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77 results for “neural circuits”
Bayesian Surprise Shapes Neural Responses in Somatosensory Cortical Circuit
<p>Numerous psychophysical studies demonstrate that Bayesian inference governs sensory decision-making, however the specific neural circuitry underlying this probabilistic mechanism remains unknown. We record extracellular neural activity along the somatosensory pathway of mice while delivering sensory stimulation paradigms designed to isolate the response to the surprise generated by Bayesian inference. Our results demonstrate that laminar cortical circuits in early sensory areas encode Bayesian surprise. Systematic sensitivity to surprise is not identified in the somatosensory thalamus, rather emerging in the primary (S1) and secondary (S2) somatosensory cortices. Multiunit spiking activity and evoked potentials in layer 6 of these regions exhibit the highest sensitivity to surprise. Gamma power in S1 layer 2/3 exhibits an NMDAR-dependent scaling with surprise, as does alpha power in layers 2/3 and 6 of S2. These results demonstrate a precise spatiotemporal neural representation of Bayesian surprise<br> and suggest that Bayesian inference is a fundamental component of cortical proc</p>
Petrucco et al, Neural dynamics and architecture of the heading direction circuit in a vertebrate brain [Dataset]
<p>Data supporting the paper <a href="https://www.biorxiv.org/content/10.1101/2022.04.27.489672v1.full">Neural dynamics and architecture of the heading direction circuit in a vertebrate brain</a>. </p>
A recurrent neural circuit in Drosophila temporally sharpens visual inputs
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Data from: The neural basis of resting-state fMRI functional connectivity in fronto-limbic circuits revealed by chemogenetic manipulation
<p>Included are raw neuroimaging and preprocessed neural recording data from "The neural basis of resting-state fMRI functional connectivity in fronto-limbic circuits revealed by chemogenetic manipulation" (see Related Works section; citation will be updated after publication). Please cite this paper if you use any of these data. Refer to the linked github repository for associated code.</p> <p>Neuroimaging data is organized in BIDS format and saved as NIfTI files. We used MION (monocrystalline iron oxide nanoparticle) as a contrast agent. Functional resting state files can be found in the 'func' folder for each imaging session. The final six runs are resting state data (the first two/three are short EPI sequences used to test that MION is present in the brain; all resting state data used in our analyses consist of 300 volumes). The first three of these six runs consist of baseline data with no drug treatment. Four through six are resting state data recorded after I.M. injection of vehicle (2% DMSO in saline), dechloroclozapine (DCZ) or clozapine-N-oxide (CNO). </p> <p>Neural recording data is separated into LFP data, organized by folder, and putative single units, organized the 'Sorted neurons' folder. LFP data folders are named by subject's intial and date of recording. Single units are labeled according to this same system. All data are stored in .mat format and can be opened in MATLAB. KB2.mat files store timing information: the first event in the KBD2 file indicates the start of baseline, pre-injection data acquisition, and the second event indicates the start of post-injection treatment data. The KB3.mat files contains the timing information of the drug injection. As with the fMRI data, we treated animals with I.M. injection of vehicle, DCZ, or CNO. </p> <p>Treatment information for both modalities is as follows. Neuroimaging: 2020/03/16 Animal L DCZ 1; 2020/05/27 Animal H vehicle 1; 2020/06/01 Animal L vehicle 1; 2020/06/08 Animal H DCZ 1; 2020/06/22 Animal L DCZ 2; 2020/06/24 Animal H vehicle 2; 2020/07/06 Animal L vehicle 2; 2020/07/08 Animal H DCZ 2; 2021/10/25 Animal L CNO; 2022/01/13 Animal H CNO. Neural recordings: 2022/04/14 Animal H DCZ 1; 2022/04/21 Animal H vehicle 1; 2022/05/12 Animal H DCZ 2; 2022/05/24 Animal H vehicle 2; 2022/06/03 Animal H CNO; 2022/08/18 Animal L vehicle 1; 2022/08/25 Animal L DCZ 1; 2022/09/01 Animal L DCZ 2; 2022/09/08 Animal L vehicle 2; 2022/09/22 Animal L CNO.</p>
A neural circuit for wind-guided olfactory navigation
<p>Data for Matheson et al., 2022 </p>
Data for: A zinc-finger fusion protein refines Gal4-defined neural circuits
<p>This is microscopy image data and related information for</p> <p><strong>A zinc-finger fusion protein refines Gal4-defined neural circuits</strong></p> <p>The analysis of behavior requires that the underlying neuronal circuits are identified and genetically isolated. In several major model species—most notably Drosophila, neurogeneticists identify and isolate neural circuits with a binary, heterologous, expression control system: Gal4-UASG. One limitation of Gal4-UASG is that expression patterns are often too broad to map circuits precisely. To help refine the expression of Gal4 lines, we developed an intersectional genetic AND operator. Interoperable with Gal4, the new system’s key component is a fusion protein in which the DNA-binding domain of Gal4 has been replaced with a zinc finger domain with a different DNA-binding specificity. In combination with its cognate binding site (UASZ) the zinc-finger-replaced Gal4 (‘Zal1’) was functional as a standalone transcription factor. Zal1 transgenes also refined Gal4 expression ranges when combined with UASGZ, a hybrid upstream activation sequence. In this way, combining Gal4 and Zal1 drivers captured restricted cell sets compared with single drivers and improved genetic fidelity. This intersectional genetic AND operation presumably derives from the action of a heterodimeric transcription factor: Gal4-Zal1. Configurations of Zal1-UASZ and Zal1-Gal4-UASGZ are versatile tools for defining, refining, and manipulating targeted neural expression patterns with precision.</p>
Evolutionary conservation and diversification of auditory neural circuits that process courtship songs in Drosophila
