共查询到20条相似文献,搜索用时 7 毫秒
1.
This paper investigates finite-time synchronization of an array of coupled neural networks via discontinuous controllers.
Based on Lyapunov function method and the discontinuous version of finite-time stability theory, some sufficient criteria
for finite-time synchronization are obtained. Furthermore, we propose switched control and adaptive tuning parameter strategies
in order to reduce the settling time. In addition, pinning control scheme via a single controller is also studied in this
paper. With the hypothesis that the coupling network topology contains a directed spanning tree and each of the strongly connected
components is detail-balanced, we prove that finite-time synchronization can be achieved via pinning control. Finally, some
illustrative examples are given to show the validity of the theoretical results. 相似文献
2.
Noise-induced complete synchronization and frequency synchronization in coupled spiking and bursting neurons are studied firstly.
The effects of noise and coupling are discussed. It is found that bursting neurons are easier to achieve firing synchronization
than spiking ones, which means that bursting activities are more important for information transfer in neuronal networks.
Secondly, the effects of noise on firing synchronization in a noisy map neuronal network are presented. Noise-induced synchronization
and temporal order are investigated by means of the firing rate function and the order index. Firing synchronization and temporal
order of excitatory neurons can be greatly enhanced by subthreshold stimuli with resonance frequency. Finally, it is concluded
that random perturbations play an important role in firing activities and temporal order in neuronal networks. 相似文献
3.
The influence of unreliable synapses on the dynamic properties of a neural network is investigated for a homogeneous integrate-and-fire
network with delayed inhibitory synapses. Numerical and analytical calculations show that the network relaxes to a state with
dynamic clusters of identical size which permanently exchange neurons. We present analytical results for the number of clusters
and their distribution of firing times which are determined by the synaptic properties. The number of possible configurations
increases exponentially with network size. In addition to states with a maximal number of clusters, metastable ones with a
smaller number of clusters survive for an exponentially large time scale. An externally excited cluster survives for some
time, too, thus clusters may encode information. 相似文献
4.
In this paper, the synchronization problem for delayed continuous time nonlinear complex neural networks is considered. The
delay dependent state feed back synchronization gain matrix is obtained by considering more general case of time-varying delay.
Using Lyapunov stability theory, the sufficient synchronization criteria are derived in terms of Linear Matrix Inequalities
(LMIs). By decomposing the delay interval into multiple equidistant subintervals, Lyapunov-Krasovskii functionals (LKFs) are
constructed on these intervals. Employing these LKFs, new delay dependent synchronization criteria are proposed in terms of
LMIs for two cases with and without derivative of time-varying delay. Numerical examples are illustrated to show the effectiveness
of the proposed method. 相似文献
5.
Inspired by the temporal correlation theory of brain functions, researchers have presented a number of neural oscillator networks
to implement visual scene segmentation problems. Recently, it is shown that many biological neural networks are typical small-world
networks. In this paper, we propose and investigate two small-world models derived from the well-known LEGION (locally excitatory
and globally inhibitory oscillator network) model. To form a small-world network, we add a proper proportion of unidirectional
shortcuts (random long-range connections) to the original LEGION model. With local connections and shortcuts, the neural oscillators
can not only communicate with neighbors but also exchange phase information with remote partners. Model 1 introduces excitatory
shortcuts to enhance the synchronization within an oscillator group representing the same object. Model 2 goes further to
replace the global inhibitor with a sparse set of inhibitory shortcuts. Simulation results indicate that the proposed small-world
models could achieve synchronization faster than the original LEGION model and are more likely to bind disconnected image
regions belonging together. In addition, we argue that these two models are more biologically plausible. 相似文献
6.
Y Salu 《Bio Systems》1985,18(1):93-103
Our environment consists of virtually an infinite number of scenarios in which we have to function. In order to respond properly to an incoming stimulus, the brain has first to analyze it, and to find out the basic familiar elements that are part of it. In other words, by using a library which contains a relatively small number of basic concepts, the brain analyzes the multitude of incoming events. Some of those basic concepts are innate, but many of them must be learned, in order to accommodate for the arbitrary environment around us. A classifying box is defined as the neural network that finds out the familiar concepts that are present in an incoming stimulus. Models for classifying boxes are introduced, and possible mechanisms by which they may establish their libraries of concepts are suggested, and then compared and evaluated by computer simulations. 相似文献
7.
