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At least 883 records · Page 49Linked to original sources
PubMed1992

Simulating vestibular compensation using recurrent back-propagation.

Vestibular compensation is simulated as learning in a dynamic neural network model of the horizontal vestibulo-ocular reflex (VOR). The bilateral, three-layered VOR model consists of nonlinear units representing horizontal canal afferents, vestibular nuclei (VN) neurons and eye muscle motoneurons. Dynamic processing takes place via commissural connections that link the VN bilaterally. The intact network is trained, using recurrent back-propagation, to produce the VOR with velocity storage integration. Compensation is simulated by removing vestibular afferent input from one side and retraining the network. The time course of simulated compensation matches that observed experimentally. The behavior of model VN neurons in the compensated network also matches real data, but only if connections at the motoneurons, as well as at the VN, are allowed to be plastic. The dynamic properties of real VN neurons in compensated and normal animals are found to differ when tested with sinusoidal but not with step stimuli. The model reproduces these conflicting data, and suggests that the disagreement may be due to VN neuron nonlinearity.

Algorithms↗
PubMed2003

Dynamic presynaptic varicosities: a role in activity-dependent synaptogenesis.

Recent developments in confocal imaging techniques have opened new avenues for investigating how synaptic networks evolve over time. These studies have revealed not only that synaptic structures are motile but also, most importantly, that a fraction of synapses undergo a continuous elimination and formation process, and that these mechanisms are markedly enhanced by activity. Turnover of dendritic spines was recently demonstrated in the somatosensory cortex upon sensory stimulation. De Paola et al. have now provided evidence for correlated remodelling of presynaptic structures. These results, together with others, indicate that activation of neuronal networks enhances dynamic mechanisms at both the presynaptic and the postsynaptic level, resulting in an increased turnover of synapses and, accordingly, a reorganization of synaptic circuitry.

Animals↗
PubMed2006

Dynamic functional tuning of nonlinear cortical networks.

The mammalian neocortex is a highly complex and nonlinear dynamic system. One of its most prominent features is an omnipresent spontaneous neuronal activity. Here the possible functional role of this global background for cognitive flexibility is studied in a prototypic mean-field model area. It is demonstrated that the level of global background current efficiently controls the stimulus-response threshold and the stability and properties of short-term memory states. Moreover, it can dynamically gate arbitrary cortical subnetworks, when applied to parts of the area as a weak bias signal. These results suggest a central functional role of the level of background activation: the dynamic functional tuning of neocortical circuits.

Action Potentials↗
PubMed2026

Co-mutation Based Genetic Networks to Infer Temporal Mutation Dynamics in Ancient Human Mitochondrial Genomes.

The evolutionary history of Homo sapiens is marked by complex interactions between environmental, cultural, and genetic factors. To investigate the molecular signatures of these processes, we analyzed ancient mitochondrial DNA (mtDNA) across temporal and geographic contexts using principles of co-occurrence of minor alleles defined as co-mutation, through spatiotemporal co-mutation networks of variable sites. Haplogroup-based assessments of variable sites revealed a major transition from foraging to agrarian lifestyles during the Copper-Bronze Age. Genetic network analyses demonstrated that COX and CYB loci exhibited distinct temporal dynamics, with their interactions modulated by NADH dehydrogenase genes in a geological age-dependent manner. To complement the network approach, we constructed phylogeny-based gene interaction networks and assessed polymorphism-to-divergence from chimpanzee ratios. The tree-based networks displayed topologies consistent with co-mutation analyses but showed reduced gene-gene connectivity. Polymorphism/divergence analysis further indicated that the CYB gene has been under long-term purifying selection, whereas ATP6, COX, and NADH dehydrogenase genes experienced episodic purifying selection aligned with distinct historical phases. Collectively, our findings demonstrate that network-based analysis of ancient mtDNA provides insights into early human lifestyle transitions and haplogroup diversification, contributing to the evolutionary foundations of modern human populations.

Ancient humans↗
PubMed1975

Mathematical modeling of differentiation in Dictyostelium discoideum.

