heart Rate Variability

PubMed Central

Wei, Luqing; Chen, Hong; Wu, Guo-Rong

2018-01-01

The neurovisceral integration model has shown a key role of the amygdala in neural circuits underlying heart rate variability (HRV) modulation, and suggested that reciprocal connections from amygdala to brain regions centered on the central autonomic network (CAN) are associated with HRV. To provide neuroanatomical evidence for these theoretical perspectives, the current study used covariance analysis of MRI-based gray matter volume (GMV) to map structural covariance network of the amygdala, and then determined whether the interregional structural correlations related to individual differences in HRV. The results showed that covariance patterns of the amygdala encompassed large portions of cortical (e.g., prefrontal, cingulate, and insula) and subcortical (e.g., striatum, hippocampus, and midbrain) regions, lending evidence from structural covariance analysis to the notion that the amygdala was a pivotal node in neural pathways for HRV modulation. Importantly, participants with higher resting HRV showed increased covariance of amygdala to dorsal medial prefrontal cortex and anterior cingulate cortex (dmPFC/dACC) extending into adjacent medial motor regions [i.e., pre-supplementary motor area (pre-SMA)/SMA], demonstrating structural covariance of the prefrontal-amygdala pathways implicated in HRV, and also implying that resting HRV may reflect the function of neural circuits underlying cognitive regulation of emotion as well as facilitation of adaptive behaviors to emotion. Our results, thus, provide anatomical substrates for the neurovisceral integration model that resting HRV may index an integrative neural network which effectively organizes emotional, cognitive, physiological and behavioral responses in the service of goal-directed behavior and adaptability. PMID:29545744

Emotional reactivity to threat modulates activity in mentalizing network during aggression.

PubMed

Beyer, Frederike; Münte, Thomas F; Erdmann, Christian; Krämer, Ulrike M

2014-10-01

Aggression is a common response to provocation, albeit with considerable interindividual differences. In this fMRI study, we investigated emotional reactivity to threat as possible link between provocation and aggression, as well as the neural correlates of this relationship. We hypothesized that emotional reactivity, measured as fear potentiation (FP) of the startle response, would be negatively associated with aggressive behavior and would modulate neural activity during an aggressive interaction. In 30 healthy female participants, FP was measured as the difference between blink amplitudes while watching threatening vs neutral pictures. Participants subsequently engaged in a variant of the Taylor Aggression Paradigm (TAP), while being scanned. During the TAP, participants selected a punishment level for either a highly provoking or a nonprovoking opponent. There was no difference in aggressive behavior between participants high and low in FP. However, we found a negative correlation between FP and the neural provocation effect in several regions of a network previously associated with mentalizing including the medial prefrontal cortex, precuneus and the temporo-parietal junction. Independently of the FP variability, aggressive behavior correlated with the provocation effect on activity in the caudate nucleus. Our results indicate that during a provocative confrontation, high emotional reactivity to threat suppresses recruitment of the mentalizing network. © The Author (2013). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Parkinson’s disease dementia: a neural networks perspective

PubMed Central

Jahanshahi, Marjan; Foltynie, Thomas

2015-01-01

In the long-term, with progression of the illness, Parkinson’s disease dementia affects up to 90% of patients with Parkinson’s disease. With increasing life expectancy in western countries, Parkinson’s disease dementia is set to become even more prevalent in the future. However, current treatments only give modest symptomatic benefit at best. New treatments are slow in development because unlike the pathological processes underlying the motor deficits of Parkinson’s disease, the neural mechanisms underlying the dementing process and its associated cognitive deficits are still poorly understood. Recent insights from neuroscience research have begun to unravel the heterogeneous involvement of several distinct neural networks underlying the cognitive deficits in Parkinson’s disease dementia, and their modulation by both dopaminergic and non-dopaminergic transmitter systems in the brain. In this review we collate emerging evidence regarding these distinct brain networks to give a novel perspective on the pathological mechanisms underlying Parkinson’s disease dementia, and discuss how this may offer new therapeutic opportunities. PMID:25888551

USC orthogonal multiprocessor for image processing with neural networks

NASA Astrophysics Data System (ADS)

Hwang, Kai; Panda, Dhabaleswar K.; Haddadi, Navid

1990-07-01

This paper presents the architectural features and imaging applications of the Orthogonal MultiProcessor (OMP) system, which is under construction at the University of Southern California with research funding from NSF and assistance from several industrial partners. The prototype OMP is being built with 16 Intel i860 RISC microprocessors and 256 parallel memory modules using custom-designed spanning buses, which are 2-D interleaved and orthogonally accessed without conflicts. The 16-processor OMP prototype is targeted to achieve 430 MIPS and 600 Mflops, which have been verified by simulation experiments based on the design parameters used. The prototype OMP machine will be initially applied for image processing, computer vision, and neural network simulation applications. We summarize important vision and imaging algorithms that can be restructured with neural network models. These algorithms can efficiently run on the OMP hardware with linear speedup. The ultimate goal is to develop a high-performance Visual Computer (Viscom) for integrated low- and high-level image processing and vision tasks.

Parkinson's disease dementia: a neural networks perspective.

PubMed

Gratwicke, James; Jahanshahi, Marjan; Foltynie, Thomas

2015-06-01

In the long-term, with progression of the illness, Parkinson's disease dementia affects up to 90% of patients with Parkinson's disease. With increasing life expectancy in western countries, Parkinson's disease dementia is set to become even more prevalent in the future. However, current treatments only give modest symptomatic benefit at best. New treatments are slow in development because unlike the pathological processes underlying the motor deficits of Parkinson's disease, the neural mechanisms underlying the dementing process and its associated cognitive deficits are still poorly understood. Recent insights from neuroscience research have begun to unravel the heterogeneous involvement of several distinct neural networks underlying the cognitive deficits in Parkinson's disease dementia, and their modulation by both dopaminergic and non-dopaminergic transmitter systems in the brain. In this review we collate emerging evidence regarding these distinct brain networks to give a novel perspective on the pathological mechanisms underlying Parkinson's disease dementia, and discuss how this may offer new therapeutic opportunities. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain.

Monetary favors and their influence on neural responses and revealed preference

PubMed Central

Harvey, Ann H.; Kirk, Ulrich; Denfield, George H.; Montague, P. Read

2010-01-01

Favors from a sender to a receiver are known to bias decisions made by the recipient, especially when the decision relates to the sender – a feature of social exchange known as reciprocity. Using an art-viewing paradigm possessing no objectively correct answer for preferring one piece of art over another, we show that sponsorship of the experiment by a company endows the company’s logo with the capacity to bias revealed preference for art displayed next to the logo. Merely offering to sponsor the experiment similarly endowed the gesturing company’s logo with the capacity to bias revealed preferences. These effects do not depend upon the size of the displayed art or the proximity of the sponsoring logo to the piece of art. We used fMRI to show that such monetary favors do not modulate a special collection of brain responses, but instead modulate responses in neural networks normally activated by a wide range of preference judgments. The results raise the important possibility that monetary favors bias judgments in domains seemingly unrelated to the favor, but nevertheless act in an implicit way through neural networks that underlie normal, ongoing preference judgments. PMID:20631188

Neural networks for learning and prediction with applications to remote sensing and speech perception

NASA Astrophysics Data System (ADS)

Gjaja, Marin N.

1997-11-01

Neural networks for supervised and unsupervised learning are developed and applied to problems in remote sensing, continuous map learning, and speech perception. Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART networks synthesize fuzzy logic and neural networks, and supervised ARTMAP networks incorporate ART modules for prediction and classification. New ART and ARTMAP methods resulting from analyses of data structure, parameter specification, and category selection are developed. Architectural modifications providing flexibility for a variety of applications are also introduced and explored. A new methodology for automatic mapping from Landsat Thematic Mapper (TM) and terrain data, based on fuzzy ARTMAP, is developed. System capabilities are tested on a challenging remote sensing problem, prediction of vegetation classes in the Cleveland National Forest from spectral and terrain features. After training at the pixel level, performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, back propagation neural networks, and K-nearest neighbor algorithms. Best performance is obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. This work forms the foundation for additional studies exploring fuzzy ARTMAP's capability to estimate class mixture composition for non-homogeneous sites. Exploratory simulations apply ARTMAP to the problem of learning continuous multidimensional mappings. A novel system architecture retains basic ARTMAP properties of incremental and fast learning in an on-line setting while adding components to solve this class of problems. The perceptual magnet effect is a language-specific phenomenon arising early in infant speech development that is characterized by a warping of speech sound perception. An unsupervised neural network model is proposed that embodies two principal hypotheses supported by experimental data--that sensory experience guides language-specific development of an auditory neural map and that a population vector can predict psychological phenomena based on map cell activities. Model simulations show how a nonuniform distribution of map cell firing preferences can develop from language-specific input and give rise to the magnet effect.

An analog neural hardware implementation using charge-injection multipliers and neutron-specific gain control.

PubMed

Massengill, L W; Mundie, D B

1992-01-01

A neural network IC based on a dynamic charge injection is described. The hardware design is space and power efficient, and achieves massive parallelism of analog inner products via charge-based multipliers and spatially distributed summing buses. Basic synaptic cells are constructed of exponential pulse-decay modulation (EPDM) dynamic injection multipliers operating sequentially on propagating signal vectors and locally stored analog weights. Individually adjustable gain controls on each neutron reduce the effects of limited weight dynamic range. A hardware simulator/trainer has been developed which incorporates the physical (nonideal) characteristics of actual circuit components into the training process, thus absorbing nonlinearities and parametric deviations into the macroscopic performance of the network. Results show that charge-based techniques may achieve a high degree of neural density and throughput using standard CMOS processes.

Application of Gaussian beam ray-equivalent model and back-propagation artificial neural network in laser diode fast axis collimator assembly.

PubMed

Yu, Hao; Rossi, Giammarco; Braglia, Andrea; Perrone, Guido

2016-08-10

The paper presents the development of a tool based on a back-propagation artificial neural network to assist in the accurate positioning of the lenses used to collimate the beam from semiconductor laser diodes along the so-called fast axis. After training using a Gaussian beam ray-equivalent model, the network is capable of indicating the tilt, decenter, and defocus of such lenses from the measured field distribution, so the operator can determine the errors with respect to the actual lens position and optimize the diode assembly procedure. An experimental validation using a typical configuration exploited in multi-emitter diode module assembly and fast axis collimating lenses with different focal lengths and numerical apertures is reported.

Near real-time analysis of extrinsic Fabry-Perot interferometric sensors under damped vibration using artificial neural networks

NASA Astrophysics Data System (ADS)

Dua, Rohit; Watkins, Steve E.

2009-03-01

Strain analysis due to vibration can provide insight into structural health. An Extrinsic Fabry-Perot Interferometric (EFPI) sensor under vibrational strain generates a non-linear modulated output. Advanced signal processing techniques, to extract important information such as absolute strain, are required to demodulate this non-linear output. Past research has employed Artificial Neural Networks (ANN) and Fast Fourier Transforms (FFT) to demodulate the EFPI sensor for limited conditions. These demodulation systems could only handle variations in absolute value of strain and frequency of actuation during a vibration event. This project uses an ANN approach to extend the demodulation system to include the variation in the damping coefficient of the actuating vibration, in a near real-time vibration scenario. A computer simulation provides training and testing data for the theoretical output of the EFPI sensor to demonstrate the approaches. FFT needed to be performed on a window of the EFPI output data. A small window of observation is obtained, while maintaining low absolute-strain prediction errors, heuristically. Results are obtained and compared from employing different ANN architectures including multi-layered feedforward ANN trained using Backpropagation Neural Network (BPNN), and Generalized Regression Neural Networks (GRNN). A two-layered algorithm fusion system is developed and tested that yields better results.

Individual nodeʼs contribution to the mesoscale of complex networks

NASA Astrophysics Data System (ADS)

Klimm, Florian; Borge-Holthoefer, Javier; Wessel, Niels; Kurths, Jürgen; Zamora-López, Gorka

2014-12-01

The analysis of complex networks is devoted to the statistical characterization of the topology of graphs at different scales of organization in order to understand their functionality. While the modular structure of networks has become an essential element to better apprehend their complexity, the efforts to characterize the mesoscale of networks have focused on the identification of the modules rather than describing the mesoscale in an informative manner. Here we propose a framework to characterize the position every node takes within the modular configuration of complex networks and to evaluate their function accordingly. For illustration, we apply this framework to a set of synthetic networks, empirical neural networks, and to the transcriptional regulatory network of the Mycobacterium tuberculosis. We find that the architecture of both neuronal and transcriptional networks are optimized for the processing of multisensory information with the coexistence of well-defined modules of specialized components and the presence of hubs conveying information from and to the distinct functional domains.

Optical information-processing systems and architectures II; Proceedings of the Meeting, San Diego, CA, July 9-13, 1990

NASA Astrophysics Data System (ADS)

Javidi, Bahram

The present conference discusses topics in the fields of neural networks, acoustooptic signal processing, pattern recognition, phase-only processing, nonlinear signal processing, image processing, optical computing, and optical information processing. Attention is given to the optical implementation of an inner-product neural associative memory, optoelectronic associative recall via motionless-head/parallel-readout optical disk, a compact real-time acoustooptic image correlator, a multidimensional synthetic estimation filter, and a light-efficient joint transform optical correlator. Also discussed are a high-resolution spatial light modulator, compact real-time interferometric Fourier-transform processors, a fast decorrelation algorithm for permutation arrays, the optical interconnection of optical modules, and carry-free optical binary adders.

Neural feedback for instantaneous spatiotemporal modulation of afferent pathways in bi-directional brain-machine interfaces.

PubMed

Liu, Jianbo; Khalil, Hassan K; Oweiss, Karim G

2011-10-01

In bi-directional brain-machine interfaces (BMIs), precisely controlling the delivery of microstimulation, both in space and in time, is critical to continuously modulate the neural activity patterns that carry information about the state of the brain-actuated device to sensory areas in the brain. In this paper, we investigate the use of neural feedback to control the spatiotemporal firing patterns of neural ensembles in a model of the thalamocortical pathway. Control of pyramidal (PY) cells in the primary somatosensory cortex (S1) is achieved based on microstimulation of thalamic relay cells through multiple-input multiple-output (MIMO) feedback controllers. This closed loop feedback control mechanism is achieved by simultaneously varying the stimulation parameters across multiple stimulation electrodes in the thalamic circuit based on continuous monitoring of the difference between reference patterns and the evoked responses of the cortical PY cells. We demonstrate that it is feasible to achieve a desired level of performance by controlling the firing activity pattern of a few "key" neural elements in the network. Our results suggest that neural feedback could be an effective method to facilitate the delivery of information to the cortex to substitute lost sensory inputs in cortically controlled BMIs.

SoxB1-driven transcriptional network underlies neural-specific interpretation of morphogen signals.

PubMed

Oosterveen, Tony; Kurdija, Sanja; Ensterö, Mats; Uhde, Christopher W; Bergsland, Maria; Sandberg, Magnus; Sandberg, Rickard; Muhr, Jonas; Ericson, Johan

2013-04-30

The reiterative deployment of a small cadre of morphogen signals underlies patterning and growth of most tissues during embyogenesis, but how such inductive events result in tissue-specific responses remains poorly understood. By characterizing cis-regulatory modules (CRMs) associated with genes regulated by Sonic hedgehog (Shh), retinoids, or bone morphogenetic proteins in the CNS, we provide evidence that the neural-specific interpretation of morphogen signaling reflects a direct integration of these pathways with SoxB1 proteins at the CRM level. Moreover, expression of SoxB1 proteins in the limb bud confers on mesodermal cells the potential to activate neural-specific target genes upon Shh, retinoid, or bone morphogenetic protein signaling, and the collocation of binding sites for SoxB1 and morphogen-mediatory transcription factors in CRMs faithfully predicts neural-specific gene activity. Thus, an unexpectedly simple transcriptional paradigm appears to conceptually explain the neural-specific interpretation of pleiotropic signaling during vertebrate development. Importantly, genes induced in a SoxB1-dependent manner appear to constitute repressive gene regulatory networks that are directly interlinked at the CRM level to constrain the regional expression of patterning genes. Accordingly, not only does the topology of SoxB1-driven gene regulatory networks provide a tissue-specific mode of gene activation, but it also determines the spatial expression pattern of target genes within the developing neural tube.

Neural analysis of bovine ovaries ultrasound images in the identification process of the corpus luteum

NASA Astrophysics Data System (ADS)

Górna, K.; Jaśkowski, B. M.; Okoń, P.; Czechlowski, M.; Koszela, K.; Zaborowicz, M.; Idziaszek, P.

2017-07-01

The aim of the paper is to shown the neural image analysis as a method useful for identifying the development stage of the domestic bovine corpus luteum on digital USG (UltraSonoGraphy) images. Corpus luteum (CL) is a transient endocrine gland that develops after ovulation from the follicle secretory cells. The aim of CL is the production of progesterone, which regulates many reproductive functions. In the presented studies, identification of the corpus luteum was carried out on the basis of information contained in ultrasound digital images. Development stage of the corpus luteum was considered in two aspects: just before and middle of domination phase and luteolysis and degradation phase. Prior to the classification, the ultrasound images have been processed using a GLCM (Gray Level Co-occurence Matrix). To generate a classification model, a Neural Networks module implemented in the STATISTICA was used. Five representative parameters describing the ultrasound image were used as learner variables. On the output of the artificial neural network was generated information about the development stage of the corpus luteum. Results of this study indicate that neural image analysis combined with GLCM texture analysis may be a useful tool for identifying the bovine corpus luteum in the context of its development phase. Best-generated artificial neural network model was the structure of MLP (Multi Layer Perceptron) 5:5-17-1:1.

Bandwidth variable transceivers with artificial neural network-aided provisioning and capacity improvement capabilities in meshed optical networks with cascaded ROADM filtering

NASA Astrophysics Data System (ADS)

Zhou, Xingyu; Zhuge, Qunbi; Qiu, Meng; Xiang, Meng; Zhang, Fangyuan; Wu, Baojian; Qiu, Kun; Plant, David V.

2018-02-01

We investigate the capacity improvement achieved by bandwidth variable transceivers (BVT) in meshed optical networks with cascaded ROADM filtering at fixed channel spacing, and then propose an artificial neural network (ANN)-aided provisioning scheme to select optimal symbol rate and modulation format for the BVTs in this scenario. Compared with a fixed symbol rate transceiver with standard QAMs, it is shown by both experiments and simulations that BVTs can increase the average capacity by more than 17%. The ANN-aided BVT provisioning method uses parameters monitored from a coherent receiver and then employs a trained ANN to transform these parameters into the desired configuration. It is verified by simulation that the BVT with the proposed provisioning method can approach the upper limit of the system capacity obtained by brute-force search under various degrees of flexibilities.

Oxytocin And Vasopressin Modulation Of The Neural Correlates Of Motivation And Emotion: Results From Functional MRI Studies In Awake Rats

PubMed Central

Febo, Marcelo; Ferris, Craig F.

2014-01-01

Oxytocin and vasopressin modulate a range of species typical behavioral functions that include social recognition, maternal-infant attachment, and modulation of memory, offensive aggression, defensive fear reactions, and reward seeking. We have employed novel functional magnetic resonance mapping techniques in awake rats to explore the roles of these neuropeptides in the maternal and non-maternal brain. Results from the functional neuroimaging studies that are summarized here have directly and indirectly confirmed and supported previous findings. Oxytocin is released within the lactating rat brain during suckling stimulation and activates specific subcortical networks in the maternal brain. Both vasopressin and oxytocin modulate brain regions involved unconditioned fear, processing of social stimuli and the expression of agonistic behaviors. Across studies there are relatively consistent brain networks associated with internal motivational drives and emotional states that are modulated by oxytocin and vasopressin. PMID:24486356

A neurocomputational model of figure-ground discrimination and target tracking.

PubMed

Sun, H; Liu, L; Guo, A

1999-01-01

A neurocomputational model is presented for figureground discrimination and target tracking. In the model, the elementary motion detectors of the correlation type, the computational modules of saccadic and smooth pursuit eye movement, an oscillatory neural-network motion perception module and a selective attention module are involved. It is shown that through the oscillatory amplitude and frequency encoding, and selective synchronization of phase oscillators, the figure and the ground can be successfully discriminated from each other. The receptive fields developed by hidden units of the networks were surprisingly similar to the actual receptive fields and columnar organization found in the primate visual cortex. It is suggested that equivalent mechanisms may exist in the primate visual cortex to discriminate figure-ground in both temporal and spatial domains.

Distributed multisensory integration in a recurrent network model through supervised learning

NASA Astrophysics Data System (ADS)

Wang, He; Wong, K. Y. Michael

Sensory integration between different modalities has been extensively studied. It is suggested that the brain integrates signals from different modalities in a Bayesian optimal way. However, how the Bayesian rule is implemented in a neural network remains under debate. In this work we propose a biologically plausible recurrent network model, which can perform Bayesian multisensory integration after trained by supervised learning. Our model is composed of two modules, each for one modality. We assume that each module is a recurrent network, whose activity represents the posterior distribution of each stimulus. The feedforward input on each module is the likelihood of each modality. Two modules are integrated through cross-links, which are feedforward connections from the other modality, and reciprocal connections, which are recurrent connections between different modules. By stochastic gradient descent, we successfully trained the feedforward and recurrent coupling matrices simultaneously, both of which resembles the Mexican-hat. We also find that there are more than one set of coupling matrices that can approximate the Bayesian theorem well. Specifically, reciprocal connections and cross-links will compensate each other if one of them is removed. Even though trained with two inputs, the network's performance with only one input is in good accordance with what is predicted by the Bayesian theorem.

Efficient “Communication through Coherence” Requires Oscillations Structured to Minimize Interference between Signals

PubMed Central

Akam, Thomas E.; Kullmann, Dimitri M.

2012-01-01

The ‘communication through coherence’ (CTC) hypothesis proposes that selective communication among neural networks is achieved by coherence between firing rate oscillation in a sending region and gain modulation in a receiving region. Although this hypothesis has stimulated extensive work, it remains unclear whether the mechanism can in principle allow reliable and selective information transfer. Here we use a simple mathematical model to investigate how accurately coherent gain modulation can filter a population-coded target signal from task-irrelevant distracting inputs. We show that selective communication can indeed be achieved, although the structure of oscillatory activity in the target and distracting networks must satisfy certain previously unrecognized constraints. Firstly, the target input must be differentiated from distractors by the amplitude, phase or frequency of its oscillatory modulation. When distracting inputs oscillate incoherently in the same frequency band as the target, communication accuracy is severely degraded because of varying overlap between the firing rate oscillations of distracting inputs and the gain modulation in the receiving region. Secondly, the oscillatory modulation of the target input must be strong in order to achieve a high signal-to-noise ratio relative to stochastic spiking of individual neurons. Thus, whilst providing a quantitative demonstration of the power of coherent oscillatory gain modulation to flexibly control information flow, our results identify constraints imposed by the need to avoid interference between signals, and reveal a likely organizing principle for the structure of neural oscillations in the brain. PMID:23144603

Discovery and validation of a glioblastoma co-expressed gene module

PubMed Central

Dunwoodie, Leland J.; Poehlman, William L.; Ficklin, Stephen P.; Feltus, Frank Alexander

2018-01-01

Tumors exhibit complex patterns of aberrant gene expression. Using a knowledge-independent, noise-reducing gene co-expression network construction software called KINC, we created multiple RNAseq-based gene co-expression networks relevant to brain and glioblastoma biology. In this report, we describe the discovery and validation of a glioblastoma-specific gene module that contains 22 co-expressed genes. The genes are upregulated in glioblastoma relative to normal brain and lower grade glioma samples; they are also hypo-methylated in glioblastoma relative to lower grade glioma tumors. Among the proneural, neural, mesenchymal, and classical glioblastoma subtypes, these genes are most-highly expressed in the mesenchymal subtype. Furthermore, high expression of these genes is associated with decreased survival across each glioblastoma subtype. These genes are of interest to glioblastoma biology and our gene interaction discovery and validation workflow can be used to discover and validate co-expressed gene modules derived from any co-expression network. PMID:29541392

Discovery and validation of a glioblastoma co-expressed gene module.

PubMed

Dunwoodie, Leland J; Poehlman, William L; Ficklin, Stephen P; Feltus, Frank Alexander

2018-02-16

Tumors exhibit complex patterns of aberrant gene expression. Using a knowledge-independent, noise-reducing gene co-expression network construction software called KINC, we created multiple RNAseq-based gene co-expression networks relevant to brain and glioblastoma biology. In this report, we describe the discovery and validation of a glioblastoma-specific gene module that contains 22 co-expressed genes. The genes are upregulated in glioblastoma relative to normal brain and lower grade glioma samples; they are also hypo-methylated in glioblastoma relative to lower grade glioma tumors. Among the proneural, neural, mesenchymal, and classical glioblastoma subtypes, these genes are most-highly expressed in the mesenchymal subtype. Furthermore, high expression of these genes is associated with decreased survival across each glioblastoma subtype. These genes are of interest to glioblastoma biology and our gene interaction discovery and validation workflow can be used to discover and validate co-expressed gene modules derived from any co-expression network.

A Targeted Attack For Enhancing Resiliency of Intelligent Intrusion Detection Modules in Energy Cyber Physical Systems

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

Youssef, Tarek; El Hariri, Mohammad; Habib, Hani

Abstract— Secure high-speed communication is required to ensure proper operation of complex power grid systems and prevent malicious tampering activities. In this paper, artificial neural networks with temporal dependency are introduced for false data identification and mitigation for broadcasted IEC 61850 SMV messages. The fast responses of such intelligent modules in intrusion detection make them suitable for time- critical applications, such as protection. However, care must be taken in selecting the appropriate intelligence model and decision criteria. As such, this paper presents a customizable malware script to sniff and manipulate SMV messages and demonstrates the ability of the malware tomore » trigger false positives in the neural network’s response. The malware developed is intended to be as a vaccine to harden the intrusion detection system against data manipulation attacks by enhancing the neural network’s ability to learn and adapt to these attacks.« less

3-D components of a biological neural network visualized in computer generated imagery. I - Macular receptive field organization

NASA Technical Reports Server (NTRS)

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

1990-01-01

Computer-assisted, 3-dimensional reconstructions of macular receptive fields and of their linkages into a neural network have revealed new information about macular functional organization. Both type I and type II hair cells are included in the receptive fields. The fields are rounded, oblong, or elongated, but gradations between categories are common. Cell polarizations are divergent. Morphologically, each calyx of oblong and elongated fields appears to be an information processing site. Intrinsic modulation of information processing is extensive and varies with the kind of field. Each reconstructed field differs in detail from every other, suggesting that an element of randomness is introduced developmentally and contributes to endorgan adaptability.

The spiritual brain: selective cortical lesions modulate human self-transcendence.

PubMed

Urgesi, Cosimo; Aglioti, Salvatore M; Skrap, Miran; Fabbro, Franco

2010-02-11

The predisposition of human beings toward spiritual feeling, thinking, and behaviors is measured by a supposedly stable personality trait called self-transcendence. Although a few neuroimaging studies suggest that neural activation of a large fronto-parieto-temporal network may underpin a variety of spiritual experiences, information on the causative link between such a network and spirituality is lacking. Combining pre- and post-neurosurgery personality assessment with advanced brain-lesion mapping techniques, we found that selective damage to left and right inferior posterior parietal regions induced a specific increase of self-transcendence. Therefore, modifications of neural activity in temporoparietal areas may induce unusually fast modulations of a stable personality trait related to transcendental self-referential awareness. These results hint at the active, crucial role of left and right parietal systems in determining self-transcendence and cast new light on the neurobiological bases of altered spiritual and religious attitudes and behaviors in neurological and mental disorders. Copyright 2010 Elsevier Inc. All rights reserved.

Network architecture of the cerebral nuclei (basal ganglia) association and commissural connectome.

PubMed

Swanson, Larry W; Sporns, Olaf; Hahn, Joel D

2016-10-04

The cerebral nuclei form the ventral division of the cerebral hemisphere and are thought to play an important role in neural systems controlling somatic movement and motivation. Network analysis was used to define global architectural features of intrinsic cerebral nuclei circuitry in one hemisphere (association connections) and between hemispheres (commissural connections). The analysis was based on more than 4,000 reports of histologically defined axonal connections involving all 45 gray matter regions of the rat cerebral nuclei and revealed the existence of four asymmetrically interconnected modules. The modules form four topographically distinct longitudinal columns that only partly correspond to previous interpretations of cerebral nuclei structure-function organization. The network of connections within and between modules in one hemisphere or the other is quite dense (about 40% of all possible connections), whereas the network of connections between hemispheres is weak and sparse (only about 5% of all possible connections). Particularly highly interconnected regions (rich club and hubs within it) form a topologically continuous band extending through two of the modules. Connection path lengths among numerous pairs of regions, and among some of the network's modules, are relatively long, thus accounting for low global efficiency in network communication. These results provide a starting point for reexamining the connectional organization of the cerebral hemispheres as a whole (right and left cerebral cortex and cerebral nuclei together) and their relation to the rest of the nervous system.

Large-Scale Network Analysis of Whole-Brain Resting-State Functional Connectivity in Spinal Cord Injury: A Comparative Study.

PubMed

Kaushal, Mayank; Oni-Orisan, Akinwunmi; Chen, Gang; Li, Wenjun; Leschke, Jack; Ward, Doug; Kalinosky, Benjamin; Budde, Matthew; Schmit, Brian; Li, Shi-Jiang; Muqeet, Vaishnavi; Kurpad, Shekar

2017-09-01

Network analysis based on graph theory depicts the brain as a complex network that allows inspection of overall brain connectivity pattern and calculation of quantifiable network metrics. To date, large-scale network analysis has not been applied to resting-state functional networks in complete spinal cord injury (SCI) patients. To characterize modular reorganization of whole brain into constituent nodes and compare network metrics between SCI and control subjects, fifteen subjects with chronic complete cervical SCI and 15 neurologically intact controls were scanned. The data were preprocessed followed by parcellation of the brain into 116 regions of interest (ROI). Correlation analysis was performed between every ROI pair to construct connectivity matrices and ROIs were categorized into distinct modules. Subsequently, local efficiency (LE) and global efficiency (GE) network metrics were calculated at incremental cost thresholds. The application of a modularity algorithm organized the whole-brain resting-state functional network of the SCI and the control subjects into nine and seven modules, respectively. The individual modules differed across groups in terms of the number and the composition of constituent nodes. LE demonstrated statistically significant decrease at multiple cost levels in SCI subjects. GE did not differ significantly between the two groups. The demonstration of modular architecture in both groups highlights the applicability of large-scale network analysis in studying complex brain networks. Comparing modules across groups revealed differences in number and membership of constituent nodes, indicating modular reorganization due to neural plasticity.

Neural control of vascular reactions: impact of emotion and attention.

PubMed

Okon-Singer, Hadas; Mehnert, Jan; Hoyer, Jana; Hellrung, Lydia; Schaare, Herma Lina; Dukart, Juergen; Villringer, Arno

2014-03-19

This study investigated the neural regions involved in blood pressure reactions to negative stimuli and their possible modulation by attention. Twenty-four healthy human subjects (11 females; age = 24.75 ± 2.49 years) participated in an affective perceptual load task that manipulated attention to negative/neutral distractor pictures. fMRI data were collected simultaneously with continuous recording of peripheral arterial blood pressure. A parametric modulation analysis examined the impact of attention and emotion on the relation between neural activation and blood pressure reactivity during the task. When attention was available for processing the distractor pictures, negative pictures resulted in behavioral interference, neural activation in brain regions previously related to emotion, a transient decrease of blood pressure, and a positive correlation between blood pressure response and activation in a network including prefrontal and parietal regions, the amygdala, caudate, and mid-brain. These effects were modulated by attention; behavioral and neural responses to highly negative distractor pictures (compared with neutral pictures) were smaller or diminished, as was the negative blood pressure response when the central task involved high perceptual load. Furthermore, comparing high and low load revealed enhanced activation in frontoparietal regions implicated in attention control. Our results fit theories emphasizing the role of attention in the control of behavioral and neural reactions to irrelevant emotional distracting information. Our findings furthermore extend the function of attention to the control of autonomous reactions associated with negative emotions by showing altered blood pressure reactions to emotional stimuli, the latter being of potential clinical relevance.

Modalities of Thinking: State and Trait Effects on Cross-Frequency Functional Independent Brain Networks.

PubMed

Milz, Patricia; Pascual-Marqui, Roberto D; Lehmann, Dietrich; Faber, Pascal L

2016-05-01

Functional states of the brain are constituted by the temporally attuned activity of spatially distributed neural networks. Such networks can be identified by independent component analysis (ICA) applied to frequency-dependent source-localized EEG data. This methodology allows the identification of networks at high temporal resolution in frequency bands of established location-specific physiological functions. EEG measurements are sensitive to neural activity changes in cortical areas of modality-specific processing. We tested effects of modality-specific processing on functional brain networks. Phasic modality-specific processing was induced via tasks (state effects) and tonic processing was assessed via modality-specific person parameters (trait effects). Modality-specific person parameters and 64-channel EEG were obtained from 70 male, right-handed students. Person parameters were obtained using cognitive style questionnaires, cognitive tests, and thinking modality self-reports. EEG was recorded during four conditions: spatial visualization, object visualization, verbalization, and resting. Twelve cross-frequency networks were extracted from source-localized EEG across six frequency bands using ICA. RMANOVAs, Pearson correlations, and path modelling examined effects of tasks and person parameters on networks. Results identified distinct state- and trait-dependent functional networks. State-dependent networks were characterized by decreased, trait-dependent networks by increased alpha activity in sub-regions of modality-specific pathways. Pathways of competing modalities showed opposing alpha changes. State- and trait-dependent alpha were associated with inhibitory and automated processing, respectively. Antagonistic alpha modulations in areas of competing modalities likely prevent intruding effects of modality-irrelevant processing. Considerable research suggested alpha modulations related to modality-specific states and traits. This study identified the distinct electrophysiological cortical frequency-dependent networks within which they operate.

Spiking neural network model for memorizing sequences with forward and backward recall.

PubMed

Borisyuk, Roman; Chik, David; Kazanovich, Yakov; da Silva Gomes, João

2013-06-01

We present an oscillatory network of conductance based spiking neurons of Hodgkin-Huxley type as a model of memory storage and retrieval of sequences of events (or objects). The model is inspired by psychological and neurobiological evidence on sequential memories. The building block of the model is an oscillatory module which contains excitatory and inhibitory neurons with all-to-all connections. The connection architecture comprises two layers. A lower layer represents consecutive events during their storage and recall. This layer is composed of oscillatory modules. Plastic excitatory connections between the modules are implemented using an STDP type learning rule for sequential storage. Excitatory neurons in the upper layer project star-like modifiable connections toward the excitatory lower layer neurons. These neurons in the upper layer are used to tag sequences of events represented in the lower layer. Computer simulations demonstrate good performance of the model including difficult cases when different sequences contain overlapping events. We show that the model with STDP type or anti-STDP type learning rules can be applied for the simulation of forward and backward replay of neural spikes respectively. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Compact VLSI neural computer integrated with active pixel sensor for real-time ATR applications

NASA Astrophysics Data System (ADS)

Fang, Wai-Chi; Udomkesmalee, Gabriel; Alkalai, Leon

1997-04-01

A compact VLSI neural computer integrated with an active pixel sensor has been under development to mimic what is inherent in biological vision systems. This electronic eye- brain computer is targeted for real-time machine vision applications which require both high-bandwidth communication and high-performance computing for data sensing, synergy of multiple types of sensory information, feature extraction, target detection, target recognition, and control functions. The neural computer is based on a composite structure which combines Annealing Cellular Neural Network (ACNN) and Hierarchical Self-Organization Neural Network (HSONN). The ACNN architecture is a programmable and scalable multi- dimensional array of annealing neurons which are locally connected with their local neurons. Meanwhile, the HSONN adopts a hierarchical structure with nonlinear basis functions. The ACNN+HSONN neural computer is effectively designed to perform programmable functions for machine vision processing in all levels with its embedded host processor. It provides a two order-of-magnitude increase in computation power over the state-of-the-art microcomputer and DSP microelectronics. A compact current-mode VLSI design feasibility of the ACNN+HSONN neural computer is demonstrated by a 3D 16X8X9-cube neural processor chip design in a 2-micrometers CMOS technology. Integration of this neural computer as one slice of a 4'X4' multichip module into the 3D MCM based avionics architecture for NASA's New Millennium Program is also described.

Optogenetic dissection reveals multiple rhythmogenic modules underlying locomotion

PubMed Central

Hägglund, Martin; Dougherty, Kimberly J.; Borgius, Lotta; Itohara, Shigeyoshi; Iwasato, Takuji; Kiehn, Ole

2013-01-01

Neural networks in the spinal cord known as central pattern generators produce the sequential activation of muscles needed for locomotion. The overall locomotor network architectures in limbed vertebrates have been much debated, and no consensus exists as to how they are structured. Here, we use optogenetics to dissect the excitatory and inhibitory neuronal populations and probe the organization of the mammalian central pattern generator. We find that locomotor-like rhythmic bursting can be induced unilaterally or independently in flexor or extensor networks. Furthermore, we show that individual flexor motor neuron pools can be recruited into bursting without any activity in other nearby flexor motor neuron pools. Our experiments differentiate among several proposed models for rhythm generation in the vertebrates and show that the basic structure underlying the locomotor network has a distributed organization with many intrinsically rhythmogenic modules. PMID:23798384

Learning a trajectory using adjoint functions and teacher forcing

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad B.; Barhen, Jacob

1992-01-01

A new methodology for faster supervised temporal learning in nonlinear neural networks is presented which builds upon the concept of adjoint operators to allow fast computation of the gradients of an error functional with respect to all parameters of the neural architecture, and exploits the concept of teacher forcing to incorporate information on the desired output into the activation dynamics. The importance of the initial or final time conditions for the adjoint equations is discussed. A new algorithm is presented in which the adjoint equations are solved simultaneously (i.e., forward in time) with the activation dynamics of the neural network. We also indicate how teacher forcing can be modulated in time as learning proceeds. The results obtained show that the learning time is reduced by one to two orders of magnitude with respect to previously published results, while trajectory tracking is significantly improved. The proposed methodology makes hardware implementation of temporal learning attractive for real-time applications.

Modulation of neural circuits underlying temporal production by facial expressions of pain.

PubMed

Ballotta, Daniela; Lui, Fausta; Porro, Carlo Adolfo; Nichelli, Paolo Frigio; Benuzzi, Francesca

2018-01-01

According to the Scalar Expectancy Theory, humans are equipped with a biological internal clock, possibly modulated by attention and arousal. Both emotions and pain are arousing and can absorb attentional resources, thus causing distortions of temporal perception. The aims of the present single-event fMRI study were to investigate: a) whether observation of facial expressions of pain interferes with time production; and b) the neural network subserving this kind of temporal distortions. Thirty healthy volunteers took part in the study. Subjects were asked to perform a temporal production task and a concurrent gender discrimination task, while viewing faces of unknown people with either pain-related or neutral expressions. Behavioural data showed temporal underestimation (i.e., longer produced intervals) during implicit pain expression processing; this was accompanied by increased activity of right middle temporal gyrus, a region known to be active during the perception of emotional and painful faces. Psycho-Physiological Interaction analyses showed that: 1) the activity of middle temporal gyrus was positively related to that of areas previously reported to play a role in timing: left primary motor cortex, middle cingulate cortex, supplementary motor area, right anterior insula, inferior frontal gyrus, bilateral cerebellum and basal ganglia; 2) the functional connectivity of supplementary motor area with several frontal regions, anterior cingulate cortex and right angular gyrus was correlated to the produced interval during painful expression processing. Our data support the hypothesis that observing emotional expressions distorts subjective time perception through the interaction of the neural network subserving processing of facial expressions with the brain network involved in timing. Within this frame, middle temporal gyrus appears to be the key region of the interplay between the two neural systems.

Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm

PubMed Central

Dura-Bernal, Salvador; Li, Kan; Neymotin, Samuel A.; Francis, Joseph T.; Principe, Jose C.; Lytton, William W.