We developed a multicellular model characterized by a high degree of heterogeneity to investigate possible mechanisms that underlie circadian network synchronization and rhythmicity in the suprachiasmatic nucleus (SCN). We populated a two-dimensional grid with 400 model neurons coupled via γ-aminobutyric acid (GABA) and vasoactive intestinal polypeptide (VIP) neurotransmitters through a putative Ca2+ mediated signaling cascade to investigate their roles in gene expression and electrical firing activity of cell populations. As observed experimentally, our model predicted that GABA would affect the amplitude of circadian oscillations but not synchrony among individual oscillators. Our model recapitulated experimental findings of decreased synchrony and average periods, loss of rhythmicity, and reduced circadian amplitudes as VIP signaling was eliminated. In addition, simulated increases of VIP reduced periodicity and synchrony. We therefore postulated a physiological range of VIP within which the system is able to produce sustained and synchronized oscillations. Our model recapitulated experimental findings of diminished amplitudes and periodicity with decreasing intracellular Ca2+ concentrations, suggesting that such behavior could be due to simultaneous decrease of individual oscillation amplitudes and population synchrony. Simulated increases in Cl− levels resulted in increased Cl− influx into the cytosol, a decrease of inhibitory postsynaptic currents, and ultimately a shift of GABA-elicited responses from inhibitory to excitatory. The simultaneous reduction of IPSCs and increase in membrane resting potential produced GABA dose-dependent increases in firing rates across the population, as has been observed experimentally. By integrating circadian gene regulation and electrophysiology with intracellular and intercellular signaling, we were able to develop the first (to our knowledge) multicellular model that allows the effects of clock genes, electrical firing, Ca2+, GABA, and VIP on circadian system behavior to be predicted. 相似文献
8.
We propose a quantitative model for human smooth pursuit tracking of a continuously moving visual target which is based on
synchronization of an internal expectancy model of the target position coupled to the retinal target signal. The model predictions
are tested in a smooth circular pursuit eye tracking experiment with transient target blanking of variable duration. In subjects
with a high tracking accuracy, the model accounts for smooth pursuit and repeatedly reproduces quantitatively characteristic
patterns of the eye dynamics during target blanking. In its simplest form, the model has only one free parameter, a coupling
constant. An extended model with a second parameter, a time delay or memory term, accounts for predictive smooth pursuit eye
movements which advance the target. The model constitutes an example of synchronization of a complex biological system with
perceived sensory signals.
Cognitive and Neurobiological Research Consortium in Traumatic Brain Injury (CNRC-TBI). 相似文献
9.
This paper investigates drive-response synchronization for a class of
neural networks with time-varying discrete and distributed delays (mixed delays) as
well as discontinuous activations. Strict mathematical proof shows the global
existence of Filippov solutions to neural networks with discontinuous activation
functions and the mixed delays. State feedback controller and impulsive controller
are designed respectively to guarantee global exponential synchronization of the
neural networks. By using Lyapunov function and new analysis techniques, several new
synchronization criteria are obtained. Moreover, lower bound on the convergence rate
is explicitly estimated when state feedback controller is utilized. Results of this
paper are new and some existing ones are extended and improved. Finally, numerical
simulations are given to verify the effectiveness of the theoretical results. 相似文献
10.
Cessac B 《Journal of mathematical biology》2008,56(3):311-345
We derive rigorous results describing the asymptotic dynamics of a discrete time model of spiking neurons introduced in Soula
et al. (Neural Comput. 18, 1, 2006). Using symbolic dynamic techniques we show how the dynamics of membrane potential has a one to one correspondence
with sequences of spikes patterns (“raster plots”). Moreover, though the dynamics is generically periodic, it has a weak form
of initial conditions sensitivity due to the presence of a sharp threshold in the model definition. As a consequence, the
model exhibits a dynamical regime indistinguishable from chaos in numerical experiments.
相似文献
11.
In this paper, local synchronization is considered for coupled delayed neural networks with discontinuous activation functions.
Under the framework of Filippov solution and in the sense of generalized derivative, a novel sufficient condition is obtained
to ensure the synchronization based on the Lyapunov exponent and the detailed analysis in Danca (Int J Bifurcat Chaos 12(8):1813–1826,
2002; Chaos Solitons Fractals 22:605–612, 2004). Simulation results are given to illustrate the theoretical results. 相似文献
12.
Yunkai Lu Palgun Reddy Pulasani Reza Derakhshani Trent M. Guess 《Biomedical signal processing and control》2013,8(6):475-482
Traditional finite element (FE) analysis is computationally demanding. The computational time becomes prohibitively long when multiple loading and boundary conditions need to be considered such as in musculoskeletal movement simulations involving multiple joints and muscles. Presented in this study is an innovative approach that takes advantage of the computational efficiency of both the dynamic multibody (MB) method and neural network (NN) analysis. A NN model that captures the behavior of musculoskeletal tissue subjected to known loading situations is built, trained, and validated based on both MB and FE simulation data. It is found that nonlinear, dynamic NNs yield better predictions over their linear, static counterparts. The developed NN model is then capable of predicting stress values at regions of interest within the musculoskeletal system in only a fraction of the time required by FE simulation. 相似文献
13.
14.
