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At least 757 records · Page 42Linked to original sources

Self-referential phase reset based on inferior olive oscillator dynamics.

The olivo-cerebellar network is a key neuronal circuit that provides high-level motor control in the vertebrate CNS. Functionally, its network dynamics is organized around the oscillatory membrane potential properties of inferior olive (IO) neurons and their electrotonic connectivity. Because IO action potentials are generated at the peaks of the quasisinusoidal membrane potential oscillations, their temporal firing properties are defined by the IO rhythmicity. Excitatory inputs to these neurons can produce oscillatory phase shifts without modifying the amplitude or frequency of the oscillations, allowing well defined time-shift modulation of action potential generation. Moreover, the resulting phase is defined only by the amplitude and duration of the reset stimulus and is independent of the original oscillatory phase when the stimulus was delivered. This reset property, henceforth referred to as selfreferential phase reset, results in the generation of organized clusters of electrically coupled cells that oscillate in phase and are controlled by inhibitory feedback loops through the cerebellar nuclei and the cerebellar cortex. These clusters provide a dynamical representation of arbitrary motor intention patterns that are further mapped to the motor execution system. Being supplied with sensory inputs, the olivo-cerebellar network is capable of rearranging the clusters during the process of movement execution. Accordingly, the phase of the IO oscillators can be rapidly reset to a desired phase independently of the history of phase evolution. The goal of this article is to show how this selfreferential phase reset may be implemented into a motor control system by using a biologically based mathematical model.

Action Potentials↗

From basic network principles to neural architecture: emergence of orientation-selective cells.

This is the second paper in a series of three that explores the emergence of several prominent features of the functional architecture of visual cortex, in a "modular self-adaptive network" containing several layers of cells with parallel feedforward connections whose strengths develop according to a Hebb-type correlation-rewarding rule. In the present paper I show that orientation-selective cells, similar to the "simple" cortical cells of Hubel and Wiesel [Hubel, D. H. & Wiesel, T. N. (1962) J. Physiol. 160, 106-154], emerge in such a network. No orientation preference is specified to the system at any stage, the orientation-selective cell layer emerges even in the absence of environmental input to the system, and none of the basic developmental rules is specific to visual processing.

Adaptation, Physiological↗

Catecholaminergic innervation of pyramidal neurons in the human temporal cortex.

In the human neocortex, catecholaminergic connections modulate the excitatory inputs of pyramidal neurons and are involved in higher cognitive functions. Catecholaminergic fibers form a dense network in which it is difficult to distinguish whether or not target specificity exists. In order to shed some light on this issue, we set out to quantify the catecholaminergic innervation of pyramidal cells in different layers of the human temporal cortex (II, IIIa, IIIb, V and VI). For this purpose, pyramidal cells were labeled in human cortical tissue by injecting them with Lucifer Yellow, and then performed immunocytochemistry for the rate limiting catecholamine synthesizing enzyme tyrosine hydroxylase (TH) to visualize catecholaminergic fibers in the same sections. Injected cells were reconstructed in three dimensions and appositions were quantified (n = 1503) in serial confocal microscopy images of each injected cell (n = 71). We found TH-immunoreactive appositions (TH-ir) in all the pyramidal cells analyzed, in both the apical and basal dendritic regions. In general, the density of TH-ir apposition was greater in layers II, V and VI than in layers IIIa and IIIb. Furthermore, TH-ir appositions showed a regular distribution in almost all dendritic compartments of the apical and basal dendritic arbors across all layers. Hence, it appears that all pyramidal neurons in the human neocortex receive catecholaminergic afferents in a rather regular pattern, independent of the layer in which they are located. Since pyramidal cells located in different layers are involved in different intrinsic and extrinsic circuits, these results suggest that catecholaminergic afferents may modify the function of a larger variety of circuits than previously thought. Thus, this aspect of human cortical organization is likely to have important implications in cortical function.

