Amplified Signal Response by Neuronal Diversity on Complex Networks

The effect of diversity on dynamics of coupled FitzHugh-Nagumo neurons on complex networks is numerically investigated, where each neuron is subjected to an external subthreshold signal. With the diversity the network is a mixture of excitable and oscillatory neurons, and the diversity is determined by the variance of the system＇s parameter. The complex network is constructed by randomly adding long-range connections （shortcuts） on a nearest-neighbouring coupled one-dimensional chain. Numerical results show that external signals are maximally magnified at an intermediate value of the diversity, as in the case of well-known stochastic resonance, burthermore, the effects of the number of shortcuts and coupled strength on the diversity-induced phenomena are also discussed. These findings exhibit that the diversity may play a constructive role in response to external signal, and highlight the importance of the diversity on such complex networks.     

Double resonance in star networks of bistable units with disordered signals

It has been found that a hub node is better able to amplify weak external signals than leaf nodes in star networks. But hub-enhanced amplification is only attained by a single hub node and is limited to weak coupling strength. We show here that random initial phases in external weak signals do not affect the hub-enhanced amplification at weak coupling strength. Instead, they can improve the responses of all the leaf nodes to external signals at large coupling strength,resulting in a double resonance-like signal amplification. We use a reduced model to analyze the influence of the star structure and random initial phases on the emergence of double resonance.     

Relaxation dynamics and frequency response of a noisy cell signaling network

We investigate the dynamics of cell signaling using an experimentally based Boolean model of the human fibroblast signal transduction network. We determine via systematic numerical simulations the relaxation dynamics of the network in response to a constant set of inputs, both in the absence and in the presence of environmental fluctuations. We then study the network's response to periodically modulated signals, uncovering different types of behaviors for different pairs of driven input and output nodes. The phenomena observed include low-pass, high-pass, and band-pass filtering of the input modulations, among other nontrivial responses, at frequencies around the relaxation frequency of the network. The results reveal that the dynamic response to the external modulation of biologically realistic signaling networks is versatile and robust to noise.     

Opinion Dynamics on Networks under Correlated Disordered External Perturbations

We study an influence network of voters subjected to correlated disordered external perturbations, and solve the dynamical equations exactly for fully connected networks. The model has a critical phase transition between disordered unimodal and ordered bimodal distribution states, characterized by an increase in the vote-share variability of the equilibrium distributions. The fluctuations (variance and correlations) in the external perturbations are shown to reduce the impact of the external influence by increasing the critical threshold needed for the bimodal distribution of opinions to appear. The external fluctuations also have the surprising effect of driving voters towards biased opinions. Furthermore, the first and second moments of the external perturbations are shown to affect the first and second moments of the vote-share distribution. This is shown analytically in the mean field limit, and confirmed numerically for fully connected networks and other network topologies. Studying the dynamic response of complex systems to disordered external perturbations could help us understand the dynamics of a wide variety of networked systems, from social networks and financial markets to amorphous magnetic spins and population genetics.     

Fluctuation Resonance of Feed Forward Loops in Gene Regulatory Networks

应用化学主方程和线性涨落近似方法,重点研究了前馈环路(FFL)对外界输入弱信号的响应,特别考察了它的涨落共振现象．研究发现Z基因的FR行为很大程度上依赖于FFL的协同性：协同FFL中Z的FR曲线呈明显的单峰,而非协同FFL中该曲线出现明显双峰．由于振荡信号常常在实际应用中用来探测网络的调控结构,因此可以利用涨落共振曲线的定性差别来区分FFL网络的性能．     

Lateral predictive coding revisited: internal model,symmetry breaking,and response time

Predictive coding is a promising theoretical framework in neuroscience for understanding information transmission and perception. It posits that the brain perceives the external world through internal models and updates these models under the guidance of prediction errors. Previous studies on predictive coding emphasized top-down feedback interactions in hierarchical multilayered networks but largely ignored lateral recurrent interactions. We perform analytical and numerical investigations in this work on the effects of single-layer lateral interactions. We consider a simple predictive response dynamics and run it on the MNIST dataset of hand-written digits. We find that learning will generally break the interaction symmetry between peer neurons, and that high input correlation between two neurons does not necessarily bring strong direct interactions between them. The optimized network responds to familiar input signals much faster than to novel or random inputs, and it significantly reduces the correlations between the output states of pairs of neurons.     

