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150 results for “self organization”
Cortical topographic motifs emerge in a self-organized map of object space
<p>The human ventral visual stream has a highly systematic organization of object information, but the causal pressures driving these topographic motifs are highly debated. Here, we use self-organizing principles to learn a topographic representation of the data manifold of a deep neural network representational space. We find that a smooth mapping of this representational space showed many brain-like motifs, with large-scale organization by animacy and real-world object size, supported by mid-level feature tuning, with naturally emerging face- and scene-selective regions. While some theories of the object-selective cortex posit that these differently tuned regions of the brain reflect a collection of distinctly specified functional modules, the present work provides computational support for an alternate hypothesis that the tuning and topography of the object-selective cortex reflects a smooth mapping of a unified representational space.</p>
Interplay of self-organization of microtubule asters and crosslinking protein condensates Data
<p>Data sets from all figures and supplemental figures for manuscript entitled "Interplay of self-organization of microtubule asters and crosslinking protein condensates" accepted at PNAS Nexus.</p>
Reference datasets for consistency tests of GENAPOPOP 1.0 software: a user-friendly software to analyse genetic diversity and structure in partially clonal and selfed polyploid organisms.
<p>Datasets companion of the manuscript entitled GenAPoPop 1.0: a user-friendly software to analyse genetic diversity and structure in partially clonal and selfed polyploid organisms, used to achieve consistency test with Spagedi 1.5 software, and used as reference datasets to demonstrate the new possibilities allowed by GenAPoPop software.</p> <p>Raw datasets used for testing GenAPoPop 1.0, A user-friendly software for easily compute genetic analyses of autopolyploid populations packaged for Linux, MacOS and Windows; Results obtained from Spagedi 1.5 (Hardy & Vekemans 2001) and GenAPoPop1.0.</p> <p>Four pseudo-observed genotyping autotetrapolyploid SNP datasets, corresponding respectively to panmictic (A), highly clonal (B), highly selfed (C) and half-clonal-half-selfed (D) reproductive mode scenario. In all these four scenarios, we simulated two populations of 100 individuals each, connected with a migration rate of 0.01 and mutating at a rate of 0.01, genotyped at 10 SNPs. Datasets were recorded 1000 generations after an initial randomly drawing population with equal allele frequencies.</p> <p>One SNP tetraploid genotyping dataset from two French <em>Ludwigia grandiflora subsp. hexapetala</em> populations (aquatic plant from the Angiosperm clade): two populations in which we collected 75 individuals, each genotyped with 36 SNPs using the Hiplex method allowing confident allele dosage (Delord et al. 2018).</p> <p>One microsatellite tetraploid genotyping dataset on two Aulactinia stella populations (sea-anemone from the Cnidaria phylum), sampled on the coast of the arctic ocean. One population of 21 individuals and one population of 15 individuals, both genotyped with 10 microsatellites.</p> <p>We also report here the consistency tests with GenAlex and Spagedi, results of analyses (GPP tab) on 6300 independant simulations and inferences of the quantitative reproductive modes using the bayesian method on CEMP tab made on 6300 another independant simulations.</p>
Dynamic self-organization in fire ant rafts underpins collective longevity and threat responsiveness
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Cortical topographic motifs emerge in a self-organized map of object space
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Data from: Self-organizing nervous systems for robot swarms
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High frequency soil sensor data for SOM input - Complex drivers of riparian soil oxygen variability revealed using self-organizing maps
The provided datasets contain the original (non-normalized) high-frequency soil and meteorological observations that were fed to the Self-Organizing Map (SOM) in order to identify ranges of values associated with low and high soil O2 conditions. For the Champlain Valley (CV) site we used the natural breaks algorithm to subset the data into high and low O2 datasets. O2 values were consistently low at the Green Mountains (GM) site, so we ran a single SOM for all O2 values at this site. The original values were then range-normalized before they were fed to the SOM.
Tropical North Atlantic phases: A Self-Organizing Maps (SOM) approach.
