Sharif Digital Repository

Due to the growing demand for large memories, using emerging technologies such as Phase Change Memories (PCM) are inevitable. PCM with appropriate scalability, power consumption and multiple bits per cell storage capability is a probable candidate for substituting DRAM. Although storing multiple bits per cell seems to be a rational response to large memory demands, there is a significant problem to achieve this goal. Resistance drift problem is an important reliability concern that is coupled to a multi-level cell PCM (MLC PCM) memory system. In this paper, we propose a memory system architecture that, by exploiting the benefits of compression, converts resistance drift prone blocks to drift... 

Many real-world networks show community structure characterized by dense intra-community connections and sparse inter-community links. In this paper we investigated the synchronization properties of such networks. In this work we constructed such networks in a way that they consist of a number of communities with scale-free or small-world structure. Furthermore, with a probability, the intra-community connections are rewired to inter-community links. Two synchronizability measures were considered as the eigenratio of the Laplacian matrix and the phase order parameter obtained for coupled nonidentical Kuramoto oscillators. We found a power-law relation between the eigenratio and the... 

Local circuits in the cortex and hippocampus are endowed with resonant, oscillatory firing properties which underlie oscillations in various frequency ranges (e.g. gamma range) frequently observed in the local field potentials, and in electroencephalography. Synchronized oscillations are thought to play important roles in information binding in the brain. This paper addresses the collective behavior of interacting locally synchronized oscillations in realistic neural networks. A network of five neurons is proposed in order to produce locally synchronized oscillations. The neuron models are Hindmarsh-Rose type with electrical and/or chemical couplings. We construct large-scale models using... 

Random geographical networks are realistic models for wireless sensor networks which are used in many applications. Achieving average consensus is very important in sensor networks and the faster the consensus is, the durable the sensors' life, and thus, the better the performance of the network. In this paper we compared the performance of a number of linear consensus algorithms with application to distributed averaging in random geographical networks. Interestingly, the simplest algorithm - where only the degree of receiving nodes is needed for the averaging - had the best performance in terms of the consensus time. Furthermore, we proved that the network has guaranteed convergence with... 

Neuronal synchronization plays an important role in the various functionality of nervous system such as binding, cognition, information processing, and computation. In this paper, we investigated how random and intentional failures in the nodes of a network influence its phase synchronization properties. We considered both artificially constructed networks using models such as preferential attachment, Watts-Strogatz, and Erdo{combining double acute accent} s-Rényi as well as a number of real neuronal networks. The failure strategy was either random or intentional based on properties of the nodes such as degree, clustering coefficient, betweenness centrality, and vulnerability. Hindmarsh-Rose... 

Neuronal synchronization plays an important role in the various functionality of nervous system such as binding, cognition, information processing, and computation. In this paper, we investigated how random and intentional failures in the nodes of a network influence its phase synchronization properties. We considered both artificially constructed networks using models such as preferential attachment, Watts-Strogatz, and Erdos-Rényi as well as a number of real neuronal networks. The failure strategy was either random or intentional based on properties of the nodes such as degree, clustering coefficient, betweenness centrality, and vulnerability. Hindmarsh-Rose model was considered as the... 

Temporal synchronization of neuronal activity plays an important role in various brain functions such as binding, cognition, information processing, and computation. Patients suffering from disorders such as Alzheimer’s disease or schizophrenia show abnormality in the synchronization patterns. Electroencephalography (EEG) is a cheap, non-invasive, and easy-to-use method with fine temporal resolution. Modern multichannel EEG data are increasingly being used in brain studies. Traditional approaches for identifying synchronous activity in EEG are through univariate techniques such as power spectral density or bivariate techniques such as coherence. In this paper, we review two methods for... 

In this letter, a model for growing highly synchronizable networks is introduced. The model is based on preferential attachment in which the new nodes tip to old ones in a way to maximize the synchronization properties of the network. Criteria based on the eigenvectors corresponding to the second smallest and the largest eigenvalues of the Laplacian matrix of the connection graph were used to choose the old nodes to which a new node makes connection. Numerical simulations showed that these networks have considerably lower eigenratio (the largest eigenvalue of the Laplacian divided by the second smallest one), and hence better synchronizability, as compared to standard Barabási-Albert (BA)... 

In this paper we investigate the effects of social power on the evolution of opinions in model networks as well as in a number of real social networks. A continuous opinion formation model is considered and the analysis is performed through numerical simulation. Social power is given to a proportion of agents selected either randomly or based on their degrees. As artificial network structures, we consider scale-free networks constructed through preferential attachment and Watts-Strogatz networks. Numerical simulations show that scale-free networks with degree-based social power on the hub nodes have an optimal case where the largest number of the nodes reaches a consensus. However, given... 