2016-01-01

Neural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors. PMID:26903796

CdSe/ZnS quantum dot fluorescence spectra shape-based thermometry via neural network reconstruction

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

Munro, Troy; Laboratory of Soft Matter and Biophysics, Department of Physics and Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Heverlee; Liu, Liwang

As a system of interest gets small, due to the influence of the sensor mass and heat leaks through the sensor contacts, thermal characterization by means of contact temperature measurements becomes cumbersome. Non-contact temperature measurement offers a suitable alternative, provided a reliable relationship between the temperature and the detected signal is available. In this work, exploiting the temperature dependence of their fluorescence spectrum, the use of quantum dots as thermomarkers on the surface of a fiber of interest is demonstrated. The performance is assessed of a series of neural networks that use different spectral shape characteristics as inputs (peak-based—peak intensity,more » peak wavelength; shape-based—integrated intensity, their ratio, full-width half maximum, peak normalized intensity at certain wavelengths, and summation of intensity over several spectral bands) and that yield at their output the fiber temperature in the optically probed area on a spider silk fiber. Starting from neural networks trained on fluorescence spectra acquired in steady state temperature conditions, numerical simulations are performed to assess the quality of the reconstruction of dynamical temperature changes that are photothermally induced by illuminating the fiber with periodically intensity-modulated light. Comparison of the five neural networks investigated to multiple types of curve fits showed that using neural networks trained on a combination of the spectral characteristics improves the accuracy over use of a single independent input, with the greatest accuracy observed for inputs that included both intensity-based measurements (peak intensity) and shape-based measurements (normalized intensity at multiple wavelengths), with an ultimate accuracy of 0.29 K via numerical simulation based on experimental observations. The implications are that quantum dots can be used as a more stable and accurate fluorescence thermometer for solid materials and that use of neural networks for temperature reconstruction improves the accuracy of the measurement.« less

Serotonergic Modulation Differentially Targets Distinct Network Elements within the Antennal Lobe of Drosophila melanogaster

PubMed Central

Sizemore, Tyler R.; Dacks, Andrew M.

2016-01-01

Neuromodulation confers flexibility to anatomically-restricted neural networks so that animals are able to properly respond to complex internal and external demands. However, determining the mechanisms underlying neuromodulation is challenging without knowledge of the functional class and spatial organization of neurons that express individual neuromodulatory receptors. Here, we describe the number and functional identities of neurons in the antennal lobe of Drosophila melanogaster that express each of the receptors for one such neuromodulator, serotonin (5-HT). Although 5-HT enhances odor-evoked responses of antennal lobe projection neurons (PNs) and local interneurons (LNs), the receptor basis for this enhancement is unknown. We used endogenous reporters of transcription and translation for each of the five 5-HT receptors (5-HTRs) to identify neurons, based on cell class and transmitter content, that express each receptor. We find that specific receptor types are expressed by distinct combinations of functional neuronal classes. For instance, the excitatory PNs express the excitatory 5-HTRs, while distinct classes of LNs each express different 5-HTRs. This study therefore provides a detailed atlas of 5-HT receptor expression within a well-characterized neural network, and enables future dissection of the role of serotonergic modulation of olfactory processing. PMID:27845422

Classifying Sources Influencing Indoor Air Quality (IAQ) Using Artificial Neural Network (ANN)

PubMed Central

Mad Saad, Shaharil; Melvin Andrew, Allan; Md Shakaff, Ali Yeon; Mohd Saad, Abdul Rahman; Muhamad Yusof @ Kamarudin, Azman; Zakaria, Ammar

2015-01-01

Monitoring indoor air quality (IAQ) is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN—a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC), base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room’s conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity. PMID:26007724

Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables

PubMed Central

Pothula, Venu M.; Yuan, Stanley C.; Maerz, David A.; Montes, Lucresia; Oleszkiewicz, Stephen M.; Yusupov, Albert; Perline, Richard

2015-01-01

Background Advanced predictive analytical techniques are being increasingly applied to clinical risk assessment. This study compared a neural network model to several other models in predicting the length of stay (LOS) in the cardiac surgical intensive care unit (ICU) based on pre-incision patient characteristics. Methods Thirty six variables collected from 185 cardiac surgical patients were analyzed for contribution to ICU LOS. The Automatic Linear Modeling (ALM) module of IBM-SPSS software identified 8 factors with statistically significant associations with ICU LOS; these factors were also analyzed with the Artificial Neural Network (ANN) module of the same software. The weighted contributions of each factor (“trained” data) were then applied to data for a “new” patient to predict ICU LOS for that individual. Results Factors identified in the ALM model were: use of an intra-aortic balloon pump; O2 delivery index; age; use of positive cardiac inotropic agents; hematocrit; serum creatinine ≥ 1.3 mg/deciliter; gender; arterial pCO2. The r2 value for ALM prediction of ICU LOS in the initial (training) model was 0.356, p <0.0001. Cross validation in prediction of a “new” patient yielded r2 = 0.200, p <0.0001. The same 8 factors analyzed with ANN yielded a training prediction r2 of 0.535 (p <0.0001) and a cross validation prediction r2 of 0.410, p <0.0001. Two additional predictive algorithms were studied, but they had lower prediction accuracies. Our validated neural network model identified the upper quartile of ICU LOS with an odds ratio of 9.8(p <0.0001). Conclusions ANN demonstrated a 2-fold greater accuracy than ALM in prediction of observed ICU LOS. This greater accuracy would be presumed to result from the capacity of ANN to capture nonlinear effects and higher order interactions. Predictive modeling may be of value in early anticipation of risks of post-operative morbidity and utilization of ICU facilities. PMID:26710254

Targeted neural network interventions for auditory hallucinations: Can TMS inform DBS?

PubMed

Taylor, Joseph J; Krystal, John H; D'Souza, Deepak C; Gerrard, Jason Lee; Corlett, Philip R

2018-05-01

The debilitating and refractory nature of auditory hallucinations (AH) in schizophrenia and other psychiatric disorders has stimulated investigations into neuromodulatory interventions that target the aberrant neural networks associated with them. Internal or invasive forms of brain stimulation such as deep brain stimulation (DBS) are currently being explored for treatment-refractory schizophrenia. The process of developing and implementing DBS is limited by symptom clustering within psychiatric constructs as well as a scarcity of causal tools with which to predict response, refine targeting or guide clinical decisions. Transcranial magnetic stimulation (TMS), an external or non-invasive form of brain stimulation, has shown some promise as a therapeutic intervention for AH but remains relatively underutilized as an investigational probe of clinically relevant neural networks. In this editorial, we propose that TMS has the potential to inform DBS by adding individualized causal evidence to an evaluation processes otherwise devoid of it in patients. Although there are significant limitations and safety concerns regarding DBS, the combination of TMS with computational modeling of neuroimaging and neurophysiological data could provide critical insights into more robust and adaptable network modulation. Copyright © 2017 Elsevier B.V. All rights reserved.

The cerebral signature for pain perception and its modulation.

PubMed

Tracey, Irene; Mantyh, Patrick W

2007-08-02

Our understanding of the neural correlates of pain perception in humans has increased significantly since the advent of neuroimaging. Relating neural activity changes to the varied pain experiences has led to an increased awareness of how factors (e.g., cognition, emotion, context, injury) can separately influence pain perception. Tying this body of knowledge in humans to work in animal models of pain provides an opportunity to determine common features that reliably contribute to pain perception and its modulation. One key system that underpins the ability to change pain intensity is the brainstem's descending modulatory network with its pro- and antinociceptive components. We discuss not only the latest data describing the cerebral signature of pain and its modulation in humans, but also suggest that the brainstem plays a pivotal role in gating the degree of nociceptive transmission so that the resultant pain experienced is appropriate for the particular situation of the individual.

Complexity Measures in Magnetoencephalography: Measuring "Disorder" in Schizophrenia

PubMed Central

Brookes, Matthew J.; Hall, Emma L.; Robson, Siân E.; Price, Darren; Palaniyappan, Lena; Liddle, Elizabeth B.; Liddle, Peter F.; Robinson, Stephen E.; Morris, Peter G.

2015-01-01

This paper details a methodology which, when applied to magnetoencephalography (MEG) data, is capable of measuring the spatio-temporal dynamics of ‘disorder’ in the human brain. Our method, which is based upon signal entropy, shows that spatially separate brain regions (or networks) generate temporally independent entropy time-courses. These time-courses are modulated by cognitive tasks, with an increase in local neural processing characterised by localised and transient increases in entropy in the neural signal. We explore the relationship between entropy and the more established time-frequency decomposition methods, which elucidate the temporal evolution of neural oscillations. We observe a direct but complex relationship between entropy and oscillatory amplitude, which suggests that these metrics are complementary. Finally, we provide a demonstration of the clinical utility of our method, using it to shed light on aberrant neurophysiological processing in schizophrenia. We demonstrate significantly increased task induced entropy change in patients (compared to controls) in multiple brain regions, including a cingulo-insula network, bilateral insula cortices and a right fronto-parietal network. These findings demonstrate potential clinical utility for our method and support a recent hypothesis that schizophrenia can be characterised by abnormalities in the salience network (a well characterised distributed network comprising bilateral insula and cingulate cortices). PMID:25886553

Modulation of Target Recollection and Recollection Rejection Networks Due to Retrieval Facilitation and Interference

ERIC Educational Resources Information Center

Bowman, Caitlin R.; Sine, Shalome L.; Dennis, Nancy A.

2017-01-01

To better understand neural recollection processing, we induced interference in target recollection by presenting related lures before their respective targets and facilitated recollection rejection of lures by presenting targets before their related lures. Target recollection following interference recruited visual and prefrontal cortices,…

A large-scale perspective on stress-induced alterations in resting-state networks

NASA Astrophysics Data System (ADS)

Maron-Katz, Adi; Vaisvaser, Sharon; Lin, Tamar; Hendler, Talma; Shamir, Ron

2016-02-01

Stress is known to induce large-scale neural modulations. However, its neural effect once the stressor is removed and how it relates to subjective experience are not fully understood. Here we used a statistically sound data-driven approach to investigate alterations in large-scale resting-state functional connectivity (rsFC) induced by acute social stress. We compared rsfMRI profiles of 57 healthy male subjects before and after stress induction. Using a parcellation-based univariate statistical analysis, we identified a large-scale rsFC change, involving 490 parcel-pairs. Aiming to characterize this change, we employed statistical enrichment analysis, identifying anatomic structures that were significantly interconnected by these pairs. This analysis revealed strengthening of thalamo-cortical connectivity and weakening of cross-hemispheral parieto-temporal connectivity. These alterations were further found to be associated with change in subjective stress reports. Integrating report-based information on stress sustainment 20 minutes post induction, revealed a single significant rsFC change between the right amygdala and the precuneus, which inversely correlated with the level of subjective recovery. Our study demonstrates the value of enrichment analysis for exploring large-scale network reorganization patterns, and provides new insight on stress-induced neural modulations and their relation to subjective experience.

Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task

PubMed Central

2017-01-01

Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP) are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making. PMID:28961245

Model of brain activation predicts the neural collective influence map of the brain

PubMed Central

Morone, Flaviano; Roth, Kevin; Min, Byungjoon; Makse, Hernán A.

2017-01-01

Efficient complex systems have a modular structure, but modularity does not guarantee robustness, because efficiency also requires an ingenious interplay of the interacting modular components. The human brain is the elemental paradigm of an efficient robust modular system interconnected as a network of networks (NoN). Understanding the emergence of robustness in such modular architectures from the interconnections of its parts is a longstanding challenge that has concerned many scientists. Current models of dependencies in NoN inspired by the power grid express interactions among modules with fragile couplings that amplify even small shocks, thus preventing functionality. Therefore, we introduce a model of NoN to shape the pattern of brain activations to form a modular environment that is robust. The model predicts the map of neural collective influencers (NCIs) in the brain, through the optimization of the influence of the minimal set of essential nodes responsible for broadcasting information to the whole-brain NoN. Our results suggest intervention protocols to control brain activity by targeting influential neural nodes predicted by network theory. PMID:28351973

Robust fixed-time synchronization for uncertain complex-valued neural networks with discontinuous activation functions.

PubMed

Ding, Xiaoshuai; Cao, Jinde; Alsaedi, Ahmed; Alsaadi, Fuad E; Hayat, Tasawar

2017-06-01

This paper is concerned with the fixed-time synchronization for a class of complex-valued neural networks in the presence of discontinuous activation functions and parameter uncertainties. Fixed-time synchronization not only claims that the considered master-slave system realizes synchronization within a finite time segment, but also requires a uniform upper bound for such time intervals for all initial synchronization errors. To accomplish the target of fixed-time synchronization, a novel feedback control procedure is designed for the slave neural networks. By means of the Filippov discontinuity theories and Lyapunov stability theories, some sufficient conditions are established for the selection of control parameters to guarantee synchronization within a fixed time, while an upper bound of the settling time is acquired as well, which allows to be modulated to predefined values independently on initial conditions. Additionally, criteria of modified controller for assurance of fixed-time anti-synchronization are also derived for the same system. An example is included to illustrate the proposed methodologies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Innovative second-generation wavelets construction with recurrent neural networks for solar radiation forecasting.

PubMed

Capizzi, Giacomo; Napoli, Christian; Bonanno, Francesco

2012-11-01

Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit the correlation between solar radiation and timescale-related variations of wind speed, humidity, and temperature. The input to the selected WRNN is provided by timescale-related bands of wavelet coefficients obtained from meteorological time series. The experimental setup available at the University of Catania, Italy, provided this information. The novelty of this approach is that the proposed WRNN performs the prediction in the wavelet domain and, in addition, also performs the inverse wavelet transform, giving the predicted signal as output. The obtained simulation results show a very low root-mean-square error compared to the results of the solar radiation prediction approaches obtained by hybrid neural networks reported in the recent literature.

Intelligent approach to prognostic enhancements of diagnostic systems

NASA Astrophysics Data System (ADS)

Vachtsevanos, George; Wang, Peng; Khiripet, Noppadon; Thakker, Ash; Galie, Thomas R.

2001-07-01

This paper introduces a novel methodology to prognostics based on a dynamic wavelet neural network construct and notions from the virtual sensor area. This research has been motivated and supported by the U.S. Navy's active interest in integrating advanced diagnostic and prognostic algorithms in existing Naval digital control and monitoring systems. A rudimentary diagnostic platform is assumed to be available providing timely information about incipient or impending failure conditions. We focus on the development of a prognostic algorithm capable of predicting accurately and reliably the remaining useful lifetime of a failing machine or component. The prognostic module consists of a virtual sensor and a dynamic wavelet neural network as the predictor. The virtual sensor employs process data to map real measurements into difficult to monitor fault quantities. The prognosticator uses a dynamic wavelet neural network as a nonlinear predictor. Means to manage uncertainty and performance metrics are suggested for comparison purposes. An interface to an available shipboard Integrated Condition Assessment System is described and applications to shipboard equipment are discussed. Typical results from pump failures are presented to illustrate the effectiveness of the methodology.

Determination of the geophysical model function of the ERS-1 scatterometer by the use of neural networks

NASA Astrophysics Data System (ADS)

Mejia, Carlos; Thiria, Sylvie; Tran, Ngan; CréPon, Michel; Badran, Fouad

1998-06-01

We present a geophysical model function (GMF) for the ERS-1 scatterometer computed by the use of neural networks. The neural networks GMF (NN GMF) is calibrated with ERS-1 scatterometer sigma 0 collocated with European Center for Medium-Range Weather Forecasts (ECMWF) analyzed wind vectors. Four different NN GMFs have been computed: one for each antenna and an average NN GMF. These NN GMFs do not present any significant differences which means that the three antenna are quasi-identical. The NN GMFs exhibit a biharmonic dependence on the wind azimuth with a small upwind-downwind modulation as found on previous GMFs. In order to check the validity of the NN GMF systematic comparisons with the European Space Agency (ESA) C band model (CMOD4) GMF (version 2 of March 25, 1993) and the Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) CMOD213 GMF are done. It is found that the NN GMFs are highly accurate and relevant functions to model the ERS-1 scatterometer sigma 0.

Neural mechanisms of social dominance

PubMed Central

Watanabe, Noriya; Yamamoto, Miyuki

2015-01-01

In a group setting, individuals' perceptions of their own level of dominance or of the dominance level of others, and the ability to adequately control their behavior based on these perceptions are crucial for living within a social environment. Recent advances in neural imaging and molecular technology have enabled researchers to investigate the neural substrates that support the perception of social dominance and the formation of a social hierarchy in humans. At the systems' level, recent studies showed that dominance perception is represented in broad brain regions which include the amygdala, hippocampus, striatum, and various cortical networks such as the prefrontal, and parietal cortices. Additionally, neurotransmitter systems such as the dopaminergic and serotonergic systems, modulate and are modulated by the formation of the social hierarchy in a group. While these monoamine systems have a wide distribution and multiple functions, it was recently found that the Neuropeptide B/W contributes to the perception of dominance and is present in neurons that have a limited projection primarily to the amygdala. The present review discusses the specific roles of these neural regions and neurotransmitter systems in the perception of dominance and in hierarchy formation. PMID:26136644

Miniaturized neural sensing and optogenetic stimulation system for behavioral studies in the rat

NASA Astrophysics Data System (ADS)

Kim, Min Hyuck; Nam, Ilho; Ryu, Youngki; Wellman, Laurie W.; Sanford, Larry D.; Yoon, Hargsoon

2015-04-01

Real time sensing of localized electrophysiological and neurochemical signals associated with spontaneous and evoked neural activity is critically important for understanding neural networks in the brain. Our goal is to enhance the functionality and flexibility of a neural sensing and stimulation system for the observation of brain activity that will enable better understanding from the level of individual cells to that of global structures. We have thus developed a miniaturized electronic system for in-vivo neurotransmitter sensing and optogenetic stimulation amenable to behavioral studies in the rat. The system contains a potentiostat, a data acquisition unit, a control unit, and a wireless data transfer unit. For the potentiostat, we applied embedded op-amps to build single potential amperometry for electrochemical sensing of dopamine. A light emitting diode is controlled by a microcontroller and pulse width modulation utilized to control optogenetic stimulation within a sub-millisecond level. In addition, this proto-typed electronic system contains a Bluetooth module for wireless data communication. In the future, an application-specific integrated circuit (ASIC) will be designed for further miniaturization of the system.

Determination of the geophysical model function of NSCAT and its corresponding variance by the use of neural networks

NASA Astrophysics Data System (ADS)

Mejia, C.; Badran, F.; Bentamy, A.; Crepon, M.; Thiria, S.; Tran, N.

1999-05-01

We have computed two geophysical model functions (one for the vertical and one for the horizontal polarization) for the NASA scatterometer (NSCAT) by using neural networks. These neural network geophysical model functions (NNGMFs) were estimated with NSCAT scatterometer σO measurements collocated with European Centre for Medium-Range Weather Forecasts analyzed wind vectors during the period January 15 to April 15, 1997. We performed a student t test showing that the NNGMFs estimate the NSCAT σO with a confidence level of 95%. Analysis of the results shows that the mean NSCAT signal depends on the incidence angle and the wind speed and presents the classical biharmonic modulation with respect to the wind azimuth. NSCAT σO increases with respect to the wind speed and presents a well-marked change at around 7 m s-1. The upwind-downwind amplitude is higher for the horizontal polarization signal than for vertical polarization, indicating that the use of horizontal polarization can give additional information for wind retrieval. Comparison of the σO computed by the NNGMFs against the NSCAT-measured σO show a quite low rms, except at low wind speeds. We also computed two specific neural networks for estimating the variance associated to these GMFs. The variances are analyzed with respect to geophysical parameters. This led us to compute the geophysical signal-to-noise ratio, i.e., Kp. The Kp values are quite high at low wind speed and decrease at high wind speed. At constant wind speed the highest Kp are at crosswind directions, showing that the crosswind values are the most difficult to estimate. These neural networks can be expressed as analytical functions, and FORTRAN subroutines can be provided.

Structure, function, and control of the human musculoskeletal network

PubMed Central

Murphy, Andrew C.; Muldoon, Sarah F.; Baker, David; Lastowka, Adam; Bennett, Brittany; Yang, Muzhi

2018-01-01

The human body is a complex organism, the gross mechanical properties of which are enabled by an interconnected musculoskeletal network controlled by the nervous system. The nature of musculoskeletal interconnection facilitates stability, voluntary movement, and robustness to injury. However, a fundamental understanding of this network and its control by neural systems has remained elusive. Here we address this gap in knowledge by utilizing medical databases and mathematical modeling to reveal the organizational structure, predicted function, and neural control of the musculoskeletal system. We constructed a highly simplified whole-body musculoskeletal network in which single muscles connect to multiple bones via both origin and insertion points. We demonstrated that, using this simplified model, a muscle’s role in this network could offer a theoretical prediction of the susceptibility of surrounding components to secondary injury. Finally, we illustrated that sets of muscles cluster into network communities that mimic the organization of control modules in primary motor cortex. This novel formalism for describing interactions between the muscular and skeletal systems serves as a foundation to develop and test therapeutic responses to injury, inspiring future advances in clinical treatments. PMID:29346370

Salience Network Connectivity Modulates Skin Conductance Responses in Predicting Arousal Experience

PubMed Central

Xia, Chenjie; Touroutoglou, Alexandra; Quigley, Karen S.; Barrett, Lisa Feldman; Dickerson, Bradford C.

2017-01-01

Individual differences in arousal experience have been linked to differences in resting-state salience network connectivity strength. In this study, we investigated how adding task-related skin conductance responses (SCR), a measure of sympathetic autonomic nervous system activity, can predict additional variance in arousal experience. Thirty-nine young adults rated their subjective experience of arousal to emotionally evocative images while SCRs were measured. They also underwent a separate resting-state fMRI scan. Greater SCR reactivity (an increased number of task-related SCRs) to emotional images and stronger intrinsic salience network connectivity independently predicted more intense experiences of arousal. Salience network connectivity further moderated the effect of SCR reactivity: In individuals with weak salience network connectivity, SCR reactivity more significantly predicted arousal experience, whereas in those with strong salience network connectivity, SCR reactivity played little role in predicting arousal experience. This interaction illustrates the degeneracy in neural mechanisms driving individual differences in arousal experience and highlights the intricate interplay between connectivity in central visceromotor neural circuitry and peripherally expressed autonomic responses in shaping arousal experience. PMID:27991182

Adaptiveness in monotone pseudo-Boolean optimization and stochastic neural computation.

PubMed

Grossi, Giuliano

2009-08-01

Hopfield neural network (HNN) is a nonlinear computational model successfully applied in finding near-optimal solutions of several difficult combinatorial problems. In many cases, the network energy function is obtained through a learning procedure so that its minima are states falling into a proper subspace (feasible region) of the search space. However, because of the network nonlinearity, a number of undesirable local energy minima emerge from the learning procedure, significantly effecting the network performance. In the neural model analyzed here, we combine both a penalty and a stochastic process in order to enhance the performance of a binary HNN. The penalty strategy allows us to gradually lead the search towards states representing feasible solutions, so avoiding oscillatory behaviors or asymptotically instable convergence. Presence of stochastic dynamics potentially prevents the network to fall into shallow local minima of the energy function, i.e., quite far from global optimum. Hence, for a given fixed network topology, the desired final distribution on the states can be reached by carefully modulating such process. The model uses pseudo-Boolean functions both to express problem constraints and cost function; a combination of these two functions is then interpreted as energy of the neural network. A wide variety of NP-hard problems fall in the class of problems that can be solved by the model at hand, particularly those having a monotonic quadratic pseudo-Boolean function as constraint function. That is, functions easily derived by closed algebraic expressions representing the constraint structure and easy (polynomial time) to maximize. We show the asymptotic convergence properties of this model characterizing its state space distribution at thermal equilibrium in terms of Markov chain and give evidence of its ability to find high quality solutions on benchmarks and randomly generated instances of two specific problems taken from the computational graph theory.

Load matters: neural correlates of verbal working memory in children with autism spectrum disorder.

PubMed

Vogan, Vanessa M; Francis, Kaitlyn E; Morgan, Benjamin R; Smith, Mary Lou; Taylor, Margot J

2018-06-01

Autism spectrum disorder (ASD) is a pervasive neurodevelopmental disorder characterised by diminished social reciprocity and communication skills and the presence of stereotyped and restricted behaviours. Executive functioning deficits, such as working memory, are associated with core ASD symptoms. Working memory allows for temporary storage and manipulation of information and relies heavily on frontal-parietal networks of the brain. There are few reports on the neural correlates of working memory in youth with ASD. The current study identified the neural systems underlying verbal working memory capacity in youth with and without ASD using functional magnetic resonance imaging (fMRI). Fifty-seven youth, 27 with ASD and 30 sex- and age-matched typically developing (TD) controls (9-16 years), completed a one-back letter matching task (LMT) with four levels of difficulty (i.e. cognitive load) while fMRI data were recorded. Linear trend analyses were conducted to examine brain regions that were recruited as a function of increasing cognitive load. We found similar behavioural performance on the LMT in terms of reaction times, but in the two higher load conditions, the ASD youth had lower accuracy than the TD group. Neural patterns of activations differed significantly between TD and ASD groups. In TD youth, areas classically used for working memory, including the lateral and medial frontal, as well as superior parietal brain regions, increased in activation with increasing task difficulty, while areas related to the default mode network (DMN) showed decreasing activation (i.e., deactivation). The youth with ASD did not appear to use this opposing cognitive processing system; they showed little recruitment of frontal and parietal regions across the load but did show similar modulation of the DMN. In a working memory task, where the load was manipulated without changing executive demands, TD youth showed increasing recruitment with increasing load of the classic fronto-parietal brain areas and decreasing involvement in default mode regions. In contrast, although they modulated the default mode network, youth with ASD did not show the modulation of increasing brain activation with increasing load, suggesting that they may be unable to manage increasing verbal information. Impaired verbal working memory in ASD would interfere with the youths' success academically and socially. Thus, determining the nature of atypical neural processing could help establish or monitor working memory interventions for ASD.

The brain as a "hyper-network": the key role of neural networks as main producers of the integrated brain actions especially via the "broadcasted" neuroconnectomics.

PubMed

Agnati, Luigi F; Marcoli, Manuela; Maura, Guido; Woods, Amina; Guidolin, Diego

2018-06-01

Investigations of brain complex integrative actions should consider beside neural networks, glial, extracellular molecular, and fluid channels networks. The present paper proposes that all these networks are assembled into the brain hyper-network that has as fundamental components, the tetra-partite synapses, formed by neural, glial, and extracellular molecular networks. Furthermore, peri-synaptic astrocytic processes by modulating the perviousness of extracellular fluid channels control the signals impinging on the tetra-partite synapses. It has also been surmised that global signalling via astrocytes networks and highly pervasive signals, such as electromagnetic fields (EMFs), allow the appropriate integration of the various networks especially at crucial nodes level, the tetra-partite synapses. As a matter of fact, it has been shown that astrocytes can form gap-junction-coupled syncytia allowing intercellular communication characterised by a rapid and possibly long-distance transfer of signals. As far as the EMFs are concerned, the concept of broadcasted neuroconnectomics (BNC) has been introduced to describe highly pervasive signals involved in resetting the information handling of brain networks at various miniaturisation levels. In other words, BNC creates, thanks to the EMFs, generated especially by neurons, different assemblages among the various networks forming the brain hyper-network. Thus, it is surmised that neuronal networks are the "core components" of the brain hyper-network that has as special "nodes" the multi-facet tetra-partite synapses. Furthermore, it is suggested that investigations on the functional plasticity of multi-partite synapses in response to BNC can be the background for a new understanding and perhaps a new modelling of brain morpho-functional organisation and integrative actions.

Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks

PubMed Central

Clune, Jeff

2017-01-01

A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their lifetimes and continuously improve those skills via experience. A longstanding obstacle towards that goal is catastrophic forgetting, which is when learning new information erases previously learned information. Catastrophic forgetting occurs in artificial neural networks (ANNs), which have fueled most recent advances in AI. A recent paper proposed that catastrophic forgetting in ANNs can be reduced by promoting modularity, which can limit forgetting by isolating task information to specific clusters of nodes and connections (functional modules). While the prior work did show that modular ANNs suffered less from catastrophic forgetting, it was not able to produce ANNs that possessed task-specific functional modules, thereby leaving the main theory regarding modularity and forgetting untested. We introduce diffusion-based neuromodulation, which simulates the release of diffusing, neuromodulatory chemicals within an ANN that can modulate (i.e. up or down regulate) learning in a spatial region. On the simple diagnostic problem from the prior work, diffusion-based neuromodulation 1) induces task-specific learning in groups of nodes and connections (task-specific localized learning), which 2) produces functional modules for each subtask, and 3) yields higher performance by eliminating catastrophic forgetting. Overall, our results suggest that diffusion-based neuromodulation promotes task-specific localized learning and functional modularity, which can help solve the challenging, but important problem of catastrophic forgetting. PMID:29145413

Light Activated Escape Circuits: A Behavior and Neurophysiology Lab Module using Drosophila Optogenetics

PubMed Central

Titlow, Josh S.; Johnson, Bruce R.; Pulver, Stefan R.

2015-01-01

The neural networks that control escape from predators often show very clear relationships between defined sensory inputs and stereotyped motor outputs. This feature provides unique opportunities for researchers, but it also provides novel opportunities for neuroscience educators. Here we introduce new teaching modules using adult Drosophila that have been engineered to express csChrimson, a red-light sensitive channelrhodopsin, in specific sets of neurons and muscles mediating visually guided escape behaviors. This lab module consists of both behavior and electrophysiology experiments that explore the neural basis of flight escape. Three preparations are described that demonstrate photo-activation of the giant fiber circuit and how to quantify these behaviors. One of the preparations is then used to acquire intracellular electrophysiology recordings from different flight muscles. The diversity of action potential waveforms and firing frequencies observed in the flight muscles make this a rich preparation to study the ionic basic of cellular excitability. By activating different cells within the giant fiber pathway we also demonstrate principles of synaptic transmission and neural circuits. Beyond conveying core neurobiological concepts it is also expected that using these cutting edge techniques will enhance student motivation and attitudes towards biological research. Data collected from students and educators who have been involved in development of the module are presented to support this notion. PMID:26240526

Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer.

PubMed

Besserve, Michel; Lowe, Scott C; Logothetis, Nikos K; Schölkopf, Bernhard; Panzeri, Stefano

2015-01-01

Distributed neural processing likely entails the capability of networks to reconfigure dynamically the directionality and strength of their functional connections. Yet, the neural mechanisms that may allow such dynamic routing of the information flow are not yet fully understood. We investigated the role of gamma band (50-80 Hz) oscillations in transient modulations of communication among neural populations by using measures of direction-specific causal information transfer. We found that the local phase of gamma-band rhythmic activity exerted a stimulus-modulated and spatially-asymmetric directed effect on the firing rate of spatially separated populations within the primary visual cortex. The relationships between gamma phases at different sites (phase shifts) could be described as a stimulus-modulated gamma-band wave propagating along the spatial directions with the largest information transfer. We observed transient stimulus-related changes in the spatial configuration of phases (compatible with changes in direction of gamma wave propagation) accompanied by a relative increase of the amount of information flowing along the instantaneous direction of the gamma wave. These effects were specific to the gamma-band and suggest that the time-varying relationships between gamma phases at different locations mark, and possibly causally mediate, the dynamic reconfiguration of functional connections.

Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer

PubMed Central

Besserve, Michel; Lowe, Scott C.; Logothetis, Nikos K.; Schölkopf, Bernhard; Panzeri, Stefano

2015-01-01

Distributed neural processing likely entails the capability of networks to reconfigure dynamically the directionality and strength of their functional connections. Yet, the neural mechanisms that may allow such dynamic routing of the information flow are not yet fully understood. We investigated the role of gamma band (50–80 Hz) oscillations in transient modulations of communication among neural populations by using measures of direction-specific causal information transfer. We found that the local phase of gamma-band rhythmic activity exerted a stimulus-modulated and spatially-asymmetric directed effect on the firing rate of spatially separated populations within the primary visual cortex. The relationships between gamma phases at different sites (phase shifts) could be described as a stimulus-modulated gamma-band wave propagating along the spatial directions with the largest information transfer. We observed transient stimulus-related changes in the spatial configuration of phases (compatible with changes in direction of gamma wave propagation) accompanied by a relative increase of the amount of information flowing along the instantaneous direction of the gamma wave. These effects were specific to the gamma-band and suggest that the time-varying relationships between gamma phases at different locations mark, and possibly causally mediate, the dynamic reconfiguration of functional connections. PMID:26394205

Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks

PubMed Central

de Santos-Sierra, Daniel; Sanchez-Jimenez, Abel; Garcia-Vellisca, Mariano A.; Navas, Adrian; Villacorta-Atienza, Jose A.

2015-01-01

Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though, the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior of the master one. In this paper, we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh–Rose neurons. PMID:26648863

Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks.

PubMed

de Santos-Sierra, Daniel; Sanchez-Jimenez, Abel; Garcia-Vellisca, Mariano A; Navas, Adrian; Villacorta-Atienza, Jose A

2015-01-01

Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though, the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior of the master one. In this paper, we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh-Rose neurons.

A Neural Network Model to Learn Multiple Tasks under Dynamic Environments

NASA Astrophysics Data System (ADS)

Tsumori, Kenji; Ozawa, Seiichi

When environments are dynamically changed for agents, the knowledge acquired in an environment might be useless in future. In such dynamic environments, agents should be able to not only acquire new knowledge but also modify old knowledge in learning. However, modifying all knowledge acquired before is not efficient because the knowledge once acquired may be useful again when similar environment reappears and some knowledge can be shared among different environments. To learn efficiently in such environments, we propose a neural network model that consists of the following modules: resource allocating network, long-term & short-term memory, and environment change detector. We evaluate the model under a class of dynamic environments where multiple function approximation tasks are sequentially given. The experimental results demonstrate that the proposed model possesses stable incremental learning, accurate environmental change detection, proper association and recall of old knowledge, and efficient knowledge transfer.

5–HT and 5–HT-SO4, but not tryptophan or 5-HIAA levels in single feeding neurons track animal hunger state

PubMed Central

Hatcher, N. G.; Zhang, X.; Stuart, J. N.; Moroz, L. L.; Sweedler, J. V.; Gillette, R.

2014-01-01

Serotonin (5-HT) is an intrinsic modulator of neural network excitation states in gastropod molluscs. 5-HT and related indole metabolites were measured in single, well-characterized serotonergic neurons of the feeding motor network of the predatory sea-slug Pleurobranchaea californica. Indole amounts were compared between paired hungry and satiated animals. Levels of 5-HT and its metabolite 5-HT-SO4 in the metacerebral giant neurons were observed in amounts approximately four-fold and two-fold, respectively, below unfed partners 24 h after a satiating meal. Intracellular levels of 5-hydroxyindole acetic acid and of free tryptophan did not differ significantly with hunger state. These data demonstrate that neurotransmitter levels and their metabolites can vary in goal-directed neural networks in a manner that follows internal state. PMID:18036151

Two algorithms for neural-network design and training with application to channel equalization.

PubMed

Sweatman, C Z; Mulgrew, B; Gibson, G J

1998-01-01

We describe two algorithms for designing and training neural-network classifiers. The first, the linear programming slab algorithm (LPSA), is motivated by the problem of reconstructing digital signals corrupted by passage through a dispersive channel and by additive noise. It constructs a multilayer perceptron (MLP) to separate two disjoint sets by using linear programming methods to identify network parameters. The second, the perceptron learning slab algorithm (PLSA), avoids the computational costs of linear programming by using an error-correction approach to identify parameters. Both algorithms operate in highly constrained parameter spaces and are able to exploit symmetry in the classification problem. Using these algorithms, we develop a number of procedures for the adaptive equalization of a complex linear 4-quadrature amplitude modulation (QAM) channel, and compare their performance in a simulation study. Results are given for both stationary and time-varying channels, the latter based on the COST 207 GSM propagation model.

Photonics: From target recognition to lesion detection

NASA Technical Reports Server (NTRS)

Henry, E. Michael

1994-01-01

Since 1989, Martin Marietta has invested in the development of an innovative concept for robust real-time pattern recognition for any two-dimensioanal sensor. This concept has been tested in simulation, and in laboratory and field hardware, for a number of DOD and commercial uses from automatic target recognition to manufacturing inspection. We have now joined Rose Health Care Systems in developing its use for medical diagnostics. The concept is based on determining regions of interest by using optical Fourier bandpassing as a scene segmentation technique, enhancing those regions using wavelet filters, passing the enhanced regions to a neural network for analysis and initial pattern identification, and following this initial identification with confirmation by optical correlation. The optical scene segmentation and pattern confirmation are performed by the same optical module. The neural network is a recursive error minimization network with a small number of connections and nodes that rapidly converges to a global minimum.

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting.

PubMed

Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca

2016-01-01

In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (<75 pJ) synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.

Oxytocin and vasopressin modulation of the neural correlates of motivation and emotion: results from functional MRI studies in awake rats.

PubMed

Febo, Marcelo; Ferris, Craig F

2014-09-11

Oxytocin and vasopressin modulate a range of species typical behavioral functions that include social recognition, maternal-infant attachment, and modulation of memory, offensive aggression, defensive fear reactions, and reward seeking. We have employed novel functional magnetic resonance mapping techniques in awake rats to explore the roles of these neuropeptides in the maternal and non-maternal brain. Results from the functional neuroimaging studies that are summarized here have directly and indirectly confirmed and supported previous findings. Oxytocin is released within the lactating rat brain during suckling stimulation and activates specific subcortical networks in the maternal brain. Both vasopressin and oxytocin modulate brain regions involved unconditioned fear, processing of social stimuli and the expression of agonistic behaviors. Across studies there are relatively consistent brain networks associated with internal motivational drives and emotional states that are modulated by oxytocin and vasopressin. This article is part of a Special Issue entitled Oxytocin and Social Behav. Copyright © 2014 Elsevier B.V. All rights reserved.

Neural dynamics of reward probability coding: a Magnetoencephalographic study in humans

PubMed Central

Thomas, Julie; Vanni-Mercier, Giovanna; Dreher, Jean-Claude

2013-01-01

Prediction of future rewards and discrepancy between actual and expected outcomes (prediction error) are crucial signals for adaptive behavior. In humans, a number of fMRI studies demonstrated that reward probability modulates these two signals in a large brain network. Yet, the spatio-temporal dynamics underlying the neural coding of reward probability remains unknown. Here, using magnetoencephalography, we investigated the neural dynamics of prediction and reward prediction error computations while subjects learned to associate cues of slot machines with monetary rewards with different probabilities. We showed that event-related magnetic fields (ERFs) arising from the visual cortex coded the expected reward value 155 ms after the cue, demonstrating that reward value signals emerge early in the visual stream. Moreover, a prediction error was reflected in ERF peaking 300 ms after the rewarded outcome and showing decreasing amplitude with higher reward probability. This prediction error signal was generated in a network including the anterior and posterior cingulate cortex. These findings pinpoint the spatio-temporal characteristics underlying reward probability coding. Together, our results provide insights into the neural dynamics underlying the ability to learn probabilistic stimuli-reward contingencies. PMID:24302894

Semantic disturbance in schizophrenia and its relationship to the cognitive neuroscience of attention

PubMed Central

Nestor, P.G.; Han, S.D.; Niznikiewicz, M.; Salisbury, D.; Spencer, K.; Shenton, M.E.; McCarley, R.W.

2010-01-01

We view schizophrenia as producing a failure of attentional modulation that leads to a breakdown in the selective enhancement or inhibition of semantic/lexical representations whose biological substrata are widely distributed across left (dominant) temporal and frontal lobes. Supporting behavioral evidence includes word recall studies that have pointed to a disturbance in connectivity (associative strength) but not network size (number of associates) in patients with schizophrenia. Paralleling these findings are recent neural network simulation studies of the abnormal connectivity effect in schizophrenia through ‘lesioning’ network connection weights while holding constant network size. Supporting evidence at the level of biology are in vitro studies examining N-methyl-d-aspartate (NMDA) receptor antagonists on recurrent inhibition; simulations in neural populations with realistically modeled biophysical properties show NMDA antagonists produce a schizophrenia-like disturbance in pattern association. We propose a similar failure of NMDA-mediated recurrent inhibition as a candidate biological substrate for attention and semantic anomalies of schizophrenia. PMID:11454433

Modulation for emergent networks: serotonin and dopamine.

