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213 results for “Neural Activity”
Dataset for monkeys A and B from AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity
<p>ECoG data from two monkeys, affi (A) and beignet (B) used in AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity. Jingyuan Li, Leo Scholl, Trung Le, Pavithra Rajeswaran, Amy L Orsborn, and Eli Shlizerman. NeurIPS. 2023. https://openreview.net/forum?id=7ntI4kcoqG</p><p>See also https://github.com/shlizee/AMAG</p>
Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models
<p>#############</p> <p>Task-driven neural network models predict neural dynamics of proprioception, Cell 2024</p> <p>#############</p> <p>Authors: Marin Vargas, Alessandro (orcid=0000-0001-7073-4120) and Bisi, Axel (orcid=0009-0006-8602-7555) and Chiappa, Alberto Silvio (orcid=0009-0001-2764-6552) and Versteeg, Christopher (orcid=0000-0002-4269-5109) and Miller, Lee E. (orcid=0000-0001-8675-7140) and Mathis, Alexander (orcid=0000-0002-3777-2202)</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the Cell article:</p> <p><a href="https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf">https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf</a></p> <p>--------------------------------</p> <p>Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception".</p> <p>It contains the behavioral and neural experimental data (cuneate nucleus and somatosensory recordings from the Miller Lab, Northwestern University), the result dataframes for task-driven and untrained models, the activations and predictions for the *best models for all tasks* for active and passive movements and the predictions for linear models for active and passive movements. </p> <p>Note, the predictions of other models can be computed from the network weights that were deposited for all trained models. </p> <p>The overall structure of the data is:</p> <p>└── exp_analysis<br> ├── results - Contains the result dataframe of the predictions for all models, tasks and primates<br> ├── activations<br> │ ├── active - Contains activations related to active movements<br> │ └── passive - Contains activations related to passive movements<br> ├── predictions<br> │ ├── active - Contains predictions related to active movements<br> │ └── passive - Contains predictions related to passive movements<br> └── beh_exp_datasets<br> ├── matlab_data - Contains raw behavioral and neural data<br> ├── MonkeyAlignedDatasets_new - Contains padded test behavioral input for generating network activations<br> ├── MonkeyDatasets - Contains not aligned padded test behavioral input for generating network activations<br> ├── MonkeySpikeRegressDatasets - Contains datasets for training data-driven models<br> ├── MonkeySpikeRegressDatasets_new - Contains trial index for regression splits <br> └── new_beh_exp_dataframe - Contains pre-processed behavioral and neural data</p> <p>--------------------------------</p> <p>The activations and predictions for the best 3 models and for all tasks are stored in experiments folder (in .h5 format) that follows the same name convention of the checkpoints.</p> <p>The checkpoints are stored in experiment folders (experiment_***) that follow this scheme:<br>- Task: shallow exp id, deep TCNs exp id, LSTM id.</p> <p>Experiment IDs for each task:</p> <p>- Untrained: 15, 115, 45<br>- Classification: 4015, 5015, 4045</p> <p>- Torque: 8015, 8030, 8045</p> <p>- Regress joint pos: 17016, 17031, 17046<br>- Regress joint vel: 17216, 17231, 17246<br>- Regress joint pos & vel:: 17416, 17431, 17446<br>- Regress joint pos & vel & acc:: 20516, 20531, 20546</p> <p>- Regress hand pos: 4016, 5016, 4046<br>- Regress hand vel: 17316, 17331, 17346<br>- Regress hand pos & vel: 17516, 17531, 17546<br>- Regress hand pos & vel & acc: 20416, 17831, 17846</p> <p>- Regress hand and elbow pos: 20016, 20031, 20046<br>- Regress hand and elbow vel: 20916, 20931, 20946<br>- Regress hand and elbow pos & vel: 20616, 20631, 20646<br>- Regress hand and elbow pos & vel & acc: 20816, 20831, 20846</p> <p>- Redundancy reduction: 10020, 10035, 10050<br>- Autoencoder 20716 & 20717, 20731 & 20732, X</p> <p> </p> <p>The code to process the behavioral data is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/exp_data_processing">https://github.com/amathislab/Task-driven-Proprioception/tree/master/exp_data_processing</a><br>The code to load and use the models to generate activations and predictions is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/neural_prediction">https://github.com/amathislab/Task-driven-Proprioception/tree/master/neural_prediction</a></p> <p>To reproduce the results, it is possible to reproduce the main figures using the result dataframe. See our repository for more details. </p> <p>--------------------------------</p> <p>The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.</p> <p>The code is released under the MIT license, see <a href="https://github.com/amathislab/Task-driven-Proprioception">https://github.com/amathislab/Task-driven-Proprioception</a></p> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@article{vargas2024task,<br> title={Task-driven neural network models predict neural dynamics of proprioception},<br> author={{Marin Vargas}, Alessandro and Bisi, Axel and Chiappa, Alberto S and Versteeg, Chris and Miller, Lee E and Mathis, Alexander},<br> journal={Cell},<br> year={2024},<br> publisher={Elsevier}<br>}</p>