Methods for the dynamic analysis of biochemical differentiation are presented. These are demonstrated in the analysis of biochemical differentiation of the carbohydrate system in D. discoideum. Procedures for simplification which are presented are projection and contraction of the system trajectory in state space and the generation of reduced equivalent dynamic metabolic networks. The importance of the hierarchical structure of differentiating systems is discussed and the concept of a dynamic embedding diagram is introduced. It is shown that complex systems must be analyzed on an epoch by epoch basis, each epoch being a period of time characterized by a constant dynamic embedding diagram, and that widely different time scales and state space scales may be necessary in different epochs. In particular there is no a priori lower limit to the time scale which may be necessary during the analysis. Some problems in mathematically defining differentiation are discussed.

Carbohydrate Metabolism↗
PubMed2006

Stomatal patchiness and task-performing networks.

BACKGROUND: Patchy stomatal conductance is a poorly understood and little-studied phenomenon. It is relatively common, yet it appears to be detrimental to water-use efficiency under some conditions and has no immediately obvious physiological function of any kind. Much of the difficulty in studying patchy stomatal conductance is tied to its unpredictability, both in occurrence and in characteristics. SCOPE AND CONCLUSIONS: Statistical analyses of the variability of stomatal patchiness reveal remarkable similarities to structures and behaviours found in locally connected networks of dynamic units that perform tasks. Such systems solve problems that reside at the level of the entire network despite the absence of a central processor or a mechanism for directly sharing information over the entire system. Frequently, task performance is emergent, in the sense that no unit independently performs the task. Because each unit in the network can communicate with only its immediate neighbours, problem solving is accomplished by the states of the individual units self-organizing into synchronized, collective patterns. In some cases, patches of states form and move coherently over the network, thus providing a means for distantly separated parts of the network to communicate. Often, exactly what form these patches take and how they move as the units synchronize is highly unpredictable. In analogy with such networks, it is suggested that stomatal patchiness may be a signature that plants optimize gas exchange in a more sophisticated and adaptive manner than if performed by their individual stomata independently.

Chlorophyll↗
PubMed2002

GeneNet database: description and modeling of gene networks.

Almost all cellular processes in an organism are controlled by gene networks. Here we report on the analysis of gene networks functioning using two associated methods - data accumulation in GeneNet system and generalized chemical kinetic method for mathematical simulation of gene network functional dynamics. The technology of the usage of these methods is shown on the example of the gene network of macrophage activation.

Algorithms↗
PubMed2005

Robustness of the avalanche dynamics in data-packet transport on scale-free networks.

We study the avalanche dynamics in the data-packet transport on scale-free networks through a simple model. In the model, each vertex is assigned a capacity proportional to the load with the proportionality constant 1+a . When the system is perturbed by a single vertex removal, the load of each vertex is redistributed, followed by subsequent failures of overloaded vertices. The avalanche size depends on the parameter a as well as which vertex triggers it. We find that there exists a critical value a(c) at which the avalanche size distribution follows a power law. The critical exponent associated with it appears to be robust as long as the degree exponent is between 2 and 3 and is close in value to that of the distribution of the diameter changes by single vertex removal.

Journal Article↗
PubMed2001

Improvement of generalization ability for identifying dynamical systems by using universal learning networks.

This paper studies how the generalization ability of models of dynamical systems can be improved by taking advantage of the second order derivatives of the outputs with respect to the external inputs. The proposed method can be regarded as a direct implementation of the well-known regularization technique using the higher order derivatives of the Universal Learning Networks (ULNs). ULNs consist of a number of interconnected nodes where the nodes may have any continuously differentiable nonlinear functions in them and each pair of nodes can be connected by multiple branches with arbitrary time delays. A generalized learning algorithm has been derived for the ULNs, in which both the first order derivatives (gradients) and the higher order derivatives are incorporated. First, the method for computing the second order derivatives of ULNs is discussed. Then, a new method for implementing the regularization term is presented. Finally, simulation studies on identification of a nonlinear dynamical system with noises are carried out to demonstrate the effectiveness of the proposed method. Simulation results show that the proposed method can improve the generalization ability of neural networks significantly, especially in terms that (1) the robust network can be obtained even when the branches of trained ULNs are destructed, and (2) the obtained performance does not depend on the initial parameter values.

Computer Simulation↗
PubMed1994

Molecular neural network devices based on non-linear dynamic media.