PubMed

Weng, Juyang; Paslaski, Stephen; Daly, James; VanDam, Courtland; Brown, Jacob

2013-05-01

In autonomous learning, value-sensitive experiences can improve the efficiency of learning. A learning network needs be motivated so that the limited computational resources and the limited lifetime are devoted to events that are of high value for the agent to compete in its environment. The neuromodulatory system of the brain is mainly responsible for developing such a motivation system. Although reinforcement learning has been extensively studied, many existing models are symbolic whose internal nodes or modules have preset meanings. Neural networks have been used to automatically generate internal emergent representations. However, modeling an emergent motivational system for neural networks is still a great challenge. By emergent, we mean that the internal representations emerge autonomously through interactions with the external environments. This work proposes a generic emergent modulatory system for emergent networks, which includes two subsystems - the serotonin system and the dopamine system. The former signals a large class of stimuli that are intrinsically aversive (e.g., stress or pain). The latter signals a large class of stimuli that are intrinsically appetitive (e.g., pleasure or sweet). We experimented with this motivational system for two settings. The first is a visual recognition setting to investigate how such a system can learn through interactions with a teacher, who does not directly give answers, but only punishments and rewards. The second is a setting for wandering in the presence of a friend and a foe. Copyright © 2012 Elsevier Ltd. All rights reserved.

Dynamics of neuromodulatory feedback determines frequency modulation in a reduced respiratory network: a computational study.

PubMed

Toporikova, Natalia; Butera, Robert J

2013-02-01

Neuromodulators, such as amines and neuropeptides, alter the activity of neurons and neuronal networks. In this work, we investigate how neuromodulators, which activate G(q)-protein second messenger systems, can modulate the bursting frequency of neurons in a critical portion of the respiratory neural network, the pre-Bötzinger complex (preBötC). These neurons are a vital part of the ponto-medullary neuronal network, which generates a stable respiratory rhythm whose frequency is regulated by neuromodulator release from the nearby Raphe nucleus. Using a simulated 50-cell network of excitatory preBötC neurons with a heterogeneous distribution of persistent sodium conductance and Ca(2+), we determined conditions for frequency modulation in such a network by simulating interaction between Raphe and preBötC nuclei. We found that the positive feedback between the Raphe excitability and preBötC activity induces frequency modulation in the preBötC neurons. In addition, the frequency of the respiratory rhythm can be regulated via phasic release of excitatory neuromodulators from the Raphe nucleus. We predict that the application of a G(q) antagonist will eliminate this frequency modulation by the Raphe and keep the network frequency constant and low. In contrast, application of a G(q) agonist will result in a high frequency for all levels of Raphe stimulation. Our modeling results also suggest that high [K(+)] requirement in respiratory brain slice experiments may serve as a compensatory mechanism for low neuromodulatory tone. Copyright © 2012 Elsevier B.V. All rights reserved.

Sustained Activity in Hierarchical Modular Neural Networks: Self-Organized Criticality and Oscillations

PubMed Central

Wang, Sheng-Jun; Hilgetag, Claus C.; Zhou, Changsong

2010-01-01

Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information processing. PMID:21852971

Neural Circuitry of Impaired Emotion Regulation in Substance Use Disorders.

PubMed

Wilcox, Claire E; Pommy, Jessica M; Adinoff, Bryon

2016-04-01

Impaired emotion regulation contributes to the development and severity of substance use disorders (substance disorders). This review summarizes the literature on alterations in emotion regulation neural circuitry in substance disorders, particularly in relation to disorders of negative affect (without substance disorder), and it presents promising areas of future research. Emotion regulation paradigms during functional magnetic resonance imaging are conceptualized into four dimensions: affect intensity and reactivity, affective modulation, cognitive modulation, and behavioral control. The neural circuitry associated with impaired emotion regulation is compared in individuals with and without substance disorders, with a focus on amygdala, insula, and prefrontal cortex activation and their functional and structural connectivity. Hypoactivation of the rostral anterior cingulate cortex/ventromedial prefrontal cortex (rACC/vmPFC) is the most consistent finding across studies, dimensions, and clinical populations (individuals with and without substance disorders). The same pattern is evident for regions in the cognitive control network (anterior cingulate and dorsal and ventrolateral prefrontal cortices) during cognitive modulation and behavioral control. These congruent findings are possibly related to attenuated functional and/or structural connectivity between the amygdala and insula and between the rACC/vmPFC and cognitive control network. Although increased amygdala and insula activation is associated with impaired emotion regulation in individuals without substance disorders, it is not consistently observed in substance disorders. Emotion regulation disturbances in substance disorders may therefore stem from impairments in prefrontal functioning, rather than excessive reactivity to emotional stimuli. Treatments for emotion regulation in individuals without substance disorders that normalize prefrontal functioning may offer greater efficacy for substance disorders than treatments that dampen reactivity.

Neural Circuitry of Impaired Emotion Regulation in Substance Use Disorders

PubMed Central

Wilcox, Claire E.; Pommy, Jessica M.; Adinoff, Bryon

2016-01-01

Impaired emotion regulation contributes to the development and severity of substance use disorders (substance disorders). This review summarizes the literature on alterations in emotion regulation neural circuitry in substance disorders, particularly in relation to disorders of negative affect (without substance disorder), and it presents promising areas of future research. Emotion regulation paradigms during functional magnetic resonance imaging are conceptualized into four dimensions: affect intensity and reactivity, affective modulation, cognitive modulation, and behavioral control. The neural circuitry associated with impaired emotion regulation is compared in individuals with and without substance disorders, with a focus on amygdala, insula, and prefrontal cortex activation and their functional and structural connectivity. Hypoactivation of the rostral anterior cingulate cortex/ventromedial prefrontal cortex (rACC/vmPFC) is the most consistent finding across studies, dimensions, and clinical populations (individuals with and without substance disorders). The same pattern is evident for regions in the cognitive control network (anterior cingulate and dorsal and ventrolateral prefrontal cortices) during cognitive modulation and behavioral control. These congruent findings are possibly related to attenuated functional and/or structural connectivity between the amygdala and insula and between the rACC/vmPFC and cognitive control network. Although increased amygdala and insula activation is associated with impaired emotion regulation in individuals without substance disorders, it is not consistently observed in substance disorders. Emotion regulation disturbances in substance disorders may therefore stem from impairments in prefrontal functioning, rather than excessive reactivity to emotional stimuli. Treatments for emotion regulation in individuals without substance disorders that normalize prefrontal functioning may offer greater efficacy for substance disorders than treatments that dampen reactivity. PMID:26771738

When modularization fails to occur: a developmental perspective.

PubMed

D'Souza, Dean; Karmiloff-Smith, Annette

2011-05-01

We argue that models of adult cognition defined in terms of independently functioning modules cannot be applied to development, whether typical or atypical. The infant brain starts out highly interconnected, and it is only over developmental time that neural networks become increasingly specialized-that is, relatively modularized. In the case of atypical development, even when behavioural scores fall within the normal range, they are frequently underpinned by different cognitive and neural processes. In other words, in neurodevelopmental disorders the gradual process of relative modularization may fail to occur.

NeuroGrid: recording action potentials from the surface of the brain.

PubMed

Khodagholy, Dion; Gelinas, Jennifer N; Thesen, Thomas; Doyle, Werner; Devinsky, Orrin; Malliaras, George G; Buzsáki, György

2015-02-01

Recording from neural networks at the resolution of action potentials is critical for understanding how information is processed in the brain. Here, we address this challenge by developing an organic material-based, ultraconformable, biocompatible and scalable neural interface array (the 'NeuroGrid') that can record both local field potentials(LFPs) and action potentials from superficial cortical neurons without penetrating the brain surface. Spikes with features of interneurons and pyramidal cells were simultaneously acquired by multiple neighboring electrodes of the NeuroGrid, allowing for the isolation of putative single neurons in rats. Spiking activity demonstrated consistent phase modulation by ongoing brain oscillations and was stable in recordings exceeding 1 week's duration. We also recorded LFP-modulated spiking activity intraoperatively in patients undergoing epilepsy surgery. The NeuroGrid constitutes an effective method for large-scale, stable recording of neuronal spikes in concert with local population synaptic activity, enhancing comprehension of neural processes across spatiotemporal scales and potentially facilitating diagnosis and therapy for brain disorders.

Attentional modulation of neuronal variability in circuit models of cortex

PubMed Central

Kanashiro, Tatjana; Ocker, Gabriel Koch; Cohen, Marlene R; Doiron, Brent

2017-01-01

The circuit mechanisms behind shared neural variability (noise correlation) and its dependence on neural state are poorly understood. Visual attention is well-suited to constrain cortical models of response variability because attention both increases firing rates and their stimulus sensitivity, as well as decreases noise correlations. We provide a novel analysis of population recordings in rhesus primate visual area V4 showing that a single biophysical mechanism may underlie these diverse neural correlates of attention. We explore model cortical networks where top-down mediated increases in excitability, distributed across excitatory and inhibitory targets, capture the key neuronal correlates of attention. Our models predict that top-down signals primarily affect inhibitory neurons, whereas excitatory neurons are more sensitive to stimulus specific bottom-up inputs. Accounting for trial variability in models of state dependent modulation of neuronal activity is a critical step in building a mechanistic theory of neuronal cognition. DOI: http://dx.doi.org/10.7554/eLife.23978.001 PMID:28590902

Signalling properties and pharmacological analysis of two sulfakinin receptors from the red flour beetle, Tribolium castaneum

USDA-ARS?s Scientific Manuscript database

Sulfakinin is an insect neuropeptide that constitutes an important component of the complex network of hormonal and neural factors that regulate feeding and digestion. The key modulating functions of sulfakinin are mediated by binding and signaling via G-protein coupled receptors. Although a subst...

Spatial Attention-Related Modulation of the N170 by Backward Masked Fearful Faces

ERIC Educational Resources Information Center

Carlson, Joshua M.; Reinke, Karen S.

2010-01-01

Facial expressions are a basic form of non-verbal communication that convey important social information to others. The relevancy of this information is highlighted by findings that backward masked facial expressions facilitate spatial attention. This attention effect appears to be mediated through a neural network consisting of the amygdala,…

Neural networks underlying trait aggression depend on MAOA gene alleles.

PubMed

Klasen, Martin; Wolf, Dhana; Eisner, Patrick D; Habel, Ute; Repple, Jonathan; Vernaleken, Ingo; Schlüter, Thorben; Eggermann, Thomas; Zerres, Klaus; Zepf, Florian D; Mathiak, Klaus

2018-03-01

Low expressing alleles of the MAOA gene (MAOA-L) have been associated with an increased risk for developing an aggressive personality. This suggests an MAOA-L-specific neurobiological vulnerability associated with trait aggression. The neural networks underlying this vulnerability are unknown. The present study investigated genotype-specific associations between resting state brain networks and trait aggression (Buss-Perry Aggression Questionnaire) in 82 healthy Caucasian males. Genotype influences on aggression-related networks were studied for intrinsic and seed-based brain connectivity. Intrinsic connectivity was higher in the ventromedial prefrontal cortex (VMPFC) of MAOA-L compared to high expressing allele (MAOA-H) carriers. Seed-based connectivity analyses revealed genotype differences in the functional involvement of this region. MAOA genotype modulated the relationship between trait aggression and VMPFC connectivity with supramarginal gyrus (SMG) and areas of the default mode network (DMN). Separate analyses for the two groups were performed to better understand how the genotype modulated the relationship between aggression and brain networks. They revealed a positive correlation between VMPFC connectivity and aggression in right angular gyrus (AG) and a negative correlation in right SMG in the MAOA-L group. No such effect emerged in the MAOA-H carriers. The results indicate a particular relevance of VMPFC for aggression in MAOA-L carriers; in specific, a detachment from the DMN along with a strengthened coupling to the AG seems to go along with lower trait aggression. MAOA-L carriers may thus depend on a synchronization of emotion regulation systems (VMPFC) with core areas of empathy (SMG) to prevent aggression.

Frequency mode excitations in two-dimensional Hindmarsh-Rose neural networks

NASA Astrophysics Data System (ADS)

Tabi, Conrad Bertrand; Etémé, Armand Sylvin; Mohamadou, Alidou

2017-05-01

In this work, we explicitly show the existence of two frequency regimes in a two-dimensional Hindmarsh-Rose neural network. Each of the regimes, through the semi-discrete approximation, is shown to be described by a two-dimensional complex Ginzburg-Landau equation. The modulational instability phenomenon for the two regimes is studied, with consideration given to the coupling intensities among neighboring neurons. Analytical solutions are also investigated, along with their propagation in the two frequency regimes. These waves, depending on the coupling strength, are identified as breathers, impulses and trains of soliton-like structures. Although the waves in two regimes appear in some common regions of parameters, some phase differences are noticed and the global dynamics of the system is highly influenced by the values of the coupling terms. For some values of such parameters, the high-frequency regime displays modulated trains of waves, while the low-frequency dynamics keeps the original asymmetric character of action potentials. We argue that in a wide range of pathological situations, strong interactions among neurons can be responsible for some pathological states, including schizophrenia and epilepsy.

Mapping and correction of the CMM workspace error with the use of an electronic gyroscope and neural networks--practical application.

PubMed

Swornowski, Pawel J

2013-01-01

The article presents the application of neural networks in determining and correction of the deformation of a coordinate measuring machine (CMM) workspace. The information about the CMM errors is acquired using an ADXRS401 electronic gyroscope. A test device (PS-20 module) was built and integrated with a commercial measurement system based on the SP25M passive scanning probe and with a PH10M module (Renishaw). The proposed solution was tested on a Kemco 600 CMM and on a DEA Global Clima CMM. In the former case, correction of the CMM errors was performed using the source code of WinIOS software owned by The Institute of Advanced Manufacturing Technology, Cracow, Poland and in the latter on an external PC. Optimum parameters of full and simplified mapping of a given layer of the CMM workspace were determined for practical applications. The proposed method can be employed for the interim check (ISO 10360-2 procedure) or to detect local CMM deformations, occurring when the CMM works at high scanning speeds (>20 mm/s). © Wiley Periodicals, Inc.

System alignment using the Talbot effect

NASA Astrophysics Data System (ADS)

Chevallier, Raymond; Le Falher, Eric; Heggarty, Kevin

1990-08-01

The Talbot effect is utilized to correct an alignment problem related to a neural network used for image recognition, which required the alignment of a spatial light modulator (SLM) with the input module. A mathematical model which employs the Fresnel diffraction theory is presented to describe the method. The calculation of the diffracted amplitude describes the wavefront sphericity and the original object transmittance function in order to qualify the lateral shift of the Talbot image. Another explanation is set forth in terms of plane-wave illumination in the neural network. Using a Fourier series and by describing planes where all the harmonics are in phase, the reconstruction of Talbot images is explained. The alignment is effective when the lenslet array is aligned on the even Talbot images of the SLM pixels and the incident wave is a plane wave. The alignment is evaluated in terms of source and periodicity errors, tilt of the incident plane waves, and finite object dimensions. The effects of the error sources are concluded to be negligible, the lenslet array is shown to be successfully aligned with the SLM, and other alignment applications are shown to be possible.

On the application of neural networks to the classification of phase modulated waveforms

NASA Astrophysics Data System (ADS)

Buchenroth, Anthony; Yim, Joong Gon; Nowak, Michael; Chakravarthy, Vasu

2017-04-01

Accurate classification of phase modulated radar waveforms is a well-known problem in spectrum sensing. Identification of such waveforms aids situational awareness enabling radar and communications spectrum sharing. While various feature extraction and engineering approaches have sought to address this problem, the use of a machine learning algorithm that best utilizes these features is becomes foremost. In this effort, a comparison of a standard shallow and a deep learning approach are explored. Experiments provide insights into classifier architecture, training procedure, and performance.

Neural Network Perception for Mobile Robot Guidance

DTIC Science & Technology

1992-02-16

of this dissertation, the perceptual process - ing module (See Figure 1.1). The job of the perceptual processing module is to transform the information...organization in a perceptual net- work ", Computer, Vol. 21 pp. 105-117. [Linsker, 1989] Linsker, R. (1989) Designing a sensory processing system: What can...Dean A. Pomerleau Support for this work has come from DARPA, under contracts DACA76-35-C-0019, DACA76-85-C-0003, DACA76-85-C-0002, DACA76-89-C-0014 and

A neural mechanism for detecting the distance of a selected target by modulating the FM sweep rate of biosonar in echolocation of bat.

PubMed

Kamata, Eigo; Inoue, Satoru; Zheng, MeiHong; Kashimori, Yoshiki; Kambara, Takeshi

2004-01-01

Most species of bats making echolocation use frequency modulated (FM) ultrasonic pulses to measure the distance to targets. These bats detect with a high accuracy the arrival time differences between emitted pulses and their echoes generated by targets. In order to clarify the neural mechanism for echolocation, we present neural model of inferior colliculus (IC), medial geniculate body (MGB) and auditory cortex (AC) along which information of echo delay times is processed. The bats increase the downward frequency sweep rate of emitted FM pulse as they approach the target. The functional role of this modulation of sweep rate is not yet clear. In order to investigate the role, we calculated the response properties of our models of IC, MGB, and AC changing the target distance and the sweep rate. We found based on the simulations that the distance of a target in various ranges may be encoded the most clearly into the activity pattern of delay time map network in AC, when the sweep rate of FM pulse used is coincided with the observed value which the bats adopt for each range of target distance.

Control chart pattern recognition using RBF neural network with new training algorithm and practical features.

PubMed

Addeh, Abdoljalil; Khormali, Aminollah; Golilarz, Noorbakhsh Amiri

2018-05-04

The control chart patterns are the most commonly used statistical process control (SPC) tools to monitor process changes. When a control chart produces an out-of-control signal, this means that the process has been changed. In this study, a new method based on optimized radial basis function neural network (RBFNN) is proposed for control chart patterns (CCPs) recognition. The proposed method consists of four main modules: feature extraction, feature selection, classification and learning algorithm. In the feature extraction module, shape and statistical features are used. Recently, various shape and statistical features have been presented for the CCPs recognition. In the feature selection module, the association rules (AR) method has been employed to select the best set of the shape and statistical features. In the classifier section, RBFNN is used and finally, in RBFNN, learning algorithm has a high impact on the network performance. Therefore, a new learning algorithm based on the bees algorithm has been used in the learning module. Most studies have considered only six patterns: Normal, Cyclic, Increasing Trend, Decreasing Trend, Upward Shift and Downward Shift. Since three patterns namely Normal, Stratification, and Systematic are very similar to each other and distinguishing them is very difficult, in most studies Stratification and Systematic have not been considered. Regarding to the continuous monitoring and control over the production process and the exact type detection of the problem encountered during the production process, eight patterns have been investigated in this study. The proposed method is tested on a dataset containing 1600 samples (200 samples from each pattern) and the results showed that the proposed method has a very good performance. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Impulsivity and the Modular Organization of Resting-State Neural Networks

PubMed Central

Davis, F. Caroline; Knodt, Annchen R.; Sporns, Olaf; Lahey, Benjamin B.; Zald, David H.; Brigidi, Bart D.; Hariri, Ahmad R.

2013-01-01

Impulsivity is a complex trait associated with a range of maladaptive behaviors, including many forms of psychopathology. Previous research has implicated multiple neural circuits and neurotransmitter systems in impulsive behavior, but the relationship between impulsivity and organization of whole-brain networks has not yet been explored. Using graph theory analyses, we characterized the relationship between impulsivity and the functional segregation (“modularity”) of the whole-brain network architecture derived from resting-state functional magnetic resonance imaging (fMRI) data. These analyses revealed remarkable differences in network organization across the impulsivity spectrum. Specifically, in highly impulsive individuals, regulatory structures including medial and lateral regions of the prefrontal cortex were isolated from subcortical structures associated with appetitive drive, whereas these brain areas clustered together within the same module in less impulsive individuals. Further exploration of the modular organization of whole-brain networks revealed novel shifts in the functional connectivity between visual, sensorimotor, cortical, and subcortical structures across the impulsivity spectrum. The current findings highlight the utility of graph theory analyses of resting-state fMRI data in furthering our understanding of the neurobiological architecture of complex behaviors. PMID:22645253

Stimulus dependence of local field potential spectra: experiment versus theory.

PubMed

Barbieri, Francesca; Mazzoni, Alberto; Logothetis, Nikos K; Panzeri, Stefano; Brunel, Nicolas

2014-10-29

The local field potential (LFP) captures different neural processes, including integrative synaptic dynamics that cannot be observed by measuring only the spiking activity of small populations. Therefore, investigating how LFP power is modulated by external stimuli can offer important insights into sensory neural representations. However, gaining such insight requires developing data-driven computational models that can identify and disambiguate the neural contributions to the LFP. Here, we investigated how networks of excitatory and inhibitory integrate-and-fire neurons responding to time-dependent inputs can be used to interpret sensory modulations of LFP spectra. We computed analytically from such models the LFP spectra and the information that they convey about input and used these analytical expressions to fit the model to LFPs recorded in V1 of anesthetized macaques (Macaca mulatta) during the presentation of color movies. Our expressions explain 60%-98% of the variance of the LFP spectrum shape and its dependency upon movie scenes and we achieved this with realistic values for the best-fit parameters. In particular, synaptic best-fit parameters were compatible with experimental measurements and the predictions of firing rates, based only on the fit of LFP data, correlated with the multiunit spike rate recorded from the same location. Moreover, the parameters characterizing the input to the network across different movie scenes correlated with cross-scene changes of several image features. Our findings suggest that analytical descriptions of spiking neuron networks may become a crucial tool for the interpretation of field recordings. Copyright © 2014 the authors 0270-6474/14/3414589-17$15.00/0.

Modulation of steady state functional connectivity in the default mode and working memory networks by cognitive load.

PubMed

Newton, Allen T; Morgan, Victoria L; Rogers, Baxter P; Gore, John C

2011-10-01

Interregional correlations between blood oxygen level dependent (BOLD) magnetic resonance imaging (fMRI) signals in the resting state have been interpreted as measures of connectivity across the brain. Here we investigate whether such connectivity in the working memory and default mode networks is modulated by changes in cognitive load. Functional connectivity was measured in a steady-state verbal identity N-back task for three different conditions (N = 1, 2, and 3) as well as in the resting state. We found that as cognitive load increases, the functional connectivity within both the working memory the default mode network increases. To test whether functional connectivity between the working memory and the default mode networks changed, we constructed maps of functional connectivity to the working memory network as a whole and found that increasingly negative correlations emerged in a dorsal region of the posterior cingulate cortex. These results provide further evidence that low frequency fluctuations in BOLD signals reflect variations in neural activity and suggests interaction between the default mode network and other cognitive networks. Copyright © 2010 Wiley-Liss, Inc.

Decoding the cortical transformations for visually guided reaching in 3D space.

PubMed

Blohm, Gunnar; Keith, Gerald P; Crawford, J Douglas

2009-06-01

To explore the possible cortical mechanisms underlying the 3-dimensional (3D) visuomotor transformation for reaching, we trained a 4-layer feed-forward artificial neural network to compute a reach vector (output) from the visual positions of both the hand and target viewed from different eye and head orientations (inputs). The emergent properties of the intermediate layers reflected several known neurophysiological findings, for example, gain field-like modulations and position-dependent shifting of receptive fields (RFs). We performed a reference frame analysis for each individual network unit, simulating standard electrophysiological experiments, that is, RF mapping (unit input), motor field mapping, and microstimulation effects (unit outputs). At the level of individual units (in both intermediate layers), the 3 different electrophysiological approaches identified different reference frames, demonstrating that these techniques reveal different neuronal properties and suggesting that a comparison across these techniques is required to understand the neural code of physiological networks. This analysis showed fixed input-output relationships within each layer and, more importantly, within each unit. These local reference frame transformation modules provide the basic elements for the global transformation; their parallel contributions are combined in a gain field-like fashion at the population level to implement both the linear and nonlinear elements of the 3D visuomotor transformation.

Task-Rest Modulation of Basal Ganglia Connectivity in Mild to Moderate Parkinson’s Disease

PubMed Central

Müller-Oehring, Eva M.; Sullivan, Edith V.; Pfefferbaum, Adolf; Huang, Neng C.; Poston, Kathleen L.; Bronte-Stewart, Helen M.; Schulte, Tilman

2014-01-01

Parkinson’s disease (PD) is associated with abnormal synchronization in basal ganglia-thalamo-cortical loops. We tested whether early PD patients without demonstrable cognitive impairment exhibit abnormal modulation of functional connectivity at rest, while engaged in a task, or both. PD and healthy controls underwent two functional MRI scans: a resting-state scan and a Stroop Match-to-Sample task scan. Rest-task modulation of basal ganglia (BG) connectivity was tested using seed-to-voxel connectivity analysis with task and rest time series as conditions. Despite substantial overlap of BG–cortical connectivity patterns in both groups, connectivity differences between groups had clinical and behavioral correlates. During rest, stronger putamen–medial parietal and pallidum–occipital connectivity in PD than controls was associated with worse task performance and more severe PD symptoms suggesting that abnormalities in resting-state connectivity denote neural network dedifferentiation. During the executive task, PD patients showed weaker BG-cortical connectivity than controls, i.e., between caudate–supramarginal gyrus and pallidum–inferior prefrontal regions, that was related to more severe PD symptoms and worse task performance. Yet, task processing also evoked stronger striatal–cortical connectivity, specifically between caudate–prefrontal, caudate–precuneus, and putamen–motor/premotor regions in PD relative to controls, which was related to less severe PD symptoms and better performance on the Stroop task. Thus, stronger task-evoked striatal connectivity in PD demonstrated compensatory neural network enhancement to meet task demands and improve performance levels. fMRI-based network analysis revealed that despite resting-state BG network compromise in PD, BG connectivity to prefrontal, premotor, and precuneus regions can be adequately invoked during executive control demands enabling near normal task performance. PMID:25280970

Midfrontal conflict-related theta-band power reflects neural oscillations that predict behavior.

PubMed

Cohen, Michael X; Donner, Tobias H

2013-12-01

Action monitoring and conflict resolution require the rapid and flexible coordination of activity in multiple brain regions. Oscillatory neural population activity may be a key physiological mechanism underlying such rapid and flexible network coordination. EEG power modulations of theta-band (4-8 Hz) activity over the human midfrontal cortex during response conflict have been proposed to reflect neural oscillations that support conflict detection and resolution processes. However, it has remained unclear whether this frequency-band-specific activity reflects neural oscillations or nonoscillatory responses (i.e., event-related potentials). Here, we show that removing the phase-locked component of the EEG did not reduce the strength of the conflict-related modulation of the residual (i.e., non-phase-locked) theta power over midfrontal cortex. Furthermore, within-subject regression analyses revealed that the non-phase-locked theta power was a significantly better predictor of the conflict condition than was the time-domain phase-locked EEG component. Finally, non-phase-locked theta power showed robust and condition-specific (high- vs. low-conflict) cross-trial correlations with reaction time, whereas the phase-locked component did not. Taken together, our results indicate that most of the conflict-related and behaviorally relevant midfrontal EEG signal reflects a modulation of ongoing theta-band oscillations that occurs during the decision process but is not phase-locked to the stimulus or to the response.

Multiple roles for executive control in belief-desire reasoning: distinct neural networks are recruited for self perspective inhibition and complexity of reasoning.

PubMed

Hartwright, Charlotte E; Apperly, Ian A; Hansen, Peter C

2012-07-16

Belief-desire reasoning is a core component of 'Theory of Mind' (ToM), which can be used to explain and predict the behaviour of agents. Neuroimaging studies reliably identify a network of brain regions comprising a 'standard' network for ToM, including temporoparietal junction and medial prefrontal cortex. Whilst considerable experimental evidence suggests that executive control (EC) may support a functioning ToM, co-ordination of neural systems for ToM and EC is poorly understood. We report here use of a novel task in which psychologically relevant ToM parameters (true versus false belief; approach versus avoidance desire) were manipulated orthogonally. The valence of these parameters not only modulated brain activity in the 'standard' ToM network but also in EC regions. Varying the valence of both beliefs and desires recruits anterior cingulate cortex, suggesting a shared inhibitory component associated with negatively valenced mental state concepts. Varying the valence of beliefs additionally draws on ventrolateral prefrontal cortex, reflecting the need to inhibit self perspective. These data provide the first evidence that separate functional and neural systems for EC may be recruited in the service of different aspects of ToM. Copyright © 2012 Elsevier Inc. All rights reserved.

Computational optical tomography using 3-D deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Nguyen, Thanh; Bui, Vy; Nehmetallah, George

2018-04-01

Deep convolutional neural networks (DCNNs) offer a promising performance for many image processing areas, such as super-resolution, deconvolution, image classification, denoising, and segmentation, with outstanding results. Here, we develop for the first time, to our knowledge, a method to perform 3-D computational optical tomography using 3-D DCNN. A simulated 3-D phantom dataset was first constructed and converted to a dataset of phase objects imaged on a spatial light modulator. For each phase image in the dataset, the corresponding diffracted intensity image was experimentally recorded on a CCD. We then experimentally demonstrate the ability of the developed 3-D DCNN algorithm to solve the inverse problem by reconstructing the 3-D index of refraction distributions of test phantoms from the dataset from their corresponding diffraction patterns.

Analyzing large-scale spiking neural data with HRLAnalysis™

PubMed Central

Thibeault, Corey M.; O'Brien, Michael J.; Srinivasa, Narayan

2014-01-01

The additional capabilities provided by high-performance neural simulation environments and modern computing hardware has allowed for the modeling of increasingly larger spiking neural networks. This is important for exploring more anatomically detailed networks but the corresponding accumulation in data can make analyzing the results of these simulations difficult. This is further compounded by the fact that many existing analysis packages were not developed with large spiking data sets in mind. Presented here is a software suite developed to not only process the increased amount of spike-train data in a reasonable amount of time, but also provide a user friendly Python interface. We describe the design considerations, implementation and features of the HRLAnalysis™ suite. In addition, performance benchmarks demonstrating the speedup of this design compared to a published Python implementation are also presented. The result is a high-performance analysis toolkit that is not only usable and readily extensible, but also straightforward to interface with existing Python modules. PMID:24634655

An Investigation of the Application of Artificial Neural Networks to Adaptive Optics Imaging Systems

DTIC Science & Technology

1991-12-01

neural network and the feedforward neural network studied is the single layer perceptron artificial neural network . The recurrent artificial neural network input...features are the wavefront sensor slope outputs and neighboring actuator feedback commands. The feedforward artificial neural network input

Motor imagery learning modulates functional connectivity of multiple brain systems in resting state.

PubMed

Zhang, Hang; Long, Zhiying; Ge, Ruiyang; Xu, Lele; Jin, Zhen; Yao, Li; Liu, Yijun

2014-01-01

Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning.

Network architecture of the cerebral nuclei (basal ganglia) association and commissural connectome

PubMed Central

Swanson, Larry W.; Sporns, Olaf; Hahn, Joel D.

2016-01-01

The cerebral nuclei form the ventral division of the cerebral hemisphere and are thought to play an important role in neural systems controlling somatic movement and motivation. Network analysis was used to define global architectural features of intrinsic cerebral nuclei circuitry in one hemisphere (association connections) and between hemispheres (commissural connections). The analysis was based on more than 4,000 reports of histologically defined axonal connections involving all 45 gray matter regions of the rat cerebral nuclei and revealed the existence of four asymmetrically interconnected modules. The modules form four topographically distinct longitudinal columns that only partly correspond to previous interpretations of cerebral nuclei structure–function organization. The network of connections within and between modules in one hemisphere or the other is quite dense (about 40% of all possible connections), whereas the network of connections between hemispheres is weak and sparse (only about 5% of all possible connections). Particularly highly interconnected regions (rich club and hubs within it) form a topologically continuous band extending through two of the modules. Connection path lengths among numerous pairs of regions, and among some of the network’s modules, are relatively long, thus accounting for low global efficiency in network communication. These results provide a starting point for reexamining the connectional organization of the cerebral hemispheres as a whole (right and left cerebral cortex and cerebral nuclei together) and their relation to the rest of the nervous system. PMID:27647882

Effects of alexithymia and empathy on the neural processing of social and monetary rewards.

PubMed

Goerlich, Katharina Sophia; Votinov, Mikhail; Lammertz, Sarah E; Winkler, Lina; Spreckelmeyer, Katja N; Habel, Ute; Gründer, Gerhard; Gossen, Anna

2017-07-01

Empathy has been found to affect the neural processing of social and monetary rewards. Alexithymia, a subclinical condition showing a close inverse relationship with empathy is linked to dysfunctions of socio-emotional processing in the brain. Whether alexithymia alters the neural processing of rewards, which is currently unknown. Here, we investigated the influence of both alexithymia and empathy on reward processing using a social incentive delay (SID) task and a monetary incentive delay (MID) task in 45 healthy men undergoing functional magnetic resonance imaging. Controlling for temperament-character dimensions and rejection sensitivity, the relationship of alexithymia and empathy with neural activity in several a priori regions of interest (ROIs) was examined by means of partial correlations, while participants anticipated and received social and monetary rewards. Results were considered significant if they survived Holm-Bonferroni correction for multiple comparisons. Alexithymia modulated neural activity in several ROIs of the emotion and reward network, both during the anticipation of social and monetary rewards and in response to the receipt of monetary rewards. In contrast, empathy did not affect reward anticipation and modulated ROI activity only in response to the receipt of social rewards. These results indicate a significant influence of alexithymia on the processing of social and monetary rewards in the healthy brain.

Spike frequency adaptation is a possible mechanism for control of attractor preference in auto-associative neural networks

NASA Astrophysics Data System (ADS)

Roach, James; Sander, Leonard; Zochowski, Michal

Auto-associative memory is the ability to retrieve a pattern from a small fraction of the pattern and is an important function of neural networks. Within this context, memories that are stored within the synaptic strengths of networks act as dynamical attractors for network firing patterns. In networks with many encoded memories, some attractors will be stronger than others. This presents the problem of how networks switch between attractors depending on the situation. We suggest that regulation of neuronal spike-frequency adaptation (SFA) provides a universal mechanism for network-wide attractor selectivity. Here we demonstrate in a Hopfield type attractor network that neurons minimal SFA will reliably activate in the pattern corresponding to a local attractor and that a moderate increase in SFA leads to the network to converge to the strongest attractor state. Furthermore, we show that on long time scales SFA allows for temporal sequences of activation to emerge. Finally, using a model of cholinergic modulation within the cortex we argue that dynamic regulation of attractor preference by SFA could be critical for the role of acetylcholine in attention or for arousal states in general. This work was supported by: NSF Graduate Research Fellowship Program under Grant No. DGE 1256260 (JPR), NSF CMMI 1029388 (MRZ) and NSF PoLS 1058034 (MRZ & LMS).

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

PubMed

Chen, Liang; Xue, Wei; Tokuda, Naoyuki

2010-08-01

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

Effect of synapse dilution on the memory retrieval in structured attractor neural networks

NASA Astrophysics Data System (ADS)

Brunel, N.

1993-08-01

We investigate a simple model of structured attractor neural network (ANN). In this network a module codes for the category of the stored information, while another group of neurons codes for the remaining information. The probability distribution of stabilities of the patterns and the prototypes of the categories are calculated, for two different synaptic structures. The stability of the prototypes is shown to increase when the fraction of neurons coding for the category goes down. Then the effect of synapse destruction on the retrieval is studied in two opposite situations : first analytically in sparsely connected networks, then numerically in completely connected ones. In both cases the behaviour of the structured network and that of the usual homogeneous networks are compared. When lesions increase, two transitions are shown to appear in the behaviour of the structured network when one of the patterns is presented to the network. After the first transition the network recognizes the category of the pattern but not the individual pattern. After the second transition the network recognizes nothing. These effects are similar to syndromes caused by lesions in the central visual system, namely prosopagnosia and agnosia. In both types of networks (structured or homogeneous) the stability of the prototype is greater than the stability of individual patterns, however the first transition, for completely connected networks, occurs only when the network is structured.

Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks.

PubMed

Ertosun, Mehmet Günhan; Rubin, Daniel L

2015-01-01

Brain glioma is the most common primary malignant brain tumors in adults with different pathologic subtypes: Lower Grade Glioma (LGG) Grade II, Lower Grade Glioma (LGG) Grade III, and Glioblastoma Multiforme (GBM) Grade IV. The survival and treatment options are highly dependent of this glioma grade. We propose a deep learning-based, modular classification pipeline for automated grading of gliomas using digital pathology images. Whole tissue digitized images of pathology slides obtained from The Cancer Genome Atlas (TCGA) were used to train our deep learning modules. Our modular pipeline provides diagnostic quality statistics, such as precision, sensitivity and specificity, of the individual deep learning modules, and (1) facilitates training given the limited data in this domain, (2) enables exploration of different deep learning structures for each module, (3) leads to developing less complex modules that are simpler to analyze, and (4) provides flexibility, permitting use of single modules within the framework or use of other modeling or machine learning applications, such as probabilistic graphical models or support vector machines. Our modular approach helps us meet the requirements of minimum accuracy levels that are demanded by the context of different decision points within a multi-class classification scheme. Convolutional Neural Networks are trained for each module for each sub-task with more than 90% classification accuracies on validation data set, and achieved classification accuracy of 96% for the task of GBM vs LGG classification, 71% for further identifying the grade of LGG into Grade II or Grade III on independent data set coming from new patients from the multi-institutional repository.

Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks

PubMed Central

Ertosun, Mehmet Günhan; Rubin, Daniel L.

2015-01-01

Brain glioma is the most common primary malignant brain tumors in adults with different pathologic subtypes: Lower Grade Glioma (LGG) Grade II, Lower Grade Glioma (LGG) Grade III, and Glioblastoma Multiforme (GBM) Grade IV. The survival and treatment options are highly dependent of this glioma grade. We propose a deep learning-based, modular classification pipeline for automated grading of gliomas using digital pathology images. Whole tissue digitized images of pathology slides obtained from The Cancer Genome Atlas (TCGA) were used to train our deep learning modules. Our modular pipeline provides diagnostic quality statistics, such as precision, sensitivity and specificity, of the individual deep learning modules, and (1) facilitates training given the limited data in this domain, (2) enables exploration of different deep learning structures for each module, (3) leads to developing less complex modules that are simpler to analyze, and (4) provides flexibility, permitting use of single modules within the framework or use of other modeling or machine learning applications, such as probabilistic graphical models or support vector machines. Our modular approach helps us meet the requirements of minimum accuracy levels that are demanded by the context of different decision points within a multi-class classification scheme. Convolutional Neural Networks are trained for each module for each sub-task with more than 90% classification accuracies on validation data set, and achieved classification accuracy of 96% for the task of GBM vs LGG classification, 71% for further identifying the grade of LGG into Grade II or Grade III on independent data set coming from new patients from the multi-institutional repository. PMID:26958289

Effects of Transcranial Direct Current Stimulation on Neural Networks in Young and Older Adults

PubMed

Martin, Andrew K; Meinzer, Marcus; Lindenberg, Robert; Sieg, Mira M; Nachtigall, Laura; Flöel, Agnes

2017-11-01

Transcranial direct current stimulation (tDCS) may be a viable tool to improve motor and cognitive function in advanced age. However, although a number of studies have demonstrated improved cognitive performance in older adults, other studies have failed to show restorative effects. The neural effects of beneficial stimulation response in both age groups is lacking. In the current study, tDCS was administered during simultaneous fMRI in 42 healthy young and older participants. Semantic word generation and motor speech baseline tasks were used to investigate behavioral and neural effects of uni- and bihemispheric motor cortex tDCS in a three-way, crossover, sham tDCS controlled design. Independent components analysis assessed differences in task-related activity between the two age groups and tDCS effects at the network level. We also explored whether laterality of language network organization was effected by tDCS. Behaviorally, both active tDCS conditions significantly improved semantic word retrieval performance in young and older adults and were comparable between groups and stimulation conditions. Network-level tDCS effects were identified in the ventral and dorsal anterior cingulate networks in the combined sample during semantic fluency and motor speech tasks. In addition, a shift toward enhanced left laterality was identified in the older adults for both active stimulation conditions. Thus, tDCS results in common network-level modulations and behavioral improvements for both age groups, with an additional effect of increasing left laterality in older adults.

Advanced Aeroservoelastic Testing and Data Analysis (Les Essais Aeroservoelastiques et l’Analyse des Donnees).

DTIC Science & Technology

1995-11-01

network - based AFS concepts. Neural networks can addition of vanes in each engine exhaust for thrust provide...parameter estimation programs 19-11 8.6 Neural Network Based Methods unknown parameters of the postulated state space model Artificial neural network ...Forward Neural Network the network that the applicability of the recurrent neural and ii) Recurrent Neural Network [117-119]. network to

Autonomic innervation of immune organs and neuroimmune modulation.

