http://scientiairanica.sharif.edu/article_2093_39002dbcaa0fc5bb8f4f33b6b189a977.pdf

Hybrid multiple criteria decision making methods: a review of applications in engineering

A major trust of modal parameters identification (MPI) research in recent years has been based on using artificial and natural vibrations sources because vibration measurements can reflect the true dynamic behavior of a structure while analytical prediction methods, such as finite element models, are less accurate due to the numerous structural idealizations and uncertainties involved in the simulations. This paper presents a state-of-the-art review of the time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals as well as the factors that affect the estimation accuracy. Further, the latest signal processing techniques proposed since 2012 are also reviewed. These algorithms are worth being researched for MPI of large real-life structures because they provide good time-frequency resolution and noise-immunity.

/pdf/time-frequency-techniques-for-modal-parameters-4c9b8c24yd.pdf

Time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals

In this work, we investigated the response of a stochastic Hodgkin–Huxley (HH) neuron with an autapse to subthreshold sinusoidal signals. It is found that the autapse not only adjusts the stochastic responses, but also improves the detection of subthreshold signals. In the case of weak noise, the autapse facilitates the response of neuron to the subthreshold sinusoidal signals with a small parameter region in t d e l a y - ω space. The increased noise intensity enlarges this parameter region and increases the corresponding response frequency in such range. As the autaptic intensity increases, however, this parameter region shrunks. We also observed that there is an optimal range of the delay time of autapse, within which the stochastic HH neuron fires action potentials with high frequency. The corresponding response spike train for the optimal delay time is nearly a regular sequence with the interspike intervals approximated to the delay time. The current results reveal a novel resonance phenomenon facilitated by autapse, named autaptic delay-induced coherence resonance.

Response of autaptic Hodgkin–Huxley neuron with noise to subthreshold sinusoidal signals

Communication between neuronal populations is facilitated by synchronization of their oscillatory activity. Although nonlinearity has been observed in the sensorimotor system, its nonlinear connectivity has not been widely investigated yet. This study investigates nonlinear connectivity during the human stretch reflex based on neuronal synchronization. Healthy participants generated isotonic wrist flexion while receiving a periodic mechanical perturbation to the wrist. Using a novel cross-frequency phase coupling metric, we estimate directional nonlinear connectivity, including time delay, from the perturbation to brain and to muscle, as well as from brain to muscle. Nonlinear phase coupling is significantly stronger from the perturbation to the muscle than to the brain, with a shorter time delay. The time delay from the perturbation to the muscle is 33 ms, similar to the reported latency of the spinal stretch reflex at the wrist. Source localization of nonlinear phase coupling from the brain to the muscle suggests activity originating from the motor cortex, although its effect on the stretch reflex is weak. As such nonlinear phase coupling between the perturbation and muscle activity is dominated by the spinal reflex loop. This study provides new evidence of nonlinear neuronal synchronization in the stretch reflex at the wrist joint with respect to spinal and transcortical loops.

Nonlinear Connectivity in the Human Stretch Reflex Assessed by Cross-Frequency Phase Coupling

Whether premotor/motor neurons encode information in terms of spiking frequency or by their relative time of firing, which may display synchronization, is still undetermined. To address this issue, we used an information theory approach to analyze neuronal responses recorded in the premotor (area F5) and primary motor (area F1) cortices of macaque monkeys under four different conditions of visual feedback during hand grasping. To evaluate the sensitivity of spike timing correlation between single neurons, we investigated the stimulus dependent synchronization in our population of pairs. We first investigated the degree of correlation of trial-to-trial fluctuations in response strength between neighboring neurons for each condition, and second estimated the stimulus dependent synchronization by means of an information theoretical approach. We compared the information conveyed by pairs of simultaneously recorded neurons with the sum of information provided by the respective individual cells. The information transmission across pairs of cells in the primary motor cortex seems largely independent, whereas information transmission across pairs of premotor neurons is summed superlinearly. The brain could take advantage of both the accuracy provided by the independency of F1 and the synergy allowed by the superlinear information population coding in F5, distinguishing thus the generalizing role of F5.

