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PubMed2003

The time-organized map algorithm: extending the self-organizing map to spatiotemporal signals.

The new time-organized map (TOM) is presented for a better understanding of the self-organization and geometric structure of cortical signal representations. The algorithm extends the common self-organizing map (SOM) from the processing of purely spatial signals to the processing of spatiotemporal signals. The main additional idea of the TOM compared with the SOM is the functionally reasonable transfer of temporal signal distances into spatial signal distances in topographic neural representations. This is achieved by neural dynamics of propagating waves, allowing current and former signals to interact spatiotemporally in the neural network. Within a biologically plausible framework, the TOM algorithm (1) reveals how dynamic neural networks can self-organize to embed spatial signals in temporal context in order to realize functional meaningful invariances, (2) predicts time-organized representational structures in cortical areas representing signals with systematic temporal relation, and (3) suggests that the strength with which signals interact in the cortex determines the type of signal topology realized in topographic maps (e.g., spatially or temporally defined signal topology). Moreover, the TOM algorithm supports the explanation of topographic reorganizations based on time-to-space transformations (Wiemer, Spengler, Joublin, Stagge, & Wacquant, 2000).

Algorithms↗
PubMed1998

Local instability and oscillations of trajectories in a diluted symmetric neural network.

Using a generalized version of the signal-to-noise analysis, we study local instabilities of trajectories for the parallel dynamics of an extremely diluted, symmetric, Hopfield neural network. In order to reach a better understanding of the structure of the attractors of this model, a revision of the asymmetric version is performed in the case of zero and non-zero temperatures. New unexpected dynamical behaviours are found. Moreover, despite accepted beliefs, both analytical and numerical deviations between the dynamical properties of the two models (symmetric and asymmetric) can be exhibited. We show that, in some range of parameters, the diluted symmetric network exhibits strong dynamical oscillations of the neuronal activity, similar to those observed in synchronized networks. Furthermore, a deeper knowledge of the structure near attractors is achieved from this stability/instability analysis thanks to explicit analytical formulae for a two-step parallel dynamics for a symmetric network.

Models, Theoretical↗
PubMed2000

Modeling transcriptional control in gene networks--methods, recent results, and future directions.

Mathematical models are useful for providing a framework for integrating data and gaining insights into the static and dynamic behavior of complex biological systems such as networks of interacting genes. We review the dynamic behaviors expected from model gene networks incorporating common biochemical motifs, and we compare current methods for modeling genetic networks. A common modeling technique, based on simply modeling genes as ON-OFF switches, is readily implemented and allows rapid numerical simulations. However, this method may predict dynamic solutions that do not correspond to those seen when systems are modeled with a more detailed method using ordinary differential equations. Until now, the majority of gene network modeling studies have focused on determining the types of dynamics that can be generated by common biochemical motifs such as feedback loops or protein oligomerization. For example, these elements can generate multiple stable states for gene product concentrations, state-dependent responses to stimuli, circadian rhythms and other oscillations, and optimal stimulus frequencies for maximal transcription. In the future, as new experimental techniques increase the ease of characterization of genetic networks, qualitative modeling will need to be supplanted by quantitative models for specific systems.

Animals↗
PubMed2006

A neuronal network for the logic of Limax learning.

We construct a neuronal network to model the logic of associative conditioning as revealed in experimental results using the terrestrial mollusk Limax maximus. We show, in particular, how blocking to a previously conditioned stimulus in the presence of the unconditional stimulus, can emerge as a dynamical property of the network. We also propose experiments to test the new model.

Action Potentials↗
PubMed2001

Keratin-mediated resistance to stress and apoptosis in simple epithelial cells in relation to health and disease.