Automatic monitoring of neural activity with single-cell resolution in behaving Hydra
<p>The ability to record every spike from every neuron in a behaving animal is one of the holy grails of neuroscience. Here, we report coming one step closer towards this goal with the development of an end-to-end pipeline that automatically tracks and extracts calcium signals from individual neurons in the cnidarian <em>Hydra vulgaris</em>. We imaged dually labeled (nuclear tdTomato and cytoplasmic GCaMP7s) transgenic <em>Hydra </em>and developed an open-source Python platform (TraSE-IN) for the Tracking and Spike Estimation of Individual Neurons in the animal during behavior. The TraSE-IN platform comprises a series of modules that segments and tracks each nucleus over time and extracts the corresponding calcium activity in the GCaMP channel. Another series of signal processing modules allows robust prediction of individual spikes from each neuron's calcium signal. This complete pipeline will facilitate the automatic generation and analysis of large-scale datasets of single-cell resolution neural activity in <em>Hydra</em>, and potentially other model organisms, paving the way towards deciphering the neural code of an entire animal.</p>
Reconstruction of Natural Visual Scenes from Primary Visual Cortical Neural Activity Using Adversarial Learning
<p>This data set and code goes with "Reconstruction of Natural Visual Scenes from Primary Visual Cortical Neural Activity Using Adversarial Learning"</p>
Dataset for "Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks"
<p>This is the experimental dataset for the paper "Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks", doi/10.1073/pnas.1716933115.</p>
Automatic and feature-specific prediction related neural activity in the human auditory system
<p>Downsampled (to 100Hz) data of the paper "Automatic and feature specific prediction related neural activity in the human auditory system"</p>
Multi-Omics Visible Drug Activity Prediction with a Biologically Informed Neural Network Model
<p>Drug discovery is a challenging task, it takes several years for a drug to be introduced on the market, with most of<br> the studied drugs not even passing the first phase. The understanding of the mechanisms influencing response to drugs<br> can reduce failures and accelerate drug development. Virtual drug screening, based on Machine Learning models, is a<br> promising field for the prediction of the outcome of a treatment. However, the complex relationships between the features<br> learned by these models are still poorly understood and not easy to interpret.<br> We have designed a Neural Network model for drug sensitivity prediction that leverages a Visible Neural Network, an<br> easily interpretable model, due to its biologically informed nature. The trained model can be inspected to study which<br> biological processes were fundamental for the prediction and to identify the drug properties that affect sensitivity. It<br> combines multi-omics data from various types of tumor tissues and drug representations based on molecular descriptors.<br> The mechanisms learned from the network can also be exploited to find candidate drugs for synergy to predict the effect<br> of combined therapies. We consider the unbalanced nature of public drug screening datasets and show that our model<br> outperforms state-of-the-art visible machine learning models.</p>
Data from: Beyond rhythm - a framework for understanding the frequency spectrum of neural activity
<p>Cognitive and behavioral processes are often accompanied by changes within well-defined frequency bands of the local field potential (LFP i.e., the voltage induced by neuronal activity). These changes are detectable in the frequency domain using the Fourier transform and are often interpreted as neuronal oscillations. However, aside some well-known exceptions, the processes underlying such changes are difficult to track in time, making their oscillatory nature hard to verify. In addition, many non-periodic neural processes can also have spectra that emphasize specific frequencies. Thus, the notion that spectral changes reflect oscillations is likely too restrictive. In this study, we use a simple yet versatile framework to understand the frequency spectra of neural recordings. Using simulations, we derive the Fourier spectra of periodic, quasi-periodic and non-periodic neural processes having diverse waveforms, illustrating how these attributes shape their spectral signatures. We then show how neural processes sum their energy in the local field potential in simulated and real-world recording scenarios. We find that the spectral power of neural processes is essentially determined by two aspects: 1) the distribution of neural events in time and 2) the waveform of the voltage induced by single neural events. Taken together, this work guides the interpretation of the Fourier spectrum of neural recordings and indicates that power increases in specific frequency bands do not necessarily reflect periodic neural activity.</p>
