Inverse stochastic resonance in modular neural network with synaptic plasticity

This work explores the inverse stochastic resonance(ISR) induced by bounded noise and the multiple inverse stochastic resonance induced by time delay by constructing a modular neural network, where the modified Oja’s synaptic learning rule is employed to characterize synaptic plasticity in this network. Meanwhile, the effects of synaptic plasticity on the ISR dynamics are investigated. Through numerical simulations, it is found that the mean firing rate curve under the influence of bounded noise has an inverted bell-like shape, which implies the appearance of ISR. Moreover, synaptic plasticity with smaller learning rate strengthens this ISR phenomenon, while synaptic plasticity with larger learning rate weakens or even destroys it. On the other hand, the mean firing rate curve under the influence of time delay is found to exhibit a decaying oscillatory process, which represents the emergence of multiple ISR. However, the multiple ISR phenomenon gradually weakens until it disappears with increasing noise amplitude. On the same time, synaptic plasticity with smaller learning rate also weakens this multiple ISR phenomenon, while synaptic plasticity with larger learning rate strengthens it. Furthermore, we find that changes of synaptic learning rate can induce the emergence of ISR phenomenon. We hope these obtained results would provide new insights into the study of ISR in neuroscience.

Prediction of NO_(x)concentration using modular long short-term memory neural network for municipal solid waste incineration

Air pollution control poses a major problem in the implementation of municipal solid waste incineration(MSWI).Accurate prediction of nitrogen oxides(NO_(x))concentration plays an important role in efficient NO_(x)emission controlling.In this study,a modular long short-term memory(M-LSTM)network is developed to design an efficient prediction model for NO_(x)concentration.First,the fuzzy C means(FCM)algorithm is utilized to divide the task into several sub-tasks,aiming to realize the divide-and-conquer ability for complex task.Second,long short-term memory(LSTM)neural networks are applied to tackle corresponding sub-tasks,which can improve the prediction accuracy of the sub-networks.Third,a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application stage.Finally,after being evaluated by a benchmark simulation,the proposed method is applied to a real MSWI process.And the experimental results demonstrate the considerable prediction ability of the M-LSTM network.

MNN-XSS:Modular Neural Network Based Approach for XSS Attack Detection

The rapid growth and uptake of network-based communication technologies have made cybersecurity a significant challenge as the number of cyber-attacks is also increasing.A number of detection systems are used in an attempt to detect known attacks using signatures in network traffic.In recent years,researchers have used different machine learning methods to detect network attacks without relying on those signatures.The methods generally have a high false-positive rate which is not adequate for an industry-ready intrusion detection product.In this study,we propose and implement a new method that relies on a modular deep neural network for reducing the false positive rate in the XSS attack detection system.Experiments were performed using a dataset consists of 1000 malicious and 10000 benign sample.The model uses 50 features selected by using Pearson correlation method and will be used in the detection and preventions of XSS attacks.The results obtained from the experiments depict improvement in the detection accuracy as high as 99.96%compared to other approaches.

Aeromagnetic Compensation Algorithm Based on Levenberg-Marquard Neural Network

The magnetic compensation of aeromagnetic survey is an important calibration work,which has a great impact on the accuracy of measurement.In an aeromagnetic survey flight,measurement data consists of diurnal variation,aircraft maneuver interference field,and geomagnetic field.In this paper,appropriate physical features and the modular feedforward neural network(MFNN)with Levenberg-Marquard(LM)back propagation algorithm are adopted to supervised learn fluctuation of measuring signals and separate the interference magnetic field from the measurement data.LM algorithm is a kind of least square estimation algorithm of nonlinear parameters.It iteratively calculates the jacobian matrix of error performance and the adjustment value of gradient with the regularization method.LM algorithm’s computing efficiency is high and fitting error is very low.The fitting performance and the compensation accuracy of LM-MFNN algorithm are proved to be much better than those of TOLLES-LAWSON(T-L)model with the linear least square(LS)solution by fitting experiments with five different aeromagnetic surveys’data.