Adult↗

Dragon TF Association Miner: a system for exploring transcription factor associations through text-mining.

We present Dragon TF Association Miner (DTFAM), a system for text-mining of PubMed documents for potential functional association of transcription factors (TFs) with terms from Gene Ontology (GO) and with diseases. DTFAM has been trained and tested in the selection of relevant documents on a manually curated dataset containing >3000 PubMed abstracts relevant to transcription control. On our test data the system achieves sensitivity of 80% with specificity of 82%. DTFAM provides comprehensive tabular and graphical reports linking terms to relevant sets of documents. These documents are color-coded for easier inspection. DTFAM complements the existing biological resources by collecting, assessing, extracting and presenting associations that can reveal some of the not so easily observable connections among the entities found which could explain the functions of TFs and help decipher parts of gene transcriptional regulatory networks. DTFAM summarizes information from a large volume of documents saving time and making analysis simpler for individual users. DTFAM is freely available for academic and non-profit users at http://research.i2r.a-star.edu.sg/DRAGON/TFAM/.

Gene Expression Regulation↗

Deformation of cross-linked semiflexible polymer networks.

Networks of filamentous proteins play a crucial role in cell mechanics. These cytoskeletal networks, together with various cross-linking and other associated proteins largely determine the (visco)elastic response of cells. In this Letter we study a model system of cross-linked, stiff filaments in order to explore the connection between the microstructure under strain and the macroscopic response of cytoskeletal networks. We find two distinct regimes as a function primarily of cross-link density and filament rigidity: one characterized by affine deformation and one by nonaffine deformation. We characterize the crossover between these two.

Biopolymers↗

A hybrid neural network model for consciousness.

A new framework for consciousness is introduced based upon traditional artificial neural network models. This framework reflects explicit connections between two parts of the brain: one global working memory and distributed modular cerebral networks relating to specific brain functions. Accordingly this framework is composed of three layers, physical mnemonic layer and abstract thinking layer, which cooperate together through a recognition layer to accomplish information storage and cognition using algorithms of how these interactions contribute to consciousness: (1) the reception process whereby cerebral subsystems group distributed signals into coherent object patterns; (2) the partial recognition process whereby patterns from particular subsystems are compared or stored as knowledge; and (3) the resonant learning process whereby global workspace stably adjusts its structure to adapt to patterns' changes. Using this framework, various sorts of human actions can be explained, leading to a general approach for analyzing brain functions.

Algorithms↗

Yeast Protein Interactome topology provides framework for coordinated-functionality.

The architecture of the network of protein-protein physical interactions in Saccharomyces cerevisiae is exposed through the combination of two complementary theoretical network measures, betweenness centrality and 'Q-modularity'. The yeast interactome is characterized by well-defined topological modules connected via a small number of inter-module protein interactions. Should such topological inter-module connections turn out to constitute a form of functional coordination between the modules, we speculate that this coordination is occurring typically in a pairwise fashion, rather than by way of high-degree hub proteins responsible for coordinating multiple modules. The unique non-hub-centric hierarchical organization of the interactome is not reproduced by gene duplication-and-divergence stochastic growth models that disregard global selective pressures.

Models, Biological↗

Identification of functional links between genes using phylogenetic profiles.