Data from: Beyond rhythm - a framework for understanding the frequency spectrum of neural activity
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Data from: Multiplexed subspaces route neural activity across brain-wide networks
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Automatic monitoring of neural activity with single-cell resolution in behaving Hydra
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Neural ensemble reactivation in REM and SWS coordinate with muscle activity to promote rapid motor skill learning
<p>Neural activity patterns of recent experiences are reactivated during sleep in structures critical for memory storage, including hippocampus and neocortex. This reactivation process is thought to aid memory consolidation. Although synaptic rearrangement dynamics following learning involve an interplay between slow-wave sleep (SWS) and rapid eye movement sleep (REM), most physiological evidence implicates SWS directly following experience as a preferred window for reactivation. Here we show that reactivation occurs in both REM and SWS, and that coordination of REM and SWS activation on the same day is associated with rapid learning of a motor skill. We performed 6-hour recordings from cells in rats' motor cortex as they were trained daily on a skilled reaching task. In addition to SWS following training, reactivation occurred in REM, primarily during the pre-task rest period, and REM and SWS reactivation occurred on the same day in rats that acquired the skill rapidly. Both pre-task REM and posttask SWS activation were coordinated with muscle activity during sleep, suggesting a functional role for reactivation in skill learning. Our results provide the first demonstration that reactivation in REM sleep occurs during motor skill learning, and that coordinated reactivation in both sleep states on the same day, although at different times, is beneficial for skill learning.</p>
Forecasting of the Geomagnetic Activity for the Next 3 Days Utilizing Neural Networks Based on Parameters Related to Large-scale Structures of the Solar Corona
<p>These are supplementary data for the paper "Forecasting of the Geomagnetic Activity for the Next 3 Days Utilizing Neural Networks Based on Parameters Related to Large-scale Structures of the Solar Corona". They are:</p> <ul> <li>Python code to forecast Kp index</li> <li><span>Code to construct a nerual network model</span></li> </ul>
Dataset of Recordings of Enteric Neural Activity using Implantable Bioelectronics (I)
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Long short-term memory (LSTM) recurrent neural network for muscle activity detection
<p><strong>Background: </strong>The accurate temporal analysis of muscle activation is of great interest in many research areas, spanning<br> from neurorobotic systems to the assessment of altered locomotion patterns in orthopedic and neurological<br> patients and the monitoring of their motor rehabilitation. The performance of the existing muscle activity detectors<br> is strongly affected by both the SNR of the surface electromyography (sEMG) signals and the set of features used to<br> detect the activation intervals. This work aims at introducing and validating a powerful approach to detect muscle<br> activation intervals from sEMG signals, based on long short-term memory (LSTM) recurrent neural networks.<br> </p> <p><strong>Methods: </strong>First, the applicability of the proposed LSTM-based muscle activity detector (LSTM-MAD) is studied<br> through simulated sEMG signals, comparing the LSTM-MAD performance against other two widely used approaches,<br> i.e., the standard approach based on Teager–Kaiser Energy Operator (TKEO) and the traditional approach, used in<br> clinical gait analysis, based on a double-threshold statistical detector (Stat). Second, the effect of the Signal-to-Noise<br> Ratio (SNR) on the performance of the LSTM-MAD is assessed considering simulated signals with nine different SNR<br> values. Finally, the newly introduced approach is validated on real sEMG signals, acquired during both physiological<br> and pathological gait. Electromyography recordings from a total of 20 subjects (8 healthy individuals, 6 orthopedic<br> patients, and 6 neurological patients) were included in the analysis.</p> <p><strong>Results</strong>: The proposed algorithm overcomes the main limitations of the other tested approaches and it works<br> directly on sEMG signals, without the need for background-noise and SNR estimation (as in Stat). Results demonstrate<br> that LSTM-MAD outperforms the other approaches, revealing higher values of F1-score (F1-score > 0.91) and Jaccard<br> similarity index (Jaccard > 0.85), and lower values of onset/offset bias (average absolute bias < 6 ms), both on simulated<br> and real sEMG signals. Moreover, the advantages of using the LSTM-MAD algorithm are particularly evident for<br> signals featuring a low to medium SNR.</p> <p><strong>Conclusions</strong>: The presented approach LSTM-MAD revealed excellent performances against TKEO and Stat. The<br> validation carried out both on simulated and real signals, considering normal as well as pathological motor function<br> during locomotion, demonstrated that it can be considered a powerful tool in the accurate and effective recognition/<br> distinction of muscle activity from background noise in sEMG signals.</p> <p> </p>
