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16 results for “neuronal representations”
BRAIN Journal-Artificial Neuron Modelling Based on Wave Shape-Figure 2. New neuronal model, based on matching a wave-like representation of the output.
<p>For the new model, shown in Figure 2, the ‘differences’ between the outputs is measured and combined, to produce a kind of wave-like shape. If output point 1 has a value of 10, and output point 2 has a value of 5, for example, then this results in the shape moving down 5 points on the wave shape. This shape will, of course, change depending on the order that the dataset values are presented in. Also, if new data is presented, then that will produce a different shape. However, there is still an association between the input values and the output values and it is still this relation that is being learnt. The neural network needs to be able to learn a function that can generalise over the presented datasets, so that it can recognise the relation in previously unseen data as well. Generalising over trying to learn a wave-like shape, or trying to match the output values exactly looks quite similar, suggesting that the process is at least valid. In Figure 2, note that a transposition to move the learned shape up or down first is possible, before a weight value would try to scale it. If the initial match of the combined inputs can be as close as possible, then these adjustments will become less and will be more for fine tuning.</p>
Changes in neuronal representations of phonemes in the ascending auditory system and their role speech recognition
<p>This dataset comprises neural responses to a set of speech sounds from several brain regions. Auditory nerve data was simulated using a computational model of the auditory nerve. Also included are multi-unit extracellular recordings or responses to the same stimuli in the inferior colliculus and auditory cortex of anaethetised guinea pigs.</p>
Input data and analyzed data of "Topology of synaptic connectivity constrains neuronal stimulus representation (...)"
<p>This dataset contains the input data, as well as the analyzed data that our <a href="http://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">preprint</a></p> <p><em><strong>Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies</strong></em></p> <p>to be found on <a href="https://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">bioRxiv</a> is based on. The input data (<em>input_data.zip</em>) contains everything that is needed to run the full <a href="https://github.com/BlueBrain/topological_sampling/">analysis pipeline</a> from start to the generation of the figures found in the manuscript. However, some of the analysis steps can be computationally heavy, so we also provide the output of these expensive steps, that can be simply used in conjunction with jupyter notebooks (<em>notebooks.zip)</em> to generate the figures.</p> <p><strong>Overview</strong></p> <p>An overview image can be found <a href="https://raw.githubusercontent.com/BlueBrain/topological_sampling/master/toposampling_pipeline_overview.png"><strong>here</strong></a></p> <p>Blue squares denote input / output files (that are part of this dataset). Grey circles denote steps of the analysis pipeline (that are implemented in the <a href="https://github.com/BlueBrain/topological_sampling/">github repository</a>). Red rectangles denote configuration files (that are part of this dataset and also in the <a href="https://github.com/BlueBrain/topological_sampling/">github repository</a>).</p> <p>This Dataset can also be browsed, downloaded and accessed as linked open data from the <a href="https://bbp.epfl.ch/nexus/web/studios/public/topological-sampling/studios/data:a7cc7e9f-53c5-4940-929c-95f4c4f57728?workspaceId=data:165e54c5-e8f6-4d85-ac94-53bc3dfe5cd4">BBP knowledge Graph based Data studios</a>.</p> <p><strong>Contained file types and their structure</strong></p> <p>Here, we provide four types of files. Configuration files specify analysis parameters and define the expected locations of the data files. Input files are the inputs into the analysis pipeline. Analyzed files are the outputs of said pipeline. Finally, we provide a number of jupyter notebooks that use the analyzed files to generate the manuscript figures. If you want to re-run the entire analysis pipeline, you need the code and configuration files from the <a href="https://github.com/BlueBrain/topological_sampling/">repository</a>, the input files and notebooks; the analyzed files will be generated as you run the pipeline. For information how to run this, refer to the <a href="https://github.com/BlueBrain/topological_sampling/blob/master/README.md">readme</a>. If you only want to generate the figures, you still need the code and configuration files from the repository, as it contains a package related to reading the result files; further, you need the analyzed files in addition to the input files. Of course, you can also run parts of the analysis pipeline and download the outputs for the rest.</p> <p>To run everything smoothly, the files have to be placed into the expected file structure. You can look up and configure the file structure in the configuration files. Below, we describe the default layout, which is very simple (<em>root</em> is where you placed the code from our <a href="https://github.com/BlueBrain/topological_sampling/">repository</a> and can be any location on your file system):</p> <ul> <li>Configuration files<em>: </em>Part of the <a href="https://github.com/BlueBrain/topological_sampling/">repository.