In this paper, we investigated phase synchronization in delayed dynamical networks. Non-identical spiking Hindmarsh-Rose neurons were considered as individual dynamical systems and coupled through a number of network structures such as scale-free, Erdos-Rényi, and modular. The individual neurons were coupled through excitatory chemical synapses with uniform or distributed time delays. The profile of spike phase synchrony was different when the delay was uniform across the edges as compared to the case when it was distributed, i.e., different delays for the edges. When an identical transmission delay was considered, a quasi-periodic pattern was observed in the spike phase synchrony. There... 

In this paper the robustness of network synchronizability against random deletion of nodes, i.e. errors, in dynamical scale-free networks was studied. To this end, two measures of network synchronizability, namely, the eigenratio of the Laplacian and the order parameter quantifying the degree of phase synchrony were adopted, and the synchronizability robustness on preferential attachment scale-free graphs was investigated. The findings revealed that as the network size decreases, the robustness of its synchronizability against random removal of nodes declines, i.e. the more the number of randomly removed nodes from the network, the worse its synchronizability. We also showed that this... 

Although diffusive electrical connections in neuronal networks are instantaneous, excitatory/inhibitory couplings via chemical synapses encompass a transmission time-delay. In this paper neural networks with instantaneous electrical couplings and time-delayed excitatory/inhibitory chemical connections are considered and scaling of the spike phase synchronization with the unified time-delay in the network is investigated. The findings revealed that in both excitatory and inhibitory chemical connections, the phase synchronization could be enhanced by introducing time-delay. The role of the variability of the neuronal external current in the phase synchronization is also investigated. As... 

In this paper, we review the literature on enhancing synchronizability of diffusively coupled dynamical networks with identical nodes. The last decade has witnessed intensive investigations on the collective behavior over complex networks and synchronization of dynamical systems is the most common form of collective behavior. For many applications, it is desired that the synchronizability - the ability of networks in synchronizing activity of their individual dynamical units - is enhanced. There are a number of methods for improving the synchronization properties of dynamical networks through structural perturbation. In this paper, we survey such methods including adding/removing nodes... 

In this paper we study how the meso-scale and micro-scale electroencephalography (EEG) synchronization measures can be used for discriminating patients suffering from Alzheimer's disease (AD) from normal control subjects. To this end, two synchronization measures, namely power spectral density and multivariate phase synchronization, are considered and the topography of the changes in patients vs. Controls is shown. The AD patients showed increased power spectral density in the frontal area in theta band and widespread decrease in the higher frequency bands. It was also characterized with decreased multivariate phase synchronization in the left fronto-temporal and medial regions, which was... 

Social networks are inevitable parts of daily life and there has been an increasing interest in analyzing social phenomena on networked structures. Evolution of opinion formation is one of the topics that has attracted many scholars in the field. In this work we consider the influence of leaders and social power in the evolution of opinion formation. A number of central nodes with specific properties (e.g. nodes with highest degree, betweenness or vulnerability values) are taken as leaders whose opinions are kept unchanged, that is, not influenced by other agents. The leaders try to coordinate the opinions of other agents where the connection structure is considered to be preferential... 

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. Network representation of EEG time series can be an efficient vehicle to understand the underlying mechanisms of brain function. Brain functional networks whose nodes are brain regions and edges correspond to functional links between them are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which graph theory metrics are sex dependent. To this end, EEGs from 24 healthy female subjects and 21 healthy male subjects were recorded in eyes-closed resting state conditions. The connectivity matrices were extracted using... 

Sensor networks are increasingly employed in many applications ranging from environmental to military cases. The network topology used in many sensor network applications has a kind of geographical structure. A graph weighting method for reducing consensus time in random geographical networks is proposed in this paper. We consider a method based on the mutually coupled oscillators for providing general consensus in the network. In this way, one can relate the consensus time to the properties of the Laplacian matrix of the connection graph, i.e. to the second smallest eigenvalue (algebraic connectivity). Our weighting algorithm is based on the node and edge between centrality measures. The... 

Network tools have been extensively applied to study the properties of brain functional and anatomical networks. In this paper, resiliency of Caenorhabditis elegans cortical networks against cascaded failures is studied. To this end, directed network formed by chemical connections and undirected network formed by electrical couplings through gap junctions are considered. Furthermore, two types of C. elegans networks are studied: the whole cortical network of the hermaphrodite type and the network of the posterior cortex in male C. elegans. The results show that resiliency of hermaphrodite and male networks is different. The male cortical network of chemical synapses shows extensively weaker... 

Neuronal temporal synchronization is one of the key issues in studying binding phenomenon in neural systems. In this paper we consider identical Hindmarsh-Rose neurons coupled over Newman-Watts small-world networks and investigate to what extent the numerical and analytic synchronizing coupling strengths are different. We use the master-stability-function approach to determine the unified coupling strength necessary for analytic synchronization. We also solve the network's differential equations numerically and track the synchronization error and consequently determine the numerical synchronizing coupling parameters. Then, we compare these two values and investigate the influence of various...