Alarm cues and alarmed conspecifics: Neural activity during social learning from different cues in Trinidadian guppies
<p>Learning to respond appropriately to novel dangers is often essential to survival and success, but carries risks. Learning about novel threats from others (social learning) can reduce these risks. Many species, including the Trinidadian guppy (<em>Poecilia reticulata</em>), respond defensively to both conspecific chemical alarm cues and conspecifix anti-predator behaviours, and in other fish such social information can lead to a learned aversion to novel threats. However, relatively little is known about the neural substrates underlying social learning and the degree to which different forms of learning share similar neural mechanisms. Here, we explored the neural substrates mediating social learning of novel threats from two different conspecific cues (i.e. social cue-based threat learning). We first demonstrated that guppies rapidly learn about threats paired with either alarm cues or with conspecific threat responses (demonstration). Then, focusing on acquisition rather than recall, we discovered that phospho-S6 expression, a marker of neural activity, was elevated in guppies during learning from alarm cues in the putative homologue of the mammalian lateral septum and the preoptic area. Surprisingly, these changes in neural activity were not observed in fish learning from conspecific demonstration. Together, these results implicate forebrain areas in social learning about threat but raise the possibility that circuits contribute to such learning in a stimulus-specific manner.</p>
The dataset for an article - An Evaluation of 3D-Printed Materials' Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data
<p>Dataset used in the research presented in the article:</p> <p>Szymanik, Barbara. 2022. "An Evaluation of 3D-Printed Materials’ Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data" <em>Materials</em> 15, no. 10: 3727. https://doi.org/10.3390/ma15103727</p> <p>The database in the .mat (matlab) format contains arrays of double type related to: A - original thermograms obtained for the plate made with the 3D printing technique Ar - thermograms with ROI included FITorg - approximation of original thermograms ImDiff, ImInt, ImProp - data obtained after subtracting the approximation.</p>
CODEX: Oscillatory brain activity during acute exercise: Tonic and transient neural response to an Oddball task.
<p><strong>2. Method</strong></p> <p><em>2.1.</em> <em>Participants</em></p> <p>We recruited 20 young males with a high level of aerobic fitness (age between 18-31 years old, average age 23.9 years old) from the University of Granada (Spain). All participants met the inclusion criteria of reporting at least 8 hours of cycling or triathlon training per week, normal or corrected to normal vision, reported no neurological, cardiovascular or musculoskeletal disorders and were taking no medication. Note that high-fit cyclists and triathletes were selected because they are capable of maintaining a pedalling cadence at moderate-to-high intensity during long periods of time. Furthermore, they are able to keep a fixed posture over time, which reduces EEG movement artifacts considerably. Their fitness level was verified by an incremental effort test (see below). Participants were required to maintain a regular sleep-wake cycle for at least one day before each experimental session and to abstain from stimulating beverages or any intense physical activity 24 hours before each session. All subjects gave written informed consent before the study. The protocol was in accordance with both, the ethical guidelines of the University of Granada, and the Declaration of Helsinki.</p> <p> </p> <p><em>2.2. Apparatus and materials</em></p> <p>All participants were fitted with a Polar RS800 CX monitor (Polar Electro Öy, Kempele, Finland) to record their heart rate (HR) during the incremental exercise test. We used a ViaSprint 150 P cycle ergometer (Ergoline GmbH, Germany) to induce physical effort and to obtain power values, and a JAEGER Master Screen gas analyser (CareFusion GmbH, Germany) to provide a measure of gas exchange during the effort test. Oddball stimuli were presented on a 21-inch BENQ screen maintaining a fixed distance of 100 cm between the head of participants and the centre of the screen. E-Prime software (Psychology Software Tools, Pittsburgh, PA, USA) was used for stimulus presentation and behavioural data collection.