The stochastic reaction network in which chemical species evolve through a set of reactions is widely used to model processes physics, chemistry and biology. To characterize the evolving joint probability distribution state space counts requires solving system ordinary differential equations, master equation, where size counting increases exponentially with type species. This makes it challengi...

Stochastic Resonance (SR) is a phenomenon observed in nonlinear systems whereby the introduction of noise enhances the detection of subthreshold signals. Both computer simulations and experimental recordings in the hippocampal brain slice have shown that stochastic resonance could play a significant role to enhance the detection of synaptic potentials generated in distal synapses. The noise var...

There is a growing recognition that discrete choice models are capable of providing a more realistic picture of route choice behavior. In particular, influential factors other than travel time that are found to affect the choice of route trigger the application of random utility models in the route choice literature. This paper focuses on path-based, logit-type stochastic route choice models, i...

One of the major challenges in neuroscience is to determine how noise that is present at the molecular and cellular levels affects dynamics and information processing at the macroscopic level of synaptically coupled neuronal populations. Often noise is incorporated into deterministic network models using extrinsic noise sources. An alternative approach is to assume that noise arises intrinsical...

In this paper we address the problem of inferring the topology, or inter-node navigability, of a sensor network given non-discriminating observations of activity in the environment. By exploiting motion present in the environment, our approach is able to recover a probabilistic model of the sensor network connectivity graph and the underlying traffic trends. We employ a reasoning system made up...

We propose an associative learning model using reward modulated spike-time dependent plasticity in reinforcement learning paradigm. The task of learning is to associate a stimulus pair, known as the predictor− choice pair, to a target response. In our model, a generic architecture of neural network has been used, with minimal assumption about the network dynamics. We demonstrate that stimulus-s...

Most current studies of neuronal activity dynamics in cortex are based on network models with completely random wiring. Such models are chosen for mathematical convenience, rather than biological grounds, and additionally reflect the notorious lack of knowledge about the neuroanatomical microstructure. Here, we describe some families of new, more realistic network models and explore some of the...

We investigate whether observed transcription network structures and network motifs are a byproduct of the mechanisms by which DNA strands evolve, or if they are fundamental to the function of the network. We explore this with an evolutionary model with stochastic Boolean network simulation. Structurally distinct regulation strategies are observed in populations evolved with and without interna...

in this work quantitative structure activity relationship (qsar) methodology was applied for modeling and prediction of cellular response to polymers that have been designed for tissue engineering. after calculation and screening of molecular descriptors, linear and nonlinear models were developed by using multiple linear regressions (mlr) and artificial neural network (ann) methods. the root m...