<p>Data from SOM analysis</p>
Self-Organized Construction with Continuous Building Material: Higher Flexibility based on Braided Structures
<p>Paper material.</p>
Controlling noise with self-organized resetting
<p>Data used to generate the figures for the manuscript "Controlling noise with self-organized resetting".</p>
Simple Physical Interactions Yield Social Self-Organization in Honeybees - datasets
<p>Empirical data of the location of the bees in complex thermal environments in specific time intervals. For more details please refer to <br> <br> Szopek M, Stokanic V, Radspieler G and Schmickl T (2021) Simple Physical Interactions Yield Social Self-Organization in Honeybees. <em>Front. Phys.</em> 9:670317. doi: 10.3389/fphy.2021.670317</p> <p> </p> <p>exp_1.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 30 min) for each of the 9 repetitions of Experiment 1 (static thermal environment with one global optimum at 36°C and a pessimum at 30°C).</p> <p>exp_2.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 30 min) for each of the 8 repetitions of Experiment 2 (static thermal environment with one global optimum at 36°C and one local optimum at 32°C).</p> <p>exp_3.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 30 min) for each of the 6 repetitions of Experiment 3 (static thermal environment with two equal optima of 36°C).</p> <p>exp_4.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 105 min) for each of the 17 repetitions of Experiment 4 (dynamic thermal environment).</p> <p>exp_5.csv contains the percentage of bees in the left, the center and the right evaluation zone at minute 30 for i) each of the 10 repetitions of Experiment 5 (static thermal environment with social stimulus) and ii) for each of the 8 repetitions of an experiment with the same thermal environment but without a social stimulus for comparison.</p>
Test data for "Imaging of cellular dynamics in vitro and in situ: from a whole organism to sub-cellular imaging with self-driving, multi-scale microscopy"
<p>This repository contains test data associated with analysis code of low- and high-resolution self-driving multi-scale data from our manuscript "Imaging of cellular dynamics <em>in vitro</em> and <em>in situ</em>: from a whole organism to sub-cellular imaging with self-driving, multi-scale microscopy"</p> <p>by Stephan Daetwyler, Hanieh Mazloom-Farsibaf, Felix Y. Zhou, Dagan Segal, Etai Sapoznik, Bingying Chen, Jill M. Westcott, Rolf A. Brekken, Gaudenz Danuser and Reto Fiolka</p> <p> </p> <p>Code repository: <a href="https://github.com/DaetwylerStephan/multi-scale-image-analysis">https://github.com/DaetwylerStephan/multi-scale-image-analysis</a></p> <p>Documentation: <a href="https://daetwylerstephan.github.io/multi-scale-image-analysis/">https://daetwylerstephan.github.io/multi-scale-image-analysis/</a></p> <p>Preprint: <a href="https://www.biorxiv.org/content/10.1101/2024.02.28.582579v1.full">https://www.biorxiv.org/content/10.1101/2024.02.28.582579v1.full</a></p>
Self-organization of conducting pathways explains complex wave trajectories in procedurally interpolated fibrotic cardiac tissue: a digital-twin study
<p><span>In precision cardiology, digital twinning technology (DT) holds promise for predicting arrhythmias by </span><span>leveraging patient data and biophysics knowledge. However, current DTs are designed to directly reproduce biopotential conduction in cardiac tissue, while only indirect non-invasive methods can be clinically implemented on real organs. This discrepancy challenges our understanding of DT applicability limits. This study aims to enhance DT by developing an in-vitro training complement. We conducted a frame-by-frame comparison of in-vitro optical mapping of biopotential conduction with machine learning (ML) optimized DT predictions. Patient-specific self-organized tissue samples of human induced pluripotent stem cells-derived cardiomyocytes (CMs) with diffuse fibrosis served as DT prototypes. High spatiotemporal resolution optical mapping recordings (</span><span>Δ</span><span>x=117 ± 4 </span><span>μ</span><span>m, </span><span>Δ</span><span>t=7.69 ms) and immunostainings were used to reproduce fibrotic samples with a linear size of 7.5 mm. Using data-driven ML-optimization of the Cellular Potts model, we examined wave propagation at the subcellular level. The modified Glazier-Graner-Hogeweg model accurately reflected the “perinatal window” until the 20th day of differentiation, affecting CMs self-organization. The percolation threshold of virtual conductive pathways reached 26% (26.7 ± 2.9% of CMs in-vitro), resulting in a spatial correlation of amplitude maps between prototype samples and their DT with Pearson’s coefficients of 0.83 ± 0.02. As a proof-of-concept, we demonstrated the ability of ML-optimized DT to predict and interpolate wavefront trajectories in optical mapping recordings. We found that mathematical approximation of fibrosis distribution played a key role in DT prediction accuracy, potentially informing the implementation of LGE-MRI detection of fibrosis within cardiac DT frameworks.