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51 results for “Hidden states”
Output files corresponding to "Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States"
<p>This dataset corresponds to the output files that were produced for the study reported in:</p> <p>Knights, Deon, Kevin C. Parks, Audrey H. Sawyer, Cédric H. David, Trevor N. Browning, Kelsey M. Danner, and Corey D. Wallace, (2017), Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States, <em>Journal of Hydrology,</em> 554, 331-341</p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 2, obtained from http://www.horizon-systems.com/nhdplus/NHDplusV2_data.php. Region used is: Great Lakes (04)</li> <li>The second phase of the North American Land Data Assimilation System (NLDAS2), obtained from ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS. Model outputs used are: NLDAS_MOS0125_MC.002, NLDAS_NOAH0125_MC.002, and NLDAS_VIC0125_MC.002.</li> <li>The United States 2011 National Land Cover Database (NLCD 2011), obtained from: http://www.mrlc.gov/nlcd2011.php.</li> </ul> <p> </p> <p><strong>Description of files</strong></p> <p>The files in this dataset contain are described below:</p> <ul> <li><em>Flowlines</em>: This folder contains a shapefile (<em>GL_coastcatchment_NHDflowline</em>) with the coastline of the Contiguous United States as described by NHDPlus V2, and was merged from a subsample of all river reaches available in the region used. </li> <li><em>Catchment</em>: This folder contains a shapefile (GL_coastcatchment_polygon) with the contributing catchments of NHDPlus V2 corresponding to the above coastline, and was merged from a subsample of all catchments available in the region used. </li> <li><em>Centroid</em>: This folder contains a shapefile (GL_coastcatchment_centroid) with the centroids of the above catchments. </li> <li><em>DischargeVulnerabilities.csv</em>. This .csv file contains the following data (units are in parentheses): <ul> <li>COMID: Unique feature identifier in NHDPlusV2 ().</li> <li>Length_km: Length of coastline feature (km).</li> <li>Area_sqkm: Area of coastal catchment feature (km<sup>2</sup>).</li> <li>Infiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>REACHCODE: Reach identifier in NHDPlusV2; reaches can include multiple features; Submarine Groundwater Discharge (SGD) is computed by reach, not feature ().</li> <li>RLength_km: Total length of coastline accumulated by REACHCODE (km).</li> <li>RArea_sqkm: Total area of coastal catchment accumulated by REACHCODE (km<sup>2</sup>).</li> <li>RInfiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>DGWD: Average annual direct groundwater discharge for REACHCODE (m<sup>2</sup>/y).</li> <li>Vulnerable_percent: Percentage of reach area with developed or agricultural land use in 2011 (%).</li> <li>Vulnerable: Vulnerability to coastal contamination (0- not vulnerable; 1-vulnerable)</li> </ul> </li> </ul> <p> </p>
Inference based decisions in a hidden state foraging task: differential contributions of prefrontal cortical areas
<p>Tabular dataset of behavioral data in the hidden state foraging task. The data is stored as a unique table, with one row per "attempt", i.e. a poke for mice and a tap for humans.</p> <p>The table includes four distinct experiments, encoded in the Experiment column. Experiment "Learning" refers to Fig. 2, experiment "VaryingParameters" refers to Fig. 2h and Fig.3. Experiment "LearningAndVaryingParameters" refers to Fig. 4. Experiment "OptogeneticInactivation" refers to Fig. 5.</p> <p>The column "TestingSession" defines whether those sessions were used for the analysis. It is used to exclude adaptation sessions to a new protocol for rodents in the "VaryingParameters" and "OptogeneticInactivation" experiments.</p> <p>In the human case, after an incorrect transition the subject receives an error cue and does not tap. This is considered a "trial" (and also a "streak") but not an attempt. This causes the StreakNumber and PokeNumber columns to increase by 2 after an error cue.</p>
Figure 5. A chromosome structure in case we have 2 visible states and 3 invisible states-Neuroevolution Mechanism for Hidden Markov Model
<p>Generating a population of size n of HMMs at random can be performed with some<br> restrictions:<br> - The weights representing the input layer in the chromosome should be always negligible as<br> initial values.<br> - The weights which are involved in summation of 1.0 in the hidden layer part of the<br> chromosome should be exactly 1.0.<br> Let us assume the following case<br> Visible states are 2 and invisible states (observations) are 3, , then we shall have a<br> chromosome as shown in Figure 5.</p>
Figure 1. HMM to describe a relation between the states Med. and High with the observations (invisible states) cold and hot.-Neuroevolution Mechanism for Hidden Markov Model
<p>The advantage of using this technique is that MCPRs are very useful in real time<br> applications and can be adapted over time based on the obtained experience of the networking<br> working process. Again Hewahi[6] proposed a mechanism (algorithm) to evolve and select the best<br> suitable HMM for a given problem using GA, this mechanism lacks to the training process that can<br> be of great usefulness in finding the best HMM.<br> Based on the above mentioned research, the importance of using HMM is increasing<br> rapidly.<br> Let us consider the HMM presented in Figure 1.</p>