Superlinear Summation of Information in Premotor Neuron Pairs

In the retina, the firing behaviors that ganglion cells exhibit when exposed to light stimuli are very important due to the significant roles they play in encoding the visual information. However, the detailed mechanisms, especially the intrinsic properties that generate and modulate these firing behaviors is not completely clear yet. In this study, 2 typical firing behaviors—i.e., tonic and phasic activities, which are widely observed in retinal ganglion cells (RGCs)—are investigated. A modified computational model was developed to explore the possible ionic mechanisms that underlie the generation of these 2 firing patterns. Computational results indicate that the generation of tonic and phasic activities may be attributed to the collective actions of 2 kinds of adaptation currents, i.e., an inactivating sodium current and a delayed-rectifier potassium current. The concentration of magnesium ions has crucial but differential effects in the modulation of tonic and phasic firings, when the model neuron is driven by N-methyl-D-aspartate (NMDA) -type synaptic input instead of constant current injections. The proposed model has robust features that account for the ionic mechanisms underlying the tonic and phasic firing behaviors, and it may also be used as a good candidate for modeling some other firing patterns in RGCs.

/pdf/ionic-mechanisms-underlying-tonic-and-phasic-firing-3dtvsnqvkx.pdf

Ionic mechanisms underlying tonic and phasic firing behaviors in retinal ganglion cells: a model study.

The capability of a biological neuron to discriminate the intensity of external stimulus is measured in its dynamic range. In previous studies, a few factors have been reported to be able to enhance the dynamic range, e.g., electrical coupling and active dendrites. Here we numerically show that intrinsic channel noise within neurons has a subtle effect in neuronal dynamic range modulation. Our simulation results indicate that for relatively weak noise intensity, the dynamic range of the neuron is enhanced significantly. However, as the noise intensity becomes stronger, the dynamic range of the neuron is weakened. Further investigation suggests that sodium channel noise and potassium channel noise play opposite roles in modulating the dynamic range. Consequently, the model results suggest a new function of channel noise, that is, a proper value of noise intensity could optimize the dynamic range of neurons.

/pdf/enhancement-of-the-neuronal-dynamic-range-by-proper-106xd511sh.pdf

Enhancement of the Neuronal Dynamic Range by Proper Intensities of Channel Noise

Effects of spike frequency adaptation (SFA) on the synchronous behavior of population neurons are investigated in electrically coupled networks with a scale-free property. By a computational approach, we corroborate that pairwise correlations between neurons would decrease if neurons exhibit the feature of SFA, which is similar to previous experimental observations. However, unlike the case of pairwise correlations, population activities of neurons show a rather complex variation mode: compared with those of non-adapted neurons, neurons in the networks having weak-degrees of SFA will impair population synchronizations; while neurons exhibiting strong-degrees of SFA will enhance population synchronizations. Moreover, a variation of coupling strength between neurons will not alter this phenomenon significantly, unless the coupling strength is too weak. Our results suggest that synchronous activity of electrically coupled population neurons is adaptation-dependent, and this adaptive feature may imply some coding strategies of neuronal populations.

/pdf/effects-of-spike-frequency-adaptation-on-synchronization-3myf77pw24.pdf

Effects of Spike Frequency Adaptation on Synchronization Transitions in Electrically Coupled Neuronal Networks with Scale-Free Connectivity

A typical feature of neurons is their ability to encode neural information dynamically through spike frequency adaptation (SFA). Previous studies of SFA on neuronal synchronization were mainly concentrated on the correlated firing between neuron pairs, while the synchronization of neuron populations in the presence of SFA is still unclear. In this study, the influence of SFA on the population synchronization of neurons was numerically explored in electrically coupled networks, with regular, small-world, and random connectivity, respectively. The simulation results indicate that cross-correlation indices decrease significantly when the neurons have adaptation compared with those of nonadapting neurons, similar to previous experimental observations. However, the synchronous activity of population neurons exhibits a rather complex adaptation-dependent manner. Specifically, synchronization strength of neuron populations changes nonmonotonically, depending on the degree of adaptation. In addition, single neurons in the networks can switch from regular spiking to bursting with the increase of adaptation degree. Furthermore, the connection probability among neurons exhibits significant influence on the population synchronous activity, but has little effect on the burst generation of single neurons. Accordingly, the results may suggest that synchronous activity and burst firing of population neurons are both adaptation-dependent.

Adaptation-dependent synchronization transitions and burst generations in electrically coupled neural networks.

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Papers

Ionic mechanisms underlying tonic and phasic firing behaviors in retinal ganglion cells: a model study.

Enhancement of the Neuronal Dynamic Range by Proper Intensities of Channel Noise

Effects of Spike Frequency Adaptation on Synchronization Transitions in Electrically Coupled Neuronal Networks with Scale-Free Connectivity

Adaptation-dependent synchronization transitions and burst generations in electrically coupled neural networks.