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110 results for “associative learning”
Dataset associated to the "ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots" paper (manuscript DOI: 10.1109/LRA.2022.3141658)
<pre><code class="language-markdown">This dataset contains data accompanying the work: @ARTICLE{9676410, author={Viceconte, Paolo Maria and Camoriano, Raffaello and Romualdi, Giulio and Ferigo, Diego and Dafarra, Stefano and Traversaro, Silvio and Oriolo, Giuseppe and Rosasco, Lorenzo and Pucci, Daniele}, journal={IEEE Robotics and Automation Letters}, title={ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots}, year={2022}, volume={7}, number={2}, pages={2779-2886}, doi={10.1109/LRA.2022.3141658}} The dataset is organized in folders, whose content can be summarized as follows: - mocap: motion capture data collected from human motion - retargeted_mocap: motion capture data retargeted on the robot - IO_features: input and output features extracted from the retargeted mocap data to train the trajectory generator - training_D2_D3_subsampled_mirrored_4ew_98%: training data - inference: data collected while generating trajectories - trajectory_control_simulation: data collected while controlling trajectories in simulation - trajectory_control_real_robot: data collected while controlling trajectories on the real robot - additional_figures: additional data to reproduce some figures in the paper and portions of the supplementary video A more detailed description of the content of each folder is provided in the README.txt file included in the dataset.</code></pre>
On the strategic learning of signal associations
<p>Signal detection theory (SDT) has been widely used to identify the optimal response of a receiver to a stimulus when it could be generated by more than one signaller type. While SDT assumes that the receiver adopts the optimal response at the outset, in reality receivers often have to learn how to respond. We therefore recast a simple signal detection problem as a multi-armed bandit (MAB) in which inexperienced receivers chose between accepting a signaller (gaining information and an uncertain payoff) and rejecting it (gaining no information but a certain payoff). An exact solution to this exploration-exploitation dilemma can be identified by solving the relevant dynamic programming equation (DPE). However, to evaluate how the problem is solved in practice, we conducted an experiment. Here humans (n = 135) were repeatedly presented with a four readily discriminable signaller types, some of which were on average profitable, and others unprofitable to accept in the long term. We then compared the performance of SDT, DPE and three candidate exploration-exploitation models (Softmax, Thompson and Greedy) in explaining the observed sequences of acceptance and rejection. All of the models predicted volunteer behaviour well when signallers were clearly profitable or clearly unprofitable to accept. Overall however, the Softmax and Thompson sampling models, which predict the optimal (SDT) response towards signallers with borderline profitability only after extensive learning, explained the responses of volunteers significantly better. By highlighting the relationship between the MAB and SDT models, we encourage others to evaluate how receivers strategically learn about their environments.</p>
A test of the role of associative learning in originating sexual preferences in the guppy
<p><span>How do female sexual preferences for male ornamental traits arise? The developmental origins of female preferences are still an understudied area, with most explanations pointing to genetic mechanisms. One intriguing, little-explored, alternative focuses on the role of associative learning in driving this process. According to this hypothesis, a preference learned in an ecological context can be transferred into a sexual context, resulting in changes in mating preferences as a by-product. I tested this hypothesis by first training female guppies to associate either orange or black colour with food delivery; I then presented videos of males with computer-manipulated coloured spots and measured female preference towards them. I also allowed females from both treatments to mate with males differing in their ratio of orange-to-black spots and measured the males' reproductive success. After training, female sexual preferences significantly diverged among treatments in the expected direction. In addition, orange males sired a greater proportion of offspring with females food-conditioned on orange compared to those conditioned on black. These results show that mating preferences can arise as a by-product of associative learning, which, via translation into variation in male fitness, can become associated with indirect genetic benefits, potentially leading to further evolution.</span></p>
Data associated with the publication 'Population-level coding of avoidance learning in medial prefrontal cortex' by Benjamin Ehret et al.
<p>This repository contains data for the following publication:</p> <p>Population-level coding of avoidance learning in medial prefrontal cortex</p> <p>Ehret B., Boehringer R., Amadei E. A., Cervera M. R., Henning C., Galgali A., Mante V., Grewe, B. F.</p> <p>Nature Neuroscience 2024</p> <p> </p> <p>The associated analysis code is published here:</p> <p>https://github.com/behret/paper_code_active_avoidance</p> <p> </p> <p>This repository contains 1) source data to reproduce all figures and 2) processed data to reproduce most analyses. </p> <p>A small subset requires access to the raw data, which is too extensive to be published online. However, raw data can be made available upon request.</p>
Dataset associated with publication of Farnworth et al "Mosaic evolution of a learning and memory circuit in Heliconiini butterflies"
<p>Dataset associated with publication of Farnworth et al "Mosaic evolution of a learning and memory circuit in Heliconiini butterflies"</p>
Figure 1 in Associative colour learning and discrimination in the South African Cape rock sengi Elephantulus edwardii (Macroscelidea, Afrotheria, Mammalia)
Figure 1: Proportion of responses (means) of twenty Elephantulus edwardii in favour of the trained colour plate (in capital letters) against a non-rewarded colour plate in choice experiments. Statistics: Binomial test: ***p <0.001, *p <0.05.
Influence of G-CSF and EPO on Associative Learning and Motor Skills
ClinicalTrials.gov study NCT00298597. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Prospective Observational Study to Predict Severe Oral Mucositis Associated With Chemoradiotherapy in Nasopharyngeal Carcinoma Based on Deep Learning
ClinicalTrials.gov study NCT06032767. IPD Sharing: UNDECIDED. Countries: 1. Publications: 8.
Enhancement of Learning Associated Neural Plasticity by Selective Serotonin Reuptake Inhibitors
ClinicalTrials.gov study NCT02753738. IPD Sharing: Not stated. Countries: 1. Publications: 2.
A Multimodal Approach to Cervical Dystonia Treatment With Association of Botulinum Toxin and Motor Learning Techniques
ClinicalTrials.gov study NCT03247868. IPD Sharing: NO. Countries: 1. Publications: 12.
tDCS Effects on Associative Learning in Older Adults of Retirement Age
ClinicalTrials.gov study NCT02839993. IPD Sharing: Not stated. Countries: 1. Publications: 3.
tDCS Effects on Associative Learning in Older Adults of Working Age
ClinicalTrials.gov study NCT02795702. IPD Sharing: Not stated. Countries: 1. Publications: 3.
tDCS Effects on Associative Learning in Younger Adults
ClinicalTrials.gov study NCT02795715. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Data from: Associative learning of flowers by generalist bumble bees can be mediated by microbes on the petals
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On the strategic learning of signal associations
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Data from: Social foraging extends associative odor-food memory expression in an automated learning assay for Drosophila melanogaster
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Heliconiini butterflies can learn time-dependent reward associations
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Wolbachia manipulate fitness benefits of olfactory associative learning in a parasitoid wasp
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Data from: Developmental changes in hippocampal CA1 single neuron firing and theta activity during associative learning
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Dissociable control of unconditioned responses and associative fear learning by parabrachial CGRP neurons
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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)
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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.