The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Rainer W Friedrich - One of the best experts on this subject based on the ideXlab platform.
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whitening of odor representations by the Wiring Diagram of the olfactory bulb
Nature Neuroscience, 2020Co-Authors: Adrian A Wanner, Rainer W FriedrichAbstract:Neuronal computations underlying higher brain functions depend on synaptic interactions among specific neurons. A mechanistic understanding of such computations requires Wiring Diagrams of neuronal networks. In this study, we examined how the olfactory bulb (OB) performs ‘whitening’, a fundamental computation that decorrelates activity patterns and supports their classification by memory networks. We measured odor-evoked activity in the OB of a zebrafish larva and subsequently reconstructed the complete Wiring Diagram by volumetric electron microscopy. The resulting functional connectome revealed an over-representation of multisynaptic connectivity motifs that mediate reciprocal inhibition between neurons with similar tuning. This connectivity suppressed redundant responses and was necessary and sufficient to reproduce whitening in simulations. Whitening of odor representations is therefore mediated by higher-order structure in the Wiring Diagram that is adapted to natural input patterns. The authors measure evoked activity and perform dense reconstruction of the olfactory bulb Wiring Diagram in a zebrafish larva, uncovering a mechanism for whitening, a computation that decorrelates activity for pattern classification by memory networks.
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whitening of odor representations by the Wiring Diagram of the olfactory bulb
bioRxiv, 2019Co-Authors: Adrian A Wanner, Rainer W FriedrichAbstract:Neuronal computations underlying higher brain functions depend on synaptic interactions among specific neurons. A mechanistic understanding of such computations requires Wiring Diagrams of neuronal networks. We examined how the olfactory bulb (OB) performs ‘whitening’, a fundamental computation that decorrelates activity patterns and supports their classification by memory networks. We measured odor-evoked activity in the OB of a zebrafish larva and subsequently reconstructed the complete Wiring Diagram by volumetric electron microscopy. The resulting functional connectome revealed an overrepresentation of multisynaptic connectivity motifs that mediate reciprocal inhibition between neurons with similar tuning. This connectivity suppressed redundant responses and was necessary and sufficient to reproduce whitening in simulations. Whitening of odor representations is therefore mediated by higher-order structure in the Wiring Diagram that is adapted to natural input patterns.
Adrian A Wanner - One of the best experts on this subject based on the ideXlab platform.
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whitening of odor representations by the Wiring Diagram of the olfactory bulb
Nature Neuroscience, 2020Co-Authors: Adrian A Wanner, Rainer W FriedrichAbstract:Neuronal computations underlying higher brain functions depend on synaptic interactions among specific neurons. A mechanistic understanding of such computations requires Wiring Diagrams of neuronal networks. In this study, we examined how the olfactory bulb (OB) performs ‘whitening’, a fundamental computation that decorrelates activity patterns and supports their classification by memory networks. We measured odor-evoked activity in the OB of a zebrafish larva and subsequently reconstructed the complete Wiring Diagram by volumetric electron microscopy. The resulting functional connectome revealed an over-representation of multisynaptic connectivity motifs that mediate reciprocal inhibition between neurons with similar tuning. This connectivity suppressed redundant responses and was necessary and sufficient to reproduce whitening in simulations. Whitening of odor representations is therefore mediated by higher-order structure in the Wiring Diagram that is adapted to natural input patterns. The authors measure evoked activity and perform dense reconstruction of the olfactory bulb Wiring Diagram in a zebrafish larva, uncovering a mechanism for whitening, a computation that decorrelates activity for pattern classification by memory networks.