MOTIVATION: Genes with identical patterns of occurrence across the phyla tend to function together in the same protein complexes or participate in the same biochemical pathway. However, the requirement that the profiles be identical (i) severely restricts the number of functional links that can be established by such phylogenetic profiling; (ii) limits detection to very strong functional links, failing to capture relations between genes that are not in the same pathway, but nevertheless subserve a common function and (iii) misses relations between analogous genes. Here we present and apply a method for relaxing the restriction, based on the probability that a given arbitrary degree of similarity between two profiles would occur by chance, with no biological pressure. Function is then inferred at any desired level of confidence. RESULTS: We derive an expression for the probability distribution of a given number of chance co-occurrences of a pair of non-homologous orthologs across a set of genomes. The method is applied to 2905 clusters of orthologous genes (COGs) from 44 fully sequenced microbial genomes representing all three domains of life. Among the results are the following. (1) Of the 51 000 annotated intrapathway gene pairs, 8935 are linked at a level of significance of 0.01. This is over 30-fold greater than the 271 intrapathway pairs obtained at the same confidence level when identical profiles are used. (2) Of the 540 000 interpathway genes pairs, some 65 000 are linked at the 0.01 level of significance, some 12 standard deviations beyond the number expected by chance at this confidence level. We speculate that many of these links involve nearest-neighbor path, and discuss some examples. (3) The difference in the percentage of linked interpathway and intrapathway genes is highly significant, consistent with the intuitive expectation that genes in the same pathway are generally under greater selective pressure than those that are not. (4) The method appears to recover well metabolic networks. This is illustrated by the TCA cycle which is recovered as a highly connected, weighted edge network of 30 of its 31 COGs. (5) The fraction of pairs having a common pathway is a symmetric function of the Hamming distance between their profiles. This finding, that the functional correlation between profiles with near maximum Hamming distance is as large as between profiles with near zero Hamming distance, and as statistically significant, is plausibly explained if the former group represents analogous genes.

Algorithms↗

GABAergic microcircuits in the neostriatum.

The vast majority of neostriatal neurons and intrinsic intrastriatal synapses are GABAergic, the latter arising from axon collaterals of spiny projection neurons and from GABAergic interneurons. An important feature of the functional organization of the neostriatum has long been assumed to be the existence of a widespread lateral inhibitory network mediated by the axon collaterals of spiny projection neurons. However, these collateral connections have recently been demonstrated electrophysiologically to be relatively weak--in contrast to feedforward interneuronal inhibition, which exerts strong effects on spike timing in spiny neurons. These new data are incompatible with current "winner-take-all" models of lateral inhibitory function in the neostriatum, and they force a modification of established concepts of the functional roles of feedback inhibition in this nucleus.

Action Potentials↗

Self-stabilization of neuronal networks. I. The compensation algorithm for synaptogenesis.

Between the extreme views concerning ontogenesis (genetic vs. environmental determination), we use a moderate approach: a somehow pre-established neuronal model network reacts to activity deviations (reflecting input to be compensated), and stabilizes itself during a complex feed-back process. Morphogenesis is based on an algorithm formalizing the compensation theory of synaptogenesis (Wolff and Wagner 1983). This algorithm is applied to randomly connected McCulloch-Pitts networks that are able to maintain oscillations of their activity patterns over time. The algorithm can lead to networks which are morphogenetically stable but preserve self-maintained oscillations in activity. This is in contrast to most of the current models of synaptogenesis and synaptic modification based on Hebbian rules of plasticity. Hebbian networks are morphogenetically unstable without additional assumptions. The effects of compensation on structural and functional properties of the networks are described. It is concluded that the compensation theory of synaptogenesis can account for the development of morphogenetically stable neuronal networks out of randomly connected networks via selective stabilization and elimination of synapses. The logic of the compensation algorithm is based on experimental results. The present paper shows that the compensation theory can not only predict the behavior of synaptic populations (Wagner and Wolff, in preparation), but it can also describe the behavior of neurons interconnected in a network, with the resulting additional system properties. The neuronal interactions--leading to equilibrium in certain cases--are a self-organizing process in the sense that all decisions are performed on the individual cell level without knowing the overall network situation or goal.

Animals↗

A protein linkage map of Escherichia coli bacteriophage T7.

Genome sequencing projects are predicting large numbers of novel proteins, whose interactions with other proteins must mediate the function of cellular processes. To analyse these networks, we used the yeast two-hybrid system on a genome-wide scale to identify 25 interactions among the proteins of Escherichia coli bacteriophage T7. Among these is a set of six interactions connecting proteins that function in DNA replication and DNA packaging. Remarkably, two genes, arranged such that one entirely overlaps the other and uses a different reading frame, encode interacting proteins. Several of the interactions reflect intramolecular associations of different domains of the same polypeptide, suggesting that the two-hybrid assay may be useful in the analysis of protein folding. This global approach to protein-protein interactions may be applicable to the analysis of more complex genomes whose sequences are becoming available.