Response of scale-free networks with community structure to external stimuli

The response of scale-free networks with community structure to external stimuli is studied. By disturbing some nodes with different strategies, it is shown that the robustness of this kind of network can be enhanced due to the existence of communities in the networks. Some of the response patterns are found to coincide with topological communities. We show that such phenomena also occur in the cat brain network which is an example of a scale-free like network with community structure. Our results provide insights into the relationship between network topology and the functional organization in complex networks from another viewpoint.     

All-optical switching and storage in a four-level tripod-type atomic system

The optical behavior of a four-level tripod-type atomic system in a ring resonator driven by a cavity field and two external coherent fields is studied. It is shown that the atomic response exhibits an ultra-sensitive switch from high absorption to nearly transparency by changing the value of one of the control fields. The optical bistable response can be controlled by means of the external fields. The system can flip from the lower to the upper branch of the hysteresis curve without changing the incident probe. Switching and information storage of a light signal are predicted under appropriate triggering of the auxiliary external optical signals.     

Optimal Population Coding for Dynamic Input by Nonequilibrium Networks

The efficient coding hypothesis states that neural response should maximize its information about the external input. Theoretical studies focus on optimal response in single neuron and population code in networks with weak pairwise interactions. However, more biological settings with asymmetric connectivity and the encoding for dynamical stimuli have not been well-characterized. Here, we study the collective response in a kinetic Ising model that encodes the dynamic input. We apply gradient-based method and mean-field approximation to reconstruct networks given the neural code that encodes dynamic input patterns. We measure network asymmetry, decoding performance, and entropy production from networks that generate optimal population code. We analyze how stimulus correlation, time scale, and reliability of the network affect optimal encoding networks. Specifically, we find network dynamics altered by statistics of the dynamic input, identify stimulus encoding strategies, and show optimal effective temperature in the asymmetric networks. We further discuss how this approach connects to the Bayesian framework and continuous recurrent neural networks. Together, these results bridge concepts of nonequilibrium physics with the analyses of dynamics and coding in networks.     

Diversity-induced resonance on weighted scale-free networks

The diversity-induced resonance in weighted scale-free networks is investigated numerically and analytically. The couplings are weighted according to node degree. It is found that self-organized phase shifts occur between large-degree and small-degree nodes, although there is no time delay in coupling terms. Such spontaneous phase shifts are relevant to the improvement of signal resonance amplification. This finding may help to better understand how the collective behavior of individual units promotes the response of the whole system to external signals.     

Vibrational resonance in excitable neuronal systems

In this paper, we investigate the effect of a high-frequency driving on the dynamical response of excitable neuronal systems to a subthreshold low-frequency signal by numerical simulation. We demonstrate the occurrence of vibrational resonance in spatially extended neuronal networks. Different network topologies from single small-world networks to modular networks of small-world subnetworks are considered. It is shown that an optimal amplitude of high-frequency driving enhances the response of neuron populations to a low-frequency signal. This effect of vibrational resonance of neuronal systems depends extensively on the network structure and parameters, such as the coupling strength between neurons, network size, and rewiring probability of single small-world networks, as well as the number of links between different subnetworks and the number of subnetworks in the modular networks. All these parameters play a key role in determining the ability of the network to enhance the outreach of the localized subthreshold low-frequency signal. Considering that two-frequency signals are ubiquity in brain dynamics, we expect the presented results could have important implications for the weak signal detection and information propagation across neuronal systems.     

Excitation Properties of the Biological Neurons with Side-Inhibition Mechanism in Small-World Networks

We have studied the excitation properties of biophysical Hodgkin-Huxley neurons with the side-inhibition mechanism in small-world networks. The result shows that the excitation properties in the networks are preferably consistent with the characteristic properties of a brain neural system under external constant stimuli, such as fatigue effect, extreme excitation principle, and the brain neural excitation response induced by different in- tensity of noise and coupling. The results of the study might shed some light on the study of the brain nerve electrophysiology and epistemological science.     

Synaptic behaviors of a single metal–oxide–metal resistive device

The mammalian brain is far superior to today’s electronic circuits in intelligence and efficiency. Its functions are realized by the network of neurons connected via synapses. Much effort has been extended in finding satisfactory electronic neural networks that act like brains, i.e., especially the electronic version of synapse that is capable of the weight control and is independent of the external data storage. We demonstrate experimentally that a single metal–oxide–metal structure successfully stores the biological synaptic weight variations (synaptic plasticity) without any external storage node or circuit. Our device also demonstrates the reliability of plasticity experimentally with the model considering the time dependence of spikes. All these properties are embodied by the change of resistance level corresponding to the history of injected voltage-pulse signals. Moreover, we prove the capability of second-order learning of the multi-resistive device by applying it to the circuit composed of transistors. We anticipate our demonstration will invigorate the study of electronic neural networks using non-volatile multi-resistive device, which is simpler and superior compared to other storage devices.     

