Log In Sign up
The largest database of trusted experimental protocols
> Anatomy > Cell > Grid Cells

Grid Cells

Grid cells are a type of neuron found in the entorhinal cortex of the brain that play a key role in spatial navigation and memory.
These cells fire in a hexagonal grid pattern, creating a cognitive map of the surrounding environment.
Grid cells work in conjunction with other spatial cells, such as place cells and head direction cells, to help animals and humans orient themselves and navigate through complex spatial environments.
By providing a metric for distance and direction, grid cells contribute to the brain's ability to encode the geometrical structure of the external world.
Understanding the function and properties of grid cells is an important area of research in neuroscience, with potential applications in the development of more efficient navigation systems and the study of spatial cognition disfunctions associated with neurological disorders.

Most cited protocols related to «Grid Cells»

1
Automated Assessment of Experimental Map Quality
A set of measures of map quality were applied to experimental maps (or structure-factor amplitudes, phases and weights) obtained from real but re-enacted structure determinations. Each of the structures considered had been determined previously, so that phases from a refined model could be used with measured amplitudes to calculate a model map to use as a standard. The ‘true’ quality of each map was taken to be the correlation with the corresponding standard map calculated at the same nominal resolution. Each measure of quality was applied to each map and the resulting scores were saved along with the corresponding ‘true’ quality. The structure-solution process was automatically carried out by the PHENIX AutoSol wizard and each experimentally phased map that was obtained during the structure-solution process was examined in this way. To reduce the number of near-duplicate solutions considered, all solutions for a structure that had nearly identical values of the map correlation to the standard map (within a range of ±0.0005 in map correlation) were considered to be the same and only the first was used in the analysis. For comparisons involving two possible enantiomers of a solution, the two enantiomers of a solution sometimes differed only slightly (i.e. the heavy-atom sub­structure was nearly centrosymmetric). In these analyses of enantiomeric pairs, only those that differed by an r.m.s.d. of at least 0.5 Å were considered.
For analysis of map quality, electron-density maps and structure factors were calculated using a high-resolution limit of 2.5 Å (if data were available to that resolution), as described above for the PHENIX AutoSol wizard. Before applying each of the measures of map quality, the experimental maps were normalized to a mean of zero and a variance of unity. They were then adjusted in two steps to reduce the contribution from high density at the coordinates of heavy-atom sites. (The high density at heavy-atom sites might otherwise lead to high values for the skewness, NCS correlation, contrast and possibly other measures.) Firstly, the electron density within a radius (r) of each heavy-atom site used in phasing (where r was given by twice the resolution of the data or 5 Å, whichever was greater) was limited to values less than or equal to twice the r.m.s. (2σ) of the map. Secondly, the electron density everywhere in the map was limited to values in the range −5σ to +5σ. This modified map is referred to below as the normalized truncated experimental electron-density map.
Weighted electron-density maps were calculated in the PHENIX environment (Adams et al., 2002 ▶ ) using RESOLVE (Terwilliger, 2000 ▶ ) on a grid with a spacing of 1/3 of the high-resolution limit of the data or finer. Map correlations were obtained by calculating the correlation coefficient of a pair of maps at all the grid points in the asymmetric unit of the unit cell. Model–map correlations were calculated in the same way, except that one map was calculated from the model and an overall B factor (b_overall) was adjusted to maximize the correlation. This correlation was further maximized by adjusting a parameter (rFFT) representing the radius around atoms in the model to be included in FFT-based density calculations (typically about equal to the high-resolution limit of the data). For protein chains, an increment in isotropic thermal factors (beta_b) for each bond between side-chain atoms and the Cβ atom was also applied to maximize the correlation.
Terwilliger T.C., Adams P.D., Read R.J., McCoy A.J., Moriarty N.W., Grosse-Kunstleve R.W., Afonine P.V., Zwart P.H, & Hung L.W. (2009). Decision-making in structure solution using Bayesian estimates of map quality: the PHENIX AutoSol wizard. Acta Crystallographica Section D: Biological Crystallography, 65(Pt 6), 582-601.
Publication 2009
Complement Factor B Electrons Grid Cells Microtubule-Associated Proteins Proteins Radius
2
Spatiotemporal Neural Network for PM2.5 Estimation
We trained a neural network with the above variables to PM2.5 monitoring data from the AQS network. The relationships between input variables and PM2.5 could be highly nonlinear with complex interactions. Neural networks have the potential to model any type of nonlinearity.71 , 72 The details of the neural network, such as its structure and training method were articulated in the supplementary material. All input variables covered the entire study area, but some of them were not available in early years or had higher proportions of missing values. Missing values were especially common in Terra and Aqua AOD data. To deal with the missing values problem and different temporal coverages, we adopted the following steps. We used a calibration method to fill in the missing values in Aqua AOD data from 2003 to 2012 and Terra AOD data from 2001 to 2012 based on the association of GEOS-Chem outputs and land-use terms with non-missing AOD.56 For the other variables with a low fraction of missing values, we interpolated at grid cells with missing values. Regarding temporal coverage, GEOS-Chem outputs, land-use terms, MODIS outputs, and meteorological variables were available throughout the study period. OMI data, Aqua AOD, and Terra AOD were unavailable in earlier years. For years with one or more unavailable variables, we fitted the model with the remaining available variables.
