scholarly journals Formulation of Pruning Maps with Rhythmic Neural Firing

Mathematics ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 1247
Author(s):  
Feng-Sheng Tsai ◽  
Yi-Li Shih ◽  
Chin-Tzong Pang ◽  
Sheng-Yi Hsu
Keyword(s):  
Network Dynamics ◽  
Network Models ◽  
Feedforward Neural Networks ◽  
Oscillatory Activity ◽  
Control Network ◽  
Neural Function ◽  
Network System ◽  
Neural Firing ◽  
Dynamical Network ◽  
Neuronal Connections

Rhythmic neural firing is thought to underlie the operation of neural function. This triggers the construction of dynamical network models to investigate how the rhythms interact with each other. Recently, an approach concerning neural path pruning has been proposed in a dynamical network system, in which critical neuronal connections are identified and adjusted according to the pruning maps, enabling neurons to produce rhythmic, oscillatory activity in simulation. Here, we construct a sort of homomorphic functions based on different rhythms of neural firing in network dynamics. Armed with the homomorphic functions, the pruning maps can be simply expressed in terms of interactive rhythms of neural firing and allow a concrete analysis of coupling operators to control network dynamics. Such formulation of pruning maps is applied to probe the consolidation of rhythmic patterns between layers of neurons in feedforward neural networks.

The Predigest Project of Vehicle-Bone Multimedia Transmission and Control Network Based on FPGA

2012 ◽  
Vol 203 ◽  
pp. 62-66
Author(s):  
Hong Li ◽  
Qiao Zhen Hou
Keyword(s):  
Network Control ◽  
Control Network ◽  
Network System ◽  
Multimedia Transmission ◽  
Transmission Control ◽  
Hardware Platform ◽  
Arm Processor ◽  
Interface Module ◽  
Fpga Chip ◽  
And Control

The design uses 32 ARM processor S3C44B0X and Spartan™ -3E500 FPGA chip produced by Xinlinx company for setting up the hardware platform, and integrates the camera, GPS module, MiniGUI interface module. And realized bus vehicle mounted multimedia transmission control network control based on MOST. All of these are in the purpose of achieving a Predigest Project of vehicle-bone multimedia transmission and control network based on FPGA. The experiment indicated that, the transmission and control network system constructed by S3C44B0X and Spartan - 3E500 FPGA is low cast, simple and reliable.

scholarly journals Modeling Network Dynamics

2020 ◽  
pp. 145-171
Keyword(s):  
Communication Networks ◽  
Random Network ◽  
Network Dynamics ◽  
Network Models ◽  
Random Networks ◽  
Social Phenomena ◽  
Disease Propagation ◽  
The Social ◽  
Wide Range ◽  
Model Dynamics

Social relationships and the social networks over these relationships do not occur arbitrarily. However, the random networks dealt with in this chapter are important tools for modeling the networks of these systems. The authors use random networks to understand and to model dynamics regarding the whole social structure. Random network models became the topic of several studies independently from social network analysis in the 1950s. These models were used in the analysis of a wide range of social and non-social phenomena, from electrical and communication networks to the speed and manner of disease propagation. This chapter explores the modeling network dynamics of random networks.

scholarly journals Exploring Residue Component Contributions to Dynamical Network Models of Allostery

2012 ◽  
Vol 8 (8) ◽  
pp. 2949-2961 ◽  
Author(s):  
Adam T. VanWart ◽  
John Eargle ◽  
Zaida Luthey-Schulten ◽  
Rommie E. Amaro
Keyword(s):  
Network Models ◽  
Dynamical Network

scholarly journals A Classification Method to Distinguish Cell-Specific Responses Elicited by Current Pulses in Hippocampal CA1 Pyramidal Cells

2008 ◽  
Vol 20 (6) ◽  
pp. 1512-1536 ◽  
Author(s):  
José Ambros-Ingerson ◽  
Lawrence M. Grover ◽  
William R. Holmes
Keyword(s):  
Distance Measure ◽  
Network Models ◽  
Pyramidal Cells ◽  
Spike Timing ◽  
Voltage Gradient ◽  
Neural Function ◽  
Ca1 Pyramidal Cells ◽  
Current Pulses ◽  
Point Distance

The suprathreshold electrophysiological responses of pyramidal cells have been grouped into large classes such as bursting and spiking. However, it is not known whether, within a class, response variability ranges uniformly across all cells or whether each cell has a unique and consistent profile that can be differentiated. A major difficulty when comparing suprathreshold responses is that slight variations in spike timing in otherwise very similar traces render traditional metrics ineffective. To address these issues, we developed a novel distance measure based on fiducial points to quantify the similarity among traces with trains of action potentials and applied it together with classification techniques to a set of in vitro patch clamp recordings from CA1 pyramidal cells. We tested if responses to repeated current stimulation of a given cell would cluster together yet remain distinct from those of other cells. We found that depolarizing and hyperpolarizing current pulses elicited responses in each cell that clustered and were systematically distinguishable from responses in other cells. The fiducial-point distance measure was more effective than other methods based on spike times and voltage-gradient phase planes. Depolarizing traces were more reliably differentiated than hyperpolarizing traces, and combining both scores was even more effective. These results suggest that each CA1 pyramidal cell has unique properties that can be detected and quantified with methods discussed here. This uniqueness may be due to slight variations in morphology or membrane channel densities and kinetics, or to large, coordinated variations in these elements. Ascertaining the actual sources and their degree of variability is important when constructing network models of neural function to ensure that key mechanisms are robust in the face of variations within these ranges. The analytical tools presented here can assist in constructing detailed cell models to match experimental records to elucidate the sources of electrophysiological variability in neurons.