<br><br>Dataset A: <span>Immunostaining images were sorted based on the day of enzymatic disaggregation (before and after day 20). We collected and sorted </span><span>α</span><span>-actinin, Connexin43 and DAPI immunostainings </span><span>for Cellular Potts Model optimization. During data processing, cell shape<span>s (n=109 and n=69 for CM and BPs respectively after day 20, n=209 and n=90 for CM and BPs respectively before day 20) were formalized.<br>Dataset C corresponds to FluoVolt recordings <span>(3 samples). Dataset B corresponds to Fluo-4 AM recordings in iPSC-CMs samples with diffuse fibrosis imitation (4 samples)</span>. </span></span></span></p>
In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks - Dataset
<p>This is the dataset of "<em>In materia</em> reservoir computing with a fully memristive architecture based on self-organizing nanowire networks"</p>
Machine-guided path sampling to discover mechanisms of molecular self-organization (Training and validation data)
<p>Training and validation data for the Nature Computational Science manuscript "Machine-guided path sampling to discover mechanisms of molecular self-organization"</p>
Reducing Uncertainty in Collective Perception using Self-organized Hierarchy
<p>This dataset accompanies an article submission and a <a href="https://github.com/BlueDiamond07/Collective_perception">code repository</a>.</p> <p><strong>Abstract:</strong><br> In collective perception, agents sample spatial data and use the samples to agree on some estimate. In this research, we identify the sources of statistical uncertainty that occur in collective perception and note that improving the accuracy of fully decentralized approaches, beyond a certain threshold, might be intractable. We propose self-organized hierarchy as an approach to improve accuracy in collective perception, by reducing or eliminating some of the sources of uncertainty. Using self-organized hierarchy, aspects of centralization and decentralization can be combined: robots can understand their relative positions system-wide and fuse their information at one point, without requiring, e.g., a fully connected or static communication network. In this way, multi-sensor fusion techniques that have been designed for fully centralized systems can be applied to a self-organized system for the first time, without losing the key practical benefits of decentralization. We implement simple proof-of-concept fusion in a self-organized hierarchy approach and test it against three fully decentralized benchmark approaches. We test the perceptual accuracy of the approaches for time-invariant and time-varying absolute conditions, and test the scalability and fault tolerance of their accuracies. We show that the self-organized hierarchy approach is substantially more accurate, more consistent, and faster than the other approaches, but also that it is comparably scalable and fault-tolerant.</p>
Tomography of memory engrams in self-organizing nanowire connectomes - Dataset
<p>This is the dataset of "Tomography of memory engrams in self-organizing nanowire connectomes"</p>
Influence of sex-organ positions on pollen transfer and self-interference in plants with stylar polymorphisms: An experimental approach using three-dimensional printed flowers
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Beyond mutations: accounting for selection and self-organization in the analysis of protein evolution
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A Bibliography on Self-Organization in Manufacturing Systems
<p>Bibliographic Review on Self-Organization in Manufacturing Systems</p> <p>Content:</p> <ul> <li><code>README.md</code> (main documentation)</li> <li><code>pipeline.png</code> (processing pipeline from bibliographic sources to results and figures)</li> <li><code>script/</code> (JS files for data transformation and N3, to use with Linked-Data-Fu, for data collection)</li> <li><code>data/</code> (intermediary RDF files including bibliographic data and citation networks)</li> <li><code>query/</code> (SPARQL query files to process intermediary RDF files)</li> <li><code>result/</code> (TSV and image files as the main result of the bibliographic review)</li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.