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4,210 results for “memory”
Supplemental image data for "Mechanical annealing and memories in a disordered solid"
<p>This archive contains raw and subtracted experimental images of a 2D colloidal disordered solid, sufficient to verify the major qualitative results of the paper "Mechanical annealing and memories in a disordered solid" by Nathan C. Keim and Dani Medina. Descriptions of the images may be found in the main text and Supplementary Information for that paper, a preprint of which is available at arXiv.org.</p>
Working memory capacity of crows and monkeys arises from similar neuronal computations
<p>Complex cognition relies on flexible working memory, which is severely limited in its capacity. The neuronal computations underlying these capacity limits have been extensively studied in humans and in monkeys, resulting in competing theoretical models. We probed the working memory capacity of crows (<em>Corvus corone</em>) in a change detection task, developed for monkeys (<em>Macaca mulatta</em>), while we performed extracellular recordings of the prefrontal-like area nidopallium caudolaterale. We found that neuronal encoding and maintenance of information were affected by item load, in a way that is virtually identical to results obtained from monkey prefrontal cortex. Contemporary neurophysiological models of working memory employ divisive normalization as an important mechanism that may result in the capacity limitation. As these models are usually conceptualized and tested in an exclusively mammalian context, it remains unclear if they fully capture a general concept of working memory or if they are restricted to the mammalian neocortex. Here we report that carrion crows and macaque monkeys share divisive normalization as a neuronal computation that is in line with mammalian models. This indicates that computational models of working memory developed in the mammalian cortex can also apply to non-cortical associative brain regions of birds.</p>
Data and Codes from: Decoding distributed oscillatory signals driven by memory and perception in the prefrontal cortex
<p>These datasets and codes were used for analysis in a manuscript titled "Decoding distributed oscillatory signals driven by memory and perception in the prefrontal cortex." For more details on the experimental and analysis methods, please refer to that manuscript. The raw data was obtained by recording signals from a 64-channel ECoG array implanted on the PFC of monkeys performing a behavioral task using custom software (NS computer service, Japan) running on LabVIEW Real-Time (National Instruments, TX, USA). The recorded raw data were converted into time-series data of power in six frequency bands using FieldTrip. The total file size of all the raw data is over 50GB, so it is not included here. It is available upon request.</p>
raw data for Optogenetic Stimulation of Prelimbic Pyramidal Neurons Maintains Fear Memories and Modulates Amygdala Pyramidal Neuron Transcriptome
<p>Figure Legend </p> <p>Modulation of cellular excitability of Prelimbic (PrL) pyramidal neurons by optogenetic stimulation. (<strong>A</strong>) Representative traces in current-clamp configuration reporting evoked firing activity triggered by a series of depolarizing current steps (0 to 400 pA) applied to PrL pyramidal neurons of SHAM FEAR (black, <em>n</em> = 8 neurons from 5 mice), OPTO FEAR (red, <em>n</em>= 8 neurons from 5 mice), and No-EX (green, <em>n</em> = 5 neurons from 3 mice) groups. The cumulative plot shows the changes in firing activity. (<strong>B</strong>) Representative traces of PrL pyramidal neurons of SHAM FEAR (black, <em>n</em> = 8 neurons from 5 mice), OPTO FEAR (red, <em>n</em>= 8 neurons from 5 mice), and No-EX (green, <em>n</em> = 5 neurons from 