Epithelial cells such as hepatocytes exhibit highly polarized properties as a result of the asymmetric distribution of subsets of receptors at unique portions of the surface membrane. While the proper targeting of these surface receptors and maintenance of the resulting polarity depend on microtubules (MTs), the Golgi sorting compartment, and different actin-filament networks, the contribution of keratin intermediate filaments (IFs) has been unclear. Recent data show that the latter cytoskeletal network plays a predominant role in providing resistance to various forms of stress and to apoptosis targeted to the surface membrane. In this context, we first summarize our knowledge of the domain- or assembly-related features of IF proteins and the dynamic properties of IF networks that may explain how the same keratin pair K8/K18 can exert multiple resistance-related functions in simple epithelial cells. We then examine the contribution of linker protein(s) that integrate interactions of keratin IFs with MTs and the actin-cytoskeleton network, polarity-dependent surface receptors and cytoplasmic organelles. We next address likely molecular mechanisms by which K8/K18 can selectively provide resistance to a mechanical or toxic stress, or to Fas-mediated apoptosis. Finally, these issues on keratin structure-function are examined within a context of pathological anomalies emerging in tissue architecture as a result of natural or targeted mutations, or posttranslational modifications at specific amino acid residues. Clearly. the data accumulated in recent years provide new and significant insights on the role of K8/K18, particularly under conditions where polarized cells resist to stressful or apoptotic insults.

Animals↗
PubMed2005

Microtubule-associated protein MAP1A, MAP1B, and MAP2 proteolysis during soluble amyloid beta-peptide-induced neuronal apoptosis. Synergistic involvement of calpain and caspase-3.

A growing body of evidence supports the notion that soluble oligomeric forms of the amyloid beta-peptide (Abeta) may be the proximate effectors of neuronal injuries and death in the early stages of Alzheimer disease. However, the molecular mechanisms associated with neuronal apoptosis induced by soluble Abeta remain to be elucidated. We recently demonstrated the involvement of an early reactive oxygen species-dependent perturbation of the microtubule network (Sponne, I., Fifre, A., Drouet, B., Klein, C., Koziel, V., Pincon-Raymond, M., Olivier, J.-L., Chambaz, J., and Pillot, T. (2003) J. Biol. Chem. 278, 3437-3445). Because microtubule-associated proteins (MAPs) are responsible for the polymerization, stabilization, and dynamics of the microtubule network, we investigated whether MAPs might represent the intracellular targets that would enable us to explain the microtubule perturbation involved in soluble Abeta-mediated neuronal apoptosis. The data presented here show that soluble Abeta oligomers induce a time-dependent degradation of MAP1A, MAP1B, and MAP2 involving a perturbation of Ca2+ homeostasis with subsequent calpain activation that, on its own, is sufficient to induce the proteolysis of isoforms MAP2a, MAP2b, and MAP2c. In contrast, MAP1A and MAP1B sequential proteolysis results from the Abeta-mediated activation of caspase-3 and calpain. The prevention of MAP1A, MAP1B, and MAP2 proteolysis by antioxidants highlights the early reactive oxygen species generation in the perturbation of the microtubule network induced by soluble Abeta. These data clearly demonstrate the impact of cytoskeletal perturbations on soluble Abeta-mediated cell death and support the notion of microtubule-stabilizing agents as effective Alzheimer disease drugs.

Amyloid beta-Peptides↗
PubMed1998

Automatic classification of transiently evoked otoacoustic emissions using an artificial neural network.

The increasing use of transiently evoked otoacoustic emissions (TEOAE) in large neonatal hearing screening programmes makes a standardized method of response classification desirable. Until now methods have been either subjective or based on arbitrary response characteristics. This study takes an expert system approach to standardize the subjective judgements of an experienced scorer. The method that is developed comprises three stages. First, it transforms TEOAEs from waveforms in the time domain into a simplified parameter set. Second, the parameter set is classified by an artificial neural network that has been taught on a large database TEOAE waveforms and corresponding expert scores. Third, additional fuzzy logic rules automatically detect probable artefacts in the waveforms and synchronized spontaneous emission components. In this way, the knowledge of the experienced scorer is encapsulated in the expert system software and thereafter can be accessed by non-experts. Teaching and evaluation of the neural network was based on TEOAEs from a database totalling 2190 neonatal hearing screening tests. The database was divided into learning and test groups with 820 and 1370 waveforms respectively. From each recorded waveform a set of 12 parameters was calculated, representing signal static and dynamic properties. The artifical network was taught with parameter sets of only the learning groups. Reproduction of the human scorer classification by the neural net in the learning group showed a sensitivity for detecting screen fails of 99.3% (299 from 301 failed results on subjective scoring) and a specificity for detecting screen passes of 81.1% (421 of 519 pass results). To quantify the post hoc performance of the net (generalization), the test group was then presented to the network input. Sensitivity was 99.4% (474 from 477) and specificity was 87.3% (780 from 893). To check the efficiency of the classification method, a second learning group was selected out of the previous test group, and the previous learning group was used as the test group. Repeating learning and test procedures yielded 99.3% sensitivity and 80.7% specificity for reproduction, and 99.4% sensitivity and 86.7% specificity for generalization. In all respects, performance was better than for a previously optimized method based simply on cross-correlation between replicate non-linear waveforms. It is concluded that classification methods based on neural networks show promise for application to large neonatal screening programmes utilizing TEOAEs.