The importance of non-linear dynamic mechanisms for implementing neural network devices at a molecular level is discussed. Information processing devices based on these mechanisms proved to be capable of performing some primitive operations important for image processing.

Algorithms↗
PubMed2005

Coevolutionary dynamics on scale-free networks.

We investigate Bak-Sneppen coevolution models on scale-free networks with various degree exponents gamma including random networks. For gamma>3 , the critical fitness value f(c) approaches a nonzero finite value in the limit N --> infinity, whereas f(c) approaches zero as 2 (N) on the networks with size N. The avalanche size distribution P (s) shows the normal power-law behavior for gamma>3. In contrast, P (s) for 2 tau(2) ). The origin of the two power regimes is explained by the dynamics on an artificially made star-linked network.

Journal Article↗
PubMed2004

Epigenesis and dynamic similarity in two regulatory networks in Pseudomonas aeruginosa.

Mucoidy and cytotoxicity arise from two independent modifications of the phenotype of the bacterium Pseudomonas aeruginosa that contribute to the mortality and morbidity of cystic fibrosis. We show that, even though the transcriptional regulatory networks controlling both processes are quite different from a molecular or mechanistic point of view, they may be identical from a dynamic point of view: epigenesis may in both cases be the cause of the acquisition of these new phenotypes. This was highlighted by the identity of formal graphs modelling these networks. A mathematical framework based on formal methods from computer science was defined and implemented with a software environment. It allows an easy and rigorous validation and certification of these models and of the experimental methods that can be proposed to falsify or validate the underlying hypothesis.

Epigenesis, Genetic↗
PubMedDate not supplied

Diagnosis of tuberculosis and other diseases caused by mycobacteria.

The adequate diagnosis and treatment of tuberculosis depends on many events, including rapid pathogen detection, patient isolation, species identification, and drug susceptibility testing. Well trained staff, using state-of-the-art technology, are necessary in the mycobacteriology laboratory to produce timely results that are necessary for the patients' care and public health measures. Mycobacteriology laboratories still play a pivotal role in the control of tuberculosis, which is especially true in view of the spread of multidrug-resistant tuberculosis. One way to optimize diagnostic efforts in spite of limited financial resources might be to sort and allocate specimens according to a system of priorities, e.g., diagnostic versus follow-up specimens. A "fast track" program for tuberculosis testing, which should be established as part of a dynamic diagnostic network, should focus on the highly infectious patient population. Collaboration between clinicians and mycobacteriologists remains the basis of dynamic diagnostic teamwork. Immediate screening of smears for acid-fast-bacilli in patients suspected of tuberculosis, followed by immediate processing of smear-positive specimens using modern mycobacteriological technology, should be given high priority. Diagnosis of disease due to nontuberculous mycobacteria can be difficult. Nontuberculous mycobacteria are commonly found in nature, and assessment as to whether a nontuberculous mycobacterium isolate is clinically significant can be a difficult task.

Humans↗
PubMed2006

Effects of patch quality and network structure on patch occupancy dynamics of a yellow-bellied marmot metapopulation.

1. The presence/absence of a species at a particular site is the simplest form of data that can be collected during ecological field studies. We used 13 years (1990-2002) of survey data to parameterize a stochastic patch occupancy model for a metapopulation of the yellow-bellied marmot in Colorado, and investigated the significance of particular patches and the influence of site quality, network characteristics and regional stochasticity on the metapopulation persistence. 2. Persistence of the yellow-bellied marmot metapopulation was strongly dependent on the high quality colony sites, and persistence probability was highly sensitive to small changes in the quality of these sites. 3. A relatively small number of colony sites was ultimately responsible for the regional persistence. However, lower quality satellite sites also made a significant contribution to long-term metapopulation persistence, especially when regional stochasticity was high. 4. The northern network of the marmot metapopulation was more stable compared to the southern network, and the persistence of the southern network depended heavily on the northern network. 5. Although complex models of metapopulation dynamics may provide a more accurate description of metapopulation dynamics, such models are data-intensive. Our study, one of the very few applications of stochastic patch occupancy models to a mammalian species, suggests that stochastic patch occupancy models can provide important insights into metapopulation dynamics using data that are easy to collect.