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whitening of odor representations by the Wiring Diagram of the olfactory bulb
bioRxiv, 2019Co-Authors: Adrian A Wanner, Rainer W FriedrichAbstract:Neuronal computations underlying higher brain functions depend on synaptic interactions among specific neurons. A mechanistic understanding of such computations requires Wiring Diagrams of neuronal networks. We examined how the olfactory bulb (OB) performs ‘whitening’, a fundamental computation that decorrelates activity patterns and supports their classification by memory networks. We measured odor-evoked activity in the OB of a zebrafish larva and subsequently reconstructed the complete Wiring Diagram by volumetric electron microscopy. The resulting functional connectome revealed an overrepresentation of multisynaptic connectivity motifs that mediate reciprocal inhibition between neurons with similar tuning. This connectivity suppressed redundant responses and was necessary and sufficient to reproduce whitening in simulations. Whitening of odor representations is therefore mediated by higher-order structure in the Wiring Diagram that is adapted to natural input patterns.
Sha Sha - One of the best experts on this subject based on the ideXlab platform.
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the recognition and information extraction of grid Wiring Diagram based on convolutional neural network
International Conference on Computer Vision, 2020Co-Authors: Weirui Yue, Xuyang Wang, Donghai Chen, Zhengwei Jiang, Sha ShaAbstract:With the rapid development of digital image processing and recognition, the intelligent grid wins a great opportunity. We could be able to get the information from digital images quickly based on computer analysis, algorithms of deep learning and machine learning, grid data extraction, model training and image recognition. The article firstly reviewed the current status of traditional grid, including a broad overview of the specific strengths and weaknesses, secondly analyzed the intelligence algorithms of deep learning and machine learning based on neural network, then achieved the recognition and reconstruction of Wiring Diagram through image recognition based on neural network, lastly showed the prospects of image recognition in power system.
Reinhard Laubenbacher - One of the best experts on this subject based on the ideXlab platform.
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AND-NOT logic framework for steady state analysis of Boolean network models
Applied Mathematics & Information Sciences, 2013Co-Authors: Alan Veliz-cuba, Kristina Buschur, Rose Hamershock, Ariel Kniss, Esther Wolff, Reinhard LaubenbacherAbstract:In this paper we propose the class of AND-NOT networks for modeling biological systems and show that it provides several advantages. Some of the advantages include: Any finite dynam ical system can be written as an AND-NOT network with similar dynamical properties. There is a one-to-one correspondence between AND-NOT networks, their Wiring Diagrams, and their dynamics. Results about AND-NOT networks can be stated at the Wiring Diagram level without losing any information. Results about AND-NOT networks are applicable to any Boolean network. We apply our results to a Boolean model of Th-cell differentiation.
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AND-NOT logic framework for steady state analysis of Boolean network models
arXiv: Molecular Networks, 2012Co-Authors: Alan Veliz-cuba, Kristina Buschur, Rose Hamershock, Ariel Kniss, Esther Wolff, Reinhard LaubenbacherAbstract:Finite dynamical systems (e.g. Boolean networks and logical models) have been used in modeling biological systems to focus attention on the qualitative features of the system, such as the Wiring Diagram. Since the analysis of such systems is hard, it is necessary to focus on subclasses that have the properties of being general enough for modeling and simple enough for theoretical analysis. In this paper we propose the class of AND-NOT networks for modeling biological systems and show that it provides several advantages. Some of the advantages include: Any finite dynamical system can be written as an AND-NOT network with similar dynamical properties. There is a one-to-one correspondence between AND-NOT networks, their Wiring Diagrams, and their dynamics. Results about AND-NOT networks can be stated at the Wiring Diagram level without losing any information. Results about AND-NOT networks are applicable to any Boolean network. We apply our results to a Boolean model of Th-cell differentiation.