Artificial Intelligence↗
PubMed2005

Coupling between lysozyme and glycerol dynamics: microscopic insights from molecular-dynamics simulations.

We explore possible molecular mechanisms behind the coupling of protein and solvent dynamics using atomistic molecular-dynamics simulations. For this purpose, we analyze the model protein lysozyme in glycerol, a well-known protein-preserving agent. We find that the dynamics of the hydrogen bond network between the solvent molecules in the first shell and the surface residues of the protein controls the structural relaxation (dynamics) of the whole protein. Specifically, we find a power-law relationship between the relaxation time of the aforementioned hydrogen bond network and the structural relaxation time of the protein obtained from the incoherent intermediate scattering function. We demonstrate that the relationship between the dynamics of the hydrogen bonds and the dynamics of the protein appears also in the dynamic transition temperature of the protein. A study of the dynamics of glycerol as a function of the distance from the surface of the protein indicates that the viscosity seen by the protein is not the one of the bulk solvent. The presence of the protein suppresses the dynamics of the surrounding solvent. This implies that the protein sees an effective viscosity higher than the one of the bulk solvent. We also found significant differences in the dynamics of surface and core residues of the protein. The former is found to follow the dynamics of the solvent more closely than the latter. These results allowed us to propose a molecular mechanism for the coupling of the solvent-protein dynamics.

Algorithms↗
PubMed2005

Spatiotemporal networks in addressable excitable media.

Spatiotemporal networks are studied in a photosensitive Belousov-Zhabotinsky medium that allows both local and nonlocal transmission of excitation. Local transmission occurs via propagating excitation waves, while nonlocal transmission takes place by nondiffusive jumps to destination sites linked to excited sites in the medium. Static, dynamic, and domain link networks are experimentally and computationally characterized. Transitions to synchronized behavior are exhibited with increasing link density, and power-law relations are observed for first-coverage time as a function of link probability.

Gels↗
PubMed1998

Novel features of intermediate filament dynamics revealed by green fluorescent protein chimeras.

In order to study the dynamic behavior of intermediate filament networks in living cells, we have prepared constructs fusing green fluorescent protein to intermediate filament proteins. Vimentin fused to green fluorescent protein labeled the endogenous intermediate filament network. We generated stable SW13 and NIH3T3 cell lines that express an enhanced green fluorescent protein fused to the N-terminus of full-length vimentin. We were able to observe the dynamic behavior of the intermediate filament network in these cells for periods as long as 4 hours (images acquired every 2 minutes). In both cell lines, the vimentin network constantly moves in a wavy manner. In the NIH3T3 cells, we observed extension of individual vimentin filaments at the edge of the cell. This movement is dependent on microtubules, since the addition of nocodazole stopped the extension of the intermediate filaments. Injection of anti-IFA causes the redistribution or 'collapse' of intermediate filaments. We injected anti-IFA antibodies into NIH3T3 cells stably expressing green fluorescent protein fused to vimentin and found that individual intermediate filaments move slowly towards the perinuclear area without obvious disassembly. These results demonstrate that individual intermediate filaments are translocated during the collapse, rather than undergoing disassembly-induced redistribution. Injections of tubulin antibodies disrupt the interactions between intermediate filaments and stable microtubules and cause the collapse of the vimentin network showing that these interactions play an important role in keeping the intermediate filament network extended. The nocodazole inhibition of intermediate filament extension and the anti-IFA microinjection experiments are consistent with a model in which intermediate filaments exhibit an extended distribution when tethered to microtubules, but are translocated to the perinuclear area when these connections are severed.