PubMed

Mignini, F; Streccioni, V; Amenta, F

2003-02-01

1. Increasing evidence indicates the occurrence of functional interconnections between immune and nervous systems, although data available on the mechanisms of this bi-directional cross-talking are frequently incomplete and not always focussed on their relevance for neuroimmune modulation. 2. Primary (bone marrow and thymus) and secondary (spleen and lymph nodes) lymphoid organs are supplied with an autonomic (mainly sympathetic) efferent innervation and with an afferent sensory innervation. Anatomical studies have revealed origin, pattern of distribution and targets of nerve fibre populations supplying lymphoid organs. 3. Classic (catecholamines and acetylcholine) and peptide transmitters of neural and non-neural origin are released in the lymphoid microenvironment and contribute to neuroimmune modulation. Neuropeptide Y, substance P, calcitonin gene-related peptide, and vasoactive intestinal peptide represent the neuropeptides most involved in neuroimmune modulation. 4. Immune cells and immune organs express specific receptors for (neuro)transmitters. These receptors have been shown to respond in vivo and/or in vitro to the neural substances and their manipulation can alter immune responses. Changes in immune function can also influence the distribution of nerves and the expression of neural receptors in lymphoid organs. 5. Data on different populations of nerve fibres supplying immune organs and their role in providing a link between nervous and immune systems are reviewed. Anatomical connections between nervous and immune systems represent the structural support of the complex network of immune responses. A detailed knowledge of interactions between nervous and immune systems may represent an important basis for the development of strategies for treating pathologies in which altered neuroimmune cross-talking may be involved.

Pain perception and hypnosis: findings from recent functional neuroimaging studies.

PubMed

Del Casale, Antonio; Ferracuti, Stefano; Rapinesi, Chiara; Serata, Daniele; Caltagirone, Saverio Simone; Savoja, Valeria; Piacentino, Daria; Callovini, Gemma; Manfredi, Giovanni; Sani, Gabriele; Kotzalidis, Georgios D; Girardi, Paolo

2015-01-01

Hypnosis modulates pain perception and tolerance by affecting cortical and subcortical activity in brain regions involved in these processes. By reviewing functional neuroimaging studies focusing on pain perception under hypnosis, the authors aimed to identify brain activation-deactivation patterns occurring in hypnosis-modulated pain conditions. Different changes in brain functionality occurred throughout all components of the pain network and other brain areas. The anterior cingulate cortex appears to be central in modulating pain circuitry activity under hypnosis. Most studies also showed that the neural functions of the prefrontal, insular, and somatosensory cortices are consistently modified during hypnosis-modulated pain conditions. Functional neuroimaging studies support the clinical use of hypnosis in the management of pain conditions.

Theta-Modulated Gamma-Band Synchronization Among Activated Regions During a Verb Generation Task

PubMed Central

Doesburg, Sam M.; Vinette, Sarah A.; Cheung, Michael J.; Pang, Elizabeth W.

2012-01-01

Expressive language is complex and involves processing within a distributed network of cortical regions. Functional MRI and magnetoencephalography (MEG) have identified brain areas critical for expressive language, but how these regions communicate across the network remains poorly understood. It is thought that synchronization of oscillations between neural populations, particularly at a gamma rate (>30 Hz), underlies functional integration within cortical networks. Modulation of gamma rhythms by theta-band oscillations (4–8 Hz) has been proposed as a mechanism for the integration of local cell coalitions into large-scale networks underlying cognition and perception. The present study tested the hypothesis that these oscillatory mechanisms of functional integration were present within the expressive language network. We recorded MEG while subjects performed a covert verb generation task. We localized activated cortical regions using beamformer analysis, calculated inter-regional phase locking between activated areas, and measured modulation of inter-regional gamma synchronization by theta phase. The results show task-dependent gamma-band synchronization among regions activated during the performance of the verb generation task, and we provide evidence that these transient and periodic instances of high-frequency connectivity were modulated by the phase of cortical theta oscillations. These findings suggest that oscillatory synchronization and cross-frequency interactions are mechanisms for functional integration among distributed brain areas supporting expressive language processing. PMID:22707946

Using neural networks and Dyna algorithm for integrated planning, reacting and learning in systems

NASA Technical Reports Server (NTRS)

Lima, Pedro; Beard, Randal

1992-01-01

The traditional AI answer to the decision making problem for a robot is planning. However, planning is usually CPU-time consuming, depending on the availability and accuracy of a world model. The Dyna system generally described in earlier work, uses trial and error to learn a world model which is simultaneously used to plan reactions resulting in optimal action sequences. It is an attempt to integrate planning, reactive, and learning systems. The architecture of Dyna is presented. The different blocks are described. There are three main components of the system. The first is the world model used by the robot for internal world representation. The input of the world model is the current state and the action taken in the current state. The output is the corresponding reward and resulting state. The second module in the system is the policy. The policy observes the current state and outputs the action to be executed by the robot. At the beginning of program execution, the policy is stochastic and through learning progressively becomes deterministic. The policy decides upon an action according to the output of an evaluation function, which is the third module of the system. The evaluation function takes the following as input: the current state of the system, the action taken in that state, the resulting state, and a reward generated by the world which is proportional to the current distance from the goal state. Originally, the work proposed was as follows: (1) to implement a simple 2-D world where a 'robot' is navigating around obstacles, to learn the path to a goal, by using lookup tables; (2) to substitute the world model and Q estimate function Q by neural networks; and (3) to apply the algorithm to a more complex world where the use of a neural network would be fully justified. In this paper, the system design and achieved results will be described. First we implement the world model with a neural network and leave Q implemented as a look up table. Next, we use a lookup table for the world model and implement the Q function with a neural net. Time limitations prevented the combination of these two approaches. The final section discusses the results and gives clues for future work.

Online dimensionality reduction using competitive learning and Radial Basis Function network.

PubMed

Tomenko, Vladimir

2011-06-01

The general purpose dimensionality reduction method should preserve data interrelations at all scales. Additional desired features include online projection of new data, processing nonlinearly embedded manifolds and large amounts of data. The proposed method, called RBF-NDR, combines these features. RBF-NDR is comprised of two modules. The first module learns manifolds by utilizing modified topology representing networks and geodesic distance in data space and approximates sampled or streaming data with a finite set of reference patterns, thus achieving scalability. Using input from the first module, the dimensionality reduction module constructs mappings between observation and target spaces. Introduction of specific loss function and synthesis of the training algorithm for Radial Basis Function network results in global preservation of data structures and online processing of new patterns. The RBF-NDR was applied for feature extraction and visualization and compared with Principal Component Analysis (PCA), neural network for Sammon's projection (SAMANN) and Isomap. With respect to feature extraction, the method outperformed PCA and yielded increased performance of the model describing wastewater treatment process. As for visualization, RBF-NDR produced superior results compared to PCA and SAMANN and matched Isomap. For the Topic Detection and Tracking corpus, the method successfully separated semantically different topics. Copyright © 2011 Elsevier Ltd. All rights reserved.

Liraglutide Modulates Appetite and Body Weight Via GLP-1R-Expressing Glutamatergic Neurons.

PubMed

Adams, Jessica M; Pei, Hongjuan; Sandoval, Darleen A; Seeley, Randy J; Chang, Rui B; Liberles, Stephen D; Olson, David P

2018-05-18

Glucagon-like peptide-1 receptor (GLP-1R) agonists are FDA-approved weight loss drugs. Despite their widespread use, the sites of action through which GLP-1R agonists (GLP1RAs) impact appetite and body weight are still not fully understood. Here, we determined whether GLP-1Rs in either GABAergic or glutamatergic neurons are necessary for the acute and chronic effects of the GLP1RA liraglutide on food intake, visceral illness, body weight and neural network activation. We found that mice lacking GLP-1Rs in vGAT -expressing GABAergic neurons responded identically to controls in all parameters measured, whereas deletion of GLP-1Rs in vGlut2 -expressing glutamatergic neurons eliminated liraglutide-induced weight loss and visceral illness and severely attenuated its effects on feeding. Concomitantly, deletion of GLP-1Rs from glutamatergic neurons completely abolished the neural network activation observed after liraglutide administration. We conclude that liraglutide activates a dispersed but discrete neural network to mediate its physiological effects, and that these effects require GLP-1R expression on glutamatergic but not GABAergic neurons. © 2018 by the American Diabetes Association.

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

PubMed

Li, Yuhuan; Wang, Jing; Zhang, Jixian

2006-06-01

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

Squeeze-SegNet: a new fast deep convolutional neural network for semantic segmentation

NASA Astrophysics Data System (ADS)

Nanfack, Geraldin; Elhassouny, Azeddine; Oulad Haj Thami, Rachid

2018-04-01

The recent researches in Deep Convolutional Neural Network have focused their attention on improving accuracy that provide significant advances. However, if they were limited to classification tasks, nowadays with contributions from Scientific Communities who are embarking in this field, they have become very useful in higher level tasks such as object detection and pixel-wise semantic segmentation. Thus, brilliant ideas in the field of semantic segmentation with deep learning have completed the state of the art of accuracy, however this architectures become very difficult to apply in embedded systems as is the case for autonomous driving. We present a new Deep fully Convolutional Neural Network for pixel-wise semantic segmentation which we call Squeeze-SegNet. The architecture is based on Encoder-Decoder style. We use a SqueezeNet-like encoder and a decoder formed by our proposed squeeze-decoder module and upsample layer using downsample indices like in SegNet and we add a deconvolution layer to provide final multi-channel feature map. On datasets like Camvid or City-states, our net gets SegNet-level accuracy with less than 10 times fewer parameters than SegNet.

Using recurrent neural networks for adaptive communication channel equalization.

PubMed

Kechriotis, G; Zervas, E; Manolakos, E S

1994-01-01

Nonlinear adaptive filters based on a variety of neural network models have been used successfully for system identification and noise-cancellation in a wide class of applications. An important problem in data communications is that of channel equalization, i.e., the removal of interferences introduced by linear or nonlinear message corrupting mechanisms, so that the originally transmitted symbols can be recovered correctly at the receiver. In this paper we introduce an adaptive recurrent neural network (RNN) based equalizer whose small size and high performance makes it suitable for high-speed channel equalization. We propose RNN based structures for both trained adaptation and blind equalization, and we evaluate their performance via extensive simulations for a variety of signal modulations and communication channel models. It is shown that the RNN equalizers have comparable performance with traditional linear filter based equalizers when the channel interferences are relatively mild, and that they outperform them by several orders of magnitude when either the channel's transfer function has spectral nulls or severe nonlinear distortion is present. In addition, the small-size RNN equalizers, being essentially generalized IIR filters, are shown to outperform multilayer perceptron equalizers of larger computational complexity in linear and nonlinear channel equalization cases.

Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals.

PubMed

Kim, Junkyeong; Lee, Chaggil; Park, Seunghee

2017-06-07

Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process.

Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals

PubMed Central

Kim, Junkyeong; Lee, Chaggil; Park, Seunghee

2017-01-01

Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process. PMID:28590456

Neural networks for aircraft control

NASA Technical Reports Server (NTRS)

Linse, Dennis

1990-01-01

Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

Neural and cognitive plasticity: from maps to minds.

PubMed

Mercado, Eduardo

2008-01-01

Some species and individuals are able to learn cognitive skills more flexibly than others. Learning experiences and cortical function are known to contribute to such differences, but the specific factors that determine an organism's intellectual capacities remain unclear. Here, an integrative framework is presented suggesting that variability in cognitive plasticity reflects neural constraints on the precision and extent of an organism's stimulus representations. Specifically, it is hypothesized that cognitive plasticity depends on the number and diversity of cortical modules that an organism has available as well as the brain's capacity to flexibly reconfigure and customize networks of these modules. The author relates this framework to past proposals on the neural mechanisms of intelligence, including (a) the relationship between brain size and intellectual capacity; (b) the role of prefrontal cortex in cognitive control and the maintenance of stimulus representations; and (c) the impact of neural plasticity and efficiency on the acquisition and performance of cognitive skills. The proposed framework provides a unified account of variability in cognitive plasticity as a function of species, age, and individual, and it makes specific predictions about how manipulations of cortical structure and function will impact intellectual capacity. Copyright (c) 2008 APA.

The tongue and its control by sleep state-dependent modulators.

PubMed

Horner, R L

2011-12-01

The neural networks controlling vital functions such as breathing are embedded in the brain, the neural and chemical environment of which changes with state, i.e., wakefulness, non-rapid eye movement (non-REM) sleep and REM sleep, and with commonly administered drugs such as anaesthetics, sedatives and ethanol. One particular output from the state-dependent chemical brain is the focus of attention in this paper; the motor output to the muscles of the tongue, specifically the actions of state-dependent modulators acting at the hypoglossal motor pool. Determining the mechanisms underlying the modulation of the hypoglossal motor output during sleep is relevant to understanding the spectrum of increased upper airway resistance, airflow limitation, hypoventilation and airway obstructions that occur during natural and drug-influenced sleep in humans. Understanding the mechanisms underlying upper airway dysfunction in sleep-disordered breathing is also important given the large and growing prevalence of obstructive sleep apnea syndrome which constitutes a major public health problem with serious clinical, social and economic consequences.

Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images.

PubMed

Gong, Kuang; Yang, Jaewon; Kim, Kyungsang; El Fakhri, Georges; Seo, Youngho; Li, Quanzheng

2018-05-23

Positron Emission Tomography (PET) is a functional imaging modality widely used in neuroscience studies. To obtain meaningful quantitative results from PET images, attenuation correction is necessary during image reconstruction. For PET/MR hybrid systems, PET attenuation is challenging as Magnetic Resonance (MR) images do not reflect attenuation coefficients directly. To address this issue, we present deep neural network methods to derive the continuous attenuation coefficients for brain PET imaging from MR images. With only Dixon MR images as the network input, the existing U-net structure was adopted and analysis using forty patient data sets shows it is superior than other Dixon based methods. When both Dixon and zero echo time (ZTE) images are available, we have proposed a modified U-net structure, named GroupU-net, to efficiently make use of both Dixon and ZTE information through group convolution modules when the network goes deeper. Quantitative analysis based on fourteen real patient data sets demonstrates that both network approaches can perform better than the standard methods, and the proposed network structure can further reduce the PET quantification error compared to the U-net structure. © 2018 Institute of Physics and Engineering in Medicine.

Local community detection as pattern restoration by attractor dynamics of recurrent neural networks.

PubMed

Okamoto, Hiroshi

2016-08-01

Densely connected parts in networks are referred to as "communities". Community structure is a hallmark of a variety of real-world networks. Individual communities in networks form functional modules of complex systems described by networks. Therefore, finding communities in networks is essential to approaching and understanding complex systems described by networks. In fact, network science has made a great deal of effort to develop effective and efficient methods for detecting communities in networks. Here we put forward a type of community detection, which has been little examined so far but will be practically useful. Suppose that we are given a set of source nodes that includes some (but not all) of "true" members of a particular community; suppose also that the set includes some nodes that are not the members of this community (i.e., "false" members of the community). We propose to detect the community from this "imperfect" and "inaccurate" set of source nodes using attractor dynamics of recurrent neural networks. Community detection by the proposed method can be viewed as restoration of the original pattern from a deteriorated pattern, which is analogous to cue-triggered recall of short-term memory in the brain. We demonstrate the effectiveness of the proposed method using synthetic networks and real social networks for which correct communities are known. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Low power signal processing research at Stanford

NASA Technical Reports Server (NTRS)

Burr, J.; Williamson, P. R.; Peterson, A.

1991-01-01

This paper gives an overview of the research being conducted at Stanford University's Space, Telecommunications, and Radioscience Laboratory in the area of low energy computation. It discusses the work we are doing in large scale digital VLSI neural networks, interleaved processor and pipelined memory architectures, energy estimation and optimization, multichip module packaging, and low voltage digital logic.

Hybrid Architectures and Their Impact on Intelligent Design

NASA Technical Reports Server (NTRS)

Kandel, Abe

1996-01-01

In this presentation we investigate a novel framework for the design of autonomous fuzzy intelligent systems. The system integrates the following modules into a single autonomous entity: (1) a fuzzy expert system; (2) artificial neural network; (3) genetic algorithm; and (4) case-base reasoning. We describe the integration of these units into one intelligent structure and discuss potential applications.

Are individuals with higher psychopathic traits better learners at lying? Behavioural and neural evidence.

PubMed

Shao, R; Lee, T M C

2017-07-25

High psychopathy is characterized by untruthfulness and manipulativeness. However, existing evidence on higher propensity or capacity to lie among non-incarcerated high-psychopathic individuals is equivocal. Of particular importance, no research has investigated whether greater psychopathic tendency is associated with better 'trainability' of lying. An understanding of whether the neurobehavioral processes of lying are modifiable through practice offers significant theoretical and practical implications. By employing a longitudinal design involving university students with varying degrees of psychopathic traits, we successfully demonstrate that the performance speed of lying about face familiarity significantly improved following two sessions of practice, which occurred only among those with higher, but not lower, levels of psychopathic traits. Furthermore, this behavioural improvement associated with higher psychopathic tendency was predicted by a reduction in lying-related neural signals and by functional connectivity changes in the frontoparietal and cerebellum networks. Our findings provide novel and pivotal evidence suggesting that psychopathic traits are the key modulating factors of the plasticity of both behavioural and neural processes underpinning lying. These findings broadly support conceptualization of high-functioning individuals with higher psychopathic traits as having preserved, or arguably superior, functioning in neural networks implicated in cognitive executive processing, but deficiencies in affective neural processes, from a neuroplasticity perspective.

Audiovisual integration in hemianopia: A neurocomputational account based on cortico-collicular interaction.

PubMed

Magosso, Elisa; Bertini, Caterina; Cuppini, Cristiano; Ursino, Mauro

2016-10-01

Hemianopic patients retain some abilities to integrate audiovisual stimuli in the blind hemifield, showing both modulation of visual perception by auditory stimuli and modulation of auditory perception by visual stimuli. Indeed, conscious detection of a visual target in the blind hemifield can be improved by a spatially coincident auditory stimulus (auditory enhancement of visual detection), while a visual stimulus in the blind hemifield can improve localization of a spatially coincident auditory stimulus (visual enhancement of auditory localization). To gain more insight into the neural mechanisms underlying these two perceptual phenomena, we propose a neural network model including areas of neurons representing the retina, primary visual cortex (V1), extrastriate visual cortex, auditory cortex and the Superior Colliculus (SC). The visual and auditory modalities in the network interact via both direct cortical-cortical connections and subcortical-cortical connections involving the SC; the latter, in particular, integrates visual and auditory information and projects back to the cortices. Hemianopic patients were simulated by unilaterally lesioning V1, and preserving spared islands of V1 tissue within the lesion, to analyze the role of residual V1 neurons in mediating audiovisual integration. The network is able to reproduce the audiovisual phenomena in hemianopic patients, linking perceptions to neural activations, and disentangles the individual contribution of specific neural circuits and areas via sensitivity analyses. The study suggests i) a common key role of SC-cortical connections in mediating the two audiovisual phenomena; ii) a different role of visual cortices in the two phenomena: auditory enhancement of conscious visual detection being conditional on surviving V1 islands, while visual enhancement of auditory localization persisting even after complete V1 damage. The present study may contribute to advance understanding of the audiovisual dialogue between cortical and subcortical structures in healthy and unisensory deficit conditions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Infrared dim small target segmentation method based on ALI-PCNN model

NASA Astrophysics Data System (ADS)

Zhao, Shangnan; Song, Yong; Zhao, Yufei; Li, Yun; Li, Xu; Jiang, Yurong; Li, Lin

2017-10-01

Pulse Coupled Neural Network (PCNN) is improved by Adaptive Lateral Inhibition (ALI), while a method of infrared (IR) dim small target segmentation based on ALI-PCNN model is proposed in this paper. Firstly, the feeding input signal is modulated by lateral inhibition network to suppress background. Then, the linking input is modulated by ALI, and linking weight matrix is generated adaptively by calculating ALI coefficient of each pixel. Finally, the binary image is generated through the nonlinear modulation and the pulse generator in PCNN. The experimental results show that the segmentation effect as well as the values of contrast across region and uniformity across region of the proposed method are better than the OTSU method, maximum entropy method, the methods based on conventional PCNN and visual attention, and the proposed method has excellent performance in extracting IR dim small target from complex background.

Time Series Neural Network Model for Part-of-Speech Tagging Indonesian Language

NASA Astrophysics Data System (ADS)

Tanadi, Theo

2018-03-01

Part-of-speech tagging (POS tagging) is an important part in natural language processing. Many methods have been used to do this task, including neural network. This paper models a neural network that attempts to do POS tagging. A time series neural network is modelled to solve the problems that a basic neural network faces when attempting to do POS tagging. In order to enable the neural network to have text data input, the text data will get clustered first using Brown Clustering, resulting a binary dictionary that the neural network can use. To further the accuracy of the neural network, other features such as the POS tag, suffix, and affix of previous words would also be fed to the neural network.

Multitask neurovision processor with extensive feedback and feedforward connections

NASA Astrophysics Data System (ADS)

Gupta, Madan M.; Knopf, George K.

1991-11-01

A multi-task neuro-vision parameter which performs a variety of information processing operations associated with the early stages of biological vision is presented. The network architecture of this neuro-vision processor, called the positive-negative (PN) neural processor, is loosely based on the neural activity fields exhibited by thalamic and cortical nervous tissue layers. The computational operation performed by the processor arises from the strength of the recurrent feedback among the numerous positive and negative neural computing units. By adjusting the feedback connections it is possible to generate diverse dynamic behavior that may be used for short-term visual memory (STVM), spatio-temporal filtering (STF), and pulse frequency modulation (PFM). The information attributes that are to be processes may be regulated by modifying the feedforward connections from the signal space to the neural processor.

Network interactions underlying mirror feedback in stroke: A dynamic causal modeling study.

PubMed

Saleh, Soha; Yarossi, Mathew; Manuweera, Thushini; Adamovich, Sergei; Tunik, Eugene

2017-01-01

Mirror visual feedback (MVF) is potentially a powerful tool to facilitate recovery of disordered movement and stimulate activation of under-active brain areas due to stroke. The neural mechanisms underlying MVF have therefore been a focus of recent inquiry. Although it is known that sensorimotor areas can be activated via mirror feedback, the network interactions driving this effect remain unknown. The aim of the current study was to fill this gap by using dynamic causal modeling to test the interactions between regions in the frontal and parietal lobes that may be important for modulating the activation of the ipsilesional motor cortex during mirror visual feedback of unaffected hand movement in stroke patients. Our intent was to distinguish between two theoretical neural mechanisms that might mediate ipsilateral activation in response to mirror-feedback: transfer of information between bilateral motor cortices versus recruitment of regions comprising an action observation network which in turn modulate the motor cortex. In an event-related fMRI design, fourteen chronic stroke subjects performed goal-directed finger flexion movements with their unaffected hand while observing real-time visual feedback of the corresponding (veridical) or opposite (mirror) hand in virtual reality. Among 30 plausible network models that were tested, the winning model revealed significant mirror feedback-based modulation of the ipsilesional motor cortex arising from the contralesional parietal cortex, in a region along the rostral extent of the intraparietal sulcus. No winning model was identified for the veridical feedback condition. We discuss our findings in the context of supporting the latter hypothesis, that mirror feedback-based activation of motor cortex may be attributed to engagement of a contralateral (contralesional) action observation network. These findings may have important implications for identifying putative cortical areas, which may be targeted with non-invasive brain stimulation as a means of potentiating the effects of mirror training.

Synaptic dynamics regulation in response to high frequency stimulation in neuronal networks

NASA Astrophysics Data System (ADS)

Su, Fei; Wang, Jiang; Li, Huiyan; Wei, Xile; Yu, Haitao; Deng, Bin

2018-02-01

High frequency stimulation (HFS) has confirmed its ability in modulating the pathological neural activities. However its detailed mechanism is unclear. This study aims to explore the effects of HFS on neuronal networks dynamics. First, the two-neuron FitzHugh-Nagumo (FHN) networks with static coupling strength and the small-world FHN networks with spike-time-dependent plasticity (STDP) modulated synaptic coupling strength are constructed. Then, the multi-scale method is used to transform the network models into equivalent averaged models, where the HFS intensity is modeled as the ratio between stimulation amplitude and frequency. Results show that in static two-neuron networks, there is still synaptic current projected to the postsynaptic neuron even if the presynaptic neuron is blocked by the HFS. In the small-world networks, the effects of the STDP adjusting rate parameter on the inactivation ratio and synchrony degree increase with the increase of HFS intensity. However, only when the HFS intensity becomes very large can the STDP time window parameter affect the inactivation ratio and synchrony index. Both simulation and numerical analysis demonstrate that the effects of HFS on neuronal network dynamics are realized through the adjustment of synaptic variable and conductance.

Synaptogenesis Is Modulated by Heparan Sulfate in Caenorhabditis elegans

PubMed Central

Lázaro-Peña, María I.; Díaz-Balzac, Carlos A.; Bülow, Hannes E.; Emmons, Scott W.

2018-01-01

The nervous system regulates complex behaviors through a network of neurons interconnected by synapses. How specific synaptic connections are genetically determined is still unclear. Male mating is the most complex behavior in Caenorhabditis elegans. It is composed of sequential steps that are governed by > 3000 chemical connections. Here, we show that heparan sulfates (HS) play a role in the formation and function of the male neural network. HS, sulfated in position 3 by the HS modification enzyme HST-3.1/HS 3-O-sulfotransferase and attached to the HS proteoglycan glypicans LON-2/glypican and GPN-1/glypican, functions cell-autonomously and nonautonomously for response to hermaphrodite contact during mating. Loss of 3-O sulfation resulted in the presynaptic accumulation of RAB-3, a molecule that localizes to synaptic vesicles, and disrupted the formation of synapses in a component of the mating circuits. We also show that the neural cell adhesion protein NRX-1/neurexin promotes and the neural cell adhesion protein NLG-1/neuroligin inhibits the formation of the same set of synapses in a parallel pathway. Thus, neural cell adhesion proteins and extracellular matrix components act together in the formation of synaptic connections. PMID:29559501

A modified artificial neural network based prediction technique for tropospheric radio refractivity

PubMed Central

Javeed, Shumaila; Javed, Wajahat; Atif, M.; Uddin, Mueen

2018-01-01

Radio refractivity plays a significant role in the development and design of radio systems for attaining the best level of performance. Refractivity in the troposphere is one of the features affecting electromagnetic waves, and hence the communication system interrupts. In this work, a modified artificial neural network (ANN) based model is applied to predict the refractivity. The suggested ANN model comprises three modules: the data preparation module, the feature selection module, and the forecast module. The first module applies pre-processing to make the data compatible for the feature selection module. The second module discards irrelevant and redundant data from the input set. The third module uses ANN for prediction. The ANN model applies a sigmoid activation function and a multi-variate auto regressive model to update the weights during the training process. In this work, the refractivity is predicted and estimated based on ten years (2002–2011) of meteorological data, such as the temperature, pressure, and humidity, obtained from the Pakistan Meteorological Department (PMD), Islamabad. The refractivity is estimated using the method suggested by the International Telecommunication Union (ITU). The refractivity is predicted for the year 2012 using the database of the previous ten years, with the help of ANN. The ANN model is implemented in MATLAB. Next, the estimated and predicted refractivity levels are validated against each other. The predicted and actual values (PMD data) of the atmospheric parameters agree with each other well, and demonstrate the accuracy of the proposed ANN method. It was further found that all parameters have a strong relationship with refractivity, in particular the temperature and humidity. The refractivity values are higher during the rainy season owing to a strong association with the relative humidity. Therefore, it is important to properly cater the signal communication system during hot and humid weather. Based on the results, the proposed ANN method can be used to develop a refractivity database, which is highly important in a radio communication system. PMID:29494609

Network modulation during complex syntactic processing

PubMed Central

den Ouden, Dirk-Bart; Saur, Dorothee; Mader, Wolfgang; Schelter, Björn; Lukic, Sladjana; Wali, Eisha; Timmer, Jens; Thompson, Cynthia K.

2011-01-01

Complex sentence processing is supported by a left-lateralized neural network including inferior frontal cortex and posterior superior temporal cortex. This study investigates the pattern of connectivity and information flow within this network. We used fMRI BOLD data derived from 12 healthy participants reported in an earlier study (Thompson, C. K., Den Ouden, D. B., Bonakdarpour, B., Garibaldi, K., & Parrish, T. B. (2010b). Neural plasticity and treatment-induced recovery of sentence processing in agrammatism. Neuropsychologia, 48(11), 3211-3227) to identify activation peaks associated with object-cleft over syntactically less complex subject-cleft processing. Directed Partial Correlation Analysis was conducted on time series extracted from participant-specific activation peaks and showed evidence of functional connectivity between four regions, linearly between premotor cortex, inferior frontal gyrus, posterior superior temporal sulcus and anterior middle temporal gyrus. This pattern served as the basis for Dynamic Causal Modeling of networks with a driving input to posterior superior temporal cortex, which likely supports thematic role assignment, and networks with a driving input to inferior frontal cortex, a core region associated with syntactic computation. The optimal model was determined through both frequentist and Bayesian model selection and turned out to reflect a network with a primary drive from inferior frontal cortex and modulation of the connection between inferior frontal and posterior superior temporal cortex by complex sentence processing. The winning model also showed a substantive role for a feedback mechanism from posterior superior temporal cortex back to inferior frontal cortex. We suggest that complex syntactic processing is driven by word-order analysis, supported by inferior frontal cortex, in an interactive relation with posterior superior temporal cortex, which supports verb argument structure processing. PMID:21820518

Recognition of handwritten similar Chinese characters by self-growing probabilistic decision-based neural network.

PubMed

Fu, H C; Xu, Y Y; Chang, H Y

1999-12-01

Recognition of similar (confusion) characters is a difficult problem in optical character recognition (OCR). In this paper, we introduce a neural network solution that is capable of modeling minor differences among similar characters, and is robust to various personal handwriting styles. The Self-growing Probabilistic Decision-based Neural Network (SPDNN) is a probabilistic type neural network, which adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Based on the SPDNN model, we have constructed a three-stage recognition system. First, a coarse classifier determines a character to be input to one of the pre-defined subclasses partitioned from a large character set, such as Chinese mixed with alphanumerics. Then a character recognizer determines the input image which best matches the reference character in the subclass. Lastly, the third module is a similar character recognizer, which can further enhance the recognition accuracy among similar or confusing characters. The prototype system has demonstrated a successful application of SPDNN to similar handwritten Chinese recognition for the public database CCL/HCCR1 (5401 characters x200 samples). Regarding performance, experiments on the CCL/HCCR1 database produced 90.12% recognition accuracy with no rejection, and 94.11% accuracy with 6.7% rejection, respectively. This recognition accuracy represents about 4% improvement on the previously announced performance. As to processing speed, processing before recognition (including image preprocessing, segmentation, and feature extraction) requires about one second for an A4 size character image, and recognition consumes approximately 0.27 second per character on a Pentium-100 based personal computer, without use of any hardware accelerator or co-processor.

Synthetic event-related potentials: a computational bridge between neurolinguistic models and experiments.

PubMed

Barrès, Victor; Simons, Arthur; Arbib, Michael

2013-01-01

Our previous work developed Synthetic Brain Imaging to link neural and schema network models of cognition and behavior to PET and fMRI studies of brain function. We here extend this approach to Synthetic Event-Related Potentials (Synthetic ERP). Although the method is of general applicability, we focus on ERP correlates of language processing in the human brain. The method has two components: Phase 1: To generate cortical electro-magnetic source activity from neural or schema network models; and Phase 2: To generate known neurolinguistic ERP data (ERP scalp voltage topographies and waveforms) from putative cortical source distributions and activities within a realistic anatomical model of the human brain and head. To illustrate the challenges of Phase 2 of the methodology, spatiotemporal information from Friederici's 2002 model of auditory language comprehension was used to define cortical regions and time courses of activation for implementation within a forward model of ERP data. The cortical regions from the 2002 model were modeled using atlas-based masks overlaid on the MNI high definition single subject cortical mesh. The electromagnetic contribution of each region was modeled using current dipoles whose position and orientation were constrained by the cortical geometry. In linking neural network computation via EEG forward modeling to empirical results in neurolinguistics, we emphasize the need for neural network models to link their architecture to geometrically sound models of the cortical surface, and the need for conceptual models to refine and adopt brain-atlas based approaches to allow precise brain anchoring of their modules. The detailed analysis of Phase 2 sets the stage for a brief introduction to Phase 1 of the program, including the case for a schema-theoretic approach to language production and perception presented in detail elsewhere. Unlike Dynamic Causal Modeling (DCM) and Bojak's mean field model, Synthetic ERP builds on models of networks that mediate the relation between the brain's inputs, outputs, and internal states in executing a specific task. The neural networks used for Synthetic ERP must include neuroanatomically realistic placement and orientation of the cortical pyramidal neurons. These constraints pose exciting challenges for future work in neural network modeling that is applicable to systems and cognitive neuroscience. Copyright © 2012 Elsevier Ltd. All rights reserved.

The mixed serotonin receptor agonist psilocybin reduces threat-induced modulation of amygdala connectivity

PubMed Central

Kraehenmann, Rainer; Schmidt, André; Friston, Karl; Preller, Katrin H.; Seifritz, Erich; Vollenweider, Franz X.

2015-01-01

Stimulation of serotonergic neurotransmission by psilocybin has been shown to shift emotional biases away from negative towards positive stimuli. We have recently shown that reduced amygdala activity during threat processing might underlie psilocybin's effect on emotional processing. However, it is still not known whether psilocybin modulates bottom-up or top-down connectivity within the visual-limbic-prefrontal network underlying threat processing. We therefore analyzed our previous fMRI data using dynamic causal modeling and used Bayesian model selection to infer how psilocybin modulated effective connectivity within the visual–limbic–prefrontal network during threat processing. First, both placebo and psilocybin data were best explained by a model in which threat affect modulated bidirectional connections between the primary visual cortex, amygdala, and lateral prefrontal cortex. Second, psilocybin decreased the threat-induced modulation of top-down connectivity from the amygdala to primary visual cortex, speaking to a neural mechanism that might underlie putative shifts towards positive affect states after psilocybin administration. These findings may have important implications for the treatment of mood and anxiety disorders. PMID:26909323

The mixed serotonin receptor agonist psilocybin reduces threat-induced modulation of amygdala connectivity.

PubMed

Kraehenmann, Rainer; Schmidt, André; Friston, Karl; Preller, Katrin H; Seifritz, Erich; Vollenweider, Franz X

2016-01-01

Stimulation of serotonergic neurotransmission by psilocybin has been shown to shift emotional biases away from negative towards positive stimuli. We have recently shown that reduced amygdala activity during threat processing might underlie psilocybin's effect on emotional processing. However, it is still not known whether psilocybin modulates bottom-up or top-down connectivity within the visual-limbic-prefrontal network underlying threat processing. We therefore analyzed our previous fMRI data using dynamic causal modeling and used Bayesian model selection to infer how psilocybin modulated effective connectivity within the visual-limbic-prefrontal network during threat processing. First, both placebo and psilocybin data were best explained by a model in which threat affect modulated bidirectional connections between the primary visual cortex, amygdala, and lateral prefrontal cortex. Second, psilocybin decreased the threat-induced modulation of top-down connectivity from the amygdala to primary visual cortex, speaking to a neural mechanism that might underlie putative shifts towards positive affect states after psilocybin administration. These findings may have important implications for the treatment of mood and anxiety disorders.

A Deep Ensemble Learning Method for Monaural Speech Separation.

PubMed

Zhang, Xiao-Lei; Wang, DeLiang

2016-03-01

Monaural speech separation is a fundamental problem in robust speech processing. Recently, deep neural network (DNN)-based speech separation methods, which predict either clean speech or an ideal time-frequency mask, have demonstrated remarkable performance improvement. However, a single DNN with a given window length does not leverage contextual information sufficiently, and the differences between the two optimization objectives are not well understood. In this paper, we propose a deep ensemble method, named multicontext networks, to address monaural speech separation. The first multicontext network averages the outputs of multiple DNNs whose inputs employ different window lengths. The second multicontext network is a stack of multiple DNNs. Each DNN in a module of the stack takes the concatenation of original acoustic features and expansion of the soft output of the lower module as its input, and predicts the ratio mask of the target speaker; the DNNs in the same module employ different contexts. We have conducted extensive experiments with three speech corpora. The results demonstrate the effectiveness of the proposed method. We have also compared the two optimization objectives systematically and found that predicting the ideal time-frequency mask is more efficient in utilizing clean training speech, while predicting clean speech is less sensitive to SNR variations.

Hybrid Spintronic-CMOS Spiking Neural Network with On-Chip Learning: Devices, Circuits, and Systems

NASA Astrophysics Data System (ADS)

Sengupta, Abhronil; Banerjee, Aparajita; Roy, Kaushik

2016-12-01

Over the past decade, spiking neural networks (SNNs) have emerged as one of the popular architectures to emulate the brain. In SNNs, information is temporally encoded and communication between neurons is accomplished by means of spikes. In such networks, spike-timing-dependent plasticity mechanisms require the online programing of synapses based on the temporal information of spikes transmitted by spiking neurons. In this work, we propose a spintronic synapse with decoupled spike-transmission and programing-current paths. The spintronic synapse consists of a ferromagnet-heavy-metal heterostructure where the programing current through the heavy metal generates spin-orbit torque to modulate the device conductance. Low programing energy and fast programing times demonstrate the efficacy of the proposed device as a nanoelectronic synapse. We perform a simulation study based on an experimentally benchmarked device-simulation framework to demonstrate the interfacing of such spintronic synapses with CMOS neurons and learning circuits operating in the transistor subthreshold region to form a network of spiking neurons that can be utilized for pattern-recognition problems.

Selection of neural network structure for system error correction of electro-optical tracker system with horizontal gimbal

NASA Astrophysics Data System (ADS)

Liu, Xing-fa; Cen, Ming

2007-12-01

Neural Network system error correction method is more precise than lest square system error correction method and spheric harmonics function system error correction method. The accuracy of neural network system error correction method is mainly related to the frame of Neural Network. Analysis and simulation prove that both BP neural network system error correction method and RBF neural network system error correction method have high correction accuracy; it is better to use RBF Network system error correction method than BP Network system error correction method for little studying stylebook considering training rate and neural network scale.

A novel recurrent neural network with finite-time convergence for linear programming.

PubMed

Liu, Qingshan; Cao, Jinde; Chen, Guanrong

2010-11-01

In this letter, a novel recurrent neural network based on the gradient method is proposed for solving linear programming problems. Finite-time convergence of the proposed neural network is proved by using the Lyapunov method. Compared with the existing neural networks for linear programming, the proposed neural network is globally convergent to exact optimal solutions in finite time, which is remarkable and rare in the literature of neural networks for optimization. Some numerical examples are given to show the effectiveness and excellent performance of the new recurrent neural network.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.

PubMed

Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro

2016-01-01

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder

PubMed Central

Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro

2016-01-01

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive. PMID:28018162

Diffusion of responsibility attenuates altruistic punishment: A functional magnetic resonance imaging effective connectivity study.

PubMed

Feng, Chunliang; Deshpande, Gopikrishna; Liu, Chao; Gu, Ruolei; Luo, Yue-Jia; Krueger, Frank

2016-02-01

Humans altruistically punish violators of social norms to enforce cooperation and pro-social behaviors. However, such altruistic behaviors diminish when others are present, due to a diffusion of responsibility. We investigated the neural signatures underlying the modulations of diffusion of responsibility on altruistic punishment, conjoining a third-party punishment task with event-related functional magnetic resonance imaging and multivariate Granger causality mapping. In our study, participants acted as impartial third-party decision-makers and decided how to punish norm violations under two different social contexts: alone (i.e., full responsibility) or in the presence of putative other third-party decision makers (i.e., diffused responsibility). Our behavioral results demonstrated that the diffusion of responsibility served as a mediator of context-dependent punishment. In the presence of putative others, participants who felt less responsible also punished less severely in response to norm violations. Our neural results revealed that underlying this behavioral effect was a network of interconnected brain regions. For unfair relative to fair splits, the presence of others led to attenuated responses in brain regions implicated in signaling norm violations (e.g., AI) and to increased responses in brain regions implicated in calculating values of norm violations (e.g., vmPFC, precuneus) and mentalizing about others (dmPFC). The dmPFC acted as the driver of the punishment network, modulating target regions, such as AI, vmPFC, and precuneus, to adjust altruistic punishment behavior. Our results uncovered the neural basis of the influence of diffusion of responsibility on altruistic punishment and highlighted the role of the mentalizing network in this important phenomenon. Hum Brain Mapp 37:663-677, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)

NASA Technical Reports Server (NTRS)

Niewoehner, Kevin R.; Carter, John (Technical Monitor)

2001-01-01

The research accomplishments for the cooperative agreement 'Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)' include the following: (1) previous IFC program data collection and analysis; (2) IFC program support site (configured IFC systems support network, configured Tornado/VxWorks OS development system, made Configuration and Documentation Management Systems Internet accessible); (3) Airborne Research Test Systems (ARTS) II Hardware (developed hardware requirements specification, developing environmental testing requirements, hardware design, and hardware design development); (4) ARTS II software development laboratory unit (procurement of lab style hardware, configured lab style hardware, and designed interface module equivalent to ARTS II faceplate); (5) program support documentation (developed software development plan, configuration management plan, and software verification and validation plan); (6) LWR algorithm analysis (performed timing and profiling on algorithm); (7) pre-trained neural network analysis; (8) Dynamic Cell Structures (DCS) Neural Network Analysis (performing timing and profiling on algorithm); and (9) conducted technical interchange and quarterly meetings to define IFC research goals.