</a> Placed into <em>root/working_dir/configs</em></li> <li>Input data: Place into <em>root/working_dir/data</em>, then unzip in place <ul> <li><em>input_data.zip</em> -- Input data. Contains details on the model used in the manuscript and the output (spike times) of the simulation described in the manuscript. Within the file: <ul> <li>For details, see <a href="https://github.com/BlueBrain/topological_sampling/blob/master/README.md">readme</a></li> </ul> </li> </ul> </li> <li>Analyzed data: Place into <em>root/working_dir/data</em>, then unzip in place <ul> <li><em>classifier_features_results.zip </em>-- Output of the "classifier" step. Results of stimulus classification on the data in <em>features.zip</em></li> <li><em>classifier_manifold_result</em>s.zip -- Output of the "classifier" step. Results of stimulus classification on the data in <em>extracted_components.zip</em></li> <li><em>community_database.zip</em> -- Output of "gen_topo_db". Various topological parameters related to the close neighborhood of neurons in the model</li> <li><em>extracted_components.zip </em>-- Output of "manifold_analysis". Results of factor analysis on the spike times in the <em>input_data</em></li> <li><em>features.zip</em> -- Output of "topological_featurization". A new dimensionality reduction method we introduce in the <a href="http://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">manuscript</a></li> <li><em>split_spike_trains.zip -- </em>Output of "split_time_windows". The spike trains, split into time windows that are the responses to individual stimuli injected in the simulation</li> <li><em>structural_parameters.zip</em><em> -- </em>Output of "Structural tribe analysis". Values for the topological parameters in <em>community_database.zip</em> associated with the neuron samples specified in <em>tribes.zip</em></li> <li><em>structural_parameters_vol.zip</em> -- Output of "Structural tribe analysis". Same as above, but for volumetric neuron samples.</li> <li><em>triads.zip</em> -- Output of "Triad-counts". Over- and under-expression of triad motifs in the samples in <em>tribes.zip</em>.</li> <li><em>tribes.zip</em><em> -- </em>Output of "sample_tribes". Specific neuron samples that are then analyzed further.</li> </ul> </li> <li>Notebooks: Place into <em>root/notebooks</em> and unzip in place <ul> <li><em>notebooks.zip</em><em> -- </em>Jupyter notebooks. Run them to generate the figures in the manuscript.</li> </ul> </li> </ul> <p> </p> <p><strong>Updates:</strong></p> <p>v1.1.0 (2020/12/11): Added some additional control cases to the results for figure 7. These results will probably not be updated on bioRxiv, but go into the submission to a journal.</p> <p>v1.2.0 (2021/10/05): Updated the notebooks.zip with changes we made in response to reviewers' feedback.</p>
Overlapping representations of food and social stimuli in VTA dopamine neurons
<p>2-photon imaging and behavioral data accompanying publication of "Overlapping representations of food and social stimuli in VTA dopamine neurons" (Lindsay Willmore, Adelaide Minerva, Ben Engelhard, Brenna McMannon, Nirja Oak, Stephan Thiberge, Malavika Murugan, Catherine Jensen Pena, Ilana Witten). This dataset contains all information required to recreate figures from the paper. </p>
Overlapping representations of food and social stimuli in VTA dopamine neurons
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DSM: Deep Sequential Model for Complete Neuronal Morphology Representation and Feature Extraction
<p>This is an open-source repository for hosting codes and data files from research, "DSM: Deep Sequential Model for Complete Neuronal Morphology Representation and Feature Extraction". We also provided a web service based on our methods, please go to http://114.117.165.134:8501/.</p><p>(1)raw_dataset.zip: </p><ol><li>1,282 neuron reconstructions from SEU-Allen dataset;</li><li>1,002 neuron reconstructions from Janelia dataset;</li><li>1,100 neuron reconstructions from ION dataset.</li></ol><p>(2)Supplementary.zip: Supplementary information, including tables and figures;</p><p>(3)DSM-tools.zip: A python package for converting neuron morphology into sequences and implementing DSM models.</p><ol><li>NeuronSequenceDataset class: to transform SWC files to sequence dataframes by binary tree traversals, and prepare for the input of DSM networks.</li><li>DSMDataConverter class: a helper to convert the sequence dataframes for classification and clustering.</li><li>DSMHierarchicalAttentionNetwork class: classification model, giving a pre-trained DSM-HAN model by default.</li><li>DSMAutoencoder class: clustering model, giving a pre-trained DSM-AE model by default.</li></ol><p>(4)neuron2seq_for_developer.zip: A repository for further development of the models, including source codes and all data files.</p>
Local synaptic inputs support opposing, network-specific odor representations in a widely projecting modulatory neuron
<p>This is the data set for Zhang et al. 2019 "Local synaptic inputs support opposing, network-specific odor representations in a widely projecting modulatory neuron" published at eLife.</p>
The joint representation of self-motion and touch in superior colliculus neurons supporting active sensation