Figure 1. HMM to describe a relation between the states Med. and High with the observations (invisible states) cold and hot.-Genetic Algorithms Principles Towards Hidden Markov Model
<p>Hewahi [4] presented a modified version of Censored Production Rule (CPR) called<br> Modified Censored Production Rules (MCPR). CPR is proposed by Michalski and Winston [6 ] to<br> capture real time situations. MCPR can fit with hidden Markov model and present a scheme to<br> compute the certainty values of the obtained conclusions out of the induced rules. To compute the<br> certainty values for the rule actions (conclusions), the approach exploited only the probability<br> values associated with the hidden Markov model without using any of the other well known<br> certainty computation approaches. Hewahi [3] also proposed an intelligent networking<br> management system based on the induced MCPRs extracted from a networking structure based on<br> HMM. The advantage of using this technique is that MCPRs are very useful in real time<br> applications and can be adapted over time based on the obtained experience of the networking<br> working process.<br> Let us consider the HMM presented in Figure 1.</p>
Accompanying empirical data for Kirchherr et al., 2023, "Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex"
<p>This repository contains data accompanying: Kirchherr et al., 2023, "Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex".</p> <p>Data collection methods:</p> <p>Two adult female rhesus macaques (Macaca mulatta) trained on a reaching, and grasping, and placing task served as the subjects. The animal handling as well as surgical and experimental procedures complied with European guideline (2010/63/UE) and authorized by the French Ministry for Higher Education and Research (project # 2016112713202878) in force on the care and use of laboratory animals, and were approved by the ethics committee CELYNE (comité d’éthique Lyonnais pour les neurosciences expérimentale, C2EA 42). After initial training, we performed a sterile surgery to implant six floating multielectrode arrays (FMA, Microprobes for Life Science, Gaithersburg, MD, USA) in the right (monkey 1) or left (monkey 2) cortical hemisphere. Each array was comprised of 32 platinum/iridium electrodes (impedance 0.5 MΩ at 1 kHz) with lengths ranging from 1 to 6 mm, and with an inter-electrode spacing of 400 μm. One electrode array was implanted in the primary motor cortex (M1), two were implanted in the ventral premotor cortex (F5), one in the dorsal premotor cortex (F2), and two in the prefrontal cortex (45a and 46/12r), as estimated according to a previous magnetic resonance imaging scan. For the purposes of this study, we analyzed data from the M1 array of each monkey.</p> <p>The wideband neural signal (bandpass filtered at 0.1 to 7500 kHz) was recorded at 30 kS/s, and amplified and digitized (16-bit; 0.192 μV resolution) with an Intan Tech-based (Intan Technologies, Los Angeles, CA, USA) open source acquisition system (Open Ephys; Siegle et al. 2017). This system uses a 256-channel Intan RHD2000 series acquisition board and 32-channel headstages (RHD2132). Spike detection was performed offline using Trisdesclous (Garcia & Pouzat,2015). The common reference was removed to reduce ambient noise. Spikes were then detected from each electrode using a threshold of 2 times the median absolute deviation (MAD), and analyzed as multi-unit activity (MUA) in 10 ms bins. All electrodes in which at least one well-isolated spike waveform was detected were selected for the following analyses. We thus used a sample of 21 electrodes out of 32 for monkey 1, and 25 out of 32 electrodes for monkey 2. Custom made detection panels were used to record the moments when the monkey’s hand released the handle, the hand contacted the target object, and when the object was placed in the groove. An Omniplex 16-channel recording system (Plexon, Dallas, TX, USA) was used to simultaneously record these behavioral events. Trials were discarded if the response time (time between the go signal and handle release) was less than 100 or greater than 1500 ms, the reach duration (time between handle release and object contact) was less than 100 or greater than 1000 ms, or the placing duration (time between object contact and placing the object in the groove) was less than 100 or greater than 1200 ms, leaving 19 - 68 trials per day for monkey 1 (M = 43.9, SD = 15.46, N = 439; left: M = 14.8, SD = 5.74; center: M = 14.4, SD = 5.15; right: M = 14.7, SD = 7.73), and 23 - 49 per day for monkey 2 (M = 38.3, SD = 9.87, N = 383; left: M = 14.2, SD = 3.91; center: M = 10.8, SD = 3.55; right: M = 13.3, SD = 3.37).</p> <p><br> Abstract:</p> <p>Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analyzing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity in many neurons, but also because of changes in the recorded signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analyzing such data in terms of discrete, latent states, but previous approaches have either not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modeled condition specific differences. We present a multilevel Bayesian HMM which addresses these shortcomings by incorporating multivariate Poisson log-normal emission probability distributions, multilevel parameter estimation, and trial-specific condition covariates. We applied this framework to multi-unit neural spiking data recorded using chronically implanted multi-electrode arrays from macaque primary motor cortex during a cued reaching, grasping, and placing task. We show that the model identifies latent neural population states which are tightly linked to behavioral events, despite the model being trained without any information about event timing. We show that these events represent specific spatiotemporal patterns of neural population activity and that their relationship to behavior is consistent over days of recording. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long-term plasticity in neural populations.</p>
