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415 results for “neuro”
A neuro-metabolic account of why daylong cognitive work alters the control of economic decisions
<p>This is the data to reproduce the main analysis of the article "A neuro-metabolic account of why daylong cognitive work alters the control of economic decisions".</p> <p>Please contact antonius.wiehler@gmail.com if you have any questions.</p>
HR-GNSS data used in Neuro-Fuzzy Kinematic Finite-Fault Inversion: 2. Application to the Mw6.2, 24/August/2016, Amatrice Earthquake
<p>Here are the high-rate GNSS data we used to infer the low-frequency components of seismic source radiation within the M 6.2, 24/August/2016, Amatrice Earthquake. In particular, the traces are used to constrain frequencies between 0.03-0.06 Hz. This data has been used to evaluate the performance of the method, in a train/test split procedure, described in the manuscript. We upload data here to comply with AGU Fair data policy (https://www.agu.org/Publish-with-AGU/Publish/Author-Resources/Policies/Data-policy)</p> <p>Please find the pre-print of the manuscript from the ESSOAR (<a href="https://doi.org/10.1002/essoar.10504341.1">https://doi.org/10.1002/essoar.10504341.1</a>).</p> <p>Notice that the complete set of data are reposited on INGV FTP server: ftp://gpsfree.gm.ingv.it/amatrice2016/</p> <p>The data is originally processed by Avallone et al. (2016), and the detailed analysis procedure has been explained there. In the case where you used this data, please cite the original articles: </p> <p>Avallone, A., Latorre, D., Serpelloni, E., Cavaliere, A., Herrero, A., Cecere, G., ... & Selvaggi, G. (2016). Coseismic displacement waveforms for the 2016 August 24 Mw 6.0 Amatrice earthquake (central Italy) carried out from High-Rate GPS data. Annals of Geophysics, 59. (<a href="https://doi.org/10.4401/ag-7275">https://doi.org/10.4401/ag-7275</a>)</p> <p>Avallone, A., Selvaggi, G., D'Anastasio, E., D'Agostino, N., Pietrantonio, G., Riguzzi, F., ... & Zarrilli, L. (2010). The RING network: improvement of a GPS velocity field in the central Mediterranean. Annals of Geophysics, 53(2), 39-54. (<a href="https://doi.org/10.4401/ag-4549">https://doi.org/10.4401/ag-4549</a>)</p> <p> </p>
The Utopian Model: How The Neuro Has Become An Open Science Institution
<p><strong>Episode Summary:</strong></p> <p>In this episode we talk to Dylan Roskams-Edris from Open Science Alliance Officer at Tanenbaum Open Science Institute and The Neuro in Canada. We discussed how The Neuro made itself into the worlds first open neuroscience institution, the challenges and opportunities of embracing Open Science at an institutional level, how Open Science itself needs to be more open, and the potential for scientists working in such a system.</p> <p><strong>Episode Links: </strong></p> <p> <a href="https://www.linkedin.com/in/dylan-roskams-edris-26690598/?originalSubdomain=ca">Dylan Roskams-Edris</a></p> <p><a href="https://twitter.com/dylanwre?lang=en">Twitter</a></p> <ul> <li><a href="https://t.co/N3BFnpiVgv?amp=1">The Neuro</a> <ul> <li><a href="https://twitter.com/TheNeuro_MNI">Twitter</a></li> </ul> </li> </ul>
NeuroGentoo Presentation - Bringing the Power of Gentoo Package Management to (Neuro)Scientific Software Environments
<p>Neuroscience, one of the most computation-reliant fields in the natural sciences, is dependent upon dozens of highly complex software suites, which scientists are often forced to manage manually. Upstream developers often ship bundled dependencies to better support this flawed workflow, and in the resulting mess documenting and reproducing analysis pipelines is neigh-impossible. We seek to correct these shortcoming of both modern software distribution as well as modern data science, by integrating high-quality ebuilds for neuroscientific software into the Gentoo Science Overlay. We also seek to publish a simple NuroGentoo world file (along with appropriate usage instructions) to allow scientists with access to OpenStack, Amazon Elastic Computing, or Docker to launch up and build a system fit for reproducing data analysis run on other NeuroGentoo systems with minimum effort.</p>
Interaction of human keratinocytes and nerve fiber terminals at the neuro-cutaneous unit
<p>These data set belongs to the following publication:</p> <p>Interaction of human keratinocytes and nerve fiber terminals at the neuro-cutaneous unit<br> Christoph Erbacher, Sebastian Britz, Philine Dinkel, Thomas Klein, Markus Sauer, Christian Stigloher, Nurcan Üçeyler</p> <p>Link to corresponding pre-print will be embedded upon upload.</p> <p>Please read the README.txt file before using these data sets</p> <p> </p>
Figure 4. Applying Neuro-Psychoanalysis for building a Technical Model of the Brain
<p>The third approach is based on research findings in psychoanalysis and the Ego-Id-Superego<br> model of Sigmund Freud [22, 23]. The particularity of this model is that it is based on top-down<br> design strategies. The basic idea is to start from the function of the whole brain and then divide the<br> brain functions like in a top-down approach into different modules starting from the Id, Superego,<br> and Ego (see figure 4).</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 9. Modular Hierarchical Organization of Perceptual Neuro-Symbolic Networks