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The Dynamics of Conjunctive and Disjunctive Boolean Network Models
Bulletin of Mathematical Biology, 2010Co-Authors: Abdul Salam Jarrah, Reinhard Laubenbacher, Alan Veliz-cubaAbstract:For many biological networks, the topology of the network constrains its dynamics. In particular, feedback loops play a crucial role. The results in this paper quantify the constraints that (unsigned) feedback loops exert on the dynamics of a class of discrete models for gene regulatory networks. Conjunctive (resp. disjunctive) Boolean networks, obtained by using only the AND (resp. OR) operator, comprise a subclass of networks that consist of canalyzing functions, used to describe many published gene regulation mechanisms. For the study of feedback loops, it is common to decompose the Wiring Diagram into linked components each of which is strongly connected. It is shown that for conjunctive Boolean networks with strongly connected Wiring Diagram, the feedback loop structure completely determines the long-term dynamics of the network. A formula is established for the precise number of limit cycles of a given length, and it is determined which limit cycle lengths can appear. For general Wiring Diagrams, the situation is much more complicated, as feedback loops in one strongly connected component can influence the feedback loops in other components. This paper provides a sharp lower bound and an upper bound on the number of limit cycles of a given length, in terms of properties of the partially ordered set of strongly connected components.
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Reverse engineering of dynamic networks.
Annals of the New York Academy of Sciences, 2007Co-Authors: Brandilyn Stigler, Abdul Salam Jarrah, Michael Stillman, Reinhard LaubenbacherAbstract:We consider the problem of reverse-engineering dynamic models of biochemical networks from experimental data using polynomial dynamic systems. In earlier work, we developed an algorithm to identify minimal Wiring Diagrams, that is, directed graphs that represent the causal relationships between network variables. Here we extend this algorithm to identify a most likely dynamic model from the set of all possible dynamic models that fit the data over a fixed Wiring Diagram. To illustrate its performance, the method is applied to simulated time-course data from a published gene regulatory network in the fruitfly Drosophila melanogaster.
Charles Boone - One of the best experts on this subject based on the ideXlab platform.
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integrating genetic and protein protein interaction networks maps a functional Wiring Diagram of a cell
Current Opinion in Microbiology, 2018Co-Authors: Benjamin Vandersluis, Michael Costanzo, Chad L. Myers, Brenda J. Andrews, Maximilian Billmann, Henry N Ward, Charles BooneAbstract:Systematic experimental approaches have led to construction of comprehensive genetic and protein-protein interaction networks for the budding yeast, Saccharomyces cerevisiae. Genetic interactions capture functional relationships between genes using phenotypic readouts, while protein-protein interactions identify physical connections between gene products. These complementary, and largely non-overlapping, networks provide a global view of the functional architecture of a cell, revealing general organizing principles, many of which appear to be evolutionarily conserved. Here, we focus on insights derived from the integration of large-scale genetic and protein-protein interaction networks, highlighting principles that apply to both unicellular and more complex systems, including human cells. Network integration reveals fundamental connections involving key functional modules of eukaryotic cells, defining a core network of cellular function, which could be elaborated to explore cell-type specificity in metazoans.
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Charting the genetic interaction map of a cell
Current opinion in biotechnology, 2010Co-Authors: Michael Costanzo, Anastasia Baryshnikova, Chad L. Myers, Brenda J. Andrews, Charles BooneAbstract:Genome sequencing projects have revealed a massive catalog of genes and astounding genetic diversity in a variety of organisms. We are now faced with the formidable challenge of assigning functions to thousands of genes, and how to use this information to understand how genes interact and coordinate cell function. Studies indicate that the majority of eukaryotic genes are dispensable, highlighting the extensive buffering of genomes against genetic and environmental perturbations. Such robustness poses a significant challenge to those seeking to understand the Wiring Diagram of the cell. Genome-scale screens for genetic interactions are an effective means to chart the network that underlies this functional redundancy. A complete atlas of genetic interactions offers the potential to assign functions to most genes identified by whole genome sequencing projects and to delineate a functional Wiring Diagram of the cell. Perhaps more importantly, mapping genetic networks on a large-scale will shed light on the general principles and rules governing genetic networks and provide valuable information regarding the important but elusive relationship between genotype and phenotype.