3 mice) groups showing the firing activity triggered by linear depolarization from 0 to 800 pA. Graph (on the right) reports the effects of optogenetic stimulation on rheobase value. Namely, PrL pyramidal neurons of OPTO FEAR and No-EX groups recorded after optogenetic stimulation showed a clear reduction in the rheobase value in comparison to neurons of SHAM FEAR group (* at least <em>p</em>= 0.01). (<strong>C</strong>) Representative traces of Excitatory Post-Synaptic Currents (EPSC) of PrL pyramidal neurons of SHAM FEAR (black, <em>n</em> = 8 neurons from 5 mice), OPTO FEAR (red, <em>n</em>= 8 neurons from 5 mice), and No-EX (green, <em>n</em> = 5 neurons from 3 mice) groups. Graph plot (in the middle) and cumulative curve (on the right) depict the clear increase in firing frequency in PrL pyramidal neurons of OPTO FEAR and No-EX groups (* at least <em>p</em> = 0.01). (<strong>D</strong>) Graphs and cumulative curves report no significant differences in cellular excitability in PrL pyramidal neurons of SHAM NOT FEAR (black) and OPTO NOT FEAR (blue) groups. Data are reported as median with interquartile range.</p>
Widespread ripples synchronize human cortical activity during sleep, waking, and memory recall
<p>These are the data and code for the article 'Widespread ripples synchronize human cortical activity during sleep, waking, and memory recall.' Please cite this article when using these data or code.</p>
FSS7B - Inhibiting human aversive memory by transcranial theta-burst stimulation to primary sensory cortex: Supplementary fMRI data
<p>Functional magnetic resonance imaging (fMRI) data supplementing a publication on inhibiting somatosensory fear memory in humans with transcranial magnetic stimulation (TMS). Contains 1) individual regions-of-interest (ROIs) masks in the bilateral primary somatosensory cortex (S1) to target with TMS, 2) S1 masks for left and right hemisphere used in restricting the ROIs to a priori expected area, 3) sum and probability maps of the ROIs over participants, and 4) summary group level fMRI NIFTI images including beta images and T-maps. Individual SPMs/beta images can be requested from the authors for academic research purposes (k.ojala@uke.de). Details on the methods are found in the Supplement of the publication (see linked DOI). </p>
Data of publication A Frequency-Multiplexed Coherent Electro-optic Memory in Rare Earth Doped Nanoparticles
<p>Data corresponding to main text figures of publication : A. Fossati, S. Liu, J. Karlsson, A. Ikesue, A. Tallaire, A. Ferrier, D. Serrano, and P. Goldner, <em>A Frequency-Multiplexed Coherent Electro-Optic Memory in Rare Earth Doped Nanoparticles</em>, Nano Lett. <strong>20</strong>, 7087 (2020).</p>
Data set of manuscript entiteld Sigma Oscillations Protect or Reinstate Motor Memory Depending on their Temporal Coordination with Slow Waves
<p>Data set of manuscript entiteld Sigma Oscillations Protect or Reinstate Motor Memory Depending on their Temporal Coordination with Slow Waves</p> <p>EEG files : Brainvision</p> <p>*.dat; *.edf : eeg files of the experimental nap </p> <p>*.vhdr: corresponding header files</p> <p>*.vmrk: corresponding marker files time starting from recording sample</p> <p>*._Pauselog.txt: markers with condition in the stimulation computer time</p> <p>Sleep Scores: </p> <p>Scores by the sleep technician (one file per participants). </p> <p>Surveys:</p> <p>General survey (first screening) and complete survey (for inclusion). Code correspondance is provided in ID_correspondance.txt</p> <p>BehavData:</p> <p>For each participant, a text file with all the cues and responses logged for each tasks. *_raw.txt are present when an issue arised during experiment and a task needed to be re-started. </p> <p> </p>
Data from two studies of learning in visual span working memory tasks.