</p> <p> </p> <p><em>2.3. Fitness Assessments</em></p> <p>Participants came to the laboratory at least one week before the first experimental session to provide the informed consent, complete an anthropometric evaluation (height, weight and body mass index [BMI]) and to familiarize with the oddball task. Subsequently, they performed an incremental cycle-ergometer test to obtain their VO<sub>2max</sub> that was used in the following experimental sessions to adjust the exercise intensity individually. The incremental effort test started with a 3 minutes warm-up at 30 Watts (W), with the power output increasing 10 W every minute. Each participant set his preferred cadence (between 60-90 rpm · min<sup>-1</sup>) during the warm-up period and was asked to maintain this cadence during the entire protocol. The test began at 60 W and was followed by an incremental protocol of 30 W every 3 minutes. Each step of the incremental protocol consisted of 2 minutes of stabilized load and 1 minute of progressive load increase (5 W every 10 seconds). The oxygen uptake (VO<sub>2</sub> ml • min<sup>-1 </sup>• kg<sup>-1</sup>), respiratory exchange ratio (RER; i.e., CO<sub>2</sub> production • O<sub>2</sub> consumption<sup>-1</sup>), relative power output (W • Kg<sup>-1</sup>) and heart rate (bpm) were continuously recorded throughout the test.</p> <p> </p> <p><em>2.4. Experimental sessions</em></p> <p>Participants completed two counterbalanced experimental sessions of approximately 100 min each. To avoid possible fatigue and/or training effects, visits to the laboratory were scheduled on different days allowing 48–72 hours between sessions. On each experimental session, after 10’ warm-up on a cycle-ergometer at a power load of 30% of their individual VO<sub>2max</sub>, participants performed an oddball task for 20’ while pedalling either at 30% (Light intensity exercise session) or 80% (Moderate-intensity exercise session) of their VO<sub>2max</sub>. Upon completion of the oddball task, a 10’ cool down period at 30% of intensity followed (see Table 1). Each participant set his preferred cadence (between 60-90 rpm · min<sup>-1</sup>) before the warm-up and was asked to maintain this cadence throughout the session in order to match conditions, as much as possible, in terms of dual-task demands.</p> <p> </p> <p><em>2.5. Oddball task</em></p> <p>The visual oddball task was based on that reported in Sawaki and Katayama (Sawaki & Katayama, 2007). It consisted of a random presentation of three visual stimuli: a frequent small blue circle (approximately 1.15º x 1.15º), a rare big blue circle (approximately 1.30º x 1.30º), and a rare red square (approximately 2.00º x 2.00º). Small blue circles were considered as standard stimuli (non-target), while big blue circles (target 1) and red squares (target 2) were considered as target stimuli. Stimuli were displayed sequentially on the centre of the screen on a black background. Each trial started with the presentation of a blank screen in a black background for 1200 ms. Then, the stimulus was presented at a random time interval (between 0 and 800 ms) during 150 ms. Participants were instructed to respond to both targets by pressing a button connected to the cycle-ergometer handlebar with the thumb of their dominant hand and to not respond when standard stimuli were shown. Participants were encouraged to respond as accurately as possible. The target stimuli were randomly presented in 20% of trials (10% of target 1, 10% target 2) and the non-target stimulus in the remaining 80% of trials. A total of 600 stimuli were presented. The task lasted for 20 minutes approximately. No breaks were allowed.</p> <p> </p> <p><em>2.6. EEG recording and analysis</em></p> <p>EEG data were recorded at 1000 Hz using a 30-channel actiCHamp System (Brain Products GmbH, Munich, Germany) with active electrodes positioned according to the 10-20 EEG International System and referenced to the Cz electrode. The cap was adapted to individual head size, and each electrode was filled with Signa Electro-Gel (Parker Laboratories, Fairfield, NJ) to optimize signal transduction. Participants were instructed to avoid postural movements as much as possible, and to keep their gaze on the centre of the screen during the task. Electrode impedances were kept below 10 kΩ.</p>
NeuroVox: Bilingual Brain-to-Speech Translation and Neural Activity Detection
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Test dataset for "Rapid estimation of cortical neuron activation thresholds by transcranial magnetic stimulation using convolutional neural networks"
<p>Data corresponding to test dataset used in Aberra AS, Lopez A, Grill WM, Peterchev AV. (2022). "Rapid estimation of cortical neuron activation thresholds by transcranial magnetic stimulation using convolutional neural networks". bioRxiv. Dataset includes:</p> <ul> <li><em>simnibs/ -</em> SimNIBS mesh and E-field solution file used in test dataset (posterior-anterior TMS of M1 in <em>ernie</em> example mesh, meshed with mri2mesh pipeline)</li> <li><em>layer_data/ - </em>surface meshes used for placing and orienting neuron models and corresponding sampling grids for CNNs</li> <li><em>nrn_sim_data/ - </em>Thresholds from NEURON simulations for all 25 model neurons included in the study, each at 4,999-5,000 positions and 12 azimuthal orientations ("ground truth" for CNN) </li> <li><em>cell_data/</em> - Coordinates and morphology information for all model neurons</li> <li><em>weights/</em> - Trained 3D convolutional neural networks for estimating neuron model-specific TMS thresholds given input E-field distributions on a 3D grid (see code/manuscript for dimensions)</li> <li><em>est_data/ </em>- Output of trained CNNs on all E-field data for test dataset <em> </em></li> </ul> <p> </p>
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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.