<p>In analogy to how it is reported for the brain by A. Luria, connections of the lowest levels of the architecture of Figure 9 are predefined. Higher-level connections are set via a learning process, concretely via a supervised learning process that was described in detail in . More recent research findings indicate that learning could also already take place at lower levels of<br> perception and that unsupervised learning could be crucial for setting these connections. In, first attempts have been made to develop an unsupervised learning strategy for the model.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 7. Function Principle of Neuro-Symbols
<p>In Figure 7, the basic function principle of neuro-symbols is illustrated. One characteristic of neuro-symbols is that they represent symbolic information. In the case of perception, this symbolic inforamtion are perceptual images like for instance a face or a voice (see Section 4.2.1.2 for more details). Furthermore, neuro-symbols show a number of analogies to biological neurons. They have an activation degree (AD), which indicates if the perceptual image that each neuro-symbol respresents is currently perceived in the environment. Each neuro-symbol has a certain number of inputs and one output. Via the inputs, information about the activation degree of other neurosymbols is collected. Like illustrated in the example of Figure 7, a neuro-symbol representing a face could for instance receive information from neuro-symbols representing a head, eyes, and a mouth.</p>
Dataset of Reaction Times for the Study of Differential Functional Changes in Visual Performance During Acute Exposure to Microgravity Analogue and their Potential Links with Spaceflight-Associated Neuro-Ocular Syndrome
<p><strong>Title</strong>: "Dataset of Reaction Times for the Study of Differential Functional Changes in Visual Performance During Acute Exposure to Microgravity Analogue and their Potential Links with Spaceflight-Associated Neuro-Ocular Syndrome"<br>Zenodo DOI: 10.5281/zenodo.11840654</p> <p><strong>Contains</strong>: simple-reaction-times-VBRHDT-data.csv<br>Dateset of SRT (simple reaction time) values to visual stimuli in different positions in visual field, binocularly observed, in a microgravity analogue study, in four body positions (vertical, horizontal, -6 deg tild, -15 deg tilt). <br>Total records contained: 3584.</p> <p><strong>Institutional Review Board Statement</strong>: The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee “Carol Davila” University of Medicine and Pharmacy Bucharest, Romania, 14877/26.05.2023. Data was collected in SpaceMed Laboratory , CIeH (Center for Innovation and eHealth) of UMF Carol Davila Bucharest, Romania.</p> <p><strong>Citation and</strong> <strong>Detailed description:</strong> see "<em>Differential functional changes in visual performance during acute exposure to microgravity analogue and their potential links with Spaceflight-Associated Neuro-Ocular Syndrome</em>", 2024, by Iftime A, Tofolean IT, Pintilie V, Călinescu O, Busnatu S, Papacocea IR, Diagnostics ,2024; 14(17):1918. doi: 10.3390/diagnostics14171918 <br>https://pubmed.ncbi.nlm.nih.gov/39272703/</p> <p><strong>Structure</strong>: Dataset is in CSV (Comma-Separated Values) format, UTF-8 encoded, text field delimited with quotation marks, in tidy format; one row is one record from the SRT task, variables values are in columns). The first row is the variable name. The variable are:</p> <p>1) "live_row"<br>- type: integer numbers, sequential;<br>- values: the index (order) of each SRT measurement performed in a body position, by a participant (ID). The value is C-based (first measurement is "0", second measurement is "1", etc).</p> <p>2) "response_time"<br>- type: real numbers, continuous values;<br>- values: response time recorded from the participant; values in miliseconds.</p> <p>3) "target_position_x_deg"<br>- type: real numbers, categorical (4 values: +/- 18.478, +/- 0.75);<br>- values: horizontal visual angle, in visual degrees in the visual field, of the position of the stimulus. Screen coordinates are computed from "0" position, foveal fixation, positive values to the right, negative values to the left.</p> <p>4) "target_position_y_deg"<br>- type: real numbers, categorical (2 values: -0.75, -7.654);<br>- values: vertical visual angle, in visual degrees in the visual field, of the position of the stimulus. Screen coordinates are computed from "0" position, foveal fixation, positive values downward, negative values upward.<br>-Note: For the equivalent polar coordinates (as in planimetry testing) see Figure 2 of the mentioned paper. </p> <p>5) "Contrast_Weber_calibrated"<br>- type: real numbers, categorical (2 values: 50.58, 99.3);<br>- values: Measured Weber contrast of the visual stimulus shown, values in percents.</p> <p>6) "hand"<br>- type: categorical, 1 value ("right");<br>- values: hand used by the participant during SRT task.</p> <p>7) "body_position"<br>- type: categorical (4 values: "vertical (90 deg)", "horizontal (0 deg)", "inclined (-6 deg)", "inclined (-15 deg)" );<br>- values: Body position during the SRT task.</p> <p>8) "ID(anon)"<br>- type: integer number, categorical, 8 values;<br>- values: anonymized ID of the participants in the study (8 persons).</p>
Dataset: ClearPoint Neuro, Inc. (CLPT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Alzamend Neuro, Inc. (ALZN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Alzamend Neuro, Inc. (ALZN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 13. Implementation of Neuro-Symbols and their Communication in AnyLogic