<p>Data from two working memory span tasks. The span set size could be up to six, and each row in each .csv is one response (i.e., one "click" on an item). Thus, each trial's data is spread out on multiple rows, with accuracy being a binary variable.</p>
Extracting Session Keys From the Main Memory Using Brute-force and Machine Learning
<p>This dataset contains:</p> <ol> <li>Heap dump of three version of OpenSSH (V_7_9_P1, V_8_0_P1 and V_8_1_P1)</li> <li>Heap dump of two applications that uses TLS (lynx and curl)</li> <li>Network recording in format of pcap</li> <li>JSON file that contains the keys' information</li> </ol> <p>The source code is available at: https://github.com/smartvmi/SSH-TLS-key-extraction</p>
STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale (validation data)
<p>Raw and refined data for the validation tests performed in the paper "STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale":<br> - rallpack 1, analytical, STEPS3, STEPS4, mesh scaling with STEPS4. It is so simple that I provide only the raw data. The space saving using refined data is minimal</p> <p>- rallpack 3, STEPS 3, STEPS 4. Both raw and refined data are provided.</p> <p>- caburst, STEPS 3, STEPS 4. Both raw and refined data are provided.</p>
Figure 9. Encoding CHG_MEMORY-Selecting Operations for Assembler Encoding
<p>The three-parameter operations are represented in a somewhat different way. Their<br> encoded form resembles classifiers from Learning Classifier Systems [1,3,6]. Similarity<br> between classifiers and the three-parameter operations results from the use of the so-called<br> don’t care symbol “#” in both cases. Each encoded operation consists of four blocks of genes.<br> The first single-bit block determines one of two possible variants of the operation (see<br> Appendix 1). The second and third block indicate location of changes performed by the<br> operation (don’t care symbol is used for this purpose). The last block specifies the value of an<br> integer parameter of the operation. The example use of don’t care symbol to locate changes in<br> NDM is illustrated in Fig 9.</p>
Figure 2. Decision Making Model based on Emotions, Drives, and Different Memory Types
<p>The first model that is briefly introduced is an approach inspired by research findings in<br> neuro-psychology and psychoanalysis (see figure 2). The model considers the environment, the<br> body, and the brain/mind of an artificial being. The environment and the body are perceived via<br> sensors. (Re-)actions can be carried out via actuators. The model focuses on the decision-making<br> process of how to (re-)act according to situations currently perceived. In this decision-making<br> process, there are involved concepts like emotions, desires, drives, and different types of memory as<br> well as concepts like the Ego-Id-Superego model of Sigmund Freud. There are considered fast<br> reactions in form of reflexes and slower reactions that need reflection, thinking, and planning. For a<br> detailed model description, see [14, 17, 18].</p>
Barcoding of episodic memories in the hippocampus of a food-caching bird
<p>The hippocampus is critical for episodic memory. Although hippocampal activity represents place and other behaviorally relevant variables, it is unclear how it encodes numerous memories of specific events in life. To study episodic coding, we leveraged the specialized behavior of chickadees – food-caching birds that form memories at well-defined moments in time whenever they cache food for subsequent retrieval. Our recordings during caching revealed very sparse, transient barcode-like patterns of firing across hippocampal neurons. Each "barcode" uniquely represented a caching event and transiently reactivated during the retrieval of that specific cache. Barcodes co-occurred with the conventional activity of place cells, but were uncorrelated even for nearby cache locations that had similar place codes. We propose that animals recall episodic memories by reactivating hippocampal barcodes. Similarly to computer hash codes, these patterns assign unique identifiers to different events and could be a mechanism for rapid formation and storage of many non-interfering memories.</p>
EEG recordings during resting-state and the maintenance periods of a spatial working memory task in humans
