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235 results for “Stimulus”
Asymmetric retinal direction tuning predicts optokinetic eye movements across stimulus conditions
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Data from: Goal-directed and stimulus-driven selection of internal representations
<p>Raw behavioral and eye-tracking datasets of experiments 1 and 2 reported in the manuscript "Goal-directed and stimulus-driven selection of internal representations", by Freek van Ede, Alexander G. Board, Anna C. Nobre<br> </p>
Data for "Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics"
<p>Experimental and precomputed data for the paper "Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics" by Oesterle et al. 2020 (DOI: <a href="https://doi.org/10.7554/eLife.54997">10.7554/eLife.54997</a>).</p> <p>The cone bipolar cell data has been described and published in the paper "Inhibition decorrelates visual feature representations in the inner retina" by Franke et al. 2017 (DOI: <a href="https://doi.org/10.1038/nature21394">10.1038/nature21394</a>). </p> <p>This data is both a supplement to the Oesterle et al. paper and the code for this paper.</p> <p>The code is available in this <a href="http://github.com/berenslab/CBC_inference">GitHub repository</a>.</p> <p>We recommend downloading the GitHub repository and to follow the instructions there.</p>
Replicating Attribute Amnesia effect in the single-stimulus design
<p>Attribute Amnesia refers to the phenomenon that subjects fail to report some features (e.g., colour) of a stimulus after they have been asked to repetitively report some other features (e.g., numeric parity) of the same stimulus. This effect has shown strong replicability in the multi-stimuli design, in which there is normally one target among three distractor stimuli. Recently, Wang et al. (2021) furtherly showed the robustness of this effect when there was only one stimulus on the screen. As the main experiment of the current project will be conducted online, we aim to pre-register a replication attempt of Experiment 1 of Wang et al. (2021) in the online-experiment setting. This study shows that one can indeed replicate previous in-lab experiments on attribute amnesia in the online setting, demonstrating convincing online data quality for serious investigations. The functions of Webcam-based eyetracking from Labvanced also make rigorous environmental control and recording saccadic data possible.</p>
Sequential maturation of stimulus-specific adaptation in the mouse lemniscal auditory system
<p>Stimulus-specific adaptation (SSA), the reduction of neural activity to a common stimulus that does not generalize to other, rare stimuli, is an essential property of our brain. Although well characterized in adults, it is still unknown how it develops during adolescence and what neuronal circuits are involved. Using in vivo electrophysiology and optogenetics in the lemniscal pathway of the mouse auditory system, we observed SSA to be stable from postnatal day 20 (P20) in the inferior colliculus, to develop until P30 in the auditory thalamus (MGV) and even later in the primary auditory cortex (A1). We found this maturation process to be experience-dependent in A1 but not in MGV, and to be related to alterations in deep but not input layers of A1. We also identified corticothalamic projections to be implicated in MGV SSA development. Together, our results reveal different circuits underlying the sequential SSA maturation and provide a unique perspective to understand predictive coding and surprise across sensory systems.</p>
Supporting information for: Discrimination ability of central visual field testing using stimulus size I, II, and III and relationship with macular ganglion cell thickness in chiasmal compression
<p><strong>Purpose</strong><strong>: </strong>To compare the relationship between macular ganglion cell layer (mGCL) thickness and 10-2 visual field (VF) sensitivity using different stimulus sizes in patients with temporal hemianopia from chiasmal compression.</p> <p><strong>Methods:</strong><strong> </strong>A cross-sectional study was conducted involving 30 eyes from 25 patients with temporal VF loss on 24-2 SITA standard automated perimetry due to previous chiasmal compression and 30 healthy eyes (23 controls). Optical coherence tomography (OCT) of the macular area and 10-2 VF testing using Goldmann stimulus size I (GI), II (GII), and III (GIII) were performed in the Octopus 900 perimeter. For the sake of analysis, mGCL thickness and VF data were segregated into four quadrants (two temporal and two nasal) and two halves (temporal and nasal) centered on the fovea, and the groups were compared using generalized estimated equations. The discrimination ability of GI, GII, and GIII was evaluated, as was the correlation between mGCL and 10-2 VF sensitivity using GI, GII, and GIII. </p> <p><strong>Results:</strong><strong> </strong>All mGCL parameters were significantly reduced in patients compared to controls. 