<p>Figure 13 and Figure 14 show screenshots of the model implementation in AnyLogic. Figure<br> 13a shows how individual neuro-symbols were implemented. Neuro-symbols are realized by socalled<br> active objects with an input port and an output port via which information is exchanged with<br> other elements. Additionally, variables are used for calculating the activation of the neuro-symbols<br> (not depicted) and for storing properties of neuro-symbols (e.g., the location property). Timers and<br> state charts serve for processing information that arrives in a certain time window or in a certain<br> temporal succession at the input port. Whenever new input information arrives at the input port, the<br> activation degree of the neuro-symbol is recalculated and checked against the threshold value.<br> Based on this, the neuro-symbol is either activated or deactivated and the corresponding<br> information is sent via the output port by using “message objects” (see Figure 13b).</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 15. Input Sources of an Affective Neuro-Symbol Representing an Emotion
<p>Based on the descriptions given above and the concept of neuro-symbolic information<br> processing outlined in Section 4.2, so-called “affective neuro-symbols” were defined for the<br> affective situation assessment architecture (see Figure 15). These affective neuro-symbols can<br> principally receive information from four different sources: (1) body states, (2) objects and events<br> perceived in the environment (external perception), (3) from other emotions and (4) cognitive<br> (reasoning) processes. An input from one of these sources can in certain circumstances already be<br> sufficient to activate an affective neuro-symbol. Different sources can either have an exhibitory or<br> inhibitory effect on the activation of an affective neuro-symbol.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 12. Affinities and Differences of Neuro-Symbolic Networks in Comparison to Classical Neural Networks
<p>After having briefly illustrated the basic function principle of neuro-symbolic networks, this<br> section aims at reviewing their affinities and differences to standard neural networks like for<br> example multi-layer perceptrons (MLPs) [58]. A summary of these affinities and differences is<br> given in Figure 12. The affinities concern certain functions of individual nodes of the networks. In<br> both cases, weighted input information is summed up and an activation function is applied to this<br> sum. In both cases, the individual nodes are interconnected to form networks. Much larger than the<br> number of affinities between neuro-symbolic networks and neural network is however the number<br> of differences. The first difference consists in the application domain. Neuro-symbolic networks<br> have so far mainly been applied for complex, large-scale sensor data processing of multimodal data<br> – an application which can so far barely be handled by neural networks.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 11. Activated Neuro-Symbols for Detecting that a Person walks around in the Room
<p>With the example of Figure 11, also the function of feedback connections can be explained.<br> According to the existing feedforward connections, the neuro-symbol “object stands” would be<br> activated together with the neuro-symbol “object moves” whenever the neuro-symbols “motion”<br> and “object moves” are active, because it is activated by a subset of the neuro-symbols that activate<br> the neuro-symbol “object moves”. This activation would however be undesired in this concrete<br> case. For this reason, an inhibitory feedback connection exists from the neuro-symbol “object<br> moves” to the neuro-symbol “object stands” that inhibits the activation of the neuro-symbol “object<br> stands”.</p>
Dataset of paper: Supervised and Dynamic Neuro-Fuzzy Systems to Classify Physiological Responses in Robot-Assisted Neurorehabilitation (PLOS One)
<p>The data set contains number of user, user's physiological signals (Pulse, SCL, SCR, Respiration rate, Skin temperature), Label, Difficulty level from relax to stress. Label is codified from 1 to 5 corresponding to the Difficulty level.</p>
Data underlying "Neuro-evolutionary evidence for a universal fractal primate brain shape"
<p>This is the data that is required to run the code underlying the paper: Neuro-evolutionary evidence for a universal fractal primate brain shape (eLife 2024)</p> <p>The code can be found here: <a href="https://github.com/cnnp-lab/2024_Folding_scales">https://github.com/cnnp-lab/2024_Folding_scales</a>, and provides more details about the file structure and purpose.</p>
Neuro COVID-19
<p><strong>Incidence of </strong><strong>neurological manifestations and complications in patients with COVID-19 </strong><strong>infection: a propensity score matching analysis</strong></p>
Dataset of A User-driven Hybrid Neuro-symbolic Approach for Knowledge Graph Creation from Relational Data
<p>This dataset contains the following:</p> <p>1. achieved percentage values of the generated RML rules using LXS and manually</p> <p>2. basic information about the example used and with which creation type users started</p> <p>3. all answers of users to the User Experience Questionnaire</p> <p>4. Answers to the structured part of the user interview</p>
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.