Amino Acid Sequence↗

Self-organizing and adaptive peer-to-peer network.

In this paper, an algorithm that forms a dynamic and self-organizing network is demonstrated. The hypothesis of this work is that in order to achieve a resilient and adaptive peer-to-peer (P2P) network, each network node must proactively maintain a minimum number of edges. Specifically, low-level communication protocols are not sufficient by themselves to achieve high-service availability, especially in the case of ad hoc or dynamic networks with a high degree of node addition and deletion. The concept has been evaluated within a P2P agent application in which each agent has a goal to maintain a preferred number of connections to a number of service providing agents. Using this algorithm, the agents update a weight value associated with each connection, based on the perceived utility of the connection to the corresponding agent. This utility function can be a combination of several node or edge parameters, such as degree k of the target node, or frequency of the message response from the node. This weight is updated using a set of Hebbian-style learning rules, such that the network as a whole exhibits adaptive self-organizing behavior. The principal result is the finding that by limiting the connection neighborhood within the overlay topology, the resulting P2P network can be made highly resilient to targeted attacks on high-degree nodes, while maintaining search efficiency.

Journal Article↗

[Research on EEG classification with evolving cascade neural networks].

To correctly classify EEG with different mental tasks, a new learning algorithm for Evolving Cascade Neural Networks (ECNNs) is described to avoid over-fitting of a neural network due to noise and redundant features. The learning algorithm calculates the value of a fitness function on validate set and accordingly updates the connection weights on training set. The learning algorithm uses the regularity criterion for selecting the neurons with relevant connection. If the value Cr calculated for the rth neuron is less than the value Cr-1 calculated for the previous (r-1) neuron, the features that feed the rth neuron are relevant, else they are irrelevant. An ECNN starts to learn with one input node and then, adding new inputs as well as new hidden neurons, evolves it. The trained ECNN has a nearly minimal number of input and hidden neurons as well as connections. The algorithm is applied to classify EEG with two mental tasks. The trained ECNN has correctly classified 83.1% of the testing segments. It shows a better result, compared with a standard BP network.

Algorithms↗

Frontal lobe networks for effective processing of ambiguously expressed emotions in humans.

This study examines the neural substrates involved in the recognition of ambiguous facial expressions using functional magnetic resonance imaging. Subjects performed two tasks, one in which they judged facial expressions and another in which they identified gender. Subtraction between ambiguous expression and clear expression conditions revealed the activation of anterior cingulate (ACC), medial frontal (MeFG) and bilateral inferior frontal gyrus (IFG). Structural equation modeling showed that the functional connectivity between these areas was greater with the ambiguous expressions than with the clear ones. The activation of the ACC, MeFG, and right IFG was greater with ambiguous expressions than with ambiguous gender. These results suggest that the neural network involving these frontal regions plays a crucial role in the processing of the ambiguously expressed facial emotions.

Adult↗

Functional integration of ecological networks through pathway proliferation.

Large-scale structural patterns commonly occur in network models of complex systems including a skewed node degree distribution and small-world topology. These patterns suggest common organizational constraints and similar functional consequences. Here, we investigate a structural pattern termed pathway proliferation. Previous research enumerating pathways that link species determined that as pathway length increases, the number of pathways tends to increase without bound. We hypothesize that this pathway proliferation influences the flow of energy, matter, and information in ecosystems. In this paper, we clarify the pathway proliferation concept, introduce a measure of the node-node proliferation rate, describe factors influencing the rate, and characterize it in 17 large empirical food-webs. During this investigation, we uncovered a modular organization within these systems. Over half of the food-webs were composed of one or more subgroups that were strongly connected internally, but weakly connected to the rest of the system. Further, these modules had distinct proliferation rates. We conclude that pathway proliferation in ecological networks reveals subgroups of species that will be functionally integrated through cyclic indirect effects.