<p><span>Acoustic communication signals diversify even on short evolutionary time scales. To understand how the auditory system underlying acoustic communication could evolve, we conducted a systematic comparison of the early stages of the auditory neural circuit involved in song information processing between closely-related fruit-fly species. Male <em>Drosophila</em> <em>melanogaster</em> and <em>D</em>. <em>simulans</em> produce different sound signals during mating rituals, known as courtship songs. Female flies from these species selectively increase their receptivity when they hear songs with conspecific temporal patterns. Here, we first confirmed interspecific differences in temporal pattern preferences; <em>D</em>. <em>simulans</em> preferred pulse songs with longer intervals than <em>D</em>. <em>melanogaster</em>. Primary and secondary song-relay neurons, JO neurons and AMMC-B1 neurons, shared similar morphology and neurotransmitters between species. The temporal pattern preferences of AMMC-B1 neurons were also relatively similar between species, with slight but significant differences in their band-pass properties. Although the shift direction of the response property matched that of the behavior, these differences are not large enough to explain behavioral differences in song preferences. This study enhances our understanding of the conservation and diversification of the architecture of the early-stage neural circuit which processes acoustic communication signals.</span></p>
Predictive neural computations in cerebellar circuits contribute to motor planning and faster behavioral responses in larval zebrafish
<p>This dataset contains raw and processed data along with jupyter notebooks to generate figures in Narayanan et al., 2023. Instructions for navigating through the dataset and for running the analysis codes are in README.pdf.</p>
Overlapping Neural Circuits in Pediatric OCD
ClinicalTrials.gov study NCT02421315. IPD Sharing: YES. Countries: 1. Publications: 4.
A Study of Neural Circuit Responses to Catechol-O-methyl Transferase (COMT) Inhibitors
ClinicalTrials.gov study NCT01158950. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Cannabinoid Control of Fear Extinction Neural Circuits in Post-traumatic Stress Disorder
ClinicalTrials.gov study NCT02069366. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Novel Neural Circuit Biomarkers of Major Depression Response to CCBT
ClinicalTrials.gov study NCT03096886. IPD Sharing: UNDECIDED. Countries: 1. Publications: 18.
Data from: Sensory expectations shape neural population dynamics in motor circuits
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Anatomical, behavioral, and imaging data for a study on neural circuits for stress modulation of pain
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Dorsal raphe nucleus to anterior cingulate cortex 5-HTergic neural circuit modulates consolation and sociability
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Evolutionary conservation and diversification of auditory neural circuits that process courtship songs in Drosophila
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Dataset for "DE-HNN: An effective neural model for Circuit Netlist representation"
<p>New version datasets added:<br>1. Raw dataset: RosettaStone-GraphData-2023-03-06.zip</p> <p>2. Processed dataset: superblue.zip</p> <p>3. Older version dataset: 2023-03-06_data.tar.gz</p> <p> </p> <p>Notice that large files downloading might require long time and stable network connection, an alternative method is to use zenodo python package:</p> <h3>1. Install the package</h3> <div> <div> <div> <div> </div> </div> </div> <div dir="ltr"><code>pip install zenodo-get </code></div> </div> <h3>2. Download a dataset by DOI or URL</h3> <div> <div> <div> <div> </div> </div> </div> <div dir="ltr"><code>zenodo_get https://zenodo.org/record/14599896</code></div> </div> <p>or</p> <div> <div dir="ltr"><code>zenodo_get 10.5281/zenodo.14599896</code></div> </div> <p>This will create a local folder with the dataset files.</p> <h3>3. Use inside Python</h3> <div> <div> <div> <div> </div> </div> </div> <div dir="ltr"><code><span>from</span> zenodo_get <span>import</span> zenodo_get <span># Download by DOI or record URL</span> zenodo_get(<span>"10.5281/zenodo.14599896"</span>) <span># or</span> zenodo_get(<span>"https://zenodo.org/record/14599896"</span>)</code></div> </div>
Functional architecture of neural circuits for leg proprioception in Drosophila
<p class="Default">To effectively control their bodies, animals rely on feedback from proprioceptive mechanosensory neurons. In the <em>Drosophila </em>leg, different proprioceptor subtypes monitor joint position, movement direction, and vibration. Here, we investigate how these diverse sensory signals are integrated by central proprioceptive circuits. We find that signals for leg joint position and directional movement converge in second-order neurons, revealing pathways for local feedback control of leg posture. Distinct populations of second-order neurons integrate tibia vibration signals across pairs of legs, suggesting a role in detecting external substrate vibration. In each pathway, the flow of sensory information is dynamically gated and sculpted by inhibition. Overall, our results reveal parallel pathways for processing of internal and external mechanosensory signals, which we propose mediate feedback control of leg movement and vibration sensing, respectively. The existence of a functional connectivity map also provides a resource for interpreting connectomic reconstruction of neural circuits for leg proprioception.</p>
A neural circuit linking two sugar sensors regulates satiety-dependent fructose drive in Drosophila (raw data)
<p>This is the raw numerical data used in Musso et al., 2021, A neural circuit linking two sugar sensors regulates satiety-dependent fructose drive in Drosophila.</p>
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>
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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.