Most previous studies used only in situ variables for modeling. However, information from a neighboring cell can be informative as well. For example, nearby road density, forest coverage and other land-use variables as well as nearby PM2.5 measurements either influence or correlate with local PM2.5 measurements. They are informative for modeling and can improve model performance. We accounted for spatial correlation by using convolutional layers in the neural network.73 A convolutional layer is computed by applying a convolution kernel on an input layer. Values from neighboring cells are combined through the use of the kernel function. The kernel takes the form a function (e.g. weighted average with Gaussian weights based on distance) that produces a scalar estimate from the multidimensional inputs. A convolution layer aggregates nearby information and can simulate some form of autocorrelation. We included convolutional layers for land-use terms and nearby PM2.5 measurements as additional predictor variables to account for spatial autocorrelation. Multiple convolution layers were incorporated to allow the neural network to model even more complex autocorrelation or possible interaction with other variables (Supplementary material). In addition to nearby grid cells, observations from nearby days for the same grid cell can be also informative. To incorporate this, we first fitted a neural network and obtained an initial prediction for PM2.5. We then computed temporal convolution layers and fitted the neural network again with them (Figure S5).
To validate model results and avoid overfitting, we used 10-fold cross-validation, in which all monitoring sites were randomly divided into 10%-90% splits. The model was trained with 90% of data and predicted PM2.5 at the remaining 10%. The same process was repeated for other splits. Assembling predicted PM2.5 at ten 10% testing sets yielded predicted PM2.5 for all the monitors. We computed correlation between predicted PM2.5 and monitored PM2.5. Spatial and temporal R2s were also calculated. Details of calculating R2 have been specified in the supplementary material.
The trained neural network was then used to make dailyPM2.5 predictions for each gridcell (1 km×1 km) for each day.
All programming was implemented in Matlab (version 2014a, The MathWorks, Inc.).
Di Q., Kloog I., Koutrakis P., Lyapustin A., Wang Y, & Schwartz J. (2016). Assessing PM2.5 Exposures with High Spatiotemporal Resolution across the Continental United States. Environmental science & technology, 50(9), 4712-4721.
Publication 2016
Cells Forests Grid Cells Substance Use
3
Modeling Species Distribution using MAXENT
We used for modeling the software MAXENT [30] , a machine learning algorithm that applies the principle of maximum entropy to predict the potential distribution of species from presence-only data and environmental variables [26] . Currently, this widely used method is particularly efficient to handle complex interactions between response and predictor variables [15] , [28] , and is little sensitive to small sample sizes [29] . All models were computed using the version 3.3.3k of MAXENT (http://www.cs.princeton.edu/~schapire/maxent/). Runs were conducted with the default variable responses settings, and a logistic output format which results in a map of habitat suitability of the species ranging from 0 to 1 per grid cell, wherein the average observation should be close to 0.5 [15] . The models were evaluated by the area under the ROC curve (AUC), and three measures of overlap with the unbiased model (see below section “Model evaluation and statistical analyses”).
Fourcade Y., Engler J.O., Rödder D, & Secondi J. (2014). Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias. PLoS ONE, 9(5), e97122.
Publication 2014
Entropy Grid Cells
4
Drivers of Emerging Infectious Diseases
Biological, temporal and spatial data on human EID ‘events’ were collected from the literature from 1940 (yellow fever virus, Nuba Mountains, Sudan) until 2004 (poliovirus type 2 in Uttar Pradesh, India) (n = 335, see Supplementary Data for data and sources). Global allocation of scientific resources for disease surveillance has been focused on rich, developed countries (Supplementary Fig. 3). It is thus likely that EID discovery is biased both temporally (by increasing research effort into human pathogens over the period of the database) and spatially (by the uneven levels of surveillance across countries). We account for these biases by quantifying reporting effort in JID and including it in our temporal and spatial analyses. JID is the premier international journal (highest ISI impact factor 2006: http://portal.isiknowledge.com/) of human infectious disease research that publishes papers on both emerging and non-emerging infectious diseases without a specific geographical bias. To investigate the drivers of the spatial pattern of EID events, we compared the location of EID events to five socio-economic, environmental and ecological variables matched onto a terrestrial one degree grid of the globe. We carried out the spatial analyses using a multivariable logistic regression to control for co-variability between drivers, with the presence/absence of EID events as the dependent variable and all drivers plus our measure of spatial reporting bias by country as independent variables (n = 18,307 terrestrial grid cells). Analyses were conducted on subsets of the EID events—those caused by zoonotic pathogens (defined in our analyses as pathogens that originated in non-human animals) originating in wildlife and non-wildlife species, and those caused by drug-resistant and vector-borne pathogens.
Jones K.E., Patel N.G., Levy M.A., Storeygard A., Balk D., Gittleman J.L, & Daszak P. (2008). Global trends in emerging infectious diseases. Nature, 451(7181), 990-993.
Publication 2008
Animals Bears Biopharmaceuticals Communicable Diseases Communicable Diseases, Emerging Eye Grid Cells Homo sapiens Human poliovirus 2 Noncommunicable Diseases pathogenesis Pharmaceutical Preparations Yellow fever virus
5
Modeling Global Risk Factor Exposures