Artificial neural network-based exploration of gene-nutrient interactions in folate and xenobiotic metabolic pathways that modulate susceptibility to breast cancer.

PubMed

Naushad, Shaik Mohammad; Ramaiah, M Janaki; Pavithrakumari, Manickam; Jayapriya, Jaganathan; Hussain, Tajamul; Alrokayan, Salman A; Gottumukkala, Suryanarayana Raju; Digumarti, Raghunadharao; Kutala, Vijay Kumar

2016-04-15

In the current study, an artificial neural network (ANN)-based breast cancer prediction model was developed from the data of folate and xenobiotic pathway genetic polymorphisms along with the nutritional and demographic variables to investigate how micronutrients modulate susceptibility to breast cancer. The developed ANN model explained 94.2% variability in breast cancer prediction. Fixed effect models of folate (400 μg/day) and B12 (6 μg/day) showed 33.3% and 11.3% risk reduction, respectively. Multifactor dimensionality reduction analysis showed the following interactions in responders to folate: RFC1 G80A × MTHFR C677T (primary), COMT H108L × CYP1A1 m2 (secondary), MTR A2756G (tertiary). The interactions among responders to B12 were RFC1G80A × cSHMT C1420T and CYP1A1 m2 × CYP1A1 m4. ANN simulations revealed that increased folate might restore ER and PR expression and reduce the promoter CpG island methylation of extra cellular superoxide dismutase and BRCA1. Dietary intake of folate appears to confer protection against breast cancer through its modulating effects on ER and PR expression and methylation of EC-SOD and BRCA1. Copyright © 2016 Elsevier B.V. All rights reserved.

Stereo-vision-based cooperative-vehicle positioning using OCC and neural networks

NASA Astrophysics Data System (ADS)

Ifthekhar, Md. Shareef; Saha, Nirzhar; Jang, Yeong Min

2015-10-01

Vehicle positioning has been subjected to extensive research regarding driving safety measures and assistance as well as autonomous navigation. The most common positioning technique used in automotive positioning is the global positioning system (GPS). However, GPS is not reliably accurate because of signal blockage caused by high-rise buildings. In addition, GPS is error prone when a vehicle is inside a tunnel. Moreover, GPS and other radio-frequency-based approaches cannot provide orientation information or the position of neighboring vehicles. In this study, we propose a cooperative-vehicle positioning (CVP) technique by using the newly developed optical camera communications (OCC). The OCC technique utilizes image sensors and cameras to receive and decode light-modulated information from light-emitting diodes (LEDs). A vehicle equipped with an OCC transceiver can receive positioning and other information such as speed, lane change, driver's condition, etc., through optical wireless links of neighboring vehicles. Thus, the target vehicle position that is too far away to establish an OCC link can be determined by a computer-vision-based technique combined with the cooperation of neighboring vehicles. In addition, we have devised a back-propagation (BP) neural-network learning method for positioning and range estimation for CVP. The proposed neural-network-based technique can estimate target vehicle position from only two image points of target vehicles using stereo vision. For this, we use rear LEDs on target vehicles as image points. We show from simulation results that our neural-network-based method achieves better accuracy than that of the computer-vision method.

Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks

PubMed Central

Avsec, Žiga; Cheng, Jun; Gagneur, Julien

2018-01-01

Abstract Motivation Regulatory sequences are not solely defined by their nucleic acid sequence but also by their relative distances to genomic landmarks such as transcription start site, exon boundaries or polyadenylation site. Deep learning has become the approach of choice for modeling regulatory sequences because of its strength to learn complex sequence features. However, modeling relative distances to genomic landmarks in deep neural networks has not been addressed. Results Here we developed spline transformation, a neural network module based on splines to flexibly and robustly model distances. Modeling distances to various genomic landmarks with spline transformations significantly increased state-of-the-art prediction accuracy of in vivo RNA-binding protein binding sites for 120 out of 123 proteins. We also developed a deep neural network for human splice branchpoint based on spline transformations that outperformed the current best, already distance-based, machine learning model. Compared to piecewise linear transformation, as obtained by composition of rectified linear units, spline transformation yields higher prediction accuracy as well as faster and more robust training. As spline transformation can be applied to further quantities beyond distances, such as methylation or conservation, we foresee it as a versatile component in the genomics deep learning toolbox. Availability and implementation Spline transformation is implemented as a Keras layer in the CONCISE python package: https://github.com/gagneurlab/concise. Analysis code is available at https://github.com/gagneurlab/Manuscript_Avsec_Bioinformatics_2017. Contact avsec@in.tum.de or gagneur@in.tum.de Supplementary information Supplementary data are available at Bioinformatics online. PMID:29155928

The functional neuroanatomy of autobiographical memory: A meta-analysis

PubMed Central

Svoboda, Eva; McKinnon, Margaret C.; Levine, Brian

2007-01-01

Autobiographical memory (AM) entails a complex set of operations, including episodic memory, self-reflection, emotion, visual imagery, attention, executive functions, and semantic processes. The heterogeneous nature of AM poses significant challenges in capturing its behavioral and neuroanatomical correlates. Investigators have recently turned their attention to the functional neuroanatomy of AM. We used the effect-location method of meta-analysis to analyze data from 24 functional imaging studies of AM. The results indicated a core neural network of left-lateralized regions, including the medial and ventrolateral prefrontal, medial and lateral temporal and retrosplenial/posterior cingulate cortices, the temporoparietal junction and the cerebellum. Secondary and tertiary regions, less frequently reported in imaging studies of AM, are also identified. We examined the neural correlates of putative component processes in AM, including, executive functions, self-reflection, episodic remembering and visuospatial processing. We also separately analyzed the effect of select variables on the AM network across individual studies, including memory age, qualitative factors (personal significance, level of detail and vividness), semantic and emotional content, and the effect of reference conditions. We found that memory age effects on medial temporal lobe structures may be modulated by qualitative aspects of memory. Studies using rest as a control task masked process-specific components of the AM neural network. Our findings support a neural distinction between episodic and semantic memory in AM. Finally, emotional events produced a shift in lateralization of the AM network with activation observed in emotion-centered regions and deactivation (or lack of activation) observed in regions associated with cognitive processes. PMID:16806314

How the amygdala affects emotional memory by altering brain network properties.

PubMed

Hermans, Erno J; Battaglia, Francesco P; Atsak, Piray; de Voogd, Lycia D; Fernández, Guillén; Roozendaal, Benno

2014-07-01

The amygdala has long been known to play a key role in supporting memory for emotionally arousing experiences. For example, classical fear conditioning depends on neural plasticity within this anterior medial temporal lobe region. Beneficial effects of emotional arousal on memory, however, are not restricted to simple associative learning. Our recollection of emotional experiences often includes rich representations of, e.g., spatiotemporal context, visceral states, and stimulus-response associations. Critically, such memory features are known to bear heavily on regions elsewhere in the brain. These observations led to the modulation account of amygdala function, which postulates that amygdala activation enhances memory consolidation by facilitating neural plasticity and information storage processes in its target regions. Rodent work in past decades has identified the most important brain regions and neurochemical processes involved in these modulatory actions, and neuropsychological and neuroimaging work in humans has produced a large body of convergent data. Importantly, recent methodological developments make it increasingly realistic to monitor neural interactions underlying such modulatory effects as they unfold. For instance, functional connectivity network modeling in humans has demonstrated how information exchanges between the amygdala and specific target regions occur within the context of large-scale neural network interactions. Furthermore, electrophysiological and optogenetic techniques in rodents are beginning to make it possible to quantify and even manipulate such interactions with millisecond precision. In this paper we will discuss that these developments will likely lead to an updated view of the amygdala as a critical nexus within large-scale networks supporting different aspects of memory processing for emotionally arousing experiences. Copyright © 2014 Elsevier Inc. All rights reserved.

Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks

PubMed Central

Burroni, Javier; Taylor, P.; Corey, Cassian; Vachnadze, Tengiz; Siegelmann, Hava T.

2017-01-01

Overview: We model energy constraints in a network of spiking neurons, while exploring general questions of resource limitation on network function abstractly. Background: Metabolic states like dietary ketosis or hypoglycemia have a large impact on brain function and disease outcomes. Glia provide metabolic support for neurons, among other functions. Yet, in computational models of glia-neuron cooperation, there have been no previous attempts to explore the effects of direct realistic energy costs on network activity in spiking neurons. Currently, biologically realistic spiking neural networks assume that membrane potential is the main driving factor for neural spiking, and do not take into consideration energetic costs. Methods: We define local energy pools to constrain a neuron model, termed Spiking Neuron Energy Pool (SNEP), which explicitly incorporates energy limitations. Each neuron requires energy to spike, and resources in the pool regenerate over time. Our simulation displays an easy-to-use GUI, which can be run locally in a web browser, and is freely available. Results: Energy dependence drastically changes behavior of these neural networks, causing emergent oscillations similar to those in networks of biological neurons. We analyze the system via Lotka-Volterra equations, producing several observations: (1) energy can drive self-sustained oscillations, (2) the energetic cost of spiking modulates the degree and type of oscillations, (3) harmonics emerge with frequencies determined by energy parameters, and (4) varying energetic costs have non-linear effects on energy consumption and firing rates. Conclusions: Models of neuron function which attempt biological realism may benefit from including energy constraints. Further, we assert that observed oscillatory effects of energy limitations exist in networks of many kinds, and that these findings generalize to abstract graphs and technological applications. PMID:28289370

Modular, Hierarchical Learning By Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Baldi, Pierre F.; Toomarian, Nikzad

1996-01-01

Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.

Accessible cultural mind-set modulates default mode activity: evidence for the culturally situated brain.

PubMed

Wang, Chenbo; Oyserman, Daphna; Liu, Qiang; Li, Hong; Han, Shihui

2013-01-01

Self-construal priming modulates human behavior and associated neural activity. However, the neural activity associated with the self-construal priming procedure itself remains unknown. It is also unclear whether and how self-construal priming affects neural activity prior to engaging in a particular task. To address this gap, we scanned Chinese adults, using functional magnetic resonance imaging, during self-construal priming and a following resting state. We found that, relative to a calculation task, both interdependent and independent self-construal priming activated the ventral medial prefrontal cortex (MPFC) and the posterior cingulate cortex (PCC). The contrast of interdependent vs. independent self-construal priming also revealed increased activity in the dorsal MPFC and left middle frontal cortex. The regional homogeneity analysis of the resting-state activity revealed increased local synchronization of spontaneous activity in the dorsal MPFC but decreased local synchronization of spontaneous activity in the PCC when contrasting interdependent vs. independent self-construal priming. The functional connectivity analysis of the resting-state activity, however, did not show significant difference in synchronization of activities in remote brain regions between different priming conditions. Our findings suggest that accessible collectivistic/individualistic mind-set induced by self-construal priming is associated with modulations of both task-related and resting-state activity in the default mode network.

Multi-Object Tracking with Correlation Filter for Autonomous Vehicle.

PubMed

Zhao, Dawei; Fu, Hao; Xiao, Liang; Wu, Tao; Dai, Bin

2018-06-22

Multi-object tracking is a crucial problem for autonomous vehicle. Most state-of-the-art approaches adopt the tracking-by-detection strategy, which is a two-step procedure consisting of the detection module and the tracking module. In this paper, we improve both steps. We improve the detection module by incorporating the temporal information, which is beneficial for detecting small objects. For the tracking module, we propose a novel compressed deep Convolutional Neural Network (CNN) feature based Correlation Filter tracker. By carefully integrating these two modules, the proposed multi-object tracking approach has the ability of re-identification (ReID) once the tracked object gets lost. Extensive experiments were performed on the KITTI and MOT2015 tracking benchmarks. Results indicate that our approach outperforms most state-of-the-art tracking approaches.

Sleep deprivation compromises resting-state emotional regulatory processes: An EEG study.

PubMed

Zhang, Jinxiao; Lau, Esther Yuet Ying; Hsiao, Janet H

2018-03-01

Resting-state spontaneous neural activities consume far more biological energy than stimulus-induced activities, suggesting their significance. However, existing studies of sleep loss and emotional functioning have focused on how sleep deprivation modulates stimulus-induced emotional neural activities. The current study aimed to investigate the impacts of sleep deprivation on the brain network of emotional functioning using electroencephalogram during a resting state. Two established resting-state electroencephalogram indexes (i.e. frontal alpha asymmetry and frontal theta/beta ratio) were used to reflect the functioning of the emotion regulatory neural network. Participants completed an 8-min resting-state electroencephalogram recording after a well-rested night or 24 hr sleep deprivation. The Sleep Deprivation group had a heightened ratio of the power density in theta band to beta band (theta/beta ratio) in the frontal area than the Sleep Control group, suggesting an effective approach with reduced frontal cortical regulation of subcortical drive after sleep deprivation. There was also marginally more left-lateralized frontal alpha power (left frontal alpha asymmetry) in the Sleep Deprivation group compared with the Sleep Control group. Besides, higher theta/beta ratio and more left alpha lateralization were correlated with higher sleepiness and lower vigilance. The results converged in suggesting compromised emotional regulatory processes during resting state after sleep deprivation. Our work provided the first resting-state neural evidence for compromised emotional functioning after sleep loss, highlighting the significance of examining resting-state neural activities within the affective brain network as a default functional mode in investigating the sleep-emotion relationship. © 2018 European Sleep Research Society.

Using a Simple Neural Network to Delineate Some Principles of Distributed Economic Choice.

PubMed

Balasubramani, Pragathi P; Moreno-Bote, Rubén; Hayden, Benjamin Y

2018-01-01

The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions. While the principles under which the brain might perform computations using modular systems have been more amenable to modeling, the principles by which the brain might make choices using distributed principles have not been explored. Our goal in this perspective is to delineate some of those distributed principles using a neural network method and use its results as a lens through which to reconsider some previously published neurophysiological data. To allow for direct comparison with our own data, we trained the neural network to perform binary risky choices. We find that value correlates are ubiquitous and are always accompanied by non-value information, including spatial information (i.e., no pure value signals). Evaluation, comparison, and selection were not distinct processes; indeed, value signals even in the earliest stages contributed directly, albeit weakly, to action selection. There was no place, other than at the level of action selection, at which dimensions were fully integrated. No units were specialized for specific offers; rather, all units encoded the values of both offers in an anti-correlated format, thus contributing to comparison. Individual network layers corresponded to stages in a continuous rotation from input to output space rather than to functionally distinct modules. While our network is likely to not be a direct reflection of brain processes, we propose that these principles should serve as hypotheses to be tested and evaluated for future studies.

Using a Simple Neural Network to Delineate Some Principles of Distributed Economic Choice

PubMed Central

Balasubramani, Pragathi P.; Moreno-Bote, Rubén; Hayden, Benjamin Y.

2018-01-01

The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions. While the principles under which the brain might perform computations using modular systems have been more amenable to modeling, the principles by which the brain might make choices using distributed principles have not been explored. Our goal in this perspective is to delineate some of those distributed principles using a neural network method and use its results as a lens through which to reconsider some previously published neurophysiological data. To allow for direct comparison with our own data, we trained the neural network to perform binary risky choices. We find that value correlates are ubiquitous and are always accompanied by non-value information, including spatial information (i.e., no pure value signals). Evaluation, comparison, and selection were not distinct processes; indeed, value signals even in the earliest stages contributed directly, albeit weakly, to action selection. There was no place, other than at the level of action selection, at which dimensions were fully integrated. No units were specialized for specific offers; rather, all units encoded the values of both offers in an anti-correlated format, thus contributing to comparison. Individual network layers corresponded to stages in a continuous rotation from input to output space rather than to functionally distinct modules. While our network is likely to not be a direct reflection of brain processes, we propose that these principles should serve as hypotheses to be tested and evaluated for future studies. PMID:29643773

Motor Imagery Learning Modulates Functional Connectivity of Multiple Brain Systems in Resting State

PubMed Central

Zhang, Hang; Long, Zhiying; Ge, Ruiyang; Xu, Lele; Jin, Zhen; Yao, Li; Liu, Yijun

2014-01-01

Background Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. Methodology/Principal Findings We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. Conclusions/Significance These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning. PMID:24465577

Human cortical activity evoked by contextual processing in attentional orienting.

PubMed

Zhao, Shuo; Li, Chunlin; Uono, Shota; Yoshimura, Sayaka; Toichi, Motomi

2017-06-07

The ability to assess another person's direction of attention is paramount in social communication, many studies have reported a similar pattern between gaze and arrow cues in attention orienting. Neuroimaging research has also demonstrated no qualitative differences in attention to gaze and arrow cues. However, these studies were implemented under simple experiment conditions. Researchers have highlighted the importance of contextual processing (i.e., the semantic congruence between cue and target) in attentional orienting, showing that attentional orienting by social gaze or arrow cues could be modulated through contextual processing. Here, we examine the neural activity of attentional orienting by gaze and arrow cues in response to contextual processing using functional magnetic resonance imaging. The results demonstrated that the influence of neural activity through contextual processing to attentional orienting occurred under invalid conditions (when the cue and target were incongruent versus congruent) in the ventral frontoparietal network, although we did not identify any differences in the neural substrates of attentional orienting in contextual processing between gaze and arrow cues. These results support behavioural data of attentional orienting modulated by contextual processing based on the neurocognitive architecture.

Associating a product with a luxury brand label modulates neural reward processing and favors choices in materialistic individuals.

PubMed

Audrin, Catherine; Ceravolo, Leonardo; Chanal, Julien; Brosch, Tobias; Sander, David

2017-11-23

The present study investigated the extent to which luxury vs. non-luxury brand labels (i.e., extrinsic cues) randomly assigned to items and preferences for these items impact choice, and how this impact may be moderated by materialistic tendencies (i.e., individual characteristics). The main objective was to investigate the neural correlates of abovementioned effects using functional magnetic resonance imaging. Behavioural results showed that the more materialistic people are, the more they choose and like items labelled with luxury brands. Neuroimaging results revealed the implication of a neural network including the dorsolateral and ventromedial prefrontal cortex and the orbitofrontal cortex that was modulated by the brand label and also by the participants' preference. Most importantly, items with randomly assigned luxurious brand labels were preferentially chosen by participants and triggered enhanced signal in the caudate nucleus. This effect increased linearly with materialistic tendencies. Our results highlight the impact of brand-item association, although random in our study, and materialism on preference, relying on subparts of the brain valuation system for the integration of extrinsic cues, preferences and individual characteristics.

Ultra low power CMOS technology

NASA Technical Reports Server (NTRS)

Burr, J.; Peterson, A.

1991-01-01

This paper discusses the motivation, opportunities, and problems associated with implementing digital logic at very low voltages, including the challenge of making use of the available real estate in 3D multichip modules, energy requirements of very large neural networks, energy optimization metrics and their impact on system design, modeling problems, circuit design constraints, possible fabrication process modifications to improve performance, and barriers to practical implementation.

Compassion-based emotion regulation up-regulates experienced positive affect and associated neural networks

PubMed Central

Singer, Tania

2015-01-01

Emotion regulation research has primarily focused on techniques that attenuate or modulate the impact of emotional stimuli. Recent evidence suggests that this mode regulation can be problematic in the context of regulation of emotion elicited by the suffering of others, resulting in reduced emotional connectedness. Here, we investigated the effects of an alternative emotion regulation technique based on the up-regulation of positive affect via Compassion-meditation on experiential and neural affective responses to depictions of individuals in distress, and compared these with the established emotion regulation strategy of Reappraisal. Using fMRI, we scanned 15 expert practitioners of Compassion-meditation either passively viewing, or using Compassion-meditation or Reappraisal to modulate their emotional reactions to film clips depicting people in distress. Both strategies effectively, but differentially regulated experienced affect, with Compassion primarily increasing positive and Reappraisal primarily decreasing negative affect. Imaging results showed that Compassion, relative to both passive-viewing and Reappraisal increased activation in regions involved in affiliation, positive affect and reward processing including ventral striatum and medial orbitfrontal cortex. This network was shown to be active prior to stimulus presentation, suggesting that the regulatory mechanism of Compassion is the stimulus-independent endogenous generation of positive affect. PMID:25698699

Neural network integration during the perception of in-group and out-group members.

PubMed

Greven, Inez M; Ramsey, Richard

2017-11-01

Group biases guide social interactions by promoting in-group favouritism, but the neural mechanisms underpinning group biases remain unclear. While neuroscience research has shown that distributed brain circuits are associated with seeing in-group and out-group members as "us" and "them", it is less clear how these networks exchange signals. This fMRI study uses functional connectivity analyses to investigate the contribution of functional integration to group bias modulation of person perception. Participants were assigned to an arbitrary group and during scanning they observed bodies of in-group or out-group members that cued the recall of positive or negative social knowledge. The results showed that functional coupling between perceptual and cognitive neural networks is tuned to particular combinations of group membership and social knowledge valence. Specifically, coupling between body perception and theory-of-mind networks is biased towards seeing a person that had previously been paired with information consistent with group bias (positive for in-group and negative for out-group). This demonstrates how brain regions associated with visual analysis of others and belief reasoning exchange and integrate signals when evaluating in-group and out-group members. The results update models of person perception by showing how and when interplay occurs between perceptual and extended systems when developing a representation of another person. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

External modulation of the sustained attention network in traumatic brain injury.

PubMed

Richard, Nadine M; O'Connor, Charlene; Dey, Ayan; Robertson, Ian H; Levine, Brian

2018-05-07

Traumatic brain injury (TBI) is associated with impairments in processing speed as well as higher-level cognitive functions that depend on distributed neural networks, such as regulating and sustaining attention. Although exogenous alerting cues have been shown to support patients in sustaining attentive, goal-directed behavior, the neural correlates of this rehabilitative effect are unclear. The purpose of this study was to explore the effects of moderate to severe TBI on activity and functional connectivity in the well-documented right-lateralized frontal-subcortical-parietal sustained attention network, and to assess the effects of alerting cues. Using multivariate analysis of fMRI data, TBI patients and matched neurologically healthy (NH) comparison participants were scanned as they performed the Sustained Attention to Response Task (SART) in 60-s blocks, with or without exogenous cueing through brief auditory alerting tones. Results documented inefficient voluntary control of attention in the TBI patients, with reduced functional connectivity in the sustained attention network relative to NH participants. When alerting cues were present during the SART, however, functional connectivity increased and became comparable to activity patterns seen in the NH group. These findings provide novel evidence of a neural mechanism for the facilitatory effects of alerting cues on goal-directed behavior in patients with damaged attentional brain systems, and support their use in cognitive rehabilitation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Linear matrix inequality approach to exponential synchronization of a class of chaotic neural networks with time-varying delays

NASA Astrophysics Data System (ADS)

Wu, Wei; Cui, Bao-Tong

2007-07-01

In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.

Brain and Social Networks: Fundamental Building Blocks of Human Experience.

PubMed

Falk, Emily B; Bassett, Danielle S

2017-09-01

How do brains shape social networks, and how do social ties shape the brain? Social networks are complex webs by which ideas spread among people. Brains comprise webs by which information is processed and transmitted among neural units. While brain activity and structure offer biological mechanisms for human behaviors, social networks offer external inducers or modulators of those behaviors. Together, these two axes represent fundamental contributors to human experience. Integrating foundational knowledge from social and developmental psychology and sociology on how individuals function within dyads, groups, and societies with recent advances in network neuroscience can offer new insights into both domains. Here, we use the example of how ideas and behaviors spread to illustrate the potential of multilayer network models. Copyright © 2017 Elsevier Ltd. All rights reserved.

Computer-aided diagnostic detection system of venous beading in retinal images

NASA Astrophysics Data System (ADS)

Yang, Ching-Wen; Ma, DyeJyun; Chao, ShuennChing; Wang, ChuinMu; Wen, Chia-Hsien; Lo, ChienShun; Chung, Pau-Choo; Chang, Chein-I.

2000-05-01

The detection of venous beading in retinal images provides an early sign of diabetic retinopathy and plays an important role as a preprocessing step in diagnosing ocular diseases. We present a computer-aided diagnostic system to automatically detect venous beading of blood vessels. It comprises of two modules, referred to as the blood vessel extraction module and the venus beading detection module. The former uses a bell-shaped Gaussian kernel with 12 azimuths to extract blood vessels while the latter applies a neural network-based shape cognitron to detect venous beading among the extracted blood vessels for diagnosis. Both modules are fully computer-automated. To evaluate the proposed system, 61 retinal images (32 beaded and 29 normal images) are used for performance evaluation.

Electronic Neural Networks

NASA Technical Reports Server (NTRS)

Thakoor, Anil

1990-01-01

Viewgraphs on electronic neural networks for space station are presented. Topics covered include: electronic neural networks; electronic implementations; VLSI/thin film hybrid hardware for neurocomputing; computations with analog parallel processing; features of neuroprocessors; applications of neuroprocessors; neural network hardware for terrain trafficability determination; a dedicated processor for path planning; neural network system interface; neural network for robotic control; error backpropagation algorithm for learning; resource allocation matrix; global optimization neuroprocessor; and electrically programmable read only thin-film synaptic array.

The neural network to determine the mechanical properties of the steels

NASA Astrophysics Data System (ADS)

Yemelyanov, Vitaliy; Yemelyanova, Nataliya; Safonova, Marina; Nedelkin, Aleksey

2018-04-01

The authors describe the neural network structure and software that is designed and developed to determine the mechanical properties of steels. The neural network is developed to refine upon the values of the steels properties. The results of simulations of the developed neural network are shown. The authors note the low standard error of the proposed neural network. To realize the proposed neural network the specialized software has been developed.

Generalized memory associativity in a network model for the neuroses

NASA Astrophysics Data System (ADS)

Wedemann, Roseli S.; Donangelo, Raul; de Carvalho, Luís A. V.

2009-03-01

We review concepts introduced in earlier work, where a neural network mechanism describes some mental processes in neurotic pathology and psychoanalytic working-through, as associative memory functioning, according to the findings of Freud. We developed a complex network model, where modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental processes. The model illustrates Freud's idea that consciousness is related to symbolic and linguistic memory activity in the brain. We have introduced a generalization of the Boltzmann machine to model memory associativity. Model behavior is illustrated with simulations and some of its properties are analyzed with methods from statistical mechanics.

Energy consumption analysis for various memristive networks under different learning strategies

NASA Astrophysics Data System (ADS)

Deng, Lei; Wang, Dong; Zhang, Ziyang; Tang, Pei; Li, Guoqi; Pei, Jing

2016-02-01

Recently, various memristive systems emerge to emulate the efficient computing paradigm of the brain cortex; whereas, how to make them energy efficient still remains unclear, especially from an overall perspective. Here, a systematical and bottom-up energy consumption analysis is demonstrated, including the memristor device level and the network learning level. We propose an energy estimating methodology when modulating the memristive synapses, which is simulated in three typical neural networks with different synaptic structures and learning strategies for both offline and online learning. These results provide an in-depth insight to create energy efficient brain-inspired neuromorphic devices in the future.

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement

PubMed Central

Cao, Xiaoguang; Wang, Peng; Meng, Cai; Gong, Guoping; Liu, Miaoming; Qi, Jun

2018-01-01

In this paper, a novel algorithm based on convolutional neural network (CNN) is proposed to detect foreign object debris (FOD) based on optical imaging sensors. It contains two modules, the improved region proposal network (RPN) and spatial transformer network (STN) based CNN classifier. In the improved RPN, some extra select rules are designed and deployed to generate high quality candidates with fewer numbers. Moreover, the efficiency of CNN detector is significantly improved by introducing STN layer. Compared to faster R-CNN and single shot multiBox detector (SSD), the proposed algorithm achieves better result for FOD detection on airfield pavement in the experiment. PMID:29494524

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement.

PubMed

Cao, Xiaoguang; Wang, Peng; Meng, Cai; Bai, Xiangzhi; Gong, Guoping; Liu, Miaoming; Qi, Jun

2018-03-01

In this paper, a novel algorithm based on convolutional neural network (CNN) is proposed to detect foreign object debris (FOD) based on optical imaging sensors. It contains two modules, the improved region proposal network (RPN) and spatial transformer network (STN) based CNN classifier. In the improved RPN, some extra select rules are designed and deployed to generate high quality candidates with fewer numbers. Moreover, the efficiency of CNN detector is significantly improved by introducing STN layer. Compared to faster R-CNN and single shot multiBox detector (SSD), the proposed algorithm achieves better result for FOD detection on airfield pavement in the experiment.

Stimulation of the Locus Ceruleus Modulates Signal-to-Noise Ratio in the Olfactory Bulb.

PubMed

Manella, Laura C; Petersen, Nicholas; Linster, Christiane

2017-11-29

Norepinephrine (NE) has been shown to influence sensory, and specifically olfactory processing at the behavioral and physiological levels, potentially by regulating signal-to-noise ratio (S/N). The present study is the first to look at NE modulation of olfactory bulb (OB) in regards to S/N in vivo We show, in male rats, that locus ceruleus stimulation and pharmacological infusions of NE into the OB modulate both spontaneous and odor-evoked neural responses. NE in the OB generated a non-monotonic dose-response relationship, suppressing mitral cell activity at high and low, but not intermediate, NE levels. We propose that NE enhances odor responses not through direct potentiation of the afferent signal per se, but rather by reducing the intrinsic noise of the system. This has important implications for the ways in which an animal interacts with its olfactory environment, particularly as the animal shifts from a relaxed to an alert behavioral state. SIGNIFICANCE STATEMENT Sensory perception can be modulated by behavioral states such as hunger, fear, stress, or a change in environmental context. Behavioral state often affects neural processing via the release of circulating neurochemicals such as hormones or neuromodulators. We here show that the neuromodulator norepinephrine modulates olfactory bulb spontaneous activity and odor responses so as to generate an increased signal-to-noise ratio at the output of the olfactory bulb. Our results help interpret and improve existing ideas for neural network mechanisms underlying behaviorally observed improvements in near-threshold odor detection and discrimination. Copyright © 2017 the authors 0270-6474/17/3711605-11$15.00/0.

Locking of correlated neural activity to ongoing oscillations

PubMed Central

Helias, Moritz

2017-01-01

Population-wide oscillations are ubiquitously observed in mesoscopic signals of cortical activity. In these network states a global oscillatory cycle modulates the propensity of neurons to fire. Synchronous activation of neurons has been hypothesized to be a separate channel of signal processing information in the brain. A salient question is therefore if and how oscillations interact with spike synchrony and in how far these channels can be considered separate. Experiments indeed showed that correlated spiking co-modulates with the static firing rate and is also tightly locked to the phase of beta-oscillations. While the dependence of correlations on the mean rate is well understood in feed-forward networks, it remains unclear why and by which mechanisms correlations tightly lock to an oscillatory cycle. We here demonstrate that such correlated activation of pairs of neurons is qualitatively explained by periodically-driven random networks. We identify the mechanisms by which covariances depend on a driving periodic stimulus. Mean-field theory combined with linear response theory yields closed-form expressions for the cyclostationary mean activities and pairwise zero-time-lag covariances of binary recurrent random networks. Two distinct mechanisms cause time-dependent covariances: the modulation of the susceptibility of single neurons (via the external input and network feedback) and the time-varying variances of single unit activities. For some parameters, the effectively inhibitory recurrent feedback leads to resonant covariances even if mean activities show non-resonant behavior. Our analytical results open the question of time-modulated synchronous activity to a quantitative analysis. PMID:28604771

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

PubMed

Bao, Gang; Zeng, Zhigang; Shen, Yanjun

2018-02-01

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

A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints.

PubMed

Liang, X B; Wang, J

2000-01-01

This paper presents a continuous-time recurrent neural-network model for nonlinear optimization with any continuously differentiable objective function and bound constraints. Quadratic optimization with bound constraints is a special problem which can be solved by the recurrent neural network. The proposed recurrent neural network has the following characteristics. 1) It is regular in the sense that any optimum of the objective function with bound constraints is also an equilibrium point of the neural network. If the objective function to be minimized is convex, then the recurrent neural network is complete in the sense that the set of optima of the function with bound constraints coincides with the set of equilibria of the neural network. 2) The recurrent neural network is primal and quasiconvergent in the sense that its trajectory cannot escape from the feasible region and will converge to the set of equilibria of the neural network for any initial point in the feasible bound region. 3) The recurrent neural network has an attractivity property in the sense that its trajectory will eventually converge to the feasible region for any initial states even at outside of the bounded feasible region. 4) For minimizing any strictly convex quadratic objective function subject to bound constraints, the recurrent neural network is globally exponentially stable for almost any positive network parameters. Simulation results are given to demonstrate the convergence and performance of the proposed recurrent neural network for nonlinear optimization with bound constraints.

Transcriptome analysis reveals determinant stages controlling human embryonic stem cell commitment to neuronal cells.

PubMed

Li, Yuanyuan; Wang, Ran; Qiao, Nan; Peng, Guangdun; Zhang, Ke; Tang, Ke; Han, Jing-Dong J; Jing, Naihe

2017-12-01

Proper neural commitment is essential for ensuring the appropriate development of the human brain and for preventing neurodevelopmental diseases such as autism spectrum disorders, schizophrenia, and intellectual disorders. However, the molecular mechanisms underlying the neural commitment in humans remain elusive. Here, we report the establishment of a neural differentiation system based on human embryonic stem cells (hESCs) and on comprehensive RNA sequencing analysis of transcriptome dynamics during early hESC differentiation. Using weighted gene co-expression network analysis, we reveal that the hESC neurodevelopmental trajectory has five stages: pluripotency (day 0); differentiation initiation (days 2, 4, and 6); neural commitment (days 8-10); neural progenitor cell proliferation (days 12, 14, and 16); and neuronal differentiation (days 18, 20, and 22). These stages were characterized by unique module genes, which may recapitulate the early human cortical development. Moreover, a comparison of our RNA-sequencing data with several other transcriptome profiling datasets from mice and humans indicated that Module 3 associated with the day 8-10 stage is a critical window of fate switch from the pluripotency to the neural lineage. Interestingly, at this stage, no key extrinsic signals were activated. In contrast, using CRISPR/Cas9-mediated gene knockouts, we also found that intrinsic hub transcription factors, including the schizophrenia-associated SIX3 gene and septo-optic dysplasia-related HESX1 gene, are required to program hESC neural determination. Our results improve the understanding of the mechanism of neural commitment in the human brain and may help elucidate the etiology of human mental disorders and advance therapies for managing these conditions. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives.

PubMed

Zhong, Junpei; Cangelosi, Angelo; Wermter, Stefan

2014-01-01

The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context.

Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives

PubMed Central

Zhong, Junpei; Cangelosi, Angelo; Wermter, Stefan

2014-01-01

The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context. PMID:24550798

Neural pathways in processing of sexual arousal: a dynamic causal modeling study.

PubMed

Seok, J-W; Park, M-S; Sohn, J-H

2016-09-01

Three decades of research have investigated brain processing of visual sexual stimuli with neuroimaging methods. These researchers have found that sexual arousal stimuli elicit activity in a broad neural network of cortical and subcortical brain areas that are known to be associated with cognitive, emotional, motivational and physiological components. However, it is not completely understood how these neural systems integrate and modulated incoming information. Therefore, we identify cerebral areas whose activations were correlated with sexual arousal using event-related functional magnetic resonance imaging and used the dynamic causal modeling method for searching the effective connectivity about the sexual arousal processing network. Thirteen heterosexual males were scanned while they passively viewed alternating short trials of erotic and neutral pictures on a monitor. We created a subset of seven models based on our results and previous studies and selected a dominant connectivity model. Consequently, we suggest a dynamic causal model of the brain processes mediating the cognitive, emotional, motivational and physiological factors of human male sexual arousal. These findings are significant implications for the neuropsychology of male sexuality.

Neural functional architecture and modulation during decision making under uncertainty in individuals with generalized anxiety disorder.

PubMed

Assaf, Michal; Rabany, Liron; Zertuche, Luis; Bragdon, Laura; Tolin, David; Goethe, John; Diefenbach, Gretchen

2018-06-21

Recent evidence suggests that repetitive transcranial magnetic stimulation (rTMS) might be effective in treating generalized anxiety disorder (GAD). Cognitive models of GAD highlight the role of intolerance of uncertainty (IU) in precipitating and maintaining worry, and it has been hypothesized that patients with GAD exhibit decision-making deficits under uncertain conditions. Improving understanding of the neural mechanisms underlying cognitive deficits associated with IU may lead to the identification of novel rTMS treatment targets and optimization of treatment parameters. The current report describes two interrelated studies designed to identify and verify a potential neural target for rTMS treatment of GAD. Study I explored the integrity of prefrontal cortex (PFC) and amygdala neural networks, which underlie decision making under conditions of uncertainty, in GAD. Individuals diagnosed with GAD (n = 31) and healthy controls (n = 20) completed a functional magnetic resonance imaging (fMRI) gambling task that manipulated uncertainty using high versus low error rates. In a subsequent randomized-controlled trial (Study II), a subset of the GAD sample (n = 16) completed the fMRI gambling task again after 30 sessions of active versus sham rTMS (1 Hz, right dorsolateral prefrontal cortex) to investigate the modulation of functional networks and symptoms. In Study I, participants with GAD demonstrated impairments in PFC-PFC and PFC-amygdala functional connectivity (FC) mostly during the high uncertainty condition. In Study II, one region of interest pair, dorsal anterior cingulate (ACC) - subgenual ACC, showed "normalization" of FC following active, but not sham, rTMS, and neural changes were associated with improvement in worry symptoms. These results outline a possible treatment mechanism of rTMS in GAD, and pave the way for future studies of treatment optimization. © 2018 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.

Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

USGS Publications Warehouse

Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

1997-01-01

One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

PubMed Central

Neftci, Emre O.; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning. PMID:28680387

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.

PubMed

Neftci, Emre O; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Neuromodulation: Selected approaches and challenges

PubMed Central

Parpura, Vladimir; Silva, Gabriel A.; Tass, Peter A.; Bennet, Kevin E.; Meyyappan, Meyya; Koehne, Jessica; Lee, Kendall H.; Andrews, Russell J.

2012-01-01

The brain operates through complex interactions in the flow of information and signal processing within neural networks. The “wiring” of such networks, being neuronal or glial, can physically and/or functionally go rogue in various pathological states. Neuromodulation, as a multidisciplinary venture, attempts to correct such faulty nets. In this review, selected approaches and challenges in neuromoduation are discussed. The use of water-dispersible carbon nanotubes have proven effective in modulation of neurite outgrowth in culture as well as in aiding regeneration after spinal cord injury in vivo. Studying neural circuits using computational biology and analytical engineering approaches brings to light geometrical mapping of dynamics within neural networks, much needed information for stimulation interventions in medical practice. Indeed, sophisticated desynchronization approaches used for brain stimulation have been successful in coaxing “misfiring” neuronal circuits to resume productive firing patterns in various human disorders. Devices have been developed for the real time measurement of various neurotransmitters as well as electrical activity in the human brain during electrical deep brain stimulation. Such devices can establish the dynamics of electrochemical changes in the brain during stimulation. With increasing application of nanomaterials in devices for electrical and chemical recording and stimulating in the brain, the era of cellular, and even intracellular, precision neuromodulation will soon be upon us. PMID:23190025

Dynamic Changes in Amygdala Psychophysiological Connectivity Reveal Distinct Neural Networks for Facial Expressions of Basic Emotions.