<p>Scripts used to analyze data for the manuscript submitted for publication in EJN</p> <p><strong>Script_Curve_Fitting_HBM.rtf</strong></p> <p>Dr. Hadj Boumediene Meziane: hbmeziane@gmail.com </p> <p><span>We therefore considered this continuous change in power as an extraneous variable </span><em><span>y<sub>k</sub>(x)</span></em><span> impacting the measured power spectrum </span><em><span>Pow(E<sub>k</sub>)</span></em><span>, and modeled it with a binomial equation that best fit the data, where the coefficients in <em>p<sub>i</sub></em> are in descending powers, and the length of <em>p</em> is <em>(n+1), k </em>is trial number (<em>k = 1 to 10</em>):</span></p> <p><strong><em><span>y<sub>k</sub>(x) = p<sub><span>1 </span></sub>. x<sup><span>2</span></sup><span><span> </span></span>+ p<sub><span>2 </span></sub>. x<span> </span>+ p<sub><span>3</span></sub></span></em></strong></p> <p><span>In order to statistically compare the topographies between the trials with perfect recall and the trials with failed recall, we subtracted this variable from the mean spectral topographies of each subject and for each electrode by first producing the mean spectral curves of each maintenance trial in the theta and alpha frequency bands, taking into account the IAF, and then calculating the coefficients (</span><em><span>p<sub>1</sub></span></em><span>, </span><em><span>p<sub>2</sub></span></em><span> and </span><em><span>p<sub>3</sub></span></em><span>) of the binomial equation using the Matlab function <em>polyfit.m.</em> Once the coefficients were determined, this estimate was subtracted from each power spectrum matrix using the following formula:</span></p> <p><strong><em><span>PowFit(E<sub><span>k</span></sub>) = Pow (E<sub><span>k</span></sub>) – </span></em></strong><strong><em><span>y<sub>k</sub>(x)</span></em></strong></p> <p> </p> <p><strong>Script_Perf_Fail_EEG_Power_Spec_HBM.rtf</strong></p> <p>Dr. Hadj Meziane: hbmeziane@gmail.com<br>This script calculates EEG power spectra then compares perf and fail conditions, then plots brain topographies with statical results</p> <p> </p> <p><strong>Script_Perf_Fail_EEG_Sources_Spec_HBM.rtf</strong></p> <p>Dr. Hadj Boumediene Meziane: hbmeziane@gmail.com<br>This script compares EEG source spectra then compares Perf vs. Fail conditions then plot statistical results (significant voxels) on MRI volume</p>
Data for: Serial dependence: a matter of memory load
<p>The dataset of each experiment is stored in csv format for each experiment separately. Tables are stored in long format, with rows coding the event in each trial, and columns coding variables.<br>The subject identifier (number) is coded by the variable <strong>obs</strong>. <strong>theta</strong> is orientation of the presented gabor, <strong>error </strong>is calculated as the acute angle between the reported and true orientations, expressed in degrees. <strong>nitems</strong>: (1) load trial, (0) no load trial.<br>The meaning of the other variables should be self-explanatory by the column headers name.<br>Additional information on each variable is described in the method section.</p>
Fig. 6 in Archive reports and memories. The Brera Botanical Garden of Milan (1982-2001)
Fig. 6 - The Garden today. / L'Orto oggi. (Courtesy Università degli Studi di Milano - MOBE, Museo Orto Botanico di Brera ed Erbario).
Fig. 5 in Archive reports and memories. The Brera Botanical Garden of Milan (1982-2001)
Fig. 5 - Poster of the Northern Park showing the activity carried out with the Botanical Garden in 1999. / Poster del Parco Nord con l'indicazione dell'attività svolta con l'Orto nel 1999. (Courtesy Università degli Studi di Milano - MOBE, Museo Orto Botanico di Brera ed Erbario).
Fig. 4 - The school adopts a in Archive reports and memories. The Brera Botanical Garden of Milan (1982-2001)
Fig. 4 - The school adopts a monument, activity carried out by the Garden and the Parini School in the school years 1996-1997 and 1997-1998. / La scuola adotta un monumento, attività coordinata dall'Orto e dalla Scuola Media Parini negli anni scolastici 1996-1997 e 1997-1998. (Courtesy Università degli Studi di Milano - MOBE, Museo Orto Botanico di Brera ed Erbario).
Fig. 3 in Archive reports and memories. The Brera Botanical Garden of Milan (1982-2001)
Fig. 3 - Layout of the Project La Pietra & Magistretti (1989). / Pianta del Progetto La Pietra & Magistretti (1989). (Courtesy Università degli Studi di Milano - MOBE, Museo Orto Botanico di Brera ed Erbario).
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.