Animals↗
PubMed1998

Hybrid artificial neural network segmentation and classification of dynamic contrast-enhanced MR imaging (DEMRI) of osteosarcoma.

The evaluation of pediatric osteosarcoma has suffered from the lack of an accurate imaging measure of response. One major problem is that osteosarcoma do not shrink in response to chemotherapy; instead, viable tumor is replaced by necrotic tissue. Currently available techniques that use dynamic contrast-enhanced magnetic resonance imaging to quantitatively evaluate tumor response fail to assess the percentage of necrosis. At present, histopathologic evaluation of resected tissue is the only means of measuring the percentage of necrosis in treated osteosarcoma. The current study presents a non-invasive method to visualize necrotic and viable tumor and quantitatively assess the response of osteosarcoma. Our technique uses a hybrid neural network consisting of a Kohonen self-organizing map to segment dynamic contrast-enhanced magnetic resonance images and a multi-layer backpropagation neural network to classify the segmented images. Because the hybrid neural network is completely automated, our technique removes both inter- and intra-operator error. An analysis comparing the percentage of necrosis from our technique to the histopathologic analysis revealed a highly significant Spearman correlation coefficient of 0.617 with p < 0.001.

Adolescent↗
PubMed2004

A Bayesian approach to reconstructing genetic regulatory networks with hidden factors.

MOTIVATION: We have used state-space models (SSMs) to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T cell activation. SSMs are a class of dynamic Bayesian networks in which the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects that cannot be directly measured in a gene expression profiling experiment, for example: genes that have not been included in the microarray, levels of regulatory proteins, the effects of mRNA and protein degradation, etc. RESULTS: We have approached the problem of inferring the model structure of these state-space models using both classical and Bayesian methods. In our previous work, a bootstrap procedure was used to derive classical confidence intervals for parameters representing 'gene-gene' interactions over time. In this article, variational approximations are used to perform the analogous model selection task in the Bayesian context. Certain interactions are present in both the classical and the Bayesian analyses of these regulatory networks. The resulting models place JunB and JunD at the centre of the mechanisms that control apoptosis and proliferation. These mechanisms are key for clonal expansion and for controlling the long term behavior (e.g. programmed cell death) of these cells. AVAILABILITY: Supplementary data is available at http://public.kgi.edu/wild/index.htm and Matlab source code for variational Bayesian learning of SSMs is available at http://www.cse.ebuffalo.edu/faculty/mbeal/software.html.

Bayes Theorem↗
PubMed2001

On the relationship between fractal geometry of space and time in which a system of interacting cells exists and dynamics of gene expression.

We report that both space and time, in which a system of interacting cells exists, possess fractal structure. Each single cell of the system can restore the hierarchical organization and dynamic features of the entire tumor. There is a relationship between dynamics of gene expression and connectivity (i.e., interconnectedness which denotes the existence of complex, dynamic relationships in a population of cells leading to the emergence of global features in the system that would never appear in a single cell existing out of the system). Fractal structure emerges owing to non-bijectivity of dynamic cellular network of genes and their regulatory elements. It disappears during tumor progression. This latter state is characterized by damped dynamics of gene expression, loss of connectivity, loss of collectivity (i.e., capability of the interconnected cells to interact in a common mode), and metastatic phenotype. Fractal structure of both space and time is necessary for a cellular system to self-organize. Our findings indicate that results of molecular studies on gene expression should be interpreted in terms of space-time geometry of the cellular system. In particular, the dynamics of gene expression in cancer cells existing in a malignant tumor is not identical with the dynamics of gene expression in the same cells cultured in the monolayer system.

Animals↗
PubMed1998

Stochastic resonance in mammalian neuronal networks.

We present stochastic resonance observed in the dynamics of neuronal networks from mammalian brain. Both sinusoidal signals and random noise were superimposed into an applied electric field. As the amplitude of the noise component was increased, an optimization (increase then decrease) in the signal-to-noise ratio of the network response to the sinusoidal signal was observed. The relationship between the measures used to characterize the dynamics is discussed. Finally, a computational model of these neuronal networks that includes the neuronal interactions with the electric field is presented to illustrate the physics behind the essential features of the experiment. (c) 1998 American Institute of Physics.

Journal Article↗