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28 results for “Discriminant learning”
PsPM-DoxMem2: Pupil, SCR, ECG, EMG and respiration measurement in a classical pavlovian discriminant delay fear conditioning task, reminder under doxycycline/placebo, retention and re-learning
<p>This dataset includes eyetracker, skin conductance response (SCR), electrocardiogram (ECG), respiration and electromyogram (EMG, only relevant for retention phase) measurements. Also included are CS and US information, keypress responses and keypress response times for 79 healthy participants (40 males and 39 females aged 24.8+/-4.9 years). Participants underwent a classical (Pavlovian) discriminant delay fear conditioning task with 1 CS- and 2 CS+ (50% reinforcement), were reminded of one CS+ one week later under either doxycycline or placebo, and were tested in a retention/extinction and re-learning task another week later. CS were isoluminant coloured triangles. US consisted of 0.5 s square electric pulses with 0.2 ms duration and 500 Hz frequency. SOA between the CS onset and US was 3.5 s. CS and US co-terminated. Before the fear conditioning task, participants completed several questionnaires. During the retention/extinction phase, an auditory startle probe (ST) and no US was delivered 3.5 s after CS onset via headphones (102 dB, 40 ms duration with 2 ms on- and offset ramp). In an immediately following re-learning phase, the ST was omitted and the CS reinforced with the same schedule as during acquisition. The ITI was randomly determined on each trial to be 7, 9, or 11 s.</p> <p> </p>
Improving Algorithm-Selectors and Performance-Predictors via Learning Discriminating Training Samples - Code and Data
<p>This repository contains the code and data for reproducibility of the paper 'Improving Algorithm-Selectors and Performance-Predictors via Learning Discriminating Training Samples'. </p> <p>The following files are included:</p> <ul> <li>Plots: Additional plots not in the paper;</li> <li>Code: Python scripts to generate trajectories and perform classification/regression;</li> <li>best_algo.csv : Labels for the classification;</li> <li>performances.csv : Performances used for the regression;</li> <li>SA_parameters.csv : SA parameters for all machine learning tasks;</li> <li>irace_scenario.txt : scenario used for the tuning.</li> </ul>
Sex-dependent discrimination learning in lizards: a meta-analysis
<p>Raw data and R code used for analysis and to create plots</p>
Discrimination of Quartz Genesis Based on Explainable Machine Learning
<p>The investigation of trace elements in quartz samples was conducted via ambient laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The sampled deposits encompassed diverse geological formations, namely porphyry, pegmatite, granite, skarn, epithermal, carlin, greisen, and orogenic deposits. The objective of the study was to employ eight specific trace elements (Al, Ti, Li, Ge, Fe, Na, K, and P) for the characterization of the training samples.</p>
Fruit flies can learn non-elemental olfactory discriminations
Associative learning allows animals to establish links between stimuli based on their concomitance. In the case of Pavlovian conditioning, a single stimulus A (the conditional stimulus, CS) is reinforced unambiguously with an unconditional stimulus (US) eliciting an innate response. This conditioning constitutes an 'elemental' association enabling to elicit a learnt response from A+ without US presentation after learning. However, associative learning may involve a 'complex' CS composed of several components. In that case, the compound may predict a different outcome than the components taken separately, leading to an ambiguity and requiring the animal to perform a so-called 'non-elemental' discrimination. Here we focus on such a non-elemental task, the negative patterning (NP) problem, and provide the first evidence of NP solving in Drosophila. We show that Drosophila learn to discriminate a simple component (A or B) associated to electric shocks (+) from an odour mixture composed either partly (called 'feature-negative discrimination' A+ vs. AB-) or entirely (called 'NP' A+B+ vs. AB-) of the shock associated components. Furthermore, we show that conditioning repetition results in a transition from an elemental to a configural representation of the mixture required to solve the NP task, highlighting the cognitive flexibility of Drosophila.
Discriminative feature learning for Zero resource spoken term discovery (system #1)
<p>This is a preliminary version. More details about the STD system can be found here:<br> <a href="http://raiith.iith.ac.in/5161/1/1476.PDF">http://raiith.iith.ac.in/5161/1/1476.PDF</a><br> <a href="https://pdfs.semanticscholar.org/d235/7870f53eed854fc65b6b0f78fc62b968f0c6.pdf">https://pdfs.semanticscholar.org/d235/7870f53eed854fc65b6b0f78fc62b968f0c6.pdf</a></p>
Discriminative feature learning for Zero resource spoken term discovery (system #1)
<p>This is a preliminary version. More details about the STD system can be found here:<br> <a href="http://raiith.iith.ac.in/5161/1/1476.PDF">http://raiith.iith.ac.in/5161/1/1476.PDF</a><br> <a href="https://pdfs.semanticscholar.org/d235/7870f53eed854fc65b6b0f78fc62b968f0c6.pdf">https://pdfs.semanticscholar.org/d235/7870f53eed854fc65b6b0f78fc62b968f0c6.pdf</a></p>
Data from: Keep numbers in view: Red-eared sliders (Trachemys scripta elegans) learn to discriminate relative quantities
<p><span>The ability to discriminate relative quantities, one of the numerical competences, is considered as an adaptive trait in uncertain environments. Besides humans, previous studies have reported this capacity in several non-human primates and birds.</span><span> Here, we test whether red-eared sliders (<em>Trachemys scripta elegans</em>) can </span><span>discriminate different relative quantities</span><span>. Subjects were first trained to distinguish different stimuli with food reward. Then, they were tested with novel stimuli pairs to demonstrate how they distinguished the stimuli. The r</span><span>esults show that most subjects can complete the initial training and use relative quantity rather than absolute quantity to make choices during testing phase. This study provides behavioural evidence of relative quantity</span> <span>discrimination in a reptile species, and suggests that such capacity may be widespread among vertebrates.</span></p>
Data from: Keep numbers in view: Red-eared sliders (Trachemys scripta elegans) learn to discriminate relative quantities
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Fruit flies can learn non-elemental olfactory discriminations
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Data from: Stimulus discriminability may bias value-based probabilistic learning
Reinforcement learning tasks are often used to assess participants' tendency to learn more from the positive or more from the negative consequences of one's action. However, this assessment often requires comparison in learning performance across different task conditions, which may differ in the relative salience or discriminability of the stimuli associated with more and less rewarding outcomes, respectively. To address this issue, in a first set of studies, participants were subjected to two versions of a common probabilistic learning task. The two versions differed with respect to the stimulus (Hiragana) characters associated with reward probability. The assignment of character to reward probability was fixed within version but reversed between versions. We found that performance was highly influenced by task version, which could be explained by the relative perceptual discriminability of characters assigned to high or low reward probabilities, as assessed by a separate discrimination experiment. Participants were more reliable in selecting rewarding characters that were more discriminable, leading to differences in learning curves and their sensitivity to reward probability. This difference in experienced reinforcement history was accompanied by performance biases in a test phase assessing ability to learn from positive vs. negative outcomes. In a subsequent large-scale web-based experiment, this impact of task version on learning and test measures was replicated and extended. Collectively, these findings imply a key role for perceptual factors in guiding reward learning and underscore the need to control stimulus discriminability when making inferences about individual differences in reinforcement learning.