Stochastic resonance in mammalian neuronal networks

We present stochastic resonance observed in the dynamics of neuronal networks from mammalian brain. Both sinusoidal signals and random noise were superimposed into an applied electric field. As the amplitude of the noise component was increased, an optimization (increase then decrease) in the signal-to-noise ratio of the network response to the sinusoidal signal was observed. The relationship between the measures used to characterize the dynamics is discussed. Finally, a computational model of these neuronal networks that includes the neuronal interactions with the electric field is presented to illustrate the physics behind the essential features of the experiment. (c) 1998 American Institute of Physics.     

Zur phänomenologischen linearen Antwort von Onsager-Systemen

The Onsager separability of time-dependent correlation functions is used to construct the linearphenomenological response of large physical systems to an external perturbation. Both the classical and the quantum case are included. Simple electrical networks and the Brownian particle in the harmonic chain are treated as examples.     

Phase Transition in Recovery Process of Complex Networks

The dynamic characteristic of complex network failure and recovery is one of the main research topics in complex networks. Real world systems such as traffic jams and Internet recovery could be described by the complex network theory. We propose a model to study the recovery process in complex networks. Two different recovery mechanisms are considered in three kinds of networks: external recovery and internal recovery. By simulating the process of the nodes recovery in networks, it is found that the system exhibits the feature of first-order phase transition only when the external recovery is considered. Internal recovery cannot induce such a kind of transitions. As external recovery and internal recovery coexist on networks, the systems will retain the most efficient part of external recovery and internal recovery. Meanwhile, a hysteresis could be observed when increasing or decreasing the failure probability. Finally; a largest degree node protection strategy is proposed for improving the robustness of networks.     

TXRF quantification of interfering heavy metals using deconvolution,cross‐correlation,and external standard calibration

We developed a general procedure that includes external standard calibration and a deconvolution of spectral signals, which determined with high accuracy the relative proportion and absolute quantification of elements that habitually overlap in total reflection X‐ray fluorescence spectroscopy. The deconvolution proposed takes advantage of the well‐known method of a cross‐correlation technique, offering improvements in identification of simultaneous signals and in the determination of the relative elemental proportions. The external standard calibration was studied determining its range of application for the elements of interest and implemented in certified standard samples producing excellent results for the quantification of the mentioned elements. In this paper, we show the quality of the results obtained for specific heavy metals of currently health and industrial interest in the range of linear response of 0.2–200 ng. Copyright © 2013 John Wiley & Sons, Ltd.     

Reversal current observed in micro-and submicro-channel flow under non-continuous DC electric field

In practical applications of biochips and bio-sensors, electrokinetic mechanisms are commonly employed to manipulate and analyze the characteristics of single bio-molecules. To accurately and flexibly control the movement of single molecule within micro-/submicro-fluidic channels, the characteristics of current signals at the initial stage of the flow are systematically studied based on a three-electrode system. The current response of micro-/submicro-fluidic channels filled with different electrolyte solutions in non-continuous external electric field are investigated. It is found, there always exists a current reversal phenomenon, which is an inherent property of the current signals in micro/submicro-fluidics Each solution has an individual critical voltage under which the steady current value is equal to zero The interaction between the steady current and external applied voltage follows an exponential function. All these results can be attributed to the overpotentials of the electric double layer on the electrodes. These results are helpful for the design and fabrication of functional micro/nano-scale fluidic sensors and biochips.     

Synthesization of high-capacity auto-associative memories using complex-valued neural networks

In this paper, a novel design procedure is proposed for synthesizing high-capacity auto-associative memories based on complex-valued neural networks with real-imaginary-type activation functions and constant delays. Stability criteria dependent on external inputs of neural networks are derived. The designed networks can retrieve the stored patterns by external inputs rather than initial conditions. The derivation can memorize the desired patterns with lower-dimensional neural networks than real-valued neural networks, and eliminate spurious equilibria of complex-valued neural networks.One numerical example is provided to show the effectiveness and superiority of the presented results.     

High-Q microwave photonic filter with a tuned modulator

We propose the use of tuned electro-optic or electroabsorption external modulators to implement high-quality (high-Q) factor, single-bandpass photonic filters for microwave signals. Using this approach, we experimentally demonstrate a transversal finite impulse response with a Q factor of 237. This is to our knowledge the highest value ever reported for a passive finite impulse-response microwave photonic filter.