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At least 793 records · Page 44Linked to original sources
PubMed2000

Computational dynamics of gradient bistable networks.

We describe a neural-like, homogeneous network consisting of coupled bistable elements and we study its abilities of learning, pattern recognition and computation. The technique allows new possibilities of pattern recognition, including the memorization and perfect recall of several memory patterns, without interference from spurious states. When the coupling strength between elements exceeds a critical value, the network readily converges to a unique attractor. Below this critical value one could perfectly recall all memorized patterns.

Amino Acid Sequence↗
PubMed1995

"Dynamical confinement" in neural networks and cell cycle.

In this paper randomization of well-known former mathematical models is proposed (i.e., the Hopfield model for neural networks and the Hahn model for the cell cycle) in order to facilitate the study of their asymptotic behavior: in fact, we replace the determination of the stability basins for attractors and boundaries by the study of a unique (or a small number of) invariant measure(s), whose distribution function maxima (or, respectively, percentile contour lines) correspond to the location of the attractors (or, respectively, boundaries of their stability basins). We give the name of "confinement" to this localization of the mass of the invariant measure(s). We intend to show here that the study of the confinement is in certain cases easier than the study of underlying attractors, in particular if these last are numerous and possess small stability basins (for example, for the first time we calculate the invariant measure in the random Hopfield model in a case for which the deterministic version exhibits many attractors, and in a case of phase transition). (c) 1995 American Institute of Physics.

Journal Article↗
PubMed2004

Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data.

MOTIVATION: High-throughput technologies have facilitated the acquisition of large genomics and proteomics datasets. However, these data provide snapshots of cellular behavior, rather than help us reveal causal relations. Here, we propose how these technologies can be utilized to infer the topology and strengths of connections among genes, proteins and metabolites by monitoring time-dependent responses of cellular networks to experimental interventions. RESULTS: We demonstrate that all connections leading to a given network node, e.g. to a particular gene, can be deduced from responses to perturbations none of which directly influences that node, e.g. using strains with knock-outs to other genes. To infer all interactions from stationary data, each node should be perturbed separately or in combination with other nodes. Monitoring time series provides richer information and does not require perturbations to all nodes. Overall, the methods we propose are capable of deducing and quantifying functional interactions within and across cellular gene, signaling and metabolic networks. SUPPLEMENTARY INFORMATION: Supplementary material is available at http://www.dbi.tju.edu/bioinformatics2004.pdf

Algorithms↗
PubMed2004

A dynamic shift of neural network activity before and after learning-set formation.

Learning-set (LS) is a property of insight and hypothesis testing characterized by the ability to solve novel problems based on previous experiences with problem solving. However, the neural organization and mechanisms underlying LS remain unclear. To further characterize this process, positron emission tomography (PET) studies with [15O]H2O were performed to measure regional cerebral blood flow (rCBF) during the learning phase of the two-choice visual discrimination task under the LS paradigm in rhesus monkeys. When comparing studies before and after LS formation, the orbitofrontal and lateral prefrontal cortices were differentially activated, and functional connections between these structures and the striatum, which contributes to habit learning, were altered. We conclude that changes in the lateral prefrontal cortex during problem solving may contribute to the executive function of working memory and also inhibit control of a primitive learning system, thereby promoting LS formation.

Animals↗
PubMed2005

Spectral analysis and the dynamic response of complex networks.

The eigenvalues and eigenvectors of the connectivity matrix of complex networks contain information about its topology and its collective behavior. In particular, the spectral density rho(lambda) of this matrix reveals important network characteristics: random networks follow Wigner's semicircular law whereas scale-free networks exhibit a triangular distribution. In this paper we show that the spectral density of hierarchical networks follows a very different pattern, which can be used as a fingerprint of modularity. Of particular importance is the value rho(0), related to the homeostatic response of the network: it is maximum for random and scale-free networks but very small for hierarchical modular networks. It is also large for an actual biological protein-protein interaction network, demonstrating that the current leading model for such networks is not adequate.

Journal Article↗
PubMed2006

Dynamics of moment neuronal networks.

A theoretical framework is developed for moment neuronal networks (MNNs). Within this framework, the behavior of the system of spiking neurons is specified in terms of the first- and second-order statistics of their interspike intervals, i.e., the mean, the variance, and the cross correlations of spike activity. Since neurons emit and receive spike trains which can be described by renewal--but generally non-Poisson--processes, we first derive a suitable diffusion-type approximation of such processes. Two approximation schemes are introduced: the usual approximation scheme (UAS) and the Ornstein-Uhlenbeck scheme. It is found that both schemes approximate well the input-output characteristics of spiking models such as the IF and the Hodgkin-Huxley models. The MNN framework is then developed according to the UAS scheme, and its predictions are tested on a few examples.

Action Potentials↗