scholarly journals Expansion and contraction of resource allocation in sensory bottlenecks

2021 ◽  
Author(s):  
Laura R. Edmondson ◽  
Alejandro Jiménez Rodríguez ◽  
Hannes P. Saal
Keyword(s):  
Resource Allocation ◽  
Information Transmission ◽  
Model System ◽  
Second Order ◽  
Receptor Density ◽  
Close Match ◽  
Efficient Coding ◽  
Cortical Magnification

Topographic sensory representations often do not scale proportionally to the size of their input regions, with some expanded and others contracted. In vision, the foveal representation is magnified cortically, as are the fingertips in touch. What principles drive this allocation, and how should receptor density, e.g. the high innervation of the fovea or the fingertips, and stimulus statistics, e.g. the higher contact frequencies on the fingertips, contribute? Building on work in efficient coding, we address this problem using linear second-order models that maximize information transmission through decorrelation. We introduce a sensory bottleneck to impose constraints on resource allocation and derive the optimal neural allocation. We find that bottleneck width is a crucial factor in resource allocation, inducing either expansion or contraction. Both receptor density and stimulus statistics affect allocation and jointly determine convergence for wider bottlenecks. Furthermore, we show a close match between the predicted and empirical cortical allocations in a well-studied model system, the star-nosed mole. Overall, our results suggest that the strength of cortical magnification depends on resource limits.

scholarly journals Protein Complexes Detection Based on Node Local Properties and Gene Expression On PPI Weighted Networks

2021 ◽  
Author(s):  
Yang Yu ◽  
Dezhou Kong
Keyword(s):  
Gene Expression ◽  
Resource Allocation ◽  
Protein Complex ◽  
Protein Complexes ◽  
Second Order ◽  
Topological Properties ◽  
Ppi Network ◽  
Weighted Networks ◽  
Gene Expression Information ◽  
Local Properties

Abstract Background Identifying protein complexes from protein–protein interaction (PPI) networks is a crucial task, and many related algorithms have been developed to solve this issue. These algorithms usually consider a node’s direct neighbors and ignore resource allocation and second-order neighbors. The effective use of such information is crucial to protein complex detection.Results To overcome this deficiency, this paper proposes a new protein complex identification method based on node-local topological properties and gene expression information on a new weighted PPI network, named NLPGE-WPN (joint node-local topological properties and gene expression information on weighted PPI network). First, based on the resource allocation of the PPI network and gene expression, a new weight metric is designed to describe the interaction between proteins. Second, our method constructs a series of dense complex cores based on density and network diameter constraints; the final complexes are recognized by expanding the second-order neighbor nodes of core complexes. Experimental results demonstrate that this algorithm has improved the performances of precision and f-measure, which is more valid in identifying protein complexes．Conclusions This identification method is simple and can accurately identify more complexes by integrating node-local properties and gene expression on PPI weighted networks.

scholarly journals Direct visualization of superselective colloid-surface binding mediated by multivalent interactions

2021 ◽  
Vol 118 (36) ◽  
pp. e2106036118
Author(s):  
Christine Linne ◽  
Daniele Visco ◽  
Stefano Angioletti-Uberti ◽  
Liedewij Laan ◽  
Daniela J. Kraft
Keyword(s):  
Colloidal Particles ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
Model System ◽  
Receptor Density ◽  
Mobile Dna ◽  
Surface Binding ◽  
Receptor Level ◽  
Foerster Resonance Energy Transfer ◽  
Multivalent Interactions

Reliably distinguishing between cells based on minute differences in receptor density is crucial for cell–cell or virus–cell recognition, the initiation of signal transduction, and selective targeting in directed drug delivery. Such sharp differentiation between different surfaces based on their receptor density can only be achieved by multivalent interactions. Several theoretical and experimental works have contributed to our understanding of this “superselectivity.” However, a versatile, controlled experimental model system that allows quantitative measurements on the ligand–receptor level is still missing. Here, we present a multivalent model system based on colloidal particles equipped with surface-mobile DNA linkers that can superselectively target a surface functionalized with the complementary mobile DNA-linkers. Using a combined approach of light microscopy and Foerster resonance energy transfer (FRET), we can directly observe the binding and recruitment of the ligand–receptor pairs in the contact area. We find a nonlinear transition in colloid-surface binding probability with increasing ligand or receptor concentration. In addition, we observe an increased sensitivity with weaker ligand–receptor interactions, and we confirm that the timescale of binding reversibility of individual linkers has a strong influence on superselectivity. These unprecedented insights on the ligand–receptor level provide dynamic information into the multivalent interaction between two fluidic membranes mediated by both mobile receptors and ligands and will enable future work on the role of spatial–temporal ligand–receptor dynamics on colloid-surface binding.

