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468 results for “Spikes”
Data from: Single mechanosensory neurons encode lateral displacements using precise spike timing and thresholds
During locomotion, animals rely on multiple sensory modalities to maintain stability. External cues may guide behavior, but they must be interpreted in the context of the animal's own body movements. Mechanosensory cues that can resolve dynamic internal and environmental conditions, like those from vertebrate vestibular systems or other proprioceptors, are essential for guided movement. How do afferent proprioceptor neurons transform movement into a neural code? In flies, modified hindwings known as halteres detect forces produced by body rotations, and are essential for flight. The mechanisms by which haltere neurons transform forces resulting from three-dimensional body rotations into patterns of neural spikes are unknown, however. We use intracellular electrodes to record from haltere primary afferent neurons during a range of haltere motions. We find that spike timing activity of individual neurons changes with displacement, and propose a mechanism by which single neurons can encode three-dimensional haltere movements during flight.
Data from: Using matrix and tensor factorizations for the single-trial analysis of population spike trains
Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding.
FIGURE 4 in A new species of ' spike-nosed' frog from northern New Guinea (Anura: Hylidae: Litoria)
FIGURE 4. Litoria chrisdahli sp. nov. paratype (SAMA R62506) in life.
FIGURE 6 in A new species of ' spike-nosed' frog from northern New Guinea (Anura: Hylidae: Litoria)
FIGURE 6. Detail of pulse structure of final two notes illustrated in Fig. 5.
FIGURE 3. Racinaea condorensis Manzan. & Gouda. A. Spike. B. Flower bract. C in Four new species of the genus Racinaea (Bromeliaceae) from Ecuador
FIGURE 3. Racinaea condorensis Manzan. & Gouda. A. Spike. B. Flower bract. C. Sepal.
Data Sets "Light-Induced Spiking in Proteinoids Yields Boolean Gates"
<p>The CSV files comprise the data upon which the figures in the published manuscript are based. The recorded electrical potentials (mV) in seconds (sec) for various experimental conditions on the proteinoids samples are detailed in each individual file. The timestamps enable the reconstruction of temporal dynamics, whereas the potential levels characterise the form and amplitude of the electrical responses exhibited by the proteinoids in response to different light stimuli. In the end, these measurements facilitated the implementation of logic operations that determined the behaviours of the Boolean gates.</p><p> </p>
Analogue Memristive Devices based on La2NiO4+δ as Synapses for Spiking Neural Networks
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Spike Trains in PANI--Proteinoid Nanofibers with Different Light Pulse Rates
<p>Origin Pro datasets for the paper ''Spike Trains in PANI--Proteinoid Nanofibers with Different Light Pulse Rates.''</p>
Figure 4 from: Bozhanov S, Smerikarova M, Maslarska V (2024) Simultaneous HPLC determination of remdesivir and dexamethasone in the presence of metformin, sitagliptin, and glimepiride in a synthetic mixture and spiked human plasma. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e120463
Figure 4 Representative chromatograms obtained from A. The blank and B. Spiked plasma samples. Legend: Peak numbers: DXM-2, STG-3, RDV-4 and GLM-5.
Figure 3 from: Bozhanov S, Smerikarova M, Maslarska V (2024) Simultaneous HPLC determination of remdesivir and dexamethasone in the presence of metformin, sitagliptin, and glimepiride in a synthetic mixture and spiked human plasma. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e120463
Figure 3 Representative chromatograms obtained from A. The mobile phase and B. Synthetic mixture. Legend: Peak numbers: MTF-1, DXM-2, STG-3, RDV-4, and GLM-5.
Exploring Conformational Landscapes and Binding Mechanisms of Convergent Evolition for the SARS-CoV-2 Spike Omicron Variant Complexes with the ACE2 Receptor Using AlphaFold2-Based Structural Ensembles and Molecular Dynamics Simulations
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neural recordings - spike trains
SciDraw upload
Spike Dataset for NeurIPS Submission
<p>Anonymous</p>
Squeegee: de novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes, simulation dataset 0.5% spike-in contaminant sequences
<p>Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic "bread crumbs” across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision.</p> <p> </p> <p>Simulation Dataset 0.5% contaminant spike-in. </p>
AlphaFold2 Modeling and Molecular Dynamics Simulations of the Conformational Ensembles for the SARS-CoV-2 Spike Omicron JN.1, KP.2 and KP.3 Variants : Mutational Profiling of Binding Energetics Reveals Epistatic Drivers of the ACE2 Affinity and Escape Hotspots of Antibody Resistance
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Supplementary Figure 1. Comparison of diagnostic sensitivity of African swine fever virus (ASFV) real-time PCR kits using artificially spiked samples (ASSs)
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Machine Learning on the Impacts of Mutations in the SARS-CoV-2 Spike RBD on Binding Affinity to Human ACE2 based on Deep Mutational Scanning Data
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Figure 2 in giant, spike-toothed salmon, Oncorhynchus rastrosus and the "Proto-Tuolumne River" (early Pliocene) of Central California
Figure 2. Physical differences in marine vs freshwater (spawning) stages of extant Sockeye salmon, the closest living relative of O. rastrosus (photos from www.arkive.com).
Figure 6 in giant, spike-toothed salmon, Oncorhynchus rastrosus and the "Proto-Tuolumne River" (early Pliocene) of Central California
Figure 6. Stratigraphy of the upper Mehrten Formation (Modesto Reservoir member) at Turlock Lake. Modified from Wagner, 1981. Important and published fossil localities are indicated by arrows. Badger locality (Wagner 1976); Tortoise locality (Biewer et al. 2016); Plant localities (Axelrod 1980). The two highlighted, deep channels are what we refer to as the "proto-Tuolumne River" deposits.
Aqueous Release to Treat IOP Spikes Post-cataract Surgery
ClinicalTrials.gov study NCT03500809. IPD Sharing: NO. Countries: 0. Publications: 7.
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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.