Abstruct -A digital architecture which uses stochastic logic for simulating the behavior of Hopfield neural networks is described. This stochastic architecture provides mussiw paruf/e/ism (since stochastic logic is very space efficient), reprogrammability (since synaptic weights are stored in digital shift registers), large dynumic runge (by using either fixed or floating-point weights), unneul...

Homophily and stochastic equivalence are two primary features of interest in social networks. Recently, the multiplicative latent factor model (MLFM) is proposed to model social networks with directed links. Although MLFM can capture stochastic equivalence, it cannot model well homophily in networks. However, many real-world networks exhibit homophily or both homophily and stochastic equivalenc...

Molecular communication networks consist of transmitters and receivers distributed in a fluid medium. The communication in these networks is realised by the transmitters emitting signalling molecules, which are diffused in the medium to reach the receivers. This paper investigates the properties of noise, or the variance of the receiver output, in molecular communication networks. The noise in ...

Continuous-time Markov chains are used extensively to analyze the performance of various computer networks. However, constructing and solving continuous-time Markov chain is a tedious and error-prone procedure, especially when the studied systems are complex. Stochastic Petri nets and the corresponding software packages provide automated generation and solution to continuous-time Markov chains....

A stochastic flow network is a directed graph with incoming edges (inputs) and outgoing edges (outputs), tokens enter through the input edges, travel stochastically in the network and can exit the network through the output edges. Each node in the network is a splitter, namely, a token can enter a node through an incoming edge and exit on one of the output edges according to a predefined probab...

This paper presents a theoretical design of how a minimax equilibrium of differential game is achieved in stochastic cellular neural networks. In order to realize the equilibrium, two opposing players are selected for the model of stochastic cellular neural networks. One is the vector of external inputs and the other is the vector of internal noises. The design procedure follows the nonlinear H...

Deterministic neural networks such as backpropagation of error, multilayer perceptrons, and locally based radial basis methods have been a major focus of the neural network community in recent years. However, there has been a distinct, albeit less pronounced, interest in stochastic neural networks. In this review we provide the reader with a sense of the defining components of a stochastic neur...

The global dynamics of gene regulatory networks are known to show robustness to perturbations in the form of intrinsic and extrinsic noise, as well as mutations of individual genes. One molecular mechanism underlying this robustness has been identified as the action of so-called microRNAs that operate via feedforward loops. We present results of a computational study, using the modeling framewo...

The following analytical approaches: queuing network models, stochastic automata networks, stochastic timed Petri nets, stochastic process algebra, Markov chains can be used in performance evaluation of multi–agent systems. In this paper, new approach which is based on PERT networks is presented. This approach is applied in performance evaluation of layered multi–agent system. Time-out mechanis...

Structural and behavioral parameters of many real networks such as social networks are unpredictable, uncertain and time varying parameters and for these reasons deterministic graphs for modeling such networks are too restrictive to solve most of the real network problems. It seems that stochastic graphs, in which weights associated to the vertices are random variable, may be a better graph mod...