Physically-Augmented Deep Learning (PADL): Integration of Physical Context for Improved Seismic Event Discrimination
<p>Data for the publication <em>Physically-Augmented Deep Learning (PADL): Integration of Physical Context for Improved Seismic Event Discrimination. </em>Submitted to Geophysical Research Letters<em> </em>(peer review in progress). </p>
Human latent-state generalization through prototype learning with discriminative attention - Subject Data
<p>This is the human subject data associated with the Nature Human Behavior paper, "Human latent-state generalization through prototype learning with discriminative attention." The associated analysis code can be found at the github repository: https://github.com/murraylab/instrumentalLatentStateLearning. </p>
Data and code for: Discrimination between the facial gestures of vocalizing and non-vocalizing lemurs and small apes using deep learning.
<p>Data and code for: Discrimination between the facial gestures of vocalizing and non-vocalizing lemurs and small apes using deep learning</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.
The Influence of Amitriptyline on Learning in a Visual Discrimination Task
ClinicalTrials.gov study NCT01566825. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Multiple factors affect discrimination learning performance, but not between-individual variation, in wild mixed-species flocks of birds
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Data from: Stimulus discriminability may bias value-based probabilistic learning
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Data for: Theropod dinosaur diversity of the lower English Wealden: analysis of a tooth-based fauna from the Wadhurst Clay Formation (Lower Cretaceous: Valanginian) via phylogenetic, discriminant and machine learning methods
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Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods
<p>The physiology of living organisms, such as living plants, is complex and particularly difficult to understand on a macroscopic, organism-holistic level. Among the many options for studying plant physiology, electrical potential and tissue impedance are arguably simple measurement techniques that can be used to gather plant-level information. Despite the many possible uses, our research is exclusively driven by the idea of phytosensing, that is, interpreting living plants’ signals to gather information about surrounding environmental conditions. As ready-to-use plant-level physiological models are not available, we consider the plant as a blackbox and apply statistics and machine learning to automatically interpret measured signals. In simple plant experiments, we expose <em>Zamioculcas zamiifolia</em> and <em>Solanum lycopersicum</em> (tomato) to four different stimuli: wind, heat, red light and blue light. We measure electrical potential and tissue impedance signals. Given these signals, we evaluate a large variety of methods from statistical discriminant analysis and from deep learning, for the classification problem of determining the stimulus to which the plant was exposed. We identify a set of methods that successfully classify stimuli with good accuracy, without a clear winner. The statistical approach is competitive, partially depending on data availability for the machine learning approach. Our extensive results show the feasibility of the blackbox approach and can be used in future research to select appropriate classifier techniques for a given use case. In our own future research, we will exploit these methods to derive a phytosensing approach to monitoring air pollution in urban areas.</p> <p>Data repository for our paper " <em>Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods</em> ", submitted to the journal Bioinspiration & Biomimetics . Please refer to the paper for more information.</p> <p> </p> <p><strong>Contents of this repository</strong></p> <ul> <li><em>mu_interface:</em> Code for our data collection plant experiments, based on Raspberry Pis and the <a href="http://cybertronica.co/?q=products/phytosensor">Cybertronica phytosensing and phytoactuating system</a>.</li> <li><em>SupplementaryCode</em>: Includes the discriminant analysis classifier, raw datasets, calculated features, test-train split and the corresponding code.</li> <li><em>dl-4-tsc:</em> Deep learning framework developed by <a href="https://doi.org/10.1007/s10618-019-00619-1">Fawaz et. al (Deep learning for time series classification: a review)</a> and adapted to our use case. </li> <li><em>DeepClassifier: </em>Trained deep learning time series classifier.</li> <li><em>classification_results.xlsx: </em>Overview of the results from the deep learning framework (accuracy, precision, recall, training time, confusion matrix) and the achieved accuracies using discriminant analysis with sequential forward section (further evaluation metrics of the discriminant analysis can be found in SupplementaryCode.</li> </ul>
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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)
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.