10-2 VF test sensitivity using GI, GII, and GIII was significantly lower in patients than in controls (p≤0.008) for all parameters, except the three nasal divisions when using GI (p=0.41, 0.07 and 0.18) Significant correlations were found between temporal VF sectors (all stimulus sizes) and the corresponding nasal mGCL measurements, with similar discrimination ability. Significant correlations were also observed between all three nasal VF divisions and the corresponding temporal mGCL thickness when using stimulus sizes I and II, but not stimulus size III.</p> <p><strong>Conclusions</strong><strong>:</strong> On 10-2 VF testing, GII outperformed GI and GIII with regard to discrimination ability and structure-function correlation with mGCL thickness in chiasmal compression. Our findings suggest that the use of GII can enhance the diagnostic power of 10-2 VF testing, although further studies are necessary to support this conclusion.</p>
Data from: Stimulus dependent emergence of understanding analogical relations in budgerigars
<p>The ability for analogical reasoning, such as understanding that the relationship between two items is similar to the relationship between two other items, has long been considered to be unique to humans. Whether and to what extent non-human animals can recognize such relational analogies is still debated. We tested such abilities in two groups of budgerigars with a diverse set of item categories in which the figures of the stimuli were quite different in nature (size, color, shape, geometric type, and number). Budgerigars trained to discriminate two identical figures from the same two figures differing in size, generalized the discrimination to test stimuli belonging to the novel categories, demonstrating the spontaneous use of analogical relations. In contrast, budgerigars trained to discriminate two circles that were either the same or different in color seemed to generalize to novel stimuli by perceptual rather than conceptual similarity. The results thus demonstrate the spontaneous emergence of perceiving analogical relations in budgerigars of a nature beyond that demonstrated in previous studies of non-primate species. However, the results also show that whether budgerigars generalize a relationship by perceptual or conceptual similarity seems dependent on the nature of the training stimuli.</p>
tci20111028 - Put-project stimulus task
<p>This dataset contains files recorded from Nakre Ruth Abia. Her descriptions are prompted by a set of videos from the <a href="http://fieldmanuals.mpi.nl/volumes/2004/put-project/">put-project</a>, developed at the Max Planck Institute for Psycholinguistics, Nijmegen.</p> <p>The dataset includes:</p> <ul> <li>audio files (tci20111028-01.wav, tci20111028-02.wav, tci20111028-03.wav)</li> <li>transcription file (tci20111028a-01.eaf)</li> </ul> <p><strong>Note that the transcription file in this record is not up to date!</strong> For an updated version of this file, please download the most recent version of the Komnzo text corpus as a zip-file under <a href="https://doi.org/10.5281/zenodo.1306246">10.5281/zenodo.1306246</a></p> <p>The material was recorded by Christian Döhler as part of a language documentation project for his PhD. The project was located at the <a href="http://chl.anu.edu.au/">School of Culture, History and Language</a> at the <a href="http://anu.edu.au">Australian National University, Canberra</a>. For the most part it was funded by the <a href="http://dobes.mpi.nl/">DOBES project</a> of the <a href="https://www.volkswagenstiftung.de/en/foundation">Volkswagen Foundation</a>.</p> <p>Reference: Bowerman, M., Gullberg, M., Majid, A., & Narasimhan, B. (2004). Put project: The cross-linguistic encoding of placement events. In A. Majid (Ed.), <em>Field Manual Volume 9</em> (pp. 10-24). Nijmegen: Max Planck Institute for Psycholinguistics. doi:<a href="https://doi.org/10.17617/2.492916">10.17617/2.492916</a>.</p>
Ipsilateral stimulus encoding in primary and secondary somatosensory cortex of awake mice
<p>Data and code to accompany "Ipsilateral stimulus encoding in primary and secondary somatosensory cortex of awake mice" by Pala and Stanley, in press, Journal of Neuroscience, 2022.</p>
Data associated with the paper "Exploiting Color space geometry for visual stimulus design across animals"
<p>These are the pickled data files (pickled using pandas.DataFrame objects in Python) associated with the supplementary figures S4 and S7. </p> <p>* amp_1155: Data for S4B</p> <p>* amp_1156: Data for S4A</p> <p>* amp_518: Data for S7C-D</p> <p>* amp_529: Data for S7E-F</p>
Stimulus presentation can enhance spiking irregularity across subcortical and cortical regions