Back-propagation, feed-forward neural networks are used to predict the secondary structures of membrane proteins whose structures are known to atomic resolution. These networks are trained on globular proteins and can predict globular protein structures having no homology to those of the training set with correlation coefficients (C) of 0.45, 0.32 and 0.43 for a-helix, -strand and random coil structures, respectively. When tested on membrane proteins, neural networks trained on globular proteins do, on average, correctly predict (Qi) 62%, 38% and 69% of the residues in the -helix, -strand and random coil structures. These scores rank higher than those obtained with the currently used statistical methods and are comparable to those obtained with the joint approaches tested so far on membrane proteins. The lower success score for -strand as compared to the other structures suggests that the sample of -strand patterns contained in the training set is less representative than those of a-helix and random coil. Our analysis, which includes the effects of the network parameters and of the structural composition of the training set on the prediction, shows that regular patterns of secondary structures can be successfully extrapolated from globular to membrane proteins.
Correspondence to: R. Casadio 相似文献
15.
This paper is concerned with the stability analysis for neural networks with interval time-varying delays and parameter uncertainties.
An approach combining the Lyapunov-Krasovskii functional with the differential inequality and linear matrix inequality techniques
is taken to investigate this problem. By constructing a new Lyapunov-Krasovskii functional and introducing some free weighting
matrices, some less conservative delay-derivative-dependent and delay-derivative-independent stability criteria are established
in term of linear matrix inequality. And the new criteria are applicable to both fast and slow time-varying delays. Three
numerical examples show that the proposed criterion are effective and is an improvement over some existing results in the
literature. 相似文献
16.
Lo JT 《Cognitive neurodynamics》2010,4(4):295-313
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. 相似文献
17.
In this paper, we study general protocell models aiming to understand the synchronization phenomenon of genetic material and container productions, a necessary condition to ensure sustainable growth in protocells and eventually leading to Darwinian evolution when applied to a population of protocells.Synchronization has been proved to be an emergent property in many relevant protocell models in the class of the so-called surface reaction models, assuming both linear- and non-linear dynamics for the involved chemical reactions. We here extend this analysis by introducing and studying a new class of models where the relevant chemical reactions are assumed to occur inside the protocell, in contrast with the former model where the reaction site was the external surface.While in our previous studies, the replicators were assumed to compete for resources, without any direct interaction among them, we here improve both models by allowing linear interaction between replicators: catalysis and/or inhibition. Extending some techniques previously introduced, we are able to give a quite general analytical answer about the synchronization phenomenon in this more general context. We also report on results of numerical simulations to support the theory, where applicable, and allow the investigation of cases which are not amenable to analytical calculations. 相似文献
18.
Neural progenitor cells and developing neurons show periodic, synchronous Ca2+ rises even before synapse formation, and the origin of the synchronous activity remains unknown. Here, fluorescence measurement revealed that the membrane potential of the nuclear envelope, which forms an intracellular Ca2+ store, changed with a release of Ca2+ and generated spontaneous, periodic bursts of fluctuations in potential. Furthermore, changes in the nuclear envelope’s potential underlay spike burst generations. These results support the model that voltage fluctuations of the nuclear envelope synchronize Ca2+ release between cells and also function as a current noise generator to cause synchronous burst discharges. 相似文献
19.
Astrocytes can sense local synaptic release of glutamate by metabotropic glutamate receptors. Receptor activation in turn can mediate transient increases of astrocytic intracellular calcium concentration through inositol 1,4,5-trisphosphate production. Notably, the perturbation of calcium concentration can propagate to other adjacent astrocytes. Astrocytic calcium signaling can therefore be linked to synaptic information transfer between neurons. On the other hand, astrocytes can also modulate neuronal activity by feeding back onto synaptic terminals in a fashion that depends on their intracellular calcium concentration. Thus, astrocytes can also be active partners in neuronal network activity. The aim of our study is to provide a computationally simple network model of mutual neuron–astrocyte interactions, in order to investigate the possible roles of astrocytes in neuronal network dynamics. In particular, we focus on the information entropy of neuronal firing of the whole network, considering how it could be affected by neuron–glial interactions. 相似文献
20.
Nykamp DQ 《Journal of mathematical biology》2009,59(2):147-173
We present an analysis of interactions among neurons in stimulus-driven networks that is designed to control for effects from
unmeasured neurons. This work builds on previous connectivity analyses that assumed connectivity strength to be constant with
respect to the stimulus. Since unmeasured neuron activity can modulate with the stimulus, the effective strength of common
input connections from such hidden neurons can also modulate with the stimulus. By explicitly accounting for the resulting
stimulus-dependence of effective interactions among measured neurons, we are able to remove ambiguity in the classification
of causal interactions that resulted from classification errors in the previous analyses. In this way, we can more reliably
distinguish causal connections among measured neurons from common input connections that arise from hidden network nodes.
The approach is derived in a general mathematical framework that can be applied to other types of networks. We illustrate
the effects of stimulus-dependent connectivity estimates with simulations of neurons responding to a visual stimulus.
This research was supported by the National Science Foundation grants DMS-0415409 and DMS-0748417. 相似文献