scholarly journals Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Tiberiu Tesileanu ◽  
Mary M Conte ◽  
John J Briguglio ◽  
Ann M Hermundstad ◽  
Jonathan D Victor ◽  
...  
Keyword(s):  
Dimensional Space ◽  
Second Order ◽  
Visual Sensitivity ◽  
Natural Scenes ◽  
Large Set ◽  
Natural Scene Statistics ◽  
Image Statistics ◽  
Efficient Coding ◽  
Fractional Error ◽  
Relative Salience

Previously, in Hermundstad et al., 2014, we showed that when sampling is limiting, the efficient coding principle leads to a ‘variance is salience’ hypothesis, and that this hypothesis accounts for visual sensitivity to binary image statistics. Here, using extensive new psychophysical data and image analysis, we show that this hypothesis accounts for visual sensitivity to a large set of grayscale image statistics at a striking level of detail, and also identify the limits of the prediction. We define a 66-dimensional space of local grayscale light-intensity correlations, and measure the relevance of each direction to natural scenes. The ‘variance is salience’ hypothesis predicts that two-point correlations are most salient, and predicts their relative salience. We tested these predictions in a texture-segregation task using un-natural, synthetic textures. As predicted, correlations beyond second order are not salient, and predicted thresholds for over 300 second-order correlations match psychophysical thresholds closely (median fractional error <0.13).

scholarly journals Protein complexes detection based on node local properties and gene expression in PPI weighted networks

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Yang Yu ◽  
Dezhou Kong
Keyword(s):  
Gene Expression ◽  
Resource Allocation ◽  
Protein Complexes ◽  
Second Order ◽  
Protein Protein Interaction ◽  
Protein Complex Detection ◽  
Ppi Networks ◽  
Weight Protein ◽  
Local Properties ◽  
Effective Use

Abstract Background Identifying protein complexes from protein–protein interaction (PPI) networks is a crucial task, and many related algorithms have been developed. Most algorithms usually employ direct neighbors of nodes and ignore resource allocation and second-order neighbors. The effective use of such information is crucial to protein complex detection. Result Based on this observation, we propose a new way by combining node resource allocation and gene expression information to weight protein network (NRAGE-WPN), in which protein complexes are detected based on core-attachment and second-order neighbors. Conclusions Through comparison with eleven methods in Yeast and Human PPI network, the experimental results demonstrate that this algorithm not only performs better than other methods on 75% in terms of f-measure+, but also can achieve an ideal overall performance in terms of a composite score consisting of five performance measures. This identification method is simple and can accurately identify more complexes.

scholarly journals Unveiling uncertain forces in second-order driven oscillatorsviainternal model: A discrete-time feedback

2000 ◽  
Vol 5 (2) ◽  
pp. 89-96 ◽  
Author(s):  
R. Femat ◽  
C. Jiménez-gallegos
Keyword(s):  
Time Series ◽  
Nonlinear System ◽  
Discrete Time ◽  
Internal Model ◽  
Main Idea ◽  
Model System ◽  
Second Order

Unveil uncertain forces acting into (or onto) systems is a very interesting and old problem. Indeed, a lot effort has been devoted to develop procedures which results in the understanding of the uncertain forces and its effects. This contribution deals with recovering of the dynamics of the uncertain forces from measurements (time series). The main idea is to construct aninternal modelof the nonlinear system and design a discretetime feedback in such way that the model/system differences be stabilized at origin. In principle, if the internal model tracks the trajectory of the nonlinear system, then the uncertain force is recovered by the stabilizing command.

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