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2012
Aerosols Age Groups Airborne Particulate Matter Asbestos Child Conferences Diarrhea Diet Disease, Chronic Extinction, Psychological Food Grid Cells Light Lung Cancer Malignant Neoplasms Malnutrition Mesothelioma Nicotiana tabacum Non-Smokers Occupational Exposure Ozone physiology Respiration Disorders Respiratory Rate Respiratory Tract Infections Serum Simulate composite resin Zinc

Most recents protocols related to «Grid Cells»

1
Spatio-Temporal Modeling of Population Trends
To evaluate the trend analysis we used generalized additive mixed models GAMM where the number of individuals in the given grid cells (ygr) was considered as an additive effect of year and analytical surface (grid cells). In the modeling framework we used ‘year’ as the fixed factor (f(year)) and the id of grid cells as the random factor. The model was developed with Poisson distribution. The analysis was based on the ptrend procedure implemented in the poptrend library (for theoretical background of the analysis see Knape24 (link)).
We also used generalized additive mixed models GAMM25 to analyze not only in time (temporal) but also in space (geographical) population trends. Namely, the annual rate of population change was modeled as a function of the latitude and longitude of a given grid cell. In this way, apart from determining the population trend, we were able to indicate on a specific map for the variability of the population trend (positive/negative) in the whole of Israel. As the response variable we used the logarithm of the number of individuals of a given grid cell “g” in year “r”. This variable was modeled with a Poisson-Gamma distribution (pg). The ‘Year’ variable was added to the modeling framework as a fixed factor (f(year)) and as a continuous variable in interactive relation with the longitude and latitude (s(longitude, latitude, year)). Furthermore, the id of the grid cells (id_gc) was used as the random factor. The model was considered in two variants: spline s and linear l; such that the equation was: ygr∼pgagrσ2,agr∼logagr=ϕgrϕgr=fyear+slongitude,latitude+slongitude