Synchronization transition of a modular neural network containing subnetworks of different scales

Time delay and coupling strength are important factors that affect the synchronization of neural networks.In this study,a modular neural network containing subnetworks of different scales was constructed using the Hodgkin–Huxley(HH)neural model;i.e.,a small-scale random network was unidirectionally connected to a large-scale small-world network through chemical synapses.Time delays were found to induce multiple synchronization transitions in the network.An increase in coupling strength also promoted synchronization of the network when the time delay was an integer multiple of the firing period of a single neuron.Considering that time delays at different locations in a modular network may have different effects,we explored the influence of time delays within each subnetwork and between two subnetworks on the synchronization of modular networks.We found that when the subnetworks were well synchronized internally,an increase in the time delay within both subnetworks induced multiple synchronization transitions of their own.In addition,the synchronization state of the small-scale network affected the synchronization of the large-scale network.It was surprising to find that an increase in the time delay between the two subnetworks caused the synchronization factor of the modular network to vary periodically,but it had essentially no effect on the synchronization within the receiving subnetwork.By analyzing the phase difference between the two subnetworks,we found that the mechanism of the periodic variation of the synchronization factor of the modular network was the periodic variation of the phase difference.Finally,the generality of the results was demonstrated by investigating modular networks at different scales.

Functional modular organization unfolded by chimera-like dynamics in a large-scale brain network model

The brain is organized as a complex network architecture, which can be mapped into structural(SC) and functional connectivity(FC) by advanced neuroimaging techniques. Achievements in brain network research have revealed that modularity is a universal trait in brain networks and may be vital for cognitive segregation and integration. Large-scale brain network modeling is a promising computational approach to combine neuroimaging data with generative rules for brain dynamics. Recently, it has been proposed that chimera states, a type of dynamics referring to the coexistence of coherent and incoherent participants, have traits in common with cognitive functions like segregated and integrated brain processing. Previous studies have reported the existence of chimera-like dynamics in large-scale brain network models, whereas they did not account for the relationship between chimeralike dynamics and corresponding functional modular organizations of the brain network. By specifying qualitatively different network dynamics in an anatomically-constrained brain network model, we compare the different modular organizations of FC unfolded by network dynamics. Our simulations reveal that chimera-like dynamics support a meaningful pattern of functional modular organization, which promotes a diversity of node roles with a distributed pattern of functional cartography. The distinct node roles in modular FC are also found to occur with a spatial preference in speciflc brain regions, and, to some extent, reflect the underlying structure constraints. Our results support the view that chimera-like dynamics is a functionally meaningful scenario that may play a fundamental role in the segregation and integration of brain functioning.

Collective Computation,Information Flow,and the Emergence of Hunter-Gatherer Small-Worlds

Two key features of human sociality are anatomically complex brains with neuron-dense cerebral cortices,and the propensity to form complex social networks with non-kin.Complex brains and complex social networks facilitate flows of fitness-enhancing energy and information at multiple scales of social organization.Here,we consider how these flows interact to shape the emergence of macroscopic regularities in hunter-gatherer macroecology relative to other mammals and non-human primates.Collective computation is the processing of information by complex adaptive systems to generate inferences in order to solve adaptive problems.In hunter-gatherer societies the adaptive problem is to resolve uncertainty in generative models used to predict complex environments in order to maximize inclusive fitness.The macroecological solution is to link complex brains in social networks to form collective brains that perform collective computations.By developing theory and analyzing data,the author shows hunter-gatherers bands of~16 people,or~4 co-residing families,form the largest collective brains of any social mammal.Moreover,because individuals,families,and bands interact at multiple time scales,these fission-fusion dynamics lead to the emergence of the macroscopic regularities in hunter-gatherer macroecology we observe in cross-cultural data.These results show how computation is distributed across spatially-extended social networks forming decentralized knowledge systems characteristic of hunter-gatherer societies.The flow of information at scales far beyond daily interactions leads to the emergence of small-worlds where highly clustered local interactions are embedded within much larger,but sparsely connected multilevel metapopulations.