<p>Stimulus presentation is believed to quench neural response variability as measured by fano-factor (FF). However, the relative contribution of within-trial spike irregularity and trial-to-trial rate variability to FF reduction has remained elusive. Here, we introduce a principled approach for accurate estimation of spiking irregularity and rate variability in time for doubly stochastic point processes. Consistent with previous evidence, analysis showed stimulus-induced reduction in rate variability across multiple cortical and subcortical areas. However, unlike what was previously thought, spiking irregularity, was not constant in time and could be enhanced partly due factors beyond local CV measures such as bursting abating the quench in the post-stimulus FF. Simulations confirmed plausibility of a time varying spiking irregularity arising from within and between pool correlations of excitatory and inhibitory neural inputs. By accurate parsing of neural variability, our approach reveals previously unnoticed changes in neural response variability and constrains candidate mechanisms that give rise to observed rate variability and spiking irregularity within brain regions.</p>
Adult-born granule cells improve stimulus encoding and discrimination in the dentate gyrus
<p>Heterogeneity plays an important role in diversifying neural responses to support brain function. Adult neurogenesis provides the dentate gyrus with a heterogeneous population of granule cells (GCs) that were born and developed their properties at different times. Immature GCs have distinct intrinsic and synaptic properties than mature GCs and are needed for correct encoding and discrimination in spatial tasks. How immature GCs enhance the encoding of information to support these functions is not well understood. Here, we record the responses to fluctuating current injections of GCs of different ages to study how they encode stimuli. Immature GCs produce unreliable responses compared to mature GCs, exhibiting imprecise spike timings across repeated stimulation. We use a statistical model to describe the stimulus-response transformation performed by GCs of different ages. We fit this model to the data and obtain parameters that capture GCs encoding properties. Parameter values from this fit reflect the maturational differences of the population and indicate that immature GCs perform a differential encoding of stimuli. To study how this age heterogeneity influences encoding by a population, we perform stimulus decoding using populations that contain GCs of different ages. We find that, despite their individual unreliability, immature GCs enhance the fidelity of the signal encoded by the population and improve the discrimination of similar time dependent stimuli. Thus, the observed heterogeneity confers the population with enhanced encoding capabilities.</p>
Thalamocortical interactions shape hierarchical neural variability during stimulus perception dataset
<p>Dataset used in the Thalamocortical interactions shape hierarchical neural variability during stimulus perception article.</p> <p> </p> <p>Dataset contains neural activity recordings of a vibrotactile detection task recorded in four monkeys in the following areas: somatosensory thalamus (VPL), 3b and area 1 of the somatosensory cortex (S1)</p>
RAW DATA form - Speech Auditory Brainstem Responses: Effects of Background, Stimulus Duration, Consonant-Vowel, and Number of Epochs
<p><strong>Speech Auditory Brainstem Responses: Effects of Background, Stimulus Duration, Consonant-Vowel, and Number of Epochs</strong></p> <p>Ghada BinKhamis, Agnès Léger, Steven L. Bell, Garreth Prendergast, Martin O’Driscoll, and Karolina Kluk</p> <p><strong>doi: 10.1097/AUD.0000000000000648</strong></p> <p><em>(<strong>Please site above article)</strong></em></p> <p> </p> <p><strong>Description of raw EEG (speech-ABR) data main folder, subfolders, and raw EEG files</strong></p> <p><strong>Folder Information</strong></p> <p><strong>Main folder:</strong></p> <ul> <li>Contains 144 subfolders with raw data from 12 participants</li> </ul> <p><strong>Subfolder names:</strong></p> <ul> <li>Each subfolder starts with the participant code <ul> <li>S01, S02, S03, S04, S05, S06, S07, S08, S09, S10, S11, S12</li> </ul> </li> </ul> <ul> <li>Next is the stimulus duration: <ul> <li>40ms, 50ms, 170ms</li> </ul> </li> <li>Next is the CV used to evoke speech-ABRs <ul> <li>ba, da, ga</li> </ul> </li> <li>And finally the background condition <ul> <li>quiet, noise</li> </ul> </li> </ul> <p><strong>Example subfolder names:</strong></p> <ul> <li><em>S01 40ms da noise:</em>Participant number 1, speech-ABRs in response to the 40ms [da] in background noise</li> <li><em>S07 170ms ga quiet:</em>Participant number 7, speech-ABRs in response to the 170ms [ga] in quiet</li> </ul> <p><strong>Each participant has 12 subfolders:</strong></p> <ol> <li>S__ 