Animals↗

[A discrete dynamic model of a neuronal network with space-time organization].

A dynamical model of the neuronal network based on the principles of spatial-temporal organization of information processing was constructed. The additivity law for the formation of input connection factors in this model provides its versatility for a variety of logical functions. The model takes into account real properties of the systemic organization of brain neurones.

Neural Networks, Computer↗

Interaction between a verbal working memory network and the medial temporal lobe.

The irrelevant speech effect illustrates that sounds that are irrelevant to a visually presented short-term memory task still interfere with neuronal function. In the present study we explore the functional and effective connectivity of such interference. The functional connectivity analysis suggested an interaction between the level of irrelevant speech and the correlation between in particular the left superior temporal region, associated with verbal working memory, and the left medial temporal lobe. Based on this psycho-physiological interaction, and to broaden the understanding of this result, we performed a network analysis, using a simple network model for verbal working memory, to analyze its interaction with the medial temporal lobe memory system. The results showed dissociations in terms of network interactions between frontal as well as parietal and temporal areas in relation to the medial temporal lobe. The results of the present study suggest that a transition from phonological loop processing towards an engagement of episodic processing might take place during the processing of interfering irrelevant sounds. We speculate that, in response to the irrelevant sounds, this reflects a dynamic shift in processing as suggested by a closer interaction between a verbal working memory system and the medial temporal lobe memory system.

Humans↗

Organization and distribution of intramuscular connective tissue in normal and immobilized skeletal muscles. An immunohistochemical, polarization and scanning electron microscopic study.

Collagen fiber network is a major contributor to the coherence and tensile strength of normal skeletal muscle. Despite the well-recognized importance of the intramuscular connective tissue to the normal integrity and function of the skeletal muscle, the specific architecture including the location and three-dimensional orientation of the intramuscular connective tissue within the muscle tissue is poorly described. The structure of the intramuscular connective tissue was studied by immunohistochemistry, polarization microscopy (the crimp length and angle of the collagen fibers) and scanning electron microscope (SEM) in rat skeletal muscles (gastrocnemius, soleus and tibialis anterior) in normal situation and after 3 weeks of disuse (immobilization). Three separate networks of collagen fibers were distinguished by SEM in the normal endomysium; fibers running longitudinally on the surface of the muscle fibers (the main collagen orientation), fibers running perpendicularly to the long axis of the muscle fibers and having contacts with adjacent muscle fibers, and fibers attached to the intramuscular nerves and arteries. Similarly, the SEM analysis also disclosed three distinct collagen fiber networks running in different directions in the perimysium, but, contrary to the endomysium, the main fiber orientation could not be established. Immobilization resulted in a marked increase in the endo- and perimysial connective tissue, the majority of the increased endomysial collagen being deposited directly on the sarcolemma of the muscle cells. Immobilization also resulted in substantial increase in the number of perpendicularly oriented collagen fibers with contacts to two adjacent muscle fibers in the endomysium. Further, immobilization clearly disturbed the normal structure of the endomysium making it impossible to distinguish the various networks of fibers from each other. In the perimysium, immobilization-induced changes were similar, the number of longitudinally oriented collagen fibers was increased, the connective tissue was very dense, the number of irregularly oriented collagen fibers was markedly increased, and consequently, the different networks of collagen fibers could not be distinguished from each other. Of the three studied intact muscles, the crimp angle of the collagen fibers was lowest in the soleus and highest in the gastrocnemius muscle, and the crimp angle decreased over 10% in all muscles after the immobilization-period. Altogether, the above described quantitative and qualitative changes in the intramuscular connective tissue are likely to contribute to the deteriorated function and biomechanical properties of the immobilized skeletal muscle.

Animals↗