<p><span><span>Localizing</span><span> objects using active </span><span>sensing</span><span> requires </span><span>the</span><span> brain </span><span>to</span> <span>integrate</span> <span>a </span><span>map </span><span>sensory</span><span> space against</span><span> an ongoing </span><span>estimate</span><span> of </span><span>body position</span><span>. </span><span>While </span><span>such computations </span><span>are</span><span> often</span> <span>ascribed to </span><span>the </span><span>cerebral </span><span>corte</span><span>x</span><span>, we examined the </span><span>midbrain</span><span> superior colliculus (SC), due to its </span><span>close relationship</span><span> with</span> <span>the sensory periphery </span><span>as well as</span><span> higher, motor-related brain regions. </span><span>Using high-density electrophysiology and movement tracking, w</span><span>e discovered tha</span><span>t</span> <span>the </span><span>on-going </span><span>kinematics of </span><span>whisker motion</span> <span>and locomotion </span><span>speed </span><span>accurately predict </span><span>the firing rate of</span><span> mouse</span><span> SC neurons</span><span>. </span><span>Neural activity was best predicted by m</span><span>ovement</span><span>s </span><span>occurring</span> <span>either </span><span>in the past, present, or future, </span><span>revealing</span><span> that the SC population continuously </span><span>estimates</span> <span>a</span> <span>trajector</span><span>y of self-motion</span><span>. </span><span>Neural </span><span>selectivity</span><span> for </span><span>combinations </span><span>of kinematic features built </span><span>an accurate</span><span> representation of</span><span> whisker </span><span>position</span> <span>with</span><span> high temporal resolution.</span> <span>Half of all self-motion encoding</span> <span>neurons displayed a </span><span>touch</span><span> response </span><span>as</span><span> an object entered the active whisking field. </span><span>Trial-to-trial v</span><span>ariation in the size of this response was explained by the </span><span>position</span><span> of </span><span>the whisker</span><span> upon touch.</span><span> Taken together, these data </span><span>indicate</span><span> that SC neurons linearly combine an internal estimate of </span><span>body </span><span>movements</span><span> with </span><span>external</span><span> stimulation to </span><span>enable active tactile localization</span><span>.</span></span><span> </span></p>
The neuronal implementation of representational geometry in primate prefrontal cortex
<p><span>Modern neuroscience has seen the rise of a population-doctrine that represents cognitive variables using geometrical structures in activity space. Representational geometry does not, however, account for how individual neurons implement these representations. Here, leveraging the principle of sparse coding, we present a framework to dissect representational geometry into biologically interpretable components that retain links to single neurons. Applied to extracellular recordings from the primate prefrontal cortex in a working memory task with interference, the identified components revealed disentangled and sequential memory representations including the recovery of memory content after distraction, signals hidden to conventional analyses. Remarkably, each component was contributed by small subpopulations of neurons with distinct electrophysiological properties and response dynamics. Modelling showed that such sparse implementations are supported by recurrently connected circuits as in prefrontal cortex. The perspective of neuronal implementation links representational geometries to their cellular constituents, providing mechanistic insights into how neural systems encode and process information.</span></p>
Pronouns reactivate conceptual representations in human hippocampal neurons
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The neuronal implementation of representational geometry in primate prefrontal cortex
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Behavioral context affects social signal representations within single primate prefrontal cortex neurons
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Neural representation of cytokines by vagal sensory neurons
GEO Series GSE294447. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Overlapping representations of food and social stimuli in VTA dopamine neurons
GEO Series GSE235149. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
Learning-induced reorganization of number neurons and emergence of numerical representations in a biologically-inspired neural network
<p>Data published along with paper: <em>Learning-induced reorganization of number neurons and emergence of numerical representations in a biologically-inspired neural network</em></p> <p>In this data is shared: models (before & after training), stimuli used to train and test, and activity of the model in test.</p>
Differences in DNA Methylation between Human Neuronal and Glial Cells are Concentrated in Enhancers and non-CpG Sites [Enhanced Reduced Representation Bisulfite Sequencing data]
GEO Series GSE50852. Homo sapiens. 6 samples. Type: Methylation profiling by high throughput sequencing.
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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.
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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.