PubMed

Diano, Matteo; Tamietto, Marco; Celeghin, Alessia; Weiskrantz, Lawrence; Tatu, Mona-Karina; Bagnis, Arianna; Duca, Sergio; Geminiani, Giuliano; Cauda, Franco; Costa, Tommaso

2017-03-27

The quest to characterize the neural signature distinctive of different basic emotions has recently come under renewed scrutiny. Here we investigated whether facial expressions of different basic emotions modulate the functional connectivity of the amygdala with the rest of the brain. To this end, we presented seventeen healthy participants (8 females) with facial expressions of anger, disgust, fear, happiness, sadness and emotional neutrality and analyzed amygdala's psychophysiological interaction (PPI). In fact, PPI can reveal how inter-regional amygdala communications change dynamically depending on perception of various emotional expressions to recruit different brain networks, compared to the functional interactions it entertains during perception of neutral expressions. We found that for each emotion the amygdala recruited a distinctive and spatially distributed set of structures to interact with. These changes in amygdala connectional patters characterize the dynamic signature prototypical of individual emotion processing, and seemingly represent a neural mechanism that serves to implement the distinctive influence that each emotion exerts on perceptual, cognitive, and motor responses. Besides these differences, all emotions enhanced amygdala functional integration with premotor cortices compared to neutral faces. The present findings thus concur to reconceptualise the structure-function relation between brain-emotion from the traditional one-to-one mapping toward a network-based and dynamic perspective.

Selective attention to temporal features on nested time scales.

PubMed

Henry, Molly J; Herrmann, Björn; Obleser, Jonas

2015-02-01

Meaningful auditory stimuli such as speech and music often vary simultaneously along multiple time scales. Thus, listeners must selectively attend to, and selectively ignore, separate but intertwined temporal features. The current study aimed to identify and characterize the neural network specifically involved in this feature-selective attention to time. We used a novel paradigm where listeners judged either the duration or modulation rate of auditory stimuli, and in which the stimulation, working memory demands, response requirements, and task difficulty were held constant. A first analysis identified all brain regions where individual brain activation patterns were correlated with individual behavioral performance patterns, which thus supported temporal judgments generically. A second analysis then isolated those brain regions that specifically regulated selective attention to temporal features: Neural responses in a bilateral fronto-parietal network including insular cortex and basal ganglia decreased with degree of change of the attended temporal feature. Critically, response patterns in these regions were inverted when the task required selectively ignoring this feature. The results demonstrate how the neural analysis of complex acoustic stimuli with multiple temporal features depends on a fronto-parietal network that simultaneously regulates the selective gain for attended and ignored temporal features. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

An Introduction to Neural Networks for Hearing Aid Noise Recognition.

ERIC Educational Resources Information Center

Kim, Jun W.; Tyler, Richard S.

1995-01-01

This article introduces the use of multilayered artificial neural networks in hearing aid noise recognition. It reviews basic principles of neural networks, and offers an example of an application in which a neural network is used to identify the presence or absence of noise in speech. The ability of neural networks to "learn" the…

Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

PubMed

Wan, Ying; Cao, Jinde; Wen, Guanghui

In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.

Neural complexity: A graph theoretic interpretation

NASA Astrophysics Data System (ADS)

Barnett, L.; Buckley, C. L.; Bullock, S.

2011-04-01

One of the central challenges facing modern neuroscience is to explain the ability of the nervous system to coherently integrate information across distinct functional modules in the absence of a central executive. To this end, Tononi [Proc. Natl. Acad. Sci. USA.PNASA60027-842410.1073/pnas.91.11.5033 91, 5033 (1994)] proposed a measure of neural complexity that purports to capture this property based on mutual information between complementary subsets of a system. Neural complexity, so defined, is one of a family of information theoretic metrics developed to measure the balance between the segregation and integration of a system’s dynamics. One key question arising for such measures involves understanding how they are influenced by network topology. Sporns [Cereb. Cortex53OPAV1047-321110.1093/cercor/10.2.127 10, 127 (2000)] employed numerical models in order to determine the dependence of neural complexity on the topological features of a network. However, a complete picture has yet to be established. While De Lucia [Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.71.016114 71, 016114 (2005)] made the first attempts at an analytical account of this relationship, their work utilized a formulation of neural complexity that, we argue, did not reflect the intuitions of the original work. In this paper we start by describing weighted connection matrices formed by applying a random continuous weight distribution to binary adjacency matrices. This allows us to derive an approximation for neural complexity in terms of the moments of the weight distribution and elementary graph motifs. In particular, we explicitly establish a dependency of neural complexity on cyclic graph motifs.

Modified neural networks for rapid recovery of tokamak plasma parameters for real time control

NASA Astrophysics Data System (ADS)

Sengupta, A.; Ranjan, P.

2002-07-01

Two modified neural network techniques are used for the identification of the equilibrium plasma parameters of the Superconducting Steady State Tokamak I from external magnetic measurements. This is expected to ultimately assist in a real time plasma control. As different from the conventional network structure where a single network with the optimum number of processing elements calculates the outputs, a multinetwork system connected in parallel does the calculations here in one of the methods. This network is called the double neural network. The accuracy of the recovered parameters is clearly more than the conventional network. The other type of neural network used here is based on the statistical function parametrization combined with a neural network. The principal component transformation removes linear dependences from the measurements and a dimensional reduction process reduces the dimensionality of the input space. This reduced and transformed input set, rather than the entire set, is fed into the neural network input. This is known as the principal component transformation-based neural network. The accuracy of the recovered parameters in the latter type of modified network is found to be a further improvement over the accuracy of the double neural network. This result differs from that obtained in an earlier work where the double neural network showed better performance. The conventional network and the function parametrization methods have also been used for comparison. The conventional network has been used for an optimization of the set of magnetic diagnostics. The effective set of sensors, as assessed by this network, are compared with the principal component based network. Fault tolerance of the neural networks has been tested. The double neural network showed the maximum resistance to faults in the diagnostics, while the principal component based network performed poorly. Finally the processing times of the methods have been compared. The double network and the principal component network involve the minimum computation time, although the conventional network also performs well enough to be used in real time.

Control of magnetic bearing systems via the Chebyshev polynomial-based unified model (CPBUM) neural network.

PubMed

Jeng, J T; Lee, T T

2000-01-01

A Chebyshev polynomial-based unified model (CPBUM) neural network is introduced and applied to control a magnetic bearing systems. First, we show that the CPBUM neural network not only has the same capability of universal approximator, but also has faster learning speed than conventional feedforward/recurrent neural network. It turns out that the CPBUM neural network is more suitable in the design of controller than the conventional feedforward/recurrent neural network. Second, we propose the inverse system method, based on the CPBUM neural networks, to control a magnetic bearing system. The proposed controller has two structures; namely, off-line and on-line learning structures. We derive a new learning algorithm for each proposed structure. The experimental results show that the proposed neural network architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

ChainMail based neural dynamics modeling of soft tissue deformation for surgical simulation.

PubMed

Zhang, Jinao; Zhong, Yongmin; Smith, Julian; Gu, Chengfan

2017-07-20

Realistic and real-time modeling and simulation of soft tissue deformation is a fundamental research issue in the field of surgical simulation. In this paper, a novel cellular neural network approach is presented for modeling and simulation of soft tissue deformation by combining neural dynamics of cellular neural network with ChainMail mechanism. The proposed method formulates the problem of elastic deformation into cellular neural network activities to avoid the complex computation of elasticity. The local position adjustments of ChainMail are incorporated into the cellular neural network as the local connectivity of cells, through which the dynamic behaviors of soft tissue deformation are transformed into the neural dynamics of cellular neural network. Experiments demonstrate that the proposed neural network approach is capable of modeling the soft tissues' nonlinear deformation and typical mechanical behaviors. The proposed method not only improves ChainMail's linear deformation with the nonlinear characteristics of neural dynamics but also enables the cellular neural network to follow the principle of continuum mechanics to simulate soft tissue deformation.

Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data.

PubMed

Sun, Wenqing; Tseng, Tzu-Liang Bill; Zhang, Jianying; Qian, Wei

2017-04-01

In this study we developed a graph based semi-supervised learning (SSL) scheme using deep convolutional neural network (CNN) for breast cancer diagnosis. CNN usually needs a large amount of labeled data for training and fine tuning the parameters, and our proposed scheme only requires a small portion of labeled data in training set. Four modules were included in the diagnosis system: data weighing, feature selection, dividing co-training data labeling, and CNN. 3158 region of interests (ROIs) with each containing a mass extracted from 1874 pairs of mammogram images were used for this study. Among them 100 ROIs were treated as labeled data while the rest were treated as unlabeled. The area under the curve (AUC) observed in our study was 0.8818, and the accuracy of CNN is 0.8243 using the mixed labeled and unlabeled data. Copyright © 2016. Published by Elsevier Ltd.

Integrating conflict detection and attentional control mechanisms.

PubMed

Walsh, Bong J; Buonocore, Michael H; Carter, Cameron S; Mangun, George R

2011-09-01

Human behavior involves monitoring and adjusting performance to meet established goals. Performance-monitoring systems that act by detecting conflict in stimulus and response processing have been hypothesized to influence cortical control systems to adjust and improve performance. Here we used fMRI to investigate the neural mechanisms of conflict monitoring and resolution during voluntary spatial attention. We tested the hypothesis that the ACC would be sensitive to conflict during attentional orienting and influence activity in the frontoparietal attentional control network that selectively modulates visual information processing. We found that activity in ACC increased monotonically with increasing attentional conflict. This increased conflict detection activity was correlated with both increased activity in the attentional control network and improved speed and accuracy from one trial to the next. These results establish a long hypothesized interaction between conflict detection systems and neural systems supporting voluntary control of visual attention.

Modulations of the executive control network by stimulus onset asynchrony in a Stroop task

PubMed Central

2013-01-01

Background Manipulating task difficulty is a useful way of elucidating the functional recruitment of the brain’s executive control network. In a Stroop task, pre-exposing the irrelevant word using varying stimulus onset asynchronies (‘negative’ SOAs) modulates the amount of behavioural interference and facilitation, suggesting disparate mechanisms of cognitive processing in each SOA. The current study employed a Stroop task with three SOAs (−400, -200, 0 ms), using functional magnetic resonance imaging to investigate for the first time the neural effects of SOA manipulation. Of specific interest were 1) how SOA affects the neural representation of interference and facilitation; 2) response priming effects in negative SOAs; and 3) attentional effects of blocked SOA presentation. Results The results revealed three regions of the executive control network that were sensitive to SOA during Stroop interference; the 0 ms SOA elicited the greatest activation of these areas but experienced relatively smaller behavioural interference, suggesting that the enhanced recruitment led to more efficient conflict processing. Response priming effects were localized to the right inferior frontal gyrus, which is consistent with the idea that this region performed response inhibition in incongruent conditions to overcome the incorrectly-primed response, as well as more general action updating and response preparation. Finally, the right superior parietal lobe was sensitive to blocked SOA presentation and was most active for the 0 ms SOA, suggesting that this region is involved in attentional control. Conclusions SOA exerted both trial-specific and block-wide effects on executive processing, providing a unique paradigm for functional investigations of the cognitive control network. PMID:23902451

Nested Neural Networks

NASA Technical Reports Server (NTRS)

Baram, Yoram

1992-01-01

Report presents analysis of nested neural networks, consisting of interconnected subnetworks. Analysis based on simplified mathematical models more appropriate for artificial electronic neural networks, partly applicable to biological neural networks. Nested structure allows for retrieval of individual subpatterns. Requires fewer wires and connection devices than fully connected networks, and allows for local reconstruction of damaged subnetworks without rewiring entire network.

Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science.

PubMed

Mocanu, Decebal Constantin; Mocanu, Elena; Stone, Peter; Nguyen, Phuong H; Gibescu, Madeleine; Liotta, Antonio

2018-06-19

Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-freeness), we argue that (contrary to general practice) artificial neural networks, too, should not have fully-connected layers. Here we propose sparse evolutionary training of artificial neural networks, an algorithm which evolves an initial sparse topology (Erdős-Rényi random graph) of two consecutive layers of neurons into a scale-free topology, during learning. Our method replaces artificial neural networks fully-connected layers with sparse ones before training, reducing quadratically the number of parameters, with no decrease in accuracy. We demonstrate our claims on restricted Boltzmann machines, multi-layer perceptrons, and convolutional neural networks for unsupervised and supervised learning on 15 datasets. Our approach has the potential to enable artificial neural networks to scale up beyond what is currently possible.

Quantum neural networks: Current status and prospects for development

NASA Astrophysics Data System (ADS)

Altaisky, M. V.; Kaputkina, N. E.; Krylov, V. A.

2014-11-01

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

Geometric properties-dependent neural synchrony modulated by extracellular subthreshold electric field

NASA Astrophysics Data System (ADS)

Wei, Xile; Si, Kaili; Yi, Guosheng; Wang, Jiang; Lu, Meili

2016-07-01

In this paper, we use a reduced two-compartment neuron model to investigate the interaction between extracellular subthreshold electric field and synchrony in small world networks. It is observed that network synchronization is closely related to the strength of electric field and geometric properties of the two-compartment model. Specifically, increasing the electric field induces a gradual improvement in network synchrony, while increasing the geometric factor results in an abrupt decrease in synchronization of network. In addition, increasing electric field can make the network become synchronous from asynchronous when the geometric parameter is set to a given value. Furthermore, it is demonstrated that network synchrony can also be affected by the firing frequency and dynamical bifurcation feature of single neuron. These results highlight the effect of weak field on network synchrony from the view of biophysical model, which may contribute to further understanding the effect of electric field on network activity.

Neural network approaches to capture temporal information

NASA Astrophysics Data System (ADS)

van Veelen, Martijn; Nijhuis, Jos; Spaanenburg, Ben

2000-05-01

The automated design and construction of neural networks receives growing attention of the neural networks community. Both the growing availability of computing power and development of mathematical and probabilistic theory have had severe impact on the design and modelling approaches of neural networks. This impact is most apparent in the use of neural networks to time series prediction. In this paper, we give our views on past, contemporary and future design and modelling approaches to neural forecasting.

Attention to pain! A neurocognitive perspective on attentional modulation of pain in neuroimaging studies.

PubMed

Torta, D M; Legrain, V; Mouraux, A; Valentini, E

2017-04-01

Several studies have used neuroimaging techniques to investigate brain correlates of the attentional modulation of pain. Although these studies have advanced the knowledge in the field, important confounding factors such as imprecise theoretical definitions of attention, incomplete operationalization of the construct under exam, and limitations of techniques relying on measuring regional changes in cerebral blood flow have hampered the potential relevance of the conclusions. Here, we first provide an overview of the major theories of attention and of attention in the study of pain to bridge theory and experimental results. We conclude that load and motivational/affective theories are particularly relevant to study the attentional modulation of pain and should be carefully integrated in functional neuroimaging studies. Then, we summarize previous findings and discuss the possible neural correlates of the attentional modulation of pain. We discuss whether classical functional neuroimaging techniques are suitable to measure the effect of a fluctuating process like attention, and in which circumstances functional neuroimaging can be reliably used to measure the attentional modulation of pain. Finally, we argue that the analysis of brain networks and spontaneous oscillations may be a crucial future development in the study of attentional modulation of pain, and why the interplay between attention and pain, as examined so far, may rely on neural mechanisms shared with other sensory modalities. Copyright © 2017 Elsevier Ltd. All rights reserved.

The role of symmetry in neural networks and their Laplacian spectra.

PubMed

de Lange, Siemon C; van den Heuvel, Martijn P; de Reus, Marcel A

2016-11-01

Human and animal nervous systems constitute complexly wired networks that form the infrastructure for neural processing and integration of information. The organization of these neural networks can be analyzed using the so-called Laplacian spectrum, providing a mathematical tool to produce systems-level network fingerprints. In this article, we examine a characteristic central peak in the spectrum of neural networks, including anatomical brain network maps of the mouse, cat and macaque, as well as anatomical and functional network maps of human brain connectivity. We link the occurrence of this central peak to the level of symmetry in neural networks, an intriguing aspect of network organization resulting from network elements that exhibit similar wiring patterns. Specifically, we propose a measure to capture the global level of symmetry of a network and show that, for both empirical networks and network models, the height of the main peak in the Laplacian spectrum is strongly related to node symmetry in the underlying network. Moreover, examination of spectra of duplication-based model networks shows that neural spectra are best approximated using a trade-off between duplication and diversification. Taken together, our results facilitate a better understanding of neural network spectra and the importance of symmetry in neural networks. Copyright © 2016 Elsevier Inc. All rights reserved.

Subclinical Doses of ATP-Sensitive Potassium Channel Modulators Prevent Alterations in Memory and Synaptic Plasticity Induced by Amyloid-β.

PubMed

Salgado-Puga, Karla; Rodríguez-Colorado, Javier; Prado-Alcalá, Roberto A; Peña-Ortega, Fernando

2017-01-01

In addition to coupling cell metabolism and excitability, ATP-sensitive potassium channels (KATP) are involved in neural function and plasticity. Moreover, alterations in KATP activity and expression have been observed in Alzheimer's disease (AD) and during amyloid-β (Aβ)-induced pathology. Thus, we tested whether KATP modulators can influence Aβ-induced deleterious effects on memory, hippocampal network function, and plasticity. We found that treating animals with subclinical doses (those that did not change glycemia) of a KATP blocker (Tolbutamide) or a KATP opener (Diazoxide) differentially restrained Aβ-induced memory deficit, hippocampal network activity inhibition, and long-term synaptic plasticity unbalance (i.e., inhibition of LTP and promotion of LTD). We found that the protective effect of Tolbutamide against Aβ-induced memory deficit was strong and correlated with the reestablishment of synaptic plasticity balance, whereas Diazoxide treatment produced a mild protection against Aβ-induced memory deficit, which was not related to a complete reestablishment of synaptic plasticity balance. Interestingly, treatment with both KATP modulators renders the hippocampus resistant to Aβ-induced inhibition of hippocampal network activity. These findings indicate that KATP are involved in Aβ-induced pathology and they heighten the potential role of KATP modulation as a plausible therapeutic strategy against AD.

Synchronization Control of Neural Networks With State-Dependent Coefficient Matrices.

PubMed

Zhang, Junfeng; Zhao, Xudong; Huang, Jun

2016-11-01

This brief is concerned with synchronization control of a class of neural networks with state-dependent coefficient matrices. Being different from the existing drive-response neural networks in the literature, a novel model of drive-response neural networks is established. The concepts of uniformly ultimately bounded (UUB) synchronization and convex hull Lyapunov function are introduced. Then, by using the convex hull Lyapunov function approach, the UUB synchronization design of the drive-response neural networks is proposed, and a delay-independent control law guaranteeing the bounded synchronization of the neural networks is constructed. All present conditions are formulated in terms of bilinear matrix inequalities. By comparison, it is shown that the neural networks obtained in this brief are less conservative than those ones in the literature, and the bounded synchronization is suitable for the novel drive-response neural networks. Finally, an illustrative example is given to verify the validity of the obtained results.

The Laplacian spectrum of neural networks

PubMed Central

de Lange, Siemon C.; de Reus, Marcel A.; van den Heuvel, Martijn P.

2014-01-01

The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these “conventional” graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks. PMID:24454286

Theta phase precession and phase selectivity: a cognitive device description of neural coding

NASA Astrophysics Data System (ADS)

Zalay, Osbert C.; Bardakjian, Berj L.

2009-06-01

Information in neural systems is carried by way of phase and rate codes. Neuronal signals are processed through transformative biophysical mechanisms at the cellular and network levels. Neural coding transformations can be represented mathematically in a device called the cognitive rhythm generator (CRG). Incoming signals to the CRG are parsed through a bank of neuronal modes that orchestrate proportional, integrative and derivative transformations associated with neural coding. Mode outputs are then mixed through static nonlinearities to encode (spatio) temporal phase relationships. The static nonlinear outputs feed and modulate a ring device (limit cycle) encoding output dynamics. Small coupled CRG networks were created to investigate coding functionality associated with neuronal phase preference and theta precession in the hippocampus. Phase selectivity was found to be dependent on mode shape and polarity, while phase precession was a product of modal mixing (i.e. changes in the relative contribution or amplitude of mode outputs resulted in shifting phase preference). Nonlinear system identification was implemented to help validate the model and explain response characteristics associated with modal mixing; in particular, principal dynamic modes experimentally derived from a hippocampal neuron were inserted into a CRG and the neuron's dynamic response was successfully cloned. From our results, small CRG networks possessing disynaptic feedforward inhibition in combination with feedforward excitation exhibited frequency-dependent inhibitory-to-excitatory and excitatory-to-inhibitory transitions that were similar to transitions seen in a single CRG with quadratic modal mixing. This suggests nonlinear modal mixing to be a coding manifestation of the effect of network connectivity in shaping system dynamic behavior. We hypothesize that circuits containing disynaptic feedforward inhibition in the nervous system may be candidates for interpreting upstream rate codes to guide downstream processes such as phase precession, because of their demonstrated frequency-selective properties.

ELIPS: Toward a Sensor Fusion Processor on a Chip

NASA Technical Reports Server (NTRS)

Daud, Taher; Stoica, Adrian; Tyson, Thomas; Li, Wei-te; Fabunmi, James

1998-01-01

The paper presents the concept and initial tests from the hardware implementation of a low-power, high-speed reconfigurable sensor fusion processor. The Extended Logic Intelligent Processing System (ELIPS) processor is developed to seamlessly combine rule-based systems, fuzzy logic, and neural networks to achieve parallel fusion of sensor in compact low power VLSI. The first demonstration of the ELIPS concept targets interceptor functionality; other applications, mainly in robotics and autonomous systems are considered for the future. The main assumption behind ELIPS is that fuzzy, rule-based and neural forms of computation can serve as the main primitives of an "intelligent" processor. Thus, in the same way classic processors are designed to optimize the hardware implementation of a set of fundamental operations, ELIPS is developed as an efficient implementation of computational intelligence primitives, and relies on a set of fuzzy set, fuzzy inference and neural modules, built in programmable analog hardware. The hardware programmability allows the processor to reconfigure into different machines, taking the most efficient hardware implementation during each phase of information processing. Following software demonstrations on several interceptor data, three important ELIPS building blocks (a fuzzy set preprocessor, a rule-based fuzzy system and a neural network) have been fabricated in analog VLSI hardware and demonstrated microsecond-processing times.

Frank Beach Award Winner: Steroids as Neuromodulators of Brain Circuits and Behavior

PubMed Central

Remage-Healey, Luke

2014-01-01

Neurons communicate primarily via action potentials that transmit information on the timescale of milliseconds. Neurons also integrate information via alterations in gene transcription and protein translation that are sustained for hours to days after initiation. Positioned between these two signaling timescales are the minute-by-minute actions of neuromodulators. Over the course of minutes, the classical neuromodulators (such as serotonin, dopamine, octopamine, and norepinephrine) can alter and/or stabilize neural circuit patterning as well as behavioral states. Neuromodulators allow many flexible outputs from neural circuits and can encode information content into the firing state of neural networks. The idea that steroid molecules can operate as genuine behavioral neuromodulators - synthesized by and acting within brain circuits on a minute-by-minute timescale - has gained traction in recent years. Evidence for brain steroid synthesis at synaptic terminals has converged with evidence for the rapid actions of brain-derived steroids on neural circuits and behavior. The general principle emerging from this work is that the production of steroid hormones within brain circuits can alter their functional connectivity and shift sensory representations by enhancing their information coding. Steroids produced in the brain can therefore change the information content of neuronal networks to rapidly modulate sensory experience and sensorimotor functions. PMID:25110187

Social cognition in members of conflict groups: behavioural and neural responses in Arabs, Israelis and South Americans to each other's misfortunes.

PubMed

Bruneau, Emile G; Dufour, Nicholas; Saxe, Rebecca

2012-03-05

In contexts of cultural conflict, people delegitimize the other group's perspective and lose compassion for the other group's suffering. These psychological biases have been empirically characterized in intergroup settings, but rarely in groups involved in active conflict. Similarly, the basic brain networks involved in recognizing others' narratives and misfortunes have been identified, but how these brain networks are modulated by intergroup conflict is largely untested. In the present study, we examined behavioural and neural responses in Arab, Israeli and South American participants while they considered the pain and suffering of individuals from each group. Arabs and Israelis reported feeling significantly less compassion for each other's pain and suffering (the 'conflict outgroup'), but did not show an ingroup bias relative to South Americans (the 'distant outgroup'). In contrast, the brain regions that respond to others' tragedies showed an ingroup bias relative to the distant outgroup but not the conflict outgroup, particularly for descriptions of emotional suffering. Over all, neural responses to conflict group members were qualitatively different from neural responses to distant group members. This is the first neuroimaging study to examine brain responses to others' suffering across both distant and conflict groups, and provides a first step towards building a foundation for the biological basis of conflict.

Introduction to Neural Networks.

DTIC Science & Technology

1992-03-01

parallel processing of information that can greatly reduce the time required to perform operations which are needed in pattern recognition. Neural network, Artificial neural network , Neural net, ANN.

Cortical network models of impulse firing in the resting and active states predict cortical energetics

PubMed Central

Bennett, Maxwell R.; Farnell, Les; Gibson, William G.; Lagopoulos, Jim

2015-01-01

Measurements of the cortical metabolic rate of glucose oxidation [CMRglc(ox)] have provided a number of interesting and, in some cases, surprising observations. One is the decline in CMRglc(ox) during anesthesia and non-rapid eye movement (NREM) sleep, and another, the inverse relationship between the resting-state CMRglc(ox) and the transient following input from the thalamus. The recent establishment of a quantitative relationship between synaptic and action potential activity on the one hand and CMRglc(ox) on the other allows neural network models of such activity to probe for possible mechanistic explanations of these phenomena. We have carried out such investigations using cortical models consisting of networks of modules with excitatory and inhibitory neurons, each receiving excitatory inputs from outside the network in addition to intermodular connections. Modules may be taken as regions of cortical interest, the inputs from outside the network as arising from the thalamus, and the intermodular connections as long associational fibers. The model shows that the impulse frequency of different modules can differ from each other by less than 10%, consistent with the relatively uniform CMRglc(ox) observed across different regions of cortex. The model also shows that, if correlations of the average impulse rate between different modules decreases, there is a concomitant decrease in the average impulse rate in the modules, consistent with the observed drop in CMRglc(ox) in NREM sleep and under anesthesia. The model also explains why a transient thalamic input to sensory cortex gives rise to responses with amplitudes inversely dependent on the resting-state frequency, and therefore resting-state CMRglc(ox). PMID:25775588

Learning control of inverted pendulum system by neural network driven fuzzy reasoning: The learning function of NN-driven fuzzy reasoning under changes of reasoning environment

NASA Technical Reports Server (NTRS)

Hayashi, Isao; Nomura, Hiroyoshi; Wakami, Noboru

1991-01-01

Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.

A Neural Network Architecture For Rapid Model Indexing In Computer Vision Systems

NASA Astrophysics Data System (ADS)

Pawlicki, Ted

1988-03-01

Models of objects stored in memory have been shown to be useful for guiding the processing of computer vision systems. A major consideration in such systems, however, is how stored models are initially accessed and indexed by the system. As the number of stored models increases, the time required to search memory for the correct model becomes high. Parallel distributed, connectionist, neural networks' have been shown to have appealing content addressable memory properties. This paper discusses an architecture for efficient storage and reference of model memories stored as stable patterns of activity in a parallel, distributed, connectionist, neural network. The emergent properties of content addressability and resistance to noise are exploited to perform indexing of the appropriate object centered model from image centered primitives. The system consists of three network modules each of which represent information relative to a different frame of reference. The model memory network is a large state space vector where fields in the vector correspond to ordered component objects and relative, object based spatial relationships between the component objects. The component assertion network represents evidence about the existence of object primitives in the input image. It establishes local frames of reference for object primitives relative to the image based frame of reference. The spatial relationship constraint network is an intermediate representation which enables the association between the object based and the image based frames of reference. This intermediate level represents information about possible object orderings and establishes relative spatial relationships from the image based information in the component assertion network below. It is also constrained by the lawful object orderings in the model memory network above. The system design is consistent with current psychological theories of recognition by component. It also seems to support Marr's notions of hierarchical indexing. (i.e. the specificity, adjunct, and parent indices) It supports the notion that multiple canonical views of an object may have to be stored in memory to enable its efficient identification. The use of variable fields in the state space vectors appears to keep the number of required nodes in the network down to a tractable number while imposing a semantic value on different areas of the state space. This semantic imposition supports an interface between the analogical aspects of neural networks and the propositional paradigms of symbolic processing.

Modulation of working memory load distinguishes individuals with and without balance impairments following mild traumatic brain injury.

PubMed

Woytowicz, Elizabeth J; Sours, Chandler; Gullapalli, Rao P; Rosenberg, Joseph; Westlake, Kelly P

2018-01-01

Balance and gait deficits can persist after mild traumatic brain injury (TBI), yet an understanding of the underlying neural mechanism remains limited. The purpose of this study was to investigate differences in attention network modulation in patients with and without balance impairments 2-8 weeks following mild TBI. Using functional magnetic resonance imaging, we compared activity and functional connectivity of cognitive brain regions of the default mode, central-executive and salience networks during a 2-back working memory task in participants with mild TBI and balance impairments (n = 7, age 47 ± 15 years) or no balance impairments (n = 7, age 47 ± 15 years). We first identified greater activation in the lateral occipital cortex in the balance impaired group. Second, we observed stronger connectivity of left pre-supplementary motor cortex in the balance impaired group during the working memory task, which was related to decreased activation of regions within the salience and central executive networks and greater suppression of the default mode network. Results suggest a link between impaired balance and modulation of cognitive resources in patients in mTBI. Findings also highlight the potential importance of moving beyond traditional balance assessments towards an integrative assessment of cognition and balance in this population.

Functional vs. Structural Modularity: do they imply each other?

NASA Astrophysics Data System (ADS)

Toroczkai, Zoltan

2009-03-01

While many deterministic and stochastic processes have been proposed to produce heterogeneous graphs mimicking real-world networks, only a handful of studies attempt to connect structure and dynamics with the function(s) performed by the network. In this talk I will present an approach built on the premise that structure, dynamics, and their observed heterogeneity, are implementations of various functions and their compositions. After a brief review of real-world networks where this connection can explicitly be made, I will focus on biological networks. Biological networks are known to possess functionally specialized modules, which perform tasks almost independently of each other. While proposals have been made for the evolutionary emergence of modularity, it is far from clear that adaptation on evolutionary timescales is the sole mechanism leading to functional specialization. We show that non-evolutionary learning can also lead to the formation of functionally specialized modules in a system exposed to multiple environmental constraints. A natural example suggesting that this is possible is the cerebral cortex, where there are clearly delineated functional areas in spite of the largely uniform anatomical construction of the cortical tissue. However, as numerous experiments show, when damaged, regions specialized for a certain function can be retrained to perform functions normally attributed to other regions. We use the paradigm of neural networks to represent a multitasking system, and use several non-evolutionary learning algorithms as mechanisms for phenotypic adaptation. We show that for a network learning to perform multiple tasks, the degree of independence between the tasks dictates the degree of functional specialization emerging in the network. To uncover the functional modules, we introduce a method of node knockouts that explicitly rates the contribution of each node to different tasks (differential robustness). Through a concrete example we also demonstrate the potential inability of purely topology-based clustering methods to detect functional modules. The robustness of these results suggests that similar mechanisms might be responsible for the emergence of functional specialization in other multitasking networks, as well, including social networks.

Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles.

PubMed

Zhang, Duona; Ding, Wenrui; Zhang, Baochang; Xie, Chunyu; Li, Hongguang; Liu, Chunhui; Han, Jungong

2018-03-20

Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition and remains challenging for traditional methods due to complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include the following: (1) a convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; (2) a large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and (3) experimental results demonstrate that HDMF is very capable of coping with the AMC problem, and achieves much better performance when compared with the independent network.

Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles

PubMed Central

Ding, Wenrui; Zhang, Baochang; Xie, Chunyu; Li, Hongguang; Liu, Chunhui; Han, Jungong

2018-01-01

Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition and remains challenging for traditional methods due to complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include the following: (1) a convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; (2) a large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and (3) experimental results demonstrate that HDMF is very capable of coping with the AMC problem, and achieves much better performance when compared with the independent network. PMID:29558434

Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases

PubMed Central

Ritchie, Marylyn D; White, Bill C; Parker, Joel S; Hahn, Lance W; Moore, Jason H

2003-01-01

Background Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This can be challenging when the underlying model of the data is unknown. The goal of this study was to determine whether optimizing neural network architecture using genetic programming as a machine learning strategy would improve the ability of neural networks to model and detect nonlinear interactions among genes in studies of common human diseases. Results Using simulated data, we show that a genetic programming optimized neural network approach is able to model gene-gene interactions as well as a traditional back propagation neural network. Furthermore, the genetic programming optimized neural network is better than the traditional back propagation neural network approach in terms of predictive ability and power to detect gene-gene interactions when non-functional polymorphisms are present. Conclusion This study suggests that a machine learning strategy for optimizing neural network architecture may be preferable to traditional trial-and-error approaches for the identification and characterization of gene-gene interactions in common, complex human diseases. PMID:12846935

Medical image analysis with artificial neural networks.

PubMed

Jiang, J; Trundle, P; Ren, J

2010-12-01

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. Copyright © 2010 Elsevier Ltd. All rights reserved.

Deep nets vs expert designed features in medical physics: An IMRT QA case study.

PubMed

Interian, Yannet; Rideout, Vincent; Kearney, Vasant P; Gennatas, Efstathios; Morin, Olivier; Cheung, Joey; Solberg, Timothy; Valdes, Gilmer

2018-03-30

The purpose of this study was to compare the performance of Deep Neural Networks against a technique designed by domain experts in the prediction of gamma passing rates for Intensity Modulated Radiation Therapy Quality Assurance (IMRT QA). A total of 498 IMRT plans across all treatment sites were planned in Eclipse version 11 and delivered using a dynamic sliding window technique on Clinac iX or TrueBeam Linacs. Measurements were performed using a commercial 2D diode array, and passing rates for 3%/3 mm local dose/distance-to-agreement (DTA) were recorded. Separately, fluence maps calculated for each plan were used as inputs to a convolution neural network (CNN). The CNNs were trained to predict IMRT QA gamma passing rates using TensorFlow and Keras. A set of model architectures, inspired by the convolutional blocks of the VGG-16 ImageNet model, were constructed and implemented. Synthetic data, created by rotating and translating the fluence maps during training, was created to boost the performance of the CNNs. Dropout, batch normalization, and data augmentation were utilized to help train the model. The performance of the CNNs was compared to a generalized Poisson regression model, previously developed for this application, which used 78 expert designed features. Deep Neural Networks without domain knowledge achieved comparable performance to a baseline system designed by domain experts in the prediction of 3%/3 mm Local gamma passing rates. An ensemble of neural nets resulted in a mean absolute error (MAE) of 0.70 ± 0.05 and the domain expert model resulted in a 0.74 ± 0.06. Convolutional neural networks (CNNs) with transfer learning can predict IMRT QA passing rates by automatically designing features from the fluence maps without human expert supervision. Predictions from CNNs are comparable to a system carefully designed by physicist experts. © 2018 American Association of Physicists in Medicine.

Bifurcation Analysis on Phase-Amplitude Cross-Frequency Coupling in Neural Networks with Dynamic Synapses

PubMed Central

Sase, Takumi; Katori, Yuichi; Komuro, Motomasa; Aihara, Kazuyuki

2017-01-01

We investigate a discrete-time network model composed of excitatory and inhibitory neurons and dynamic synapses with the aim at revealing dynamical properties behind oscillatory phenomena possibly related to brain functions. We use a stochastic neural network model to derive the corresponding macroscopic mean field dynamics, and subsequently analyze the dynamical properties of the network. In addition to slow and fast oscillations arising from excitatory and inhibitory networks, respectively, we show that the interaction between these two networks generates phase-amplitude cross-frequency coupling (CFC), in which multiple different frequency components coexist and the amplitude of the fast oscillation is modulated by the phase of the slow oscillation. Furthermore, we clarify the detailed properties of the oscillatory phenomena by applying the bifurcation analysis to the mean field model, and accordingly show that the intermittent and the continuous CFCs can be characterized by an aperiodic orbit on a closed curve and one on a torus, respectively. These two CFC modes switch depending on the coupling strength from the excitatory to inhibitory networks, via the saddle-node cycle bifurcation of a one-dimensional torus in map (MT1SNC), and may be associated with the function of multi-item representation. We believe that the present model might have potential for studying possible functional roles of phase-amplitude CFC in the cerebral cortex. PMID:28424606

Involvement of mesolimbic dopaminergic network in neuropathic pain relief by treadmill exercise: A study for specific neural control with Gi-DREADD in mice.

PubMed

Wakaizumi, Kenta; Kondo, Takashige; Hamada, Yusuke; Narita, Michiko; Kawabe, Rui; Narita, Hiroki; Watanabe, Moe; Kato, Shigeki; Senba, Emiko; Kobayashi, Kazuto; Kuzumaki, Naoko; Yamanaka, Akihiro; Morisaki, Hiroshi; Narita, Minoru

2016-01-01

Exercise alleviates pain and it is a central component of treatment strategy for chronic pain in clinical setting. However, little is known about mechanism of this exercise-induced hypoalgesia. The mesolimbic dopaminergic network plays a role in positive emotions to rewards including motivation and pleasure. Pain negatively modulates these emotions, but appropriate exercise is considered to activate the dopaminergic network. We investigated possible involvement of this network as a mechanism of exercise-induced hypoalgesia. In the present study, we developed a protocol of treadmill exercise, which was able to recover pain threshold under partial sciatic nerve ligation in mice, and investigated involvement of the dopaminergic reward network in exercise-induced hypoalgesia. To temporally suppress a neural activation during exercise, a genetically modified inhibitory G-protein-coupled receptor, hM4Di, was specifically expressed on dopaminergic pathway from the ventral tegmental area to the nucleus accumbens. The chemogenetic-specific neural suppression by Gi-DREADD system dramatically offset the effect of exercise-induced hypoalgesia in transgenic mice with hM4Di expressed on the ventral tegmental area dopamine neurons. Additionally, anti-exercise-induced hypoalgesia effect was significantly observed under the suppression of neurons projecting out of the ventral tegmental area to the nucleus accumbens as well. Our findings suggest that the dopaminergic pathway from the ventral tegmental area to the nucleus accumbens is involved in the anti-nociception under low-intensity exercise under a neuropathic pain-like state. © The Author(s) 2016.

Neural-network-directed alignment of optical systems using the laser-beam spatial filter as an example

NASA Technical Reports Server (NTRS)

Decker, Arthur J.; Krasowski, Michael J.; Weiland, Kenneth E.

1993-01-01

This report describes an effort at NASA Lewis Research Center to use artificial neural networks to automate the alignment and control of optical measurement systems. Specifically, it addresses the use of commercially available neural network software and hardware to direct alignments of the common laser-beam-smoothing spatial filter. The report presents a general approach for designing alignment records and combining these into training sets to teach optical alignment functions to neural networks and discusses the use of these training sets to train several types of neural networks. Neural network configurations used include the adaptive resonance network, the back-propagation-trained network, and the counter-propagation network. This work shows that neural networks can be used to produce robust sequencers. These sequencers can learn by example to execute the step-by-step procedures of optical alignment and also can learn adaptively to correct for environmentally induced misalignment. The long-range objective is to use neural networks to automate the alignment and operation of optical measurement systems in remote, harsh, or dangerous aerospace environments. This work also shows that when neural networks are trained by a human operator, training sets should be recorded, training should be executed, and testing should be done in a manner that does not depend on intellectual judgments of the human operator.

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

PubMed

Nitta, Tohru

2017-10-01

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

The effect of the neural activity on topological properties of growing neural networks.

PubMed

Gafarov, F M; Gafarova, V R

2016-09-01

The connectivity structure in cortical networks defines how information is transmitted and processed, and it is a source of the complex spatiotemporal patterns of network's development, and the process of creation and deletion of connections is continuous in the whole life of the organism. In this paper, we study how neural activity influences the growth process in neural networks. By using a two-dimensional activity-dependent growth model we demonstrated the neural network growth process from disconnected neurons to fully connected networks. For making quantitative investigation of the network's activity influence on its topological properties we compared it with the random growth network not depending on network's activity. By using the random graphs theory methods for the analysis of the network's connections structure it is shown that the growth in neural networks results in the formation of a well-known "small-world" network.

The role of the inferior frontal gyrus in vicarious social touch: A transcranial direct current stimulation (tDCS) study.