scholarly journals Multistage ensemble of feedforward neural networks for prediction of heating energy consumption

2016 ◽  
Vol 20 (4) ◽  
pp. 1321-1331 ◽  
Author(s):  
Radisa Jovanovic ◽  
Aleksandra Sretenovic ◽  
Branislav Zivkovic
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Energy Consumption ◽  
Network Models ◽  
Feedforward Neural Networks ◽  
Neural Network Ensemble ◽  
University Campus ◽  
Neural Network Models ◽  
Feed Forward Neural Networks ◽  
Heating Energy Consumption

Feedforward neural network models are created for prediction of heating energy consumption of a university campus. Actual measured data are used for training and testing the models. Multistage neural network ensemble is proposed for the possible improvement of prediction accuracy. Previously trained feed-forward neural networks are first separated into clusters, using k-means algorithm, and then the best network of each cluster is chosen as a member of the ensemble. Three different averaging methods (simple, weighted and median) for obtaining ensemble output are applied. Besides this conventional approach, single radial basis neural network in the second level is used to aggregate the selected ensemble members. It is shown that heating energy consumption can be predicted with better accuracy by using ensemble of neural networks than using the best trained single neural network, while the best results are achieved with multistage ensemble.

scholarly journals Frontoparietal network dynamics impairments in juvenile myoclonic epilepsy revealed by MEG energy landscape

2019 ◽  
Author(s):  
Dominik Krzemiński ◽  
Naoki Masuda ◽  
Khalid Hamandi ◽  
Krish D Singh ◽  
Bethany Routley ◽  
...  
Keyword(s):  
Resting State ◽  
Energy Landscape ◽  
Network Dynamics ◽  
Juvenile Myoclonic Epilepsy ◽  
Myoclonic Epilepsy ◽  
Oscillatory Activity ◽  
Functional Networks ◽  
Occurrence Probability ◽  
Energy Landscapes ◽  
Frontoparietal Network

AbstractJuvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy affecting brain activity. It is unclear to what extent JME leads to abnormal network dynamics across functional networks. Here, we proposed a method to characterise network dynamics in MEG resting-state data, combining a pairwise maximum entropy model (pMEM) and the associated energy landscape analysis. Fifty-two JME patients and healthy controls underwent a resting-state MEG recording session. We fitted the pMEM to the oscillatory power envelopes in theta (4-7 Hz), alpha (8-13 Hz), beta (15-25 Hz) and gamma (30-60 Hz) bands in three source-localised resting-state networks: the frontoparietal network (FPN), the default mode network (DMN), and the sensorimotor network (SMN). The pMEM provided an accurate fit to the MEG oscillatory activity in both patient and control groups, and allowed estimation of the occurrence probability of each network state, with its regional activity and pairwise regional co-activation constrained by empirical data. We used energy values derived from the pMEM to depict an energy landscape of each network, with a higher energy state corresponding to a lower occurrence probability. When comparing the energy landscapes between groups, JME patients showed fewer local energy minima than controls and had elevated energy values for the FPN within the theta, beta and gamma-bands. Furthermore, numerical simulation of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of characteristic energy minima was shortened in JME patients. These network alterations were confirmed by a significant leave-one-out classification of individual participants based on a support vector machine employing the energy values of pMEM as features. Our findings suggested that JME patients had altered multi-stability in selective functional networks and frequency bands in the frontoparietal cortices.HighlightsAn energy landscape analysis characterises the dynamics of MEG oscillatory activityPatients with JME exhibit fewer local minima of the energy in their energy landscapesJME affects the network dynamics in the frontoparietal network.Energy landscape measures allow good single-patient classification.

scholarly journals The Physiological Control of Eating: Signals, Neurons, and Networks

2021 ◽  
Author(s):  
Alan G. Watts ◽  
Scott E Kanoski ◽  
Graciela Sanchez-Watts ◽  
Wolfgang Langhans
Keyword(s):  
Energy Homeostasis ◽  
Eating Behaviors ◽  
Network Models ◽  
Physiological Signals ◽  
Major Theme ◽  
Physiological Control ◽  
Neural Connections ◽  
Balanced Assessment ◽  
Neuronal Connections ◽  
The Brain

During the past 30 years, investigating the physiology of eating behaviors has generated a truly vast literature. This is fueled in part by a dramatic increase in obesity and its comorbidities that has coincided with an ever increasing sophistication of genetically based manipulations. These techniques have produced results with a remarkable level of cell-specificity-particularly at the cell signaling level-and have played a lead role in advancing the field. However, putting these findings into a brain-wide context that connects physiological signals and neurons to behavior and somatic physiology requires a thorough consideration of neuronal connections; a field that has also seen an extraordinary technological revolution. Our goal is to present a comprehensive and balanced assessment of how physiological signals associated with energy homeostasis interact at many brain levels to control eating behaviors. A major theme is that these signals engage sets of interacting neural networks throughout the brain, that are defined by specific neural connections. We begin by discussing some fundamental concepts-including ones that still engender vigorous debate-that provide the necessary frameworks for understanding how the brain controls meal initiation and termination. These include: key word definitions, ATP availability as the pivotal regulated variable in energy homeostasis, neuropeptide signaling, homeostatic and hedonic eating, and meal structure. Within this context, we discuss network models of how key regions in the endbrain (or telencephalon), hypothalamus, hindbrain, medulla, vagus nerve, and spinal cord work together with the gastrointestinal tract to enable the complex motor events that permit animals to eat in diverse situations.

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