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80 results for “neural coding”
Code and dataset for neural dynamics of causal inference in the macaque frontoparietal circuit
<p>Natural perception relies inherently on inferring causal structure in the environment. However, the neural mechanisms and functional circuits essential for representing and updating the hidden causal structure and corresponding sensory representations during multisensory processing are unknown. To address this, monkeys were trained to infer the probability of a potential common source from visual and proprioceptive signals based on their spatial disparity in a virtual reality system. The proprioceptive drift reported by monkeys demonstrated that they combined previous experience and current multisensory signals to estimate the hidden common source and subsequently updated the causal structure and sensory representation. Single-unit recordings in premotor and parietal cortices revealed that neural activity in the premotor cortex represents the core computation of causal inference, characterizing the estimation and update of the likelihood of integrating multiple sensory inputs at a trial-by-trial level. In response to signals from the premotor cortex, neural activity in the parietal cortex also represents the causal structure and further dynamically updates the sensory representation to maintain consistency with the causal inference structure. Thus, our results indicate how the premotor cortex integrates previous experience and sensory inputs to infer hidden variables and selectively updates sensory representations in the parietal cortex to support behavior. This dynamic loop of frontal-parietal interactions in the causal inference framework may provide the neural mechanism to answer long-standing questions regarding how neural circuits represent hidden structures for body awareness and agency.</p>
Code and dataset for publication "Laser Wakefield Accelerator modelling with Variational Neural Networks"
<p>Data and code for reproducing figures in published work.</p> <p> </p> <p>High Power Laser Science and Engineering</p> <p><a href="https://doi.org/10.1017/hpl.2022.47">https://doi.org/10.1017/hpl.2022.47</a></p> <p>Code used various python packages including tensorflow.</p> <p>Conda environment was created with (on 6th Jan 2022)<br> conda create --name tf tensorflow notebook tensorflow-probability pandas tqdm scikit-learn matplotlib seaborn protobuf opencv scipy scikit-image scikit-optimize Pillow PyAbel libclang flatbuffers gast --channel conda-forge</p>
Data and Code related to All-optical recreation of naturalistic neural activity with a multifunctional transgenic reporter mouse
<p>Data and code related to the publication "All-optical recreation of naturalistic neural activity with a multifunctional transgenic reporter mouse"</p> <p>Additional code for running online analysis can be found <a href="https://zenodo.org/record/8139926">here</a>, and code for analysis of all-optical calibration and activity recreation experiments can be found <a href="http://doi.org/10.5281/zenodo.8139926">here.</a></p>
Dataset and code for the manuscript 'Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks'
<p>This repository contains the code and data used in the manuscript 'Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks'. <br> Manuscript authors: Dr. Aakash Sane, Dr. Brandon G. Reichl, Dr. Alistair Adcroft, Dr. Laure Zanna<br> Manuscript preprint link: https://doi.org/10.48550/arXiv.2306.09045</p>
Code and dataset for neural dynamics of causal inference in the macaque frontoparietal circuit
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Codes and datasets associated with the paper "Day-ahead Wind Power Predictions at Regional Scales: Post-processing Operational Weather Forecasts with a Hybrid Neural Network"
<p>The jupyter notebooks and datasets associated with the EEM20 forecasts are available here. More details will be provided shortly. </p> <p>Please check the EEM20 website (<a href="https://eem20.eu/forecasting-competition/">https://eem20.eu/forecasting-competition/</a>) for the details of the forecasting competition. </p>
Data from: Stimulus background influences phase invariant coding by correlated neural activity
We recently reported that correlations between the activities of peripheral afferents mediate a phase invariant representation of natural communication stimuli that is refined across successive processing stages thereby leading to perception and behavior in the weakly electric fish Apteronotus leptorhynchus (Metzen et al., 2016). Here, we explore how phase invariant coding and perception of natural communication stimuli are affected by changes in the sinusoidal background over which they occur. We found that increasing background frequency led to phase locking, which decreased both detectability and phase invariant coding. Correlated afferent activity was a much better predictor of behavior as assessed from both invariance and detectability than single neuron activity. Thus, our results not only provide further evidence that correlated activity likely determines perception of natural communication signals, but also provide a novel explanation as to why these preferentially occur on top of low frequency as well as low intensity sinusoidal backgrounds.
Dataset underpinning: "Scalable Neural Decoder for Topological Surface Codes"
<p>The dataset belonging to the paper: Scalable Neural Decoder for Topological Surface Codes</p>
Data and codes: Automated estimation of bioturbation intensity and ichnodiversity from the core section image using convolutional neural network
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Predicting Code Comprehension: A Novel Approach to Align Human Gaze with Code Using Deep Neural Networks
<p><strong>Checkout our Github-Repo for more information, issues, and pull requests: </strong></p> <p><a href="https://github.com/Taremeh/predicting-code-comprehension-eye-tracking/">https://github.com/Taremeh/predicting-code-comprehension-eye-tracking/</a></p> <p> </p> <p>Dataset and Replication Package for our paper "Predicting Code Comprehension: A Novel Approach to Align Human Gaze with Code Using Deep Neural Networks"</p>
Data from: Stimulus background influences phase invariant coding by correlated neural activity
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Data from: Neural correlations enable invariant coding and perception of natural stimuli in weakly electric fish
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Loss of non-coding RNA expression from the DLK1-DIO3 imprinted locus correlates with reduced neural differentiation potential in human embryonic stem cell lines
GEO Series GSE58809. Homo sapiens. 12 samples. Type: Expression profiling by array.
Expression of imprinted non-coding RNAs from the DLK1-DIO3 locus in human embryonic stem cells advantages neural lineage differentiation
GEO Series GSE58508. Homo sapiens. 12 samples. Type: Expression profiling by array.
A functional genomics atlas enhanced by convolutional neural networks facilitates clinical interpretation of disease relevant variants in non-coding regulatory elements [ATAC-seq]
GEO Series GSE263338. Homo sapiens. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
A functional genomics atlas enhanced by convolutional neural networks facilitates clinical interpretation of disease relevant variants in non-coding regulatory elements [ChIP-seq]
GEO Series GSE263337. Homo sapiens. 16 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Robust Hi-C maps of enhancer-promoter interactions reveal the function of non-coding genome in neural development and diseases
GEO Series GSE116825. Homo sapiens. 24 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Other.
A functional genomics atlas enhanced by convolutional neural networks facilitates clinical interpretation of disease relevant variants in non-coding regulatory elements [wt RNA-seq]
GEO Series GSE267549. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Dual genome-wide coding and lncRNA screens in neural induction of induced pluripotent stem cells
GEO Series GSE150062. Homo sapiens. 42 samples. Type: Expression profiling by high throughput sequencing; Other; Genome binding/occupancy profiling by high throughput sequencing.
A functional genomics atlas enhanced by convolutional neural networks facilitates clinical interpretation of disease relevant variants in non-coding regulatory elements [STARR-RNA-seq]
GEO Series GSE263335. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
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.