3T3 Cells↗
PubMed2005

Generation of uncorrelated random scale-free networks.

Uncorrelated random scale-free networks are useful null models to check the accuracy and the analytical solutions of dynamical processes defined on complex networks. We propose and analyze a model capable of generating random uncorrelated scale-free networks with no multiple and self-connections. The model is based on the classical configuration model, with an additional restriction on the maximum possible degree of the vertices. We check numerically that the proposed model indeed generates scale-free networks with no two- and three-vertex correlations, as measured by the average degree of the nearest neighbors and the clustering coefficient of the vertices of degree k , respectively.

Journal Article↗
PubMed2004

The origin of hematopoietic cell type diversity.

The hematopoietic system remains robust with regards to extrinsic perturbations, in sharp contrast with the stochastic behavior of hematopoeitic stem cells (HSCs) at the single cell level, suggesting that stability may be achieved within a stem cell system that undergoes constant self-renewal, commitment to differentiation and generates cell type diversification. Converging evidence at the interface of cellular, molecular and numerical studies suggests that diversity is generated by the chaotic dynamics of transcription factor networks within a cell and of the combination of growth factors and cytokines in the environment, both involving cooperation and competition. Current evidence indicates that HSCs are primed for multilineage gene expression. A subtle shift in transcription factor dosage is sufficient to perturb this equilibrium and to drive lineage commitment that involves a resolution of complexity at the molecular level and a transition towards less chaotic behavior. This dynamical instability establishes a state of responsiveness to extrinsic signals. Evolutionary conserved environmental cues that drive pattern formation or migratory behavior during embryonic development operate in the adult to influence the decision between self-renewal and differentiation in HSCs, as exemplified by the role of Notch1, Wnt proteins, BMPs and VEGF. In contrast, a network of cytokines uniquely present in mammalians influences later developmental stages, from progenitors with more restricted potentials (tri-, bi- or unipotent) to mature functional cells. These cytokines have co-opted the ancient Jak-STAT pathway but also appear to trigger lineage-affiliated transcription factors, thus linking environmental signaling to cell fate decisions.

Acute Disease↗
PubMed2006

Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks.

A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides the ability to overcome the "explosion of complexity" problem in backstepping controllers. The SRWNNs are used to observe the arbitrary model uncertainties of FJ robots, and all their weights are trained online. From the Lyapunov stability analysis, their adaptation laws are induced, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a three-link FJ robot are utilized to validate the good position tracking performance and robustness against payload uncertainties and external disturbances of the proposed control system.

Journal Article↗
PubMed2004

Nest excavation in ants: group size effects on the size and structure of tunneling networks.

Collective digging activity was studied in the ant Messor sancta Forel in laboratory conditions and with a two dimensional set-up. We analyzed the digging dynamics and topology of tunneling networks excavated by groups of workers ranging from 50 to 200 individuals over 3 days. In all conditions, the dynamics of excavated sand volume were clearly non-linear. Excavation began with an exponential growth and after 3 days reached a saturation phase in which activity was almost totally stopped. The final volume of sand excavated was positively correlated with the number of workers. At the end of the experiments, the two-dimensional tunneling networks were mapped onto planar graphs where the vertices represent small chambers or intersections between tunnels and the edges represent tunnels. We found that all the networks belonged to a same topological family and exhibited several striking invariants such as the distribution of vertex degree that follows a power law. When increasing the number of ants, some changes occurred in the network structure, mainly an increase in the number of edges and vertices, and the progressive emergence of enlarged and highly connected vertices.

Animals↗
PubMed2004

Roles of coherent ongoing oscillations among dynamic cell assemblies in object perception.