PubMed

Peled-Avron, Leehe; Glasner, Laura; Gvirts, Hila Z; Shamay-Tsoory, Simone G

2018-04-30

The neural mechanisms facilitating the experience of vicarious social touch are largely unknown. The right inferior frontal gyrus (rIFG) has been suggested as part of a simulation observation-execution neural network that plays a key role in the perception of tactile stimuli. Considering that vicarious social touch involves vicarious sharing of emotions, we hypothesized that emotional empathy, i.e., the ability to feel what another individual is feeling, modulates the neural responses to vicarious touch. To examine the role of the rIFG in vicarious touch and its modulation by levels of emotional empathy, we used anodal transcranial direct current stimulation (tDCS) on forty participants who observed photos depicting social touch, nonsocial touch or no touch during tDCS or sham stimulation. The results show that while participants with high levels of emotional empathy exhibited no change in ratings of vicarious social touch, participants with low levels of emotional empathy rate human touch as more emotional following anodal stimulation of the rIFG than following sham stimulation. These findings indicate that emotional responses to vicarious social touch are associated with rIFG activity and are modulated by levels of emotional empathy. This result has major therapeutic potential for individuals with low empathic abilities, such as those with ASD. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Cognitive emotion regulation enhances aversive prediction error activity while reducing emotional responses.

PubMed

Mulej Bratec, Satja; Xie, Xiyao; Schmid, Gabriele; Doll, Anselm; Schilbach, Leonhard; Zimmer, Claus; Wohlschläger, Afra; Riedl, Valentin; Sorg, Christian

2015-12-01

Cognitive emotion regulation is a powerful way of modulating emotional responses. However, despite the vital role of emotions in learning, it is unknown whether the effect of cognitive emotion regulation also extends to the modulation of learning. Computational models indicate prediction error activity, typically observed in the striatum and ventral tegmental area, as a critical neural mechanism involved in associative learning. We used model-based fMRI during aversive conditioning with and without cognitive emotion regulation to test the hypothesis that emotion regulation would affect prediction error-related neural activity in the striatum and ventral tegmental area, reflecting an emotion regulation-related modulation of learning. Our results show that cognitive emotion regulation reduced emotion-related brain activity, but increased prediction error-related activity in a network involving ventral tegmental area, hippocampus, insula and ventral striatum. While the reduction of response activity was related to behavioral measures of emotion regulation success, the enhancement of prediction error-related neural activity was related to learning performance. Furthermore, functional connectivity between the ventral tegmental area and ventrolateral prefrontal cortex, an area involved in regulation, was specifically increased during emotion regulation and likewise related to learning performance. Our data, therefore, provide first-time evidence that beyond reducing emotional responses, cognitive emotion regulation affects learning by enhancing prediction error-related activity, potentially via tegmental dopaminergic pathways. Copyright © 2015 Elsevier Inc. All rights reserved.

LavaNet—Neural network development environment in a general mine planning package

NASA Astrophysics Data System (ADS)

Kapageridis, Ioannis Konstantinou; Triantafyllou, A. G.

2011-04-01

LavaNet is a series of scripts written in Perl that gives access to a neural network simulation environment inside a general mine planning package. A well known and a very popular neural network development environment, the Stuttgart Neural Network Simulator, is used as the base for the development of neural networks. LavaNet runs inside VULCAN™—a complete mine planning package with advanced database, modelling and visualisation capabilities. LavaNet is taking advantage of VULCAN's Perl based scripting environment, Lava, to bring all the benefits of neural network development and application to geologists, mining engineers and other users of the specific mine planning package. LavaNet enables easy development of neural network training data sets using information from any of the data and model structures available, such as block models and drillhole databases. Neural networks can be trained inside VULCAN™ and the results be used to generate new models that can be visualised in 3D. Direct comparison of developed neural network models with conventional and geostatistical techniques is now possible within the same mine planning software package. LavaNet supports Radial Basis Function networks, Multi-Layer Perceptrons and Self-Organised Maps.

Creative-Dynamics Approach To Neural Intelligence

NASA Technical Reports Server (NTRS)

Zak, Michail A.

1992-01-01

Paper discusses approach to mathematical modeling of artificial neural networks exhibiting complicated behaviors reminiscent of creativity and intelligence of biological neural networks. Neural network treated as non-Lipschitzian dynamical system - as described in "Non-Lipschitzian Dynamics For Modeling Neural Networks" (NPO-17814). System serves as tool for modeling of temporal-pattern memories and recognition of complicated spatial patterns.

Learning a single-hidden layer feedforward neural network using a rank correlation-based strategy with application to high dimensional gene expression and proteomic spectra datasets in cancer detection.

PubMed

Belciug, Smaranda; Gorunescu, Florin

2018-06-08

Methods based on microarrays (MA), mass spectrometry (MS), and machine learning (ML) algorithms have evolved rapidly in recent years, allowing for early detection of several types of cancer. A pitfall of these approaches, however, is the overfitting of data due to large number of attributes and small number of instances -- a phenomenon known as the 'curse of dimensionality'. A potentially fruitful idea to avoid this drawback is to develop algorithms that combine fast computation with a filtering module for the attributes. The goal of this paper is to propose a statistical strategy to initiate the hidden nodes of a single-hidden layer feedforward neural network (SLFN) by using both the knowledge embedded in data and a filtering mechanism for attribute relevance. In order to attest its feasibility, the proposed model has been tested on five publicly available high-dimensional datasets: breast, lung, colon, and ovarian cancer regarding gene expression and proteomic spectra provided by cDNA arrays, DNA microarray, and MS. The novel algorithm, called adaptive SLFN (aSLFN), has been compared with four major classification algorithms: traditional ELM, radial basis function network (RBF), single-hidden layer feedforward neural network trained by backpropagation algorithm (BP-SLFN), and support vector-machine (SVM). Experimental results showed that the classification performance of aSLFN is competitive with the comparison models. Copyright © 2018. Published by Elsevier Inc.

An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

PubMed Central

Cabessa, Jérémie; Villa, Alessandro E. P.

2014-01-01

We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of -automata, and then translating the most refined classification of -automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits. PMID:24727866

How Neural Networks Learn from Experience.

ERIC Educational Resources Information Center

Hinton, Geoffrey E.

1992-01-01

Discusses computational studies of learning in artificial neural networks and findings that may provide insights into the learning abilities of the human brain. Describes efforts to test theories about brain information processing, using artificial neural networks. Vignettes include information concerning how a neural network represents…

Optical Interconnection Via Computer-Generated Holograms

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang; Zhou, Shaomin

1995-01-01

Method of free-space optical interconnection developed for data-processing applications like parallel optical computing, neural-network computing, and switching in optical communication networks. In method, multiple optical connections between multiple sources of light in one array and multiple photodetectors in another array made via computer-generated holograms in electrically addressed spatial light modulators (ESLMs). Offers potential advantages of massive parallelism, high space-bandwidth product, high time-bandwidth product, low power consumption, low cross talk, and low time skew. Also offers advantage of programmability with flexibility of reconfiguration, including variation of strengths of optical connections in real time.

Neuromodulation of the neural circuits controlling the lower urinary tract

PubMed Central

Gad, Parag N.; Roy, Roland R.; Zhong, Hui; Gerasimenko, Yury P.; Taccola, Giuliano; Edgerton, V. Reggie

2017-01-01

The inability to control timely bladder emptying is one of the most serious challenges among the many functional deficits that occur after a spinal cord injury. We previously demonstrated that electrodes placed epidurally on the dorsum of the spinal cord can be used in animals and humans to recover postural and locomotor function after complete paralysis and can be used to enable voiding in spinal rats. In the present study, we examined the neuromodulation of lower urinary tract function associated with acute epidural spinal cord stimulation, locomotion, and peripheral nerve stimulation in adult rats. Herein we demonstrate that electrically evoked potentials in the hindlimb muscles and external urethral sphincter are modulated uniquely when the rat is stepping bipedally and not voiding, immediately pre-voiding, or when voiding. We also show that spinal cord stimulation can effectively neuromodulate the lower urinary tract via frequency-dependent stimulation patterns and that neural peripheral nerve stimulation can activate the external urethral sphincter both directly and via relays in the spinal cord. The data demonstrate that the sensorimotor networks controlling bladder and locomotion are highly integrated neurophysiologically and behaviorally and demonstrate how these two functions are modulated by sensory input from the tibial and pudental nerves. A more detailed understanding of the high level of interaction between these networks could lead to the integration of multiple neurophysiological strategies to improve bladder function. These data suggest that the development of strategies to improve bladder function should simultaneously engage these highly integrated networks in an activity-dependent manner. PMID:27381425

Neural network to diagnose lining condition

NASA Astrophysics Data System (ADS)

Yemelyanov, V. A.; Yemelyanova, N. Y.; Nedelkin, A. A.; Zarudnaya, M. V.

2018-03-01

The paper presents data on the problem of diagnosing the lining condition at the iron and steel works. The authors describe the neural network structure and software that are designed and developed to determine the lining burnout zones. The simulation results of the proposed neural networks are presented. The authors note the low learning and classification errors of the proposed neural networks. To realize the proposed neural network, the specialized software has been developed.

Effect of inhibitory feedback on correlated firing of spiking neural network.

PubMed

Xie, Jinli; Wang, Zhijie

2013-08-01

Understanding the properties and mechanisms that generate different forms of correlation is critical for determining their role in cortical processing. Researches on retina, visual cortex, sensory cortex, and computational model have suggested that fast correlation with high temporal precision appears consistent with common input, and correlation on a slow time scale likely involves feedback. Based on feedback spiking neural network model, we investigate the role of inhibitory feedback in shaping correlations on a time scale of 100 ms. Notably, the relationship between the correlation coefficient and inhibitory feedback strength is non-monotonic. Further, computational simulations show how firing rate and oscillatory activity form the basis of the mechanisms underlying this relationship. When the mean firing rate holds unvaried, the correlation coefficient increases monotonically with inhibitory feedback, but the correlation coefficient keeps decreasing when the network has no oscillatory activity. Our findings reveal that two opposing effects of the inhibitory feedback on the firing activity of the network contribute to the non-monotonic relationship between the correlation coefficient and the strength of the inhibitory feedback. The inhibitory feedback affects the correlated firing activity by modulating the intensity and regularity of the spike trains. Finally, the non-monotonic relationship is replicated with varying transmission delay and different spatial network structure, demonstrating the universality of the results.

An fMRI examination of the effects of acoustic-phonetic and lexical competition on access to the lexical-semantic network.

PubMed

Minicucci, Domenic; Guediche, Sara; Blumstein, Sheila E

2013-08-01

The current study explored how factors of acoustic-phonetic and lexical competition affect access to the lexical-semantic network during spoken word recognition. An auditory semantic priming lexical decision task was presented to subjects while in the MR scanner. Prime-target pairs consisted of prime words with the initial voiceless stop consonants /p/, /t/, and /k/ followed by word and nonword targets. To examine the neural consequences of lexical and sound structure competition, primes either had voiced minimal pair competitors or they did not, and they were either acoustically modified to be poorer exemplars of the voiceless phonetic category or not. Neural activation associated with semantic priming (Unrelated-Related conditions) revealed a bilateral fronto-temporo-parietal network. Within this network, clusters in the left insula/inferior frontal gyrus (IFG), left superior temporal gyrus (STG), and left posterior middle temporal gyrus (pMTG) showed sensitivity to lexical competition. The pMTG also demonstrated sensitivity to acoustic modification, and the insula/IFG showed an interaction between lexical competition and acoustic modification. These findings suggest the posterior lexical-semantic network is modulated by both acoustic-phonetic and lexical structure, and that the resolution of these two sources of competition recruits frontal structures. Copyright © 2013 Elsevier Ltd. All rights reserved.

[Measurement and performance analysis of functional neural network].

PubMed

Li, Shan; Liu, Xinyu; Chen, Yan; Wan, Hong

2018-04-01

The measurement of network is one of the important researches in resolving neuronal population information processing mechanism using complex network theory. For the quantitative measurement problem of functional neural network, the relation between the measure indexes, i.e. the clustering coefficient, the global efficiency, the characteristic path length and the transitivity, and the network topology was analyzed. Then, the spike-based functional neural network was established and the simulation results showed that the measured network could represent the original neural connections among neurons. On the basis of the former work, the coding of functional neural network in nidopallium caudolaterale (NCL) about pigeon's motion behaviors was studied. We found that the NCL functional neural network effectively encoded the motion behaviors of the pigeon, and there were significant differences in four indexes among the left-turning, the forward and the right-turning. Overall, the establishment method of spike-based functional neural network is available and it is an effective tool to parse the brain information processing mechanism.

Resting state electrical brain activity and connectivity in fibromyalgia

PubMed Central

Vanneste, Sven; Ost, Jan; Van Havenbergh, Tony; De Ridder, Dirk

2017-01-01

The exact mechanism underlying fibromyalgia is unknown, but increased facilitatory modulation and/or dysfunctional descending inhibitory pathway activity are posited as possible mechanisms contributing to sensitization of the central nervous system. The primary goal of this study is to identify a fibromyalgia neural circuit that can account for these abnormalities in central pain. The second goal is to gain a better understanding of the functional connectivity between the default and the executive attention network (salience network plus dorsal lateral prefrontal cortex) in fibromyalgia. We examine neural activity associated with fibromyalgia (N = 44) and compare these with healthy controls (N = 44) using resting state source localized EEG. Our data support an important role of the pregenual anterior cingulate cortex but also suggest that the degree of activation and the degree of integration between different brain areas is important. The inhibition of the connectivity between the dorsal lateral prefrontal cortex and the posterior cingulate cortex on the pain inhibitory pathway seems to be limited by decreased functional connectivity with the pregenual anterior cingulate cortex. Our data highlight the functional dynamics of brain regions integrated in brain networks in fibromyalgia patients. PMID:28650974

Differential roles of right temporal cortex and Broca's area in pitch processing: evidence from music and Mandarin.

PubMed

Nan, Yun; Friederici, Angela D

2013-09-01

Superior temporal and inferior frontal cortices are involved in the processing of pitch information in the domain of language and music. Here, we used fMRI to test the particular roles of these brain regions in the neural implementation of pitch in music and in tone language (Mandarin) with a group of Mandarin speaking musicians whose pertaining experiences in pitch are similar across domains. Our findings demonstrate that the neural network for pitch processing includes the pars triangularis of Broca's area and the right superior temporal gyrus (STG) across domains. Within this network, pitch sensitive activation in Broca's area is tightly linked to the behavioral performance of pitch congruity judgment, thereby reflecting controlled processes. Activation in the right STG is independent of performance and more sensitive to pitch congruity in music than in tone language, suggesting a domain-specific modulation of the perceptual processes. These observations provide a first glimpse at the cortical pitch processing network shared across domains. Copyright © 2012 Wiley Periodicals, Inc., a Wiley company.

Neural network error correction for solving coupled ordinary differential equations

NASA Technical Reports Server (NTRS)

Shelton, R. O.; Darsey, J. A.; Sumpter, B. G.; Noid, D. W.

1992-01-01

A neural network is presented to learn errors generated by a numerical algorithm for solving coupled nonlinear differential equations. The method is based on using a neural network to correctly learn the error generated by, for example, Runge-Kutta on a model molecular dynamics (MD) problem. The neural network programs used in this study were developed by NASA. Comparisons are made for training the neural network using backpropagation and a new method which was found to converge with fewer iterations. The neural net programs, the MD model and the calculations are discussed.

Artificial and Bayesian Neural Networks

PubMed

Korhani Kangi, Azam; Bahrampour, Abbas

2018-02-26

Introduction and purpose: In recent years the use of neural networks without any premises for investigation of prognosis in analyzing survival data has increased. Artificial neural networks (ANN) use small processors with a continuous network to solve problems inspired by the human brain. Bayesian neural networks (BNN) constitute a neural-based approach to modeling and non-linearization of complex issues using special algorithms and statistical methods. Gastric cancer incidence is the first and third ranking for men and women in Iran, respectively. The aim of the present study was to assess the value of an artificial neural network and a Bayesian neural network for modeling and predicting of probability of gastric cancer patient death. Materials and Methods: In this study, we used information on 339 patients aged from 20 to 90 years old with positive gastric cancer, referred to Afzalipoor and Shahid Bahonar Hospitals in Kerman City from 2001 to 2015. The three layers perceptron neural network (ANN) and the Bayesian neural network (BNN) were used for predicting the probability of mortality using the available data. To investigate differences between the models, sensitivity, specificity, accuracy and the area under receiver operating characteristic curves (AUROCs) were generated. Results: In this study, the sensitivity and specificity of the artificial neural network and Bayesian neural network models were 0.882, 0.903 and 0.954, 0.909, respectively. Prediction accuracy and the area under curve ROC for the two models were 0.891, 0.944 and 0.935, 0.961. The age at diagnosis of gastric cancer was most important for predicting survival, followed by tumor grade, morphology, gender, smoking history, opium consumption, receiving chemotherapy, presence of metastasis, tumor stage, receiving radiotherapy, and being resident in a village. Conclusion: The findings of the present study indicated that the Bayesian neural network is preferable to an artificial neural network for predicting survival of gastric cancer patients in Iran. Creative Commons Attribution License

Model Of Neural Network With Creative Dynamics

NASA Technical Reports Server (NTRS)

Zak, Michail; Barhen, Jacob

1993-01-01

Paper presents analysis of mathematical model of one-neuron/one-synapse neural network featuring coupled activation and learning dynamics and parametrical periodic excitation. Demonstrates self-programming, partly random behavior of suitable designed neural network; believed to be related to spontaneity and creativity of biological neural networks.

Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

PubMed

Xia, Youshen; Wang, Jun

2015-07-01

This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Thermalnet: a Deep Convolutional Network for Synthetic Thermal Image Generation

NASA Astrophysics Data System (ADS)

Kniaz, V. V.; Gorbatsevich, V. S.; Mizginov, V. A.

2017-05-01

Deep convolutional neural networks have dramatically changed the landscape of the modern computer vision. Nowadays methods based on deep neural networks show the best performance among image recognition and object detection algorithms. While polishing of network architectures received a lot of scholar attention, from the practical point of view the preparation of a large image dataset for a successful training of a neural network became one of major challenges. This challenge is particularly profound for image recognition in wavelengths lying outside the visible spectrum. For example no infrared or radar image datasets large enough for successful training of a deep neural network are available to date in public domain. Recent advances of deep neural networks prove that they are also capable to do arbitrary image transformations such as super-resolution image generation, grayscale image colorisation and imitation of style of a given artist. Thus a natural question arise: how could be deep neural networks used for augmentation of existing large image datasets? This paper is focused on the development of the Thermalnet deep convolutional neural network for augmentation of existing large visible image datasets with synthetic thermal images. The Thermalnet network architecture is inspired by colorisation deep neural networks.

Multicasting mesh AER: a scalable assembly approach for reconfigurable neuromorphic structured AER systems. Application to ConvNets.

PubMed

Zamarreno-Ramos, C; Linares-Barranco, A; Serrano-Gotarredona, T; Linares-Barranco, B

2013-02-01

This paper presents a modular, scalable approach to assembling hierarchically structured neuromorphic Address Event Representation (AER) systems. The method consists of arranging modules in a 2D mesh, each communicating bidirectionally with all four neighbors. Address events include a module label. Each module includes an AER router which decides how to route address events. Two routing approaches have been proposed, analyzed and tested, using either destination or source module labels. Our analyses reveal that depending on traffic conditions and network topologies either one or the other approach may result in better performance. Experimental results are given after testing the approach using high-end Virtex-6 FPGAs. The approach is proposed for both single and multiple FPGAs, in which case a special bidirectional parallel-serial AER link with flow control is exploited, using the FPGA Rocket-I/O interfaces. Extensive test results are provided exploiting convolution modules of 64 × 64 pixels with kernels with sizes up to 11 × 11, which process real sensory data from a Dynamic Vision Sensor (DVS) retina. One single Virtex-6 FPGA can hold up to 64 of these convolution modules, which is equivalent to a neural network with 262 × 10(3) neurons and almost 32 million synapses.

Determining geophysical properties from well log data using artificial neural networks and fuzzy inference systems

NASA Astrophysics Data System (ADS)

Chang, Hsien-Cheng

Two novel synergistic systems consisting of artificial neural networks and fuzzy inference systems are developed to determine geophysical properties by using well log data. These systems are employed to improve the determination accuracy in carbonate rocks, which are generally more complex than siliciclastic rocks. One system, consisting of a single adaptive resonance theory (ART) neural network and three fuzzy inference systems (FISs), is used to determine the permeability category. The other system, which is composed of three ART neural networks and a single FIS, is employed to determine the lithofacies. The geophysical properties studied in this research, permeability category and lithofacies, are treated as categorical data. The permeability values are transformed into a "permeability category" to account for the effects of scale differences between core analyses and well logs, and heterogeneity in the carbonate rocks. The ART neural networks dynamically cluster the input data sets into different groups. The FIS is used to incorporate geologic experts' knowledge, which is usually in linguistic forms, into systems. These synergistic systems thus provide viable alternative solutions to overcome the effects of heterogeneity, the uncertainties of carbonate rock depositional environments, and the scarcity of well log data. The results obtained in this research show promising improvements over backpropagation neural networks. For the permeability category, the prediction accuracies are 68.4% and 62.8% for the multiple-single ART neural network-FIS and a single backpropagation neural network, respectively. For lithofacies, the prediction accuracies are 87.6%, 79%, and 62.8% for the single-multiple ART neural network-FIS, a single ART neural network, and a single backpropagation neural network, respectively. The sensitivity analysis results show that the multiple-single ART neural networks-FIS and a single ART neural network possess the same matching trends in determining lithofacies. This research shows that the adaptive resonance theory neural networks enable decision-makers to clearly distinguish the importance of different pieces of data which are useful in three-dimensional subsurface modeling. Geologic experts' knowledge can be easily applied and maintained by using the fuzzy inference systems.

Reducing neural network training time with parallel processing

NASA Technical Reports Server (NTRS)

Rogers, James L., Jr.; Lamarsh, William J., II

1995-01-01

Obtaining optimal solutions for engineering design problems is often expensive because the process typically requires numerous iterations involving analysis and optimization programs. Previous research has shown that a near optimum solution can be obtained in less time by simulating a slow, expensive analysis with a fast, inexpensive neural network. A new approach has been developed to further reduce this time. This approach decomposes a large neural network into many smaller neural networks that can be trained in parallel. Guidelines are developed to avoid some of the pitfalls when training smaller neural networks in parallel. These guidelines allow the engineer: to determine the number of nodes on the hidden layer of the smaller neural networks; to choose the initial training weights; and to select a network configuration that will capture the interactions among the smaller neural networks. This paper presents results describing how these guidelines are developed.

Application of the ANNA neural network chip to high-speed character recognition.

PubMed

Sackinger, E; Boser, B E; Bromley, J; Lecun, Y; Jackel, L D

1992-01-01

A neural network with 136000 connections for recognition of handwritten digits has been implemented using a mixed analog/digital neural network chip. The neural network chip is capable of processing 1000 characters/s. The recognition system has essentially the same rate (5%) as a simulation of the network with 32-b floating-point precision.

Ratiometric Decoding of Pheromones for a Biomimetic Infochemical Communication System.

PubMed

Wei, Guangfen; Thomas, Sanju; Cole, Marina; Rácz, Zoltán; Gardner, Julian W

2017-10-30

Biosynthetic infochemical communication is an emerging scientific field employing molecular compounds for information transmission, labelling, and biochemical interfacing; having potential application in diverse areas ranging from pest management to group coordination of swarming robots. Our communication system comprises a chemoemitter module that encodes information by producing volatile pheromone components and a chemoreceiver module that decodes the transmitted ratiometric information via polymer-coated piezoelectric Surface Acoustic Wave Resonator (SAWR) sensors. The inspiration for such a system is based on the pheromone-based communication between insects. Ten features are extracted from the SAWR sensor response and analysed using multi-variate classification techniques, i.e., Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), and Multilayer Perception Neural Network (MLPNN) methods, and an optimal feature subset is identified. A combination of steady state and transient features of the sensor signals showed superior performances with LDA and MLPNN. Although MLPNN gave excellent results reaching 100% recognition rate at 400 s, over all time stations PNN gave the best performance based on an expanded data-set with adjacent neighbours. In this case, 100% of the pheromone mixtures were successfully identified just 200 s after they were first injected into the wind tunnel. We believe that this approach can be used for future chemical communication employing simple mixtures of airborne molecules.

Ratiometric Decoding of Pheromones for a Biomimetic Infochemical Communication System

PubMed Central

Wei, Guangfen; Thomas, Sanju; Cole, Marina; Rácz, Zoltán

2017-01-01

Biosynthetic infochemical communication is an emerging scientific field employing molecular compounds for information transmission, labelling, and biochemical interfacing; having potential application in diverse areas ranging from pest management to group coordination of swarming robots. Our communication system comprises a chemoemitter module that encodes information by producing volatile pheromone components and a chemoreceiver module that decodes the transmitted ratiometric information via polymer-coated piezoelectric Surface Acoustic Wave Resonator (SAWR) sensors. The inspiration for such a system is based on the pheromone-based communication between insects. Ten features are extracted from the SAWR sensor response and analysed using multi-variate classification techniques, i.e., Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), and Multilayer Perception Neural Network (MLPNN) methods, and an optimal feature subset is identified. A combination of steady state and transient features of the sensor signals showed superior performances with LDA and MLPNN. Although MLPNN gave excellent results reaching 100% recognition rate at 400 s, over all time stations PNN gave the best performance based on an expanded data-set with adjacent neighbours. In this case, 100% of the pheromone mixtures were successfully identified just 200 s after they were first injected into the wind tunnel. We believe that this approach can be used for future chemical communication employing simple mixtures of airborne molecules. PMID:29084158

Differential Evolution Based IDWNN Controller for Fault Ride-Through of Grid-Connected Doubly Fed Induction Wind Generators

PubMed Central

Manonmani, N.; Subbiah, V.; Sivakumar, L.

2015-01-01

The key objective of wind turbine development is to ensure that output power is continuously increased. It is authenticated that wind turbines (WTs) supply the necessary reactive power to the grid at the time of fault and after fault to aid the flowing grid voltage. At this juncture, this paper introduces a novel heuristic based controller module employing differential evolution and neural network architecture to improve the low-voltage ride-through rate of grid-connected wind turbines, which are connected along with doubly fed induction generators (DFIGs). The traditional crowbar-based systems were basically applied to secure the rotor-side converter during the occurrence of grid faults. This traditional controller is found not to satisfy the desired requirement, since DFIG during the connection of crowbar acts like a squirrel cage module and absorbs the reactive power from the grid. This limitation is taken care of in this paper by introducing heuristic controllers that remove the usage of crowbar and ensure that wind turbines supply necessary reactive power to the grid during faults. The controller is designed in this paper to enhance the DFIG converter during the grid fault and this controller takes care of the ride-through fault without employing any other hardware modules. The paper introduces a double wavelet neural network controller which is appropriately tuned employing differential evolution. To validate the proposed controller module, a case study of wind farm with 1.5 MW wind turbines connected to a 25 kV distribution system exporting power to a 120 kV grid through a 30 km 25 kV feeder is carried out by simulation. PMID:26516636

Differential Evolution Based IDWNN Controller for Fault Ride-Through of Grid-Connected Doubly Fed Induction Wind Generators.

PubMed

Manonmani, N; Subbiah, V; Sivakumar, L

2015-01-01

The key objective of wind turbine development is to ensure that output power is continuously increased. It is authenticated that wind turbines (WTs) supply the necessary reactive power to the grid at the time of fault and after fault to aid the flowing grid voltage. At this juncture, this paper introduces a novel heuristic based controller module employing differential evolution and neural network architecture to improve the low-voltage ride-through rate of grid-connected wind turbines, which are connected along with doubly fed induction generators (DFIGs). The traditional crowbar-based systems were basically applied to secure the rotor-side converter during the occurrence of grid faults. This traditional controller is found not to satisfy the desired requirement, since DFIG during the connection of crowbar acts like a squirrel cage module and absorbs the reactive power from the grid. This limitation is taken care of in this paper by introducing heuristic controllers that remove the usage of crowbar and ensure that wind turbines supply necessary reactive power to the grid during faults. The controller is designed in this paper to enhance the DFIG converter during the grid fault and this controller takes care of the ride-through fault without employing any other hardware modules. The paper introduces a double wavelet neural network controller which is appropriately tuned employing differential evolution. To validate the proposed controller module, a case study of wind farm with 1.5 MW wind turbines connected to a 25 kV distribution system exporting power to a 120 kV grid through a 30 km 25 kV feeder is carried out by simulation.

Towards automatic musical instrument timbre recognition

NASA Astrophysics Data System (ADS)

Park, Tae Hong

This dissertation is comprised of two parts---focus on issues concerning research and development of an artificial system for automatic musical instrument timbre recognition and musical compositions. The technical part of the essay includes a detailed record of developed and implemented algorithms for feature extraction and pattern recognition. A review of existing literature introducing historical aspects surrounding timbre research, problems associated with a number of timbre definitions, and highlights of selected research activities that have had significant impact in this field are also included. The developed timbre recognition system follows a bottom-up, data-driven model that includes a pre-processing module, feature extraction module, and a RBF/EBF (Radial/Elliptical Basis Function) neural network-based pattern recognition module. 829 monophonic samples from 12 instruments have been chosen from the Peter Siedlaczek library (Best Service) and other samples from the Internet and personal collections. Significant emphasis has been put on feature extraction development and testing to achieve robust and consistent feature vectors that are eventually passed to the neural network module. In order to avoid a garbage-in-garbage-out (GIGO) trap and improve generality, extra care was taken in designing and testing the developed algorithms using various dynamics, different playing techniques, and a variety of pitches for each instrument with inclusion of attack and steady-state portions of a signal. Most of the research and development was conducted in Matlab. The compositional part of the essay includes brief introductions to "A d'Ess Are ," "Aboji," "48 13 N, 16 20 O," and "pH-SQ." A general outline pertaining to the ideas and concepts behind the architectural designs of the pieces including formal structures, time structures, orchestration methods, and pitch structures are also presented.

Machine Learning and Quantum Mechanics

NASA Astrophysics Data System (ADS)

Chapline, George

The author has previously pointed out some similarities between selforganizing neural networks and quantum mechanics. These types of neural networks were originally conceived of as away of emulating the cognitive capabilities of the human brain. Recently extensions of these networks, collectively referred to as deep learning networks, have strengthened the connection between self-organizing neural networks and human cognitive capabilities. In this note we consider whether hardware quantum devices might be useful for emulating neural networks with human-like cognitive capabilities, or alternatively whether implementations of deep learning neural networks using conventional computers might lead to better algorithms for solving the many body Schrodinger equation.

Executive Control Over Cognition: Stronger and Earlier Rule-Based Modulation of Spatial Category Signals in Prefrontal Cortex Relative to Parietal Cortex

PubMed Central

Goodwin, Shikha J.; Blackman, Rachael K.; Sakellaridi, Sofia

2012-01-01

Human cognition is characterized by flexibility, the ability to select not only which action but which cognitive process to engage to best achieve the current behavioral objective. The ability to tailor information processing in the brain to rules, goals, or context is typically referred to as executive control, and although there is consensus that prefrontal cortex is importantly involved, at present we have an incomplete understanding of how computational flexibility is implemented at the level of prefrontal neurons and networks. To better understand the neural mechanisms of computational flexibility, we simultaneously recorded the electrical activity of groups of single neurons within prefrontal and posterior parietal cortex of monkeys performing a task that required executive control of spatial cognitive processing. In this task, monkeys applied different spatial categorization rules to reassign the same set of visual stimuli to alternative categories on a trial-by-trial basis. We found that single neurons were activated to represent spatially defined categories in a manner that was rule dependent, providing a physiological signature of a cognitive process that was implemented under executive control. We found also that neural signals coding rule-dependent categories were distributed between the parietal and prefrontal cortex—however, not equally. Rule-dependent category signals were stronger, more powerfully modulated by the rule, and earlier to emerge in prefrontal cortex relative to parietal cortex. This suggests that prefrontal cortex may initiate the switch in neural representation at a network level that is important for computational flexibility. PMID:22399773

Automated Pathogenesis-Based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning.

PubMed

Han, Zhongyi; Wei, Benzheng; Leung, Stephanie; Nachum, Ilanit Ben; Laidley, David; Li, Shuo

2018-02-15

Pathogenesis-based diagnosis is a key step to prevent and control lumbar neural foraminal stenosis (LNFS). It conducts both early diagnosis and comprehensive assessment by drawing crucial pathological links between pathogenic factors and LNFS. Automated pathogenesis-based diagnosis would simultaneously localize and grade multiple spinal organs (neural foramina, vertebrae, intervertebral discs) to diagnose LNFS and discover pathogenic factors. The automated way facilitates planning optimal therapeutic schedules and relieving clinicians from laborious workloads. However, no successful work has been achieved yet due to its extreme challenges since 1) multiple targets: each lumbar spine has at least 17 target organs, 2) multiple scales: each type of target organ has structural complexity and various scales across subjects, and 3) multiple tasks, i.e., simultaneous localization and diagnosis of all lumbar organs, are extremely difficult than individual tasks. To address these huge challenges, we propose a deep multiscale multitask learning network (DMML-Net) integrating a multiscale multi-output learning and a multitask regression learning into a fully convolutional network. 1) DMML-Net merges semantic representations to reinforce the salience of numerous target organs. 2) DMML-Net extends multiscale convolutional layers as multiple output layers to boost the scale-invariance for various organs. 3) DMML-Net joins a multitask regression module and a multitask loss module to prompt the mutual benefit between tasks. Extensive experimental results demonstrate that DMML-Net achieves high performance (0.845 mean average precision) on T1/T2-weighted MRI scans from 200 subjects. This endows our method an efficient tool for clinical LNFS diagnosis.

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

NASA Technical Reports Server (NTRS)

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.

The Reorganization of Human Brain Networks Modulated by Driving Mental Fatigue.

PubMed

Chunlin Zhao; Min Zhao; Yong Yang; Junfeng Gao; Nini Rao; Pan Lin

2017-05-01

The organization of the brain functional network is associated with mental fatigue, but little is known about the brain network topology that is modulated by the mental fatigue. In this study, we used the graph theory approach to investigate reconfiguration changes in functional networks of different electroen-cephalography (EEG) bands from 16 subjects performing a simulated driving task. Behavior and brain functional networks were compared between the normal and driving mental fatigue states. The scores of subjective self-reports indicated that 90 min of simulated driving-induced mental fatigue. We observed that coherence was significantly increased in the frontal, central, and temporal brain regions. Furthermore, in the brain network topology metric, significant increases were observed in the clustering coefficient (Cp) for beta, alpha, and delta bands and the character path length (Lp) for all EEG bands. The normalized measures γ showed significant increases in beta, alpha, and delta bands, and λ showed similar patterns in beta and theta bands. These results indicate that functional network topology can shift the network topology structure toward a more economic but less efficient configuration, which suggests low wiring costs in functional networks and disruption of the effective interactions between and across cortical regions during mental fatigue states. Graph theory analysis might be a useful tool for further understanding the neural mechanisms of driving mental fatigue.

A neural network application to classification of health status of HIV/AIDS patients.

PubMed

Kwak, N K; Lee, C

1997-04-01

This paper presents an application of neural networks to classify and to predict the health status of HIV/AIDS patients. A neural network model in classifying both the well and not-well health status of HIV/AIDS patients is developed and evaluated in terms of validity and reliability of the test. Several different neural network topologies are applied to AIDS Cost and Utilization Survey (ACSUS) datasets in order to demonstrate the neural network's capability.

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

NASA Astrophysics Data System (ADS)

Chernoded, Andrey; Dudko, Lev; Myagkov, Igor; Volkov, Petr

2017-10-01

Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

Improvement of the Hopfield Neural Network by MC-Adaptation Rule

NASA Astrophysics Data System (ADS)

Zhou, Zhen; Zhao, Hong

2006-06-01

We show that the performance of the Hopfield neural networks, especially the quality of the recall and the capacity of the effective storing, can be greatly improved by making use of a recently presented neural network designing method without altering the whole structure of the network. In the improved neural network, a memory pattern is recalled exactly from initial states having a given degree of similarity with the memory pattern, and thus one can avoids to apply the overlap criterion as carried out in the Hopfield neural networks.

The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.

PubMed

Zhu, Zhenyu; Wang, Rubin; Zhu, Fengyun

2018-01-01

Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.

An adaptive Hinfinity controller design for bank-to-turn missiles using ridge Gaussian neural networks.

PubMed

Lin, Chuan-Kai; Wang, Sheng-De

2004-11-01

A new autopilot design for bank-to-turn (BTT) missiles is presented. In the design of autopilot, a ridge Gaussian neural network with local learning capability and fewer tuning parameters than Gaussian neural networks is proposed to model the controlled nonlinear systems. We prove that the proposed ridge Gaussian neural network, which can be a universal approximator, equals the expansions of rotated and scaled Gaussian functions. Although ridge Gaussian neural networks can approximate the nonlinear and complex systems accurately, the small approximation errors may affect the tracking performance significantly. Therefore, by employing the Hinfinity control theory, it is easy to attenuate the effects of the approximation errors of the ridge Gaussian neural networks to a prescribed level. Computer simulation results confirm the effectiveness of the proposed ridge Gaussian neural networks-based autopilot with Hinfinity stabilization.

Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling.

PubMed

Yang, S; Wang, D

2000-01-01

This paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed neural network can be easily constructed and can adaptively adjust its weights of connections and biases of units based on the sequence and resource constraints of the job-shop scheduling problem during its processing. Several heuristics that can be combined with the neural network are also presented. In the combined approaches, the neural network is used to obtain feasible solutions, the heuristic algorithms are used to improve the performance of the neural network and the quality of the obtained solutions. Simulations have shown that the proposed neural network and its combined approaches are efficient with respect to the quality of solutions and the solving speed.

Financial time series prediction using spiking neural networks.

PubMed

Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam

2014-01-01

In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.

Cortical Coupling Reflects Bayesian Belief Updating in the Deployment of Spatial Attention.

PubMed

Vossel, Simone; Mathys, Christoph; Stephan, Klaas E; Friston, Karl J

2015-08-19

The deployment of visuospatial attention and the programming of saccades are governed by the inferred likelihood of events. In the present study, we combined computational modeling of psychophysical data with fMRI to characterize the computational and neural mechanisms underlying this flexible attentional control. Sixteen healthy human subjects performed a modified version of Posner's location-cueing paradigm in which the percentage of cue validity varied in time and the targets required saccadic responses. Trialwise estimates of the certainty (precision) of the prediction that the target would appear at the cued location were derived from a hierarchical Bayesian model fitted to individual trialwise saccadic response speeds. Trial-specific model parameters then entered analyses of fMRI data as parametric regressors. Moreover, dynamic causal modeling (DCM) was performed to identify the most likely functional architecture of the attentional reorienting network and its modulation by (Bayes-optimal) precision-dependent attention. While the frontal eye fields (FEFs), intraparietal sulcus, and temporoparietal junction (TPJ) of both hemispheres showed higher activity on invalid relative to valid trials, reorienting responses in right FEF, TPJ, and the putamen were significantly modulated by precision-dependent attention. Our DCM results suggested that the precision of predictability underlies the attentional modulation of the coupling of TPJ with FEF and the putamen. Our results shed new light on the computational architecture and neuronal network dynamics underlying the context-sensitive deployment of visuospatial attention. Spatial attention and its neural correlates in the human brain have been studied extensively with the help of fMRI and cueing paradigms in which the location of targets is pre-cued on a trial-by-trial basis. One aspect that has so far been neglected concerns the question of how the brain forms attentional expectancies when no a priori probability information is available but needs to be inferred from observations. This study elucidates the computational and neural mechanisms under which probabilistic inference governs attentional deployment. Our results show that Bayesian belief updating explains changes in cortical connectivity; in that directional influences from the temporoparietal junction on the frontal eye fields and the putamen were modulated by (Bayes-optimal) updates. Copyright © 2015 Vossel et al.