40ms da quiet </li> <li>S__ 40ms da noise</li> <li>S__ 50ms da quiet</li> <li>S__ 50ms da noise</li> <li>S__ 50ms ba quiet</li> <li>S__ 50ms ba noise</li> <li>S__ 50ms ga quiet</li> <li>S__ 50ms ga noise</li> <li>S__ 170ms da quiet</li> <li>S__ 170ms da noise</li> <li>S__ 170ms ba quiet</li> <li>S__ 170ms ga quiet</li> </ol> <p><strong>Each participant subfolder contains four ‘.mat’ files, ‘.mat’ file names:</strong></p> <ul> <li>Each ‘.mat’ file starts with the participant code <ul> <li>S01, S02, S03, S04, S05, S06, S07, S08, S09, S10, S11, S12 </li> </ul> </li> <li>Next is the stimulus duration: <ul> <li>40ms, 50ms, 170ms</li> </ul> </li> <li>Next is the CV used to evoke speech-ABRs <ul> <li>ba, da, ga</li> </ul> </li> <li>Next is ‘noise’ if background condition was noise</li> <li>Next is the stimulus polarity <ul> <li>Pos for positive/standard</li> <li>Neg for negative (reversed polarity stimulus)</li> </ul> </li> <li>And finally is the recording number for that polarity <ul> <li>R1 is the first recording</li> <li>R2 is the second recording</li> </ul> </li> </ul> <p><strong>Example ‘.mat’ file name:</strong></p> <ul> <li><em>S04 50 ba Neg R1:</em>Participant number 4, speech-ABR in response to the 50ms [ba] in quiet, reversed polarity stimulus, recording number one </li> <li><em>S02 40 da noise Pos R2:</em>Participant number 2, speech-ABR in response to the 40ms [da] in background noise, standard/positive stimulus, recording number two</li> </ul> <p> </p> <p><strong>File Information:</strong></p> <p><strong>Description of ‘.mat’ files that can be accessed and processed using MATLAB (MathWorks):</strong></p> <p>Each ‘.mat’ file is a structure that contains the following fields:</p> <ul> <li>The first nine fields are informational, for example: <ul> <li>xunits: ‘s’ indicates that the recording time window is in seconds, conversion to milliseconds would be required to plot the data in milliseconds</li> <li>start: ‘0’ indicates that both stimulus and recording start at 0 seconds</li> <li>points: <strong>1800</strong>is the number of sample points for speech-ABRs to the 40ms da, this number will be <strong>2200</strong>for the speech-ABRs to the 50ms stimuli (ba, da, ga), and <strong>4600</strong>for the speech-ABRs to the 170ms stimuli (ba, da, ga)</li> <li>chans: 2 is the number of channels (channel 2 is the ipsilateral channel)</li> <li>frames: 3000 is the number of epochs</li> </ul> </li> <li>The last filed <strong>‘values’</strong>is what contains the raw EEG data (1800x2x3000) <ul> <li><strong>1800 </strong>is the number of samples</li> <li><strong>2 </strong>is the number of channels (channel one is recorded from the left ear lobe (A1) and channel two is from the right ear lobe (A2))</li> <li><strong>3000 </strong>is the number of epochs</li> <li>The field <strong>‘values’ </strong>for speech-ABRs to the 50ms stimuli is <strong>2200x2x3000 </strong>and for speech-ABRs to the 170ms stimuli is <strong>4600x2x3000</strong>.</li> </ul> </li> <li>Stimulus starts at 0 seconds per epoch, pre-stimulus baseline may be extracted from the end of each epoch (i.e. before the next stimulus).</li> </ul> <p><strong>Data is recorded in Volts and will need to be converted to Micro Volts</strong></p> <p><strong>Date of data collection</strong>: May to November 2016</p>
Indoor lighting design for healthier workplaces natural and electric light assessment for suitable circadian stimulus
<p>Dataset of "Indoor lighting design for healthier workplaces natural and electric light assessment for suitable circadian stimulus" research</p>
Processing load, and not stimulus evidence, determines the duration of unconscious visual feature integration
<p>Data and MATLAB analysis script for the paper "Processing load, and not stimulus evidence, determines the duration of unconscious visual feature integration"</p>
Data for: Design of stimulus-responsive two-state hinge proteins
<p>Supplementary information for <strong>Design of stimulus-responsive two-state hinge proteins: </strong>Design scripts, design models, and aw DEER data. </p> <p> </p>
Effects of Transcranial Magnetic Stimulation (TMS) and Stimulus Controllability on Pain Perception
ClinicalTrials.gov study NCT01030133. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of a Vibratory Stimulus on Mitigating Nociception-specific Responses to Skin Puncture in Neonates
ClinicalTrials.gov study NCT04050384. IPD Sharing: NO. Countries: 1. Publications: 4.
Fundamental Asynchronous Stimulus Timing Sound Coding Study
ClinicalTrials.gov study NCT02698787. IPD Sharing: Not stated. Countries: 1. Publications: 3.
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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.
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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
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