I constructed a cortical neural network model and investigated possible roles of coherent ongoing oscillations in membrane potentials of neurons in object perception. The model has a hierarchical structure consisting of two lower networks and one higher network that are reciprocally connected via divergent/convergent projections. Information about features and their relationships (or objects) is encoded by the population activities of neurons (or dynamic cell assemblies) of the lower networks and the higher network, respectively. The ongoing state of the network is expressed by 'random itinerancy' among these dynamic cell assemblies. Under the ongoing state, the dynamic cell assemblies belonging to the same object are transiently linked across the networks and coherently oscillate at lower frequencies (approximately 15 Hz). When the model perceives a presented object, the dynamic cell assemblies corresponding to the object are persistently linked together across the networks and coherently oscillate at higher frequencies (approximately 40 Hz). When the feedback pathways are impaired, the dynamic phase transition from the slow- to fast-oscillations is not induced by the object presentation, keeping the lower frequency oscillations (approximately 15 Hz) where the activated dynamic cell assemblies oscillate incoherently. Reaction times to the object presentation are greatly reduced if the ongoing oscillation frequencies fall within a specific range (approximately 20-30 Hz). I suggest that coherent ongoing slow-oscillations in cortical activity may serve as a ready state for sensory input, whereby the brain can respond effectively to sensory stimulation. Top-down processing via feedback pathways may give an essential contribution to the induction of coherent fast-oscillations across multiple cortical areas, by which relevant features are effectively integrated into a unified percept when stimulated with a sensory object.

Action Potentials↗
PubMed1993

Convoys of social support: an intergenerational analysis.

Within the Convoy Model, social networks are viewed as dynamic hierarchic structures surrounding the individual throughout life. As a step toward empirical definition of convoy structure and function across the life span, a bicultural sample (N = 159) of young adult women, their mothers, and their grandmothers were queried. Comparable results were obtained across generations and cultures with regard to network size, the amount of support provided by the network, and the nomination of close family members as primary sources of support. However, there was a marked shift across generations in the balance of familial versus friendship relations. Younger persons included fewer family members and more friends in their networks, and they received more support from friends. These cross-sectional results are consistent with the Convoy Model and suggest that longitudinal research is warranted.

Adult↗
PubMed1989

Automata network theories in immunology: their utility and their underdetermination.

Small networks of threshold automata are used to model complex interactions between populations of regulatory cells (helpers and suppressors, antigen specific and anti-idiotypic) which participate in the immune response. The models, being discrete and semiquantitative, are well adapted to the situation of incomplete information often encountered in vivo. However, the dynamics of many different network structures usually end up in the same attractor set. Thus, many different theories are equivalent in their explicative power for the same facts. This property, known as underdetermination of the theories by the facts, is given a quantitative estimate. It appears that such an underdetermination, as a kind of irreducible complexity, can be expected in many in vivo biological processes, even when the number of interacting and functionally coupled elements is relatively small.

Animals↗
PubMed2004

Importance of mitochondrial dynamics during meiosis and sporulation.

Opposing fission and fusion events maintain the yeast mitochondrial network. Six proteins regulate these membrane dynamics during mitotic growth-Dnm1p, Mdv1p, and Fis1p mediate fission; Fzo1p, Mgm1p, and Ugo1p mediate fusion. Previous studies established that mitochondria fragment and rejoin at distinct stages during meiosis and sporulation, suggesting that mitochondrial fission and fusion are required during this process. Here we report that strains defective for mitochondrial fission alone, or both fission and fusion, complete meiosis and sporulation. However, visualization of mitochondria in sporulating cultures reveals morphological defects associated with the loss of fusion and/or fission proteins. Specifically, mitochondria collapse to one side of the cell and fail to fragment during presporulation. In addition, mitochondria are not inherited equally by newly formed spores, and mitochondrial DNA nucleoid segregation defects give rise to spores lacking nucleoids. This nucleoid inheritance defect is correlated with an increase in petite spore colonies. Unexpectedly, mitochondria fragment in mature tetrads lacking fission proteins. The latter finding suggests either that novel fission machinery operates during sporulation or that mechanical forces generate the mitochondrial fragments observed in mature spores. These results provide evidence of fitness defects caused by fission mutations and reveal new phenotypes associated with fission and fusion mutations.

Cell Respiration↗

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