A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback

PubMed Central

Maass, Wolfgang

2008-01-01

Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how behaviorally relevant adaptive changes in complex networks of spiking neurons could be achieved in a self-organizing manner through local synaptic plasticity. However, the capabilities and limitations of this learning rule could so far only be tested through computer simulations. This article provides tools for an analytic treatment of reward-modulated STDP, which allows us to predict under which conditions reward-modulated STDP will achieve a desired learning effect. These analytical results imply that neurons can learn through reward-modulated STDP to classify not only spatial but also temporal firing patterns of presynaptic neurons. They also can learn to respond to specific presynaptic firing patterns with particular spike patterns. Finally, the resulting learning theory predicts that even difficult credit-assignment problems, where it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system, can be solved in a self-organizing manner through reward-modulated STDP. This yields an explanation for a fundamental experimental result on biofeedback in monkeys by Fetz and Baker. In this experiment monkeys were rewarded for increasing the firing rate of a particular neuron in the cortex and were able to solve this extremely difficult credit assignment problem. Our model for this experiment relies on a combination of reward-modulated STDP with variable spontaneous firing activity. Hence it also provides a possible functional explanation for trial-to-trial variability, which is characteristic for cortical networks of neurons but has no analogue in currently existing artificial computing systems. In addition our model demonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without endangering the stability of the network dynamics. PMID:18846203

Non-Intrusive Gaze Tracking Using Artificial Neural Networks

DTIC Science & Technology

1994-01-05

We have developed an artificial neural network based gaze tracking, system which can be customized to individual users. A three layer feed forward...empirical analysis of the performance of a large number of artificial neural network architectures for this task. Suggestions for further explorations...for neurally based gaze trackers are presented, and are related to other similar artificial neural network applications such as autonomous road following.

Cognitive Vulnerability to Major Depression: View from the Intrinsic Network and Cross-network Interactions

PubMed Central

Wang, Xiang; Öngür, Dost; Auerbach, Randy P.; Yao, Shuqiao

2016-01-01

Abstract Although it is generally accepted that cognitive factors contribute to the pathogenesis of major depressive disorder (MDD), there are missing links between behavioral and biological models of depression. Nevertheless, research employing neuroimaging technologies has elucidated some of the neurobiological mechanisms related to cognitive-vulnerability factors, especially from a whole-brain, dynamic perspective. In this review, we integrate well-established cognitive-vulnerability factors for MDD and corresponding neural mechanisms in intrinsic networks using a dual-process framework. We propose that the dynamic alteration and imbalance among the intrinsic networks, both in the resting-state and the rest-task transition stages, contribute to the development of cognitive vulnerability and MDD. Specifically, we propose that abnormally increased resting-state default mode network (DMN) activity and connectivity (mainly in anterior DMN regions) contribute to the development of cognitive vulnerability. Furthermore, when subjects confront negative stimuli in the period of rest-to-task transition, the following three kinds of aberrant network interactions have been identified as facilitators of vulnerability and dysphoric mood, each through a different cognitive mechanism: DMN dominance over the central executive network (CEN), an impaired salience network–mediated switching between the DMN and CEN, and ineffective CEN modulation of the DMN. This focus on interrelated networks and brain-activity changes between rest and task states provides a neural-system perspective for future research on cognitive vulnerability and resilience, and may potentially guide the development of new intervention strategies for MDD. PMID:27148911

Composability-Centered Convolutional Neural Network Pruning

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

Shen, Xipeng; Guan, Hui; Lim, Seung-Hwan

This work studies the composability of the building blocks ofstructural CNN models (e.g., GoogleLeNet and Residual Networks) in thecontext of network pruning. We empirically validate that a networkcomposed of pre-trained building blocks (e.g. residual blocks andInception modules) not only gives a better initial setting fortraining, but also allows the training process to converge at asignificantly higher accuracy in much less time. Based on thatinsight, we propose a {\\em composability-centered} design for CNNnetwork pruning. Experiments show that this new scheme shortens theconfiguration process in CNN network pruning by up to 186.8X forResNet-50 and up to 30.2X for Inception-V3, and meanwhile, themore » modelsit finds that meet the accuracy requirement are significantly morecompact than those found by default schemes.« less

Neural dynamics based on the recognition of neural fingerprints

PubMed Central

Carrillo-Medina, José Luis; Latorre, Roberto

2015-01-01

Experimental evidence has revealed the existence of characteristic spiking features in different neural signals, e.g., individual neural signatures identifying the emitter or functional signatures characterizing specific tasks. These neural fingerprints may play a critical role in neural information processing, since they allow receptors to discriminate or contextualize incoming stimuli. This could be a powerful strategy for neural systems that greatly enhances the encoding and processing capacity of these networks. Nevertheless, the study of information processing based on the identification of specific neural fingerprints has attracted little attention. In this work, we study (i) the emerging collective dynamics of a network of neurons that communicate with each other by exchange of neural fingerprints and (ii) the influence of the network topology on the self-organizing properties within the network. Complex collective dynamics emerge in the network in the presence of stimuli. Predefined inputs, i.e., specific neural fingerprints, are detected and encoded into coexisting patterns of activity that propagate throughout the network with different spatial organization. The patterns evoked by a stimulus can survive after the stimulation is over, which provides memory mechanisms to the network. The results presented in this paper suggest that neural information processing based on neural fingerprints can be a plausible, flexible, and powerful strategy. PMID:25852531

Age-dependent effects of brain stimulation on network centrality.

PubMed

Antonenko, Daria; Nierhaus, Till; Meinzer, Marcus; Prehn, Kristin; Thielscher, Axel; Ittermann, Bernd; Flöel, Agnes

2018-04-18

Functional magnetic resonance imaging (fMRI) studies have suggested that advanced age may mediate the effects of transcranial direct current stimulation (tDCS) on brain function. However, studies directly comparing neural tDCS effects between young and older adults are scarce and limited to task-related imaging paradigms. Resting-state (rs-) fMRI, that is independent of age-related differences in performance, is well suited to investigate age-associated differential neural tDCS effects. Three "online" tDCS conditions (anodal, cathodal, sham) were compared in a cross-over, within-subject design, in 30 young and 30 older adults. Active stimulation targeted the left sensorimotor network (active electrode over left sensorimotor cortex with right supraorbital reference electrode). A graph-based rs-fMRI data analysis approach (eigenvector centrality mapping) and complementary seed-based analyses characterized neural tDCS effects. An interaction between anodal tDCS and age group was observed. Specifically, centrality in bilateral paracentral and posterior regions (precuneus, superior parietal cortex) was increased in young, but decreased in older adults. Seed-based analyses revealed that these opposing patterns of tDCS-induced centrality modulation originated from differential effects of tDCS on functional coupling of the stimulated left paracentral lobule. Cathodal tDCS did not show significant effects. Our study provides first evidence for differential tDCS effects on neural network organization in young and older adults. Anodal stimulation mainly affected coupling of sensorimotor with ventromedial prefrontal areas in young and decoupling with posteromedial areas in older adults. Copyright © 2018 Elsevier Inc. All rights reserved.

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

PubMed

Li, Haibin; He, Yun; Nie, Xiaobo

2018-01-01

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

Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks.

PubMed

Aguiar, Manuela A D; Dias, Ana Paula S; Ferreira, Flora

2017-01-01

We consider feed-forward and auto-regulation feed-forward neural (weighted) coupled cell networks. In feed-forward neural networks, cells are arranged in layers such that the cells of the first layer have empty input set and cells of each other layer receive only inputs from cells of the previous layer. An auto-regulation feed-forward neural coupled cell network is a feed-forward neural network where additionally some cells of the first layer have auto-regulation, that is, they have a self-loop. Given a network structure, a robust pattern of synchrony is a space defined in terms of equalities of cell coordinates that is flow-invariant for any coupled cell system (with additive input structure) associated with the network. In this paper, we describe the robust patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks. Regarding feed-forward neural networks, we show that only cells in the same layer can synchronize. On the other hand, in the presence of auto-regulation, we prove that cells in different layers can synchronize in a robust way and we give a characterization of the possible patterns of synchrony that can occur for auto-regulation feed-forward neural networks.

Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks.

PubMed

Zhang, WenJun

2007-07-01

Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance (similarity) measures. Results with the larger consistency will be more reliable.

Distinct neural substrates for visual short-term memory of actions.

PubMed

Cai, Ying; Urgolites, Zhisen; Wood, Justin; Chen, Chuansheng; Li, Siyao; Chen, Antao; Xue, Gui

2018-06-26

Fundamental theories of human cognition have long posited that the short-term maintenance of actions is supported by one of the "core knowledge" systems of human visual cognition, yet its neural substrates are still not well understood. In particular, it is unclear whether the visual short-term memory (VSTM) of actions has distinct neural substrates or, as proposed by the spatio-object architecture of VSTM, shares them with VSTM of objects and spatial locations. In two experiments, we tested these two competing hypotheses by directly contrasting the neural substrates for VSTM of actions with those for objects and locations. Our results showed that the bilateral middle temporal cortex (MT) was specifically involved in VSTM of actions because its activation and its functional connectivity with the frontal-parietal network (FPN) were only modulated by the memory load of actions, but not by that of objects/agents or locations. Moreover, the brain regions involved in the maintenance of spatial location information (i.e., superior parietal lobule, SPL) was also recruited during the maintenance of actions, consistent with the temporal-spatial nature of actions. Meanwhile, the frontoparietal network (FPN) was commonly involved in all types of VSTM and showed flexible functional connectivity with the domain-specific regions, depending on the current working memory tasks. Together, our results provide clear evidence for a distinct neural system for maintaining actions in VSTM, which supports the core knowledge system theory and the domain-specific and domain-general architectures of VSTM. © 2018 Wiley Periodicals, Inc.

Regulating the dorsal neural tube expression of Ptf1a through a distal 3' enhancer.

PubMed

Mona, Bishakha; Avila, John M; Meredith, David M; Kollipara, Rahul K; Johnson, Jane E

2016-10-01

Generating the correct balance of inhibitory and excitatory neurons in a neural network is essential for normal functioning of a nervous system. The neural network in the dorsal spinal cord functions in somatosensation where it modulates and relays sensory information from the periphery. PTF1A is a key transcriptional regulator present in a specific subset of neural progenitor cells in the dorsal spinal cord, cerebellum and retina that functions to specify an inhibitory neuronal fate while suppressing excitatory neuronal fates. Thus, the regulation of Ptf1a expression is critical for determining mechanisms controlling neuronal diversity in these regions of the nervous system. Here we identify a sequence conserved, tissue-specific enhancer located 10.8kb 3' of the Ptf1a coding region that is sufficient to direct expression to dorsal neural tube progenitors that give rise to neurons in the dorsal spinal cord in chick and mouse. DNA binding motifs for Paired homeodomain (Pd-HD) and zinc finger (ZF) transcription factors are required for enhancer activity. Mutations in these sequences implicate the Pd-HD motif for activator function and the ZF motif for repressor function. Although no repressor transcription factor was identified, both PAX6 and SOX3 can increase enhancer activity in reporter assays. Thus, Ptf1a is regulated by active and repressive inputs integrated through multiple sequence elements within a highly conserved sequence downstream of the Ptf1a gene. Copyright © 2016 Elsevier Inc. All rights reserved.

Modulation of the brain's functional network architecture in the transition from wake to sleep

PubMed Central

Larson-Prior, Linda J.; Power, Jonathan D.; Vincent, Justin L.; Nolan, Tracy S.; Coalson, Rebecca S.; Zempel, John; Snyder, Abraham Z.; Schlaggar, Bradley L.; Raichle, Marcus E.; Petersen, Steven E.

2013-01-01

The transition from quiet wakeful rest to sleep represents a period over which attention to the external environment fades. Neuroimaging methodologies have provided much information on the shift in neural activity patterns in sleep, but the dynamic restructuring of human brain networks in the transitional period from wake to sleep remains poorly understood. Analysis of electrophysiological measures and functional network connectivity of these early transitional states shows subtle shifts in network architecture that are consistent with reduced external attentiveness and increased internal and self-referential processing. Further, descent to sleep is accompanied by the loss of connectivity in anterior and posterior portions of the default-mode network and more locally organized global network architecture. These data clarify the complex and dynamic nature of the transitional period between wake and sleep and suggest the need for more studies investigating the dynamics of these processes. PMID:21854969

Centralized Networks to Generate Human Body Motions

PubMed Central

Vakulenko, Sergei; Radulescu, Ovidiu; Morozov, Ivan

2017-01-01

We consider continuous-time recurrent neural networks as dynamical models for the simulation of human body motions. These networks consist of a few centers and many satellites connected to them. The centers evolve in time as periodical oscillators with different frequencies. The center states define the satellite neurons’ states by a radial basis function (RBF) network. To simulate different motions, we adjust the parameters of the RBF networks. Our network includes a switching module that allows for turning from one motion to another. Simulations show that this model allows us to simulate complicated motions consisting of many different dynamical primitives. We also use the model for learning human body motion from markers’ trajectories. We find that center frequencies can be learned from a small number of markers and can be transferred to other markers, such that our technique seems to be capable of correcting for missing information resulting from sparse control marker settings. PMID:29240694

Centralized Networks to Generate Human Body Motions.

PubMed

Vakulenko, Sergei; Radulescu, Ovidiu; Morozov, Ivan; Weber, Andres

2017-12-14

We consider continuous-time recurrent neural networks as dynamical models for the simulation of human body motions. These networks consist of a few centers and many satellites connected to them. The centers evolve in time as periodical oscillators with different frequencies. The center states define the satellite neurons' states by a radial basis function (RBF) network. To simulate different motions, we adjust the parameters of the RBF networks. Our network includes a switching module that allows for turning from one motion to another. Simulations show that this model allows us to simulate complicated motions consisting of many different dynamical primitives. We also use the model for learning human body motion from markers' trajectories. We find that center frequencies can be learned from a small number of markers and can be transferred to other markers, such that our technique seems to be capable of correcting for missing information resulting from sparse control marker settings.

Modulating the processing of emotional stimuli by cognitive demand

PubMed Central

Sternkopf, Melanie A.; Schneider, Frank; Habel, Ute; Turetsky, Bruce I.; Zilles, Karl; Eickhoff, Simon B.

2012-01-01

Emotional processing is influenced by cognitive processes and vice versa, indicating a profound interaction of these domains. The investigation of the neural mechanisms underlying this interaction is not only highly relevant for understanding the organization of human brain function. Rather, it may also help in understanding dysregulated emotions in affective disorders and in elucidating the neurobiology of cognitive behavioural therapy (e.g. in borderline personality disorder), which aims at modulating dysfunctional emotion processes by cognitive techniques, such as restructuring. In the majority of earlier studies investigating the interaction of emotions and cognition, the main focus has been on the investigation of the effects of emotional stimuli or, more general, emotional processing, e.g. instituted by emotional material that needed to be processed, on cognitive performance and neural activation patterns. Here we pursued the opposite approach and investigated the modulation of implicit processing of emotional stimuli by cognitive demands using an event-related functional magnetic resonance imaging––study on a motor short-term memory paradigm with emotional interferences. Subjects were visually presented a finger-sequence consisting either of four (easy condition) or six (difficult condition) items, which they had to memorize. After a short pause positive, negative or neutral International affective picture system pictures or a green dot (as control condition) were presented. Subjects were instructed to reproduce the memorized sequence manually as soon as the picture disappeared. Analysis showed that with increasing cognitive demand (long relative to short sequences), neural responses to emotional pictures were significantly reduced in amygdala and orbitofrontal cortex. In contrast, the more difficult task evoked stronger activation in a widespread frontoparietal network. As stimuli were task-relevant go-cues and hence had to be processed perceptually, we would interpret this as a specific attenuation of affective responses by concurrent cognitive processing––potentially reflecting a relocation of resources mediated by the frontoparietal network. PMID:21258093

Accelerating Learning By Neural Networks

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad; Barhen, Jacob

1992-01-01

Electronic neural networks made to learn faster by use of terminal teacher forcing. Method of supervised learning involves addition of teacher forcing functions to excitations fed as inputs to output neurons. Initially, teacher forcing functions are strong enough to force outputs to desired values; subsequently, these functions decay with time. When learning successfully completed, terminal teacher forcing vanishes, and dynamics or neural network become equivalent to those of conventional neural network. Simulated neural network with terminal teacher forcing learned to produce close approximation of circular trajectory in 400 iterations.

[Biofeedback treatment for epilepsy].

PubMed

Nagai, Yoko

2014-05-01

Pharmacological treatment is the mainstay for the treatment of epilepsy. However concerns regarding long-term side effects of drugs are increasingly voiced. Behavioral treatments including biofeedback, represents an alternative management option for the control of epilepsy. Biofeedback is a non-invasive bio-behavioral procedure through which patients can learn to gain psychophysiological control over seizures. This article will first overview seizure precipitation from a psychological perspective, and then introduce three major biofeedback treatments. Sensory motor rhythm (SMR) and slow cortical potential(SCP) biofeedback uses electroencephalographic parameters and are categorized as neurofeedback. Electrodermal activity (EDA) biofeedback focuses on modulation of peripheral sympathetic tone. The neural mechanisms underlying biofeedback treatment will be discussed in relation to thalamo-cortical regulation(of neural excitability across brain networks).

Predictability and context determine differences in conflict monitoring between adolescence and adulthood.

PubMed

Chmielewski, Witold X; Roessner, Veit; Beste, Christian

2015-10-01

The ability to link contextual information to actions is an important aspect of conflict monitoring and response selection. These mechanisms depend on medial prefrontal networks. Although these areas undergo a protracted development from adolescence to adulthood, it has remained elusive how the influence of contextual information on conflict monitoring is modulated between adolescence and adulthood. Using event-related potentials (ERPs) and source localization techniques we show that the ability to link contextual information to actions is altered and that the predictability of upcoming events is an important factor to consider in this context. In adolescents, conflict monitoring functions are not as much modulated by predictability factors as in adults. It seems that adults exhibit a stronger anticipation of upcoming events than adolescents. This results in disadvantages for adults when the upcoming context is not predictable. In adolescents, problems to predict upcoming events therefore turn out to be beneficial. Two cognitive-neurophysiological factors are important for this: The first factor is related to altered conflict monitoring functions associated with modulations of neural activity in the medial frontal cortex. The second factor is related to altered perceptual processing of target stimuli associated with modulations of neural activity in parieto-occipital areas. Copyright © 2015 Elsevier B.V. All rights reserved.

Characterization of pulse amplitude and pulse rate modulation for a human vestibular implant during acute electrical stimulation

NASA Astrophysics Data System (ADS)

Nguyen, T. A. K.; DiGiovanna, J.; Cavuscens, S.; Ranieri, M.; Guinand, N.; van de Berg, R.; Carpaneto, J.; Kingma, H.; Guyot, J.-P.; Micera, S.; Perez Fornos, A.

2016-08-01

Objective. The vestibular system provides essential information about balance and spatial orientation via the brain to other sensory and motor systems. Bilateral vestibular loss significantly reduces quality of life, but vestibular implants (VIs) have demonstrated potential to restore lost function. However, optimal electrical stimulation strategies have not yet been identified in patients. In this study, we compared the two most common strategies, pulse amplitude modulation (PAM) and pulse rate modulation (PRM), in patients. Approach. Four subjects with a modified cochlear implant including electrodes targeting the peripheral vestibular nerve branches were tested. Charge-equivalent PAM and PRM were applied after adaptation to baseline stimulation. Vestibulo-ocular reflex eye movement responses were recorded to evaluate stimulation efficacy during acute clinical testing sessions. Main results. PAM evoked larger amplitude eye movement responses than PRM. Eye movement response axes for lateral canal stimulation were marginally better aligned with PRM than with PAM. A neural network model was developed for the tested stimulation strategies to provide insights on possible neural mechanisms. This model suggested that PAM would consistently cause a larger ensemble firing rate of neurons and thus larger responses than PRM. Significance. Due to the larger magnitude of eye movement responses, our findings strongly suggest PAM as the preferred strategy for initial VI modulation.

Input-dependent modulation of MEG gamma oscillations reflects gain control in the visual cortex.

PubMed

Orekhova, Elena V; Sysoeva, Olga V; Schneiderman, Justin F; Lundström, Sebastian; Galuta, Ilia A; Goiaeva, Dzerasa E; Prokofyev, Andrey O; Riaz, Bushra; Keeler, Courtney; Hadjikhani, Nouchine; Gillberg, Christopher; Stroganova, Tatiana A

2018-05-31

Gamma-band oscillations arise from the interplay between neural excitation (E) and inhibition (I) and may provide a non-invasive window into the state of cortical circuitry. A bell-shaped modulation of gamma response power by increasing the intensity of sensory input was observed in animals and is thought to reflect neural gain control. Here we sought to find a similar input-output relationship in humans with MEG via modulating the intensity of a visual stimulation by changing the velocity/temporal-frequency of visual motion. In the first experiment, adult participants observed static and moving gratings. The frequency of the MEG gamma response monotonically increased with motion velocity whereas power followed a bell-shape. In the second experiment, on a large group of children and adults, we found that despite drastic developmental changes in frequency and power of gamma oscillations, the relative suppression at high motion velocities was scaled to the same range of values across the life-span. In light of animal and modeling studies, the modulation of gamma power and frequency at high stimulation intensities characterizes the capacity of inhibitory neurons to counterbalance increasing excitation in visual networks. Gamma suppression may thus provide a non-invasive measure of inhibitory-based gain control in the healthy and diseased brain.

A 2.4 GHz ULP reconfigurable asymmetric transceiver for single-chip wireless neural recording IC.

PubMed

Tan, Jun; Liew, Wen-Sin; Heng, Chun-Huat; Lian, Yong

2014-08-01

This paper presents a 2.4 GHz ultra-low-power (ULP) reconfigurable asymmetric transceiver and demonstrates its application in wireless neural recording. Fabricated in 0.13 μm CMOS technology, the transceiver is optimized for sensor-gateway communications within a star-shaped network, and supports both the sensor and gateway operation modes. Binary phase-shift keying (BPSK) modulation with high data rate (DR) of 1 to 8 Mbps is used in the uplink from sensor to gateway, while on-off keying (OOK) modulation with low DR of 100 kbps is adopted in the downlink. A fully integrated Class-E PA with moderate output power has also been proposed and achieves power efficiency of 53%. To minimize area usage, inductor reuse is adopted between PA and LNA, and eliminates the need of lossy T/R switch in the RF signal path. When used as sensor, the transceiver with frequency locked phase-locked loop (PLL) achieves TX (BPSK) power efficiency of 28% @ 0 dBm output power, and RX (OOK) sensitivity of -80 dBm @ 100 kbps while consuming only 780 μW . When configured as gateway, the transceiver achieves sensitivity levels of -92, -84.5, and -77 dBm for 1, 5, and 8 Mbps BPSK, respectively. The transceiver is integrated with an 8-channel neural recording front-end, and neural signals from a rat are captured to verify the system functionality.

Effects of Oxytocin and Vasopressin on Preferential Brain Responses to Negative Social Feedback.

PubMed

Gozzi, Marta; Dashow, Erica M; Thurm, Audrey; Swedo, Susan E; Zink, Caroline F

2017-06-01

Receiving negative social feedback can be detrimental to emotional, cognitive, and physical well-being, and fear of negative social feedback is a prominent feature of mental illnesses that involve social anxiety. A large body of evidence has implicated the neuropeptides oxytocin and vasopressin in the modulation of human neural activity underlying social cognition, including negative emotion processing; however, the influence of oxytocin and vasopressin on neural activity elicited during negative social evaluation remains unknown. Here 21 healthy men underwent functional magnetic resonance imaging in a double-blind, placebo-controlled, crossover design to determine how intranasally administered oxytocin and vasopressin modulated neural activity when receiving negative feedback on task performance from a study investigator. We found that under placebo, a preferential response to negative social feedback compared with positive social feedback was evoked in brain regions putatively involved in theory of mind (temporoparietal junction), pain processing (anterior insula and supplementary motor area), and identification of emotionally important visual cues in social perception (right fusiform). These activations weakened with oxytocin and vasopressin administration such that neural responses to receiving negative social feedback were not significantly greater than positive social feedback. Our results show effects of both oxytocin and vasopressin on the brain network involved in negative social feedback, informing the possible use of a pharmacological approach targeting these regions in multiple disorders with impairments in social information processing.

Design, fabrication, and packaging of an integrated, wirelessly-powered optrode array for optogenetics application

PubMed Central

Kwon, Ki Yong; Lee, Hyung-Min; Ghovanloo, Maysam; Weber, Arthur; Li, Wen

2015-01-01

The recent development of optogenetics has created an increased demand for advancing engineering tools for optical modulation of neural circuitry. This paper details the design, fabrication, integration, and packaging procedures of a wirelessly-powered, light emitting diode (LED) coupled optrode neural interface for optogenetic studies. The LED-coupled optrode array employs microscale LED (μLED) chips and polymer-based microwaveguides to deliver light into multi-level cortical networks, coupled with microelectrodes to record spontaneous changes in neural activity. An integrated, implantable, switched-capacitor based stimulator (SCS) system provides high instantaneous power to the μLEDs through an inductive link to emit sufficient light and evoke neural activities. The presented system is mechanically flexible, biocompatible, miniaturized, and lightweight, suitable for chronic implantation in small freely behaving animals. The design of this system is scalable and its manufacturing is cost effective through batch fabrication using microelectromechanical systems (MEMS) technology. It can be adopted by other groups and customized for specific needs of individual experiments. PMID:25999823

Can beneficial ends justify lying? Neural responses to the passive reception of lies and truth-telling with beneficial and harmful monetary outcomes

PubMed Central

Weber, Bernd

2016-01-01

Can beneficial ends justify morally questionable means? To investigate how monetary outcomes influence the neural responses to lying, we used a modified, cheap talk sender–receiver game in which participants were the direct recipients of lies and truthful statements resulting in either beneficial or harmful monetary outcomes. Both truth-telling (vs lying) as well as beneficial (vs harmful) outcomes elicited higher activity in the nucleus accumbens. Lying (vs truth-telling) elicited higher activity in the supplementary motor area, right inferior frontal gyrus, superior temporal sulcus and left anterior insula. Moreover, the significant interaction effect was found in the left amygdala, which showed that the monetary outcomes modulated the neural activity in the left amygdala only when truth-telling rather than lying. Our study identified a neural network associated with the reception of lies and truth, including the regions linked to the reward process, recognition and emotional experiences of being treated (dis)honestly. PMID:26454816

Readout from iconic memory and selective spatial attention involve similar neural processes.

PubMed

Ruff, Christian C; Kristjánsson, Arni; Driver, Jon

2007-10-01

Iconic memory and spatial attention are often considered separately, but they may have functional similarities. Here we provide functional magnetic resonance imaging evidence for some common underlying neural effects. Subjects judged three visual stimuli in one hemifield of a bilateral array comprising six stimuli. The relevant hemifield for partial report was indicated by an auditory cue, administered either before the visual array (precue, spatial attention) or shortly after the array (postcue, iconic memory). Pre- and postcues led to similar activity modulations in lateral occipital cortex contralateral to the cued side. This finding indicates that readout from iconic memory can have some neural effects similar to those of spatial attention. We also found common bilateral activation of a fronto-parietal network for postcue and precue trials. These neuroimaging data suggest that some common neural mechanisms underlie selective spatial attention and readout from iconic memory. Some differences were also found; compared with precues, postcues led to higher activity in the right middle frontal gyrus.

Readout From Iconic Memory and Selective Spatial Attention Involve Similar Neural Processes

PubMed Central

Ruff, Christian C; Kristjánsson, Árni; Driver, Jon

2007-01-01

Iconic memory and spatial attention are often considered separately, but they may have functional similarities. Here we provide functional magnetic resonance imaging evidence for some common underlying neural effects. Subjects judged three visual stimuli in one hemifield of a bilateral array comprising six stimuli. The relevant hemifield for partial report was indicated by an auditory cue, administered either before the visual array (precue, spatial attention) or shortly after the array (postcue, iconic memory). Pre- and postcues led to similar activity modulations in lateral occipital cortex contralateral to the cued side. This finding indicates that readout from iconic memory can have some neural effects similar to those of spatial attention. We also found common bilateral activation of a fronto-parietal network for postcue and precue trials. These neuroimaging data suggest that some common neural mechanisms underlie selective spatial attention and readout from iconic memory. Some differences were also found; compared with precues, postcues led to higher activity in the right middle frontal gyrus. PMID:17894608

A quantitative framework to evaluate modeling of cortical development by neural stem cells

PubMed Central

Stein, Jason L.; de la Torre-Ubieta, Luis; Tian, Yuan; Parikshak, Neelroop N.; Hernandez, Israel A.; Marchetto, Maria C.; Baker, Dylan K.; Lu, Daning; Hinman, Cassidy R.; Lowe, Jennifer K.; Wexler, Eric M.; Muotri, Alysson R.; Gage, Fred H.; Kosik, Kenneth S.; Geschwind, Daniel H.

2014-01-01

Summary Neural stem cells have been adopted to model a wide range of neuropsychiatric conditions in vitro. However, how well such models correspond to in vivo brain has not been evaluated in an unbiased, comprehensive manner. We used transcriptomic analyses to compare in vitro systems to developing human fetal brain and observed strong conservation of in vivo gene expression and network architecture in differentiating primary human neural progenitor cells (phNPCs). Conserved modules are enriched in genes associated with ASD, supporting the utility of phNPCs for studying neuropsychiatric disease. We also developed and validated a machine learning approach called CoNTExT that identifies the developmental maturity and regional identity of in vitro models. We observed strong differences between in vitro models, including hiPSC-derived neural progenitors from multiple laboratories. This work provides a systems biology framework for evaluating in vitro systems and supports their value in studying the molecular mechanisms of human neurodevelopmental disease. PMID:24991955

Neural correlates of reappraisal considering working memory capacity and cognitive flexibility.

PubMed

Zaehringer, Jenny; Falquez, Rosalux; Schubert, Anna-Lena; Nees, Frauke; Barnow, Sven

2018-01-09

Cognitive reappraisal of emotion is strongly related to long-term mental health. Therefore, the exploration of underlying cognitive and neural mechanisms has become an essential focus of research. Considering that reappraisal and executive functions rely on a similar brain network, the question arises whether behavioral differences in executive functions modulate neural activity during reappraisal. Using functional neuroimaging, the present study aimed to analyze the role of working memory capacity (WMC) and cognitive flexibility in brain activity during down-regulation of negative emotions by reappraisal in N = 20 healthy participants. Results suggests that WMC and cognitive flexibility were negatively correlated with prefrontal activity during reappraisal condition. Here, results also revealed a negative correlation between cognitive flexibility and amygdala activation. These findings provide first hints that (1) individuals with lower WMC and lower cognitive flexibility might need more higher-order cognitive neural resources in order to down-regulate negative emotions and (2) cognitive flexibility relates to emotional reactivity during reappraisal.

Effects of Acute Alcohol Intoxication on Empathic Neural Responses for Pain

PubMed Central

Hu, Yang; Cui, Zhuoya; Fan, Mingxia; Pei, Yilai; Wang, Zhaoxin

2018-01-01

The questions whether and how empathy for pain can be modulated by acute alcohol intoxication in the non-dependent population remain unanswered. To address these questions, a double-blind, placebo-controlled, within-subject study design was adopted in this study, in which healthy social drinkers were asked to complete a pain-judgment task using pictures depicting others' body parts in painful or non-painful situations during fMRI scanning, either under the influence of alcohol intoxication or placebo conditions. Empathic neural activity for pain was reduced by alcohol intoxication only in the dorsal anterior cingulate cortex (dACC). More interestingly, we observed that empathic neural activity for pain in the right anterior insula (rAI) was significantly correlated with trait empathy only after alcohol intoxication, along with impaired functional connectivity between the rAI and the fronto-parietal attention network. Our results reveal that alcohol intoxication not only inhibits empathic neural responses for pain but also leads to trait empathy inflation, possibly via impaired top-down attentional control. These findings help to explain the neural mechanism underlying alcohol-related social problems. PMID:29354044

Effects of Acute Alcohol Intoxication on Empathic Neural Responses for Pain.

PubMed

Hu, Yang; Cui, Zhuoya; Fan, Mingxia; Pei, Yilai; Wang, Zhaoxin

2017-01-01

The questions whether and how empathy for pain can be modulated by acute alcohol intoxication in the non-dependent population remain unanswered. To address these questions, a double-blind, placebo-controlled, within-subject study design was adopted in this study, in which healthy social drinkers were asked to complete a pain-judgment task using pictures depicting others' body parts in painful or non-painful situations during fMRI scanning, either under the influence of alcohol intoxication or placebo conditions. Empathic neural activity for pain was reduced by alcohol intoxication only in the dorsal anterior cingulate cortex (dACC). More interestingly, we observed that empathic neural activity for pain in the right anterior insula (rAI) was significantly correlated with trait empathy only after alcohol intoxication, along with impaired functional connectivity between the rAI and the fronto-parietal attention network. Our results reveal that alcohol intoxication not only inhibits empathic neural responses for pain but also leads to trait empathy inflation, possibly via impaired top-down attentional control. These findings help to explain the neural mechanism underlying alcohol-related social problems.

Thermoelastic steam turbine rotor control based on neural network

NASA Astrophysics Data System (ADS)

Rzadkowski, Romuald; Dominiczak, Krzysztof; Radulski, Wojciech; Szczepanik, R.

2015-12-01

Considered here are Nonlinear Auto-Regressive neural networks with eXogenous inputs (NARX) as a mathematical model of a steam turbine rotor for controlling steam turbine stress on-line. In order to obtain neural networks that locate critical stress and temperature points in the steam turbine during transient states, an FE rotor model was built. This model was used to train the neural networks on the basis of steam turbine transient operating data. The training included nonlinearity related to steam turbine expansion, heat exchange and rotor material properties during transients. Simultaneous neural networks are algorithms which can be implemented on PLC controllers. This allows for the application neural networks to control steam turbine stress in industrial power plants.

The use of artificial neural networks in experimental data acquisition and aerodynamic design

NASA Technical Reports Server (NTRS)

Meade, Andrew J., Jr.

1991-01-01

It is proposed that an artificial neural network be used to construct an intelligent data acquisition system. The artificial neural networks (ANN) model has a potential for replacing traditional procedures as well as for use in computational fluid dynamics validation. Potential advantages of the ANN model are listed. As a proof of concept, the author modeled a NACA 0012 airfoil at specific conditions, using the neural network simulator NETS, developed by James Baffes of the NASA Johnson Space Center. The neural network predictions were compared to the actual data. It is concluded that artificial neural networks can provide an elegant and valuable class of mathematical tools for data analysis.

Research on artificial neural network intrusion detection photochemistry based on the improved wavelet analysis and transformation

NASA Astrophysics Data System (ADS)

Li, Hong; Ding, Xue

2017-03-01

This paper combines wavelet analysis and wavelet transform theory with artificial neural network, through the pretreatment on point feature attributes before in intrusion detection, to make them suitable for improvement of wavelet neural network. The whole intrusion classification model gets the better adaptability, self-learning ability, greatly enhances the wavelet neural network for solving the problem of field detection invasion, reduces storage space, contributes to improve the performance of the constructed neural network, and reduces the training time. Finally the results of the KDDCup99 data set simulation experiment shows that, this method reduces the complexity of constructing wavelet neural network, but also ensures the accuracy of the intrusion classification.

Emergence of Adaptive Computation by Single Neurons in the Developing Cortex

PubMed Central

Famulare, Michael; Gjorgjieva, Julijana; Moody, William J.

2013-01-01

Adaptation is a fundamental computational motif in neural processing. To maintain stable perception in the face of rapidly shifting input, neural systems must extract relevant information from background fluctuations under many different contexts. Many neural systems are able to adjust their input–output properties such that an input's ability to trigger a response depends on the size of that input relative to its local statistical context. This “gain-scaling” strategy has been shown to be an efficient coding strategy. We report here that this property emerges during early development as an intrinsic property of single neurons in mouse sensorimotor cortex, coinciding with the disappearance of spontaneous waves of network activity, and can be modulated by changing the balance of spike-generating currents. Simultaneously, developing neurons move toward a common intrinsic operating point and a stable ratio of spike-generating currents. This developmental trajectory occurs in the absence of sensory input or spontaneous network activity. Through a combination of electrophysiology and modeling, we demonstrate that developing cortical neurons develop the ability to perform nearly perfect gain scaling by virtue of the maturing spike-generating currents alone. We use reduced single neuron models to identify the conditions for this property to hold. PMID:23884925

Mode of effective connectivity within a putative neural network differentiates moral cognitions related to care and justice ethics.

PubMed

Cáceda, Ricardo; James, G Andrew; Ely, Timothy D; Snarey, John; Kilts, Clinton D

2011-02-25

Moral sensitivity refers to the interpretive awareness of moral conflict and can be justice or care oriented. Justice ethics is associated primarily with human rights and the application of moral rules, whereas care ethics is related to human needs and a situational approach involving social emotions. Among the core brain regions involved in moral issue processing are: medial prefrontal cortex, anterior (ACC) and posterior (PCC) cingulate cortex, posterior superior temporal sulcus (pSTS), insula and amygdala. This study sought to inform the long standing debate of whether care and justice moral ethics represent one or two different forms of cognition. Model-free and model-based connectivity analysis were used to identify functional neural networks underlying care and justice ethics for a moral sensitivity task. In addition to modest differences in patterns of associated neural activity, distinct modes of functional and effective connectivity were observed for moral sensitivity for care and justice issues that were modulated by individual variation in moral ability. These results support a neurobiological differentiation between care and justice ethics and suggest that human moral behavior reflects the outcome of integrating opposing rule-based, self-other perspectives, and emotional responses.

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

PubMed

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

2011-02-01

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

The effect of strategies, goals and stimulus material on the neural mechanisms of emotion regulation: A meta-analysis of fMRI studies.

PubMed

Morawetz, Carmen; Bode, Stefan; Derntl, Birgit; Heekeren, Hauke R

2017-01-01

Emotion regulation comprises all extrinsic and intrinsic control processes whereby people monitor, evaluate and modify the occurrence, intensity and duration of emotional reactions. Here we sought to quantitatively summarize the existing neuroimaging literature to investigate a) whether different emotion regulation strategies are based on different or the same neural networks; b) which brain regions in particular support the up- and down-regulation of emotions, respectively; and c) to which degree the neural networks realising emotion regulation depend on the stimulus material used to elicit emotions. The left ventrolateral prefrontal cortex (VLPFC), the anterior insula and the supplementary motor area were consistently activated independent of the regulation strategy. VLPFC and posterior cingulate cortex were the main regions consistently found to be recruited during the up-regulation as well as the down-regulation of emotion. The down-regulation compared to the up-regulation of emotions was associated with more right-lateralized activity while up-regulating emotions more strongly modulated activity in the ventral striatum. Finally, the process of emotion regulation appeared to be unaffected by stimulus material. Copyright © 2016 Elsevier Ltd. All rights reserved.

A class of finite-time dual neural networks for solving quadratic programming problems and its k-winners-take-all application.

PubMed

Li, Shuai; Li, Yangming; Wang, Zheng

2013-03-01

This paper presents a class of recurrent neural networks to solve quadratic programming problems. Different from most existing recurrent neural networks for solving quadratic programming problems, the proposed neural network model converges in finite time and the activation function is not required to be a hard-limiting function for finite convergence time. The stability, finite-time convergence property and the optimality of the proposed neural network for solving the original quadratic programming problem are proven in theory. Extensive simulations are performed to evaluate the performance of the neural network with different parameters. In addition, the proposed neural network is applied to solving the k-winner-take-all (k-WTA) problem. Both theoretical analysis and numerical simulations validate the effectiveness of our method for solving the k-WTA problem. Copyright © 2012 Elsevier Ltd. All rights reserved.

Satellite image analysis using neural networks

NASA Technical Reports Server (NTRS)

Sheldon, Roger A.

1990-01-01

The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, SIANN (Satellite Image Analysis using Neural Networks) that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed was completed and applied to climatological data.

Firing patterns transition and desynchronization induced by time delay in neural networks

NASA Astrophysics Data System (ADS)

Huang, Shoufang; Zhang, Jiqian; Wang, Maosheng; Hu, Chin-Kun

2018-06-01

We used the Hindmarsh-Rose (HR) model (Hindmarsh and Rose, 1984) to study the effect of time delay on the transition of firing behaviors and desynchronization in neural networks. As time delay is increased, neural networks exhibit diversity of firing behaviors, including regular spiking or bursting and firing patterns transitions (FPTs). Meanwhile, the desynchronization of firing and unstable bursting with decreasing amplitude in neural system, are also increasingly enhanced with the increase of time delay. Furthermore, we also studied the effect of coupling strength and network randomness on these phenomena. Our results imply that time delays can induce transition and desynchronization of firing behaviors in neural networks. These findings provide new insight into the role of time delay in the firing activities of neural networks, and can help to better understand the firing phenomena in complex systems of neural networks. A possible mechanism in brain that can cause the increase of time delay is discussed.