Data for: Hidden non-collinear spin-order induced topological surface states
<p>Rare-earth monopnictides are a family of materials simultaneously displaying complex magnetism, strong electronic correlation, and topological band structure. The recently discovered emergent arc-like surface states in these materials have been attributed to the multi-wave-vector antiferromagnetic order, yet the direct experimental evidence has been elusive. Here we report the observation of non-collinear antiferromagnetic order with multiple modulations using spin-polarized scanning tunneling microscopy. Moreover, we discover a hidden spin-rotation transition of single-to-multiple modulations 2 K below the Neel temperature. The hidden transition coincides with the onset of the surface state splitting observed by our angle-resolved photoemission spectroscopy measurements. Single modulation gives rise to a band inversion with induced topological surface states in a local momentum region while the full Brillouin zone carries trivial topological indices, and multiple modulation further splits the surface bands via non-collinear spin tilting, as revealed by our calculations. The direct evidence of the non-collinear spin order in NdSb not only clarifies the mechanism of the emergent topological surface states but also opens up a new paradigm of control and manipulation of band topology with magnetism.</p>
Supplementary files from: Improving inference and avoiding over-interpretation of hidden-state diversification models: Specialized plant breeding has no effect on diversification in frogs
<p>The hidden-state speciation and extinction (HiSSE) model helps avoid spurious results when testing whether a character affects diversification rates. However, care must be taken to optimally analyze models and interpret results. Recently, Tonini et al. (2020; TEA hereafter) studied anuran (frog and toad) diversification with HiSSE methods. They concluded that their focal state, breeding in phytotelmata, increases net diversification rates. Yet this conclusion is counterintuitive, because the state that purportedly increases net diversification rates is 14 times rarer among species than the alternative. Herein I revisit TEA's analyses and demonstrate problems with inferring model likelihoods, conducting post-hoc tests, and interpreting results. I also re-evaluate their top models and find that diverse strategies are necessary to reach the parameter values that maximize each model's likelihood. In contrast to TEA, I find no support for an effect of phytotelm breeding on net diversification rates in Neotropical anurans. In particular, even though the most highly supported models include the focal character, averaging parameter estimates over hidden states shows that the focal character does not influence diversification rates. Finally, I suggest ways to better analyze and interpret complex diversification models – both state-dependent and beyond – for future studies in other organisms.</p>
Dataset for the paper titled Discovering State Variables Hidden in Experimental Data
<p>Dataset for the paper titled Discovering State Variables Hidden in Experimental Data. More information is at: https://www.cs.columbia.edu/~bchen/neural-state-variables/</p> <p>For more questions, please contact: Boyuan Chen (boyuan.chen@duke.edu)</p>
Machine Learning-Assisted Discovery of Hidden States in Expanded Free Energy Space
<p>Collective variables (CVs) are crucial parameters in enhanced sampling calculations and strongly impact the quality of the obtained free energy surface. However, many existing CVs are unique to and dependent on the system they are constructed with, making the developed CV non-transferable to other systems. Herein, we develop a non-instructor-led deep autoencoder neural network (DAENN) for discovering general-purpose CVs. The DAENN is used to train a model by learning molecular representations upon unbiased trajectories that contain only the reactant conformers. The prior knowledge of nonconstraint reactants coupled with the here-introduced topology variable and loss-like penalty function are only required to make the biasing method able to expand its configurational (phase) space to unexplored energy basins. Our developed autoencoder is efficient and relatively inexpensive to use in terms of <em>a priori</em> knowledge, enabling one to automatically search for hidden CVs of the reaction of interest.</p>
Data for: Hidden non-collinear spin-order induced topological surface states
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Supplementary files from: Improving inference and avoiding over-interpretation of hidden-state diversification models: Specialized plant breeding has no effect on diversification in frogs
Open the record for dataset details and reuse information.
Figure 16 in Colour and size reveal hidden diversity of Necturus (Caudata: Proteidae) from the Gulf Coastal Plain of the United States
Figure 16. Distribution of localities of specimens assigned to N. moleri. Open circle is approximate location of type specimen.
Figure 15 in Colour and size reveal hidden diversity of Necturus (Caudata: Proteidae) from the Gulf Coastal Plain of the United States
Figure 15. Photo of dorsum (a) and venter (b) of type specimen of N. moleri (AUM 35617; Houston County, AL; photos by Craig Guyer).
Figure 14 in Colour and size reveal hidden diversity of Necturus (Caudata: Proteidae) from the Gulf Coastal Plain of the United States
Figure 14. Distribution of localities of specimens assigned to N. mounti. Open circle is approximate location of type specimen.
Figure 13 in Colour and size reveal hidden diversity of Necturus (Caudata: Proteidae) from the Gulf Coastal Plain of the United States
Figure 13. Photo of dorsum (a) and venter (b) of type specimen of N. mounti (AUM 40698; Covington County, AL; photos by Craig Guyer).
Figure 12 in Colour and size reveal hidden diversity of Necturus (Caudata: Proteidae) from the Gulf Coastal Plain of the United States
Figure 12. Distribution of localities of specimens assigned to N. beyeri. Open circle is approximate location of type specimen.
Figure 10 in Colour and size reveal hidden diversity of Necturus (Caudata: Proteidae) from the Gulf Coastal Plain of the United States
Figure 10. Type specimen of N. lodingi (USNM 61752; Mobile County, AL) dorsum (a) and venter (b; photos by Jenna L. Welch, Division of Amphibians and Reptiles, National Museum of Natural History, Smithsonian Institution; used with permission); melanistic Mobile lineage specimen (MMNS 1261, Lamar County, MS) dorsum (c) and venter (d; photos by Craig Guyer).
Figure 9 in Colour and size reveal hidden diversity of Necturus (Caudata: Proteidae) from the Gulf Coastal Plain of the United States
Figure 9. Proportional representation of composite score for COLD and COLL (a; category 2 = no dark dorsal or lateral spots; category 3 = small dark dorsal spots with no dark lateral spots or small dark lateral spots with no dark dorsal spots; category 4 = small dark dorsal and lateral spots; category 5 = small dark dorsal spots with large dark lateral spots or large dark dorsal spots with small dark lateral spots; category 6 = large dark dorsal and lateral spots) and composite score for COLC and COLV (b; category 2 = no dark spots on chin or venter; category 3 = dark chin spots on mandibles and no dark spots on venter or no dark spots on chin but small dark ventrolateral spots on venter; category 4 = dark chin spots on mandibles and small dark ventrolateral spots on venter; category 5 = dark chin spots across entire chin and small dark ventrolateral spots on venter or dark chin spots on mandibles and small dark spots across venter; category 6 = dark spots across chin and venter). Colour categories are shown for Apalachicola, Escambia, and Mobile lineages and a composite of all western lineages of N. 'beyeri'.
Figure 11 in Colour and size reveal hidden diversity of Necturus (Caudata: Proteidae) from the Gulf Coastal Plain of the United States
Figure 11. Type specimen of N. beyeri (USNM 102674; Allen Parish, LA) dorsum (a) and venter (b; photos by Jenna L. Welch, Division of Amphibians and Reptiles, National Museum of Natural History, Smithsonian Institution; used with permission); paratype of Mobile lineage (AUM 40150, Covington County, AL) dorsum (c) and venter (d; photos by Craig Guyer); paratype of Pearl lineage (MMNS 1288, Saint Tammany Parish, LA) dorsum (e) and venter (f; photos by Craig Guyer); paratype of Pontchartrain lineage (LSUMZ 99424, Tangipahoa Parish, LA) dorsum (g) and venter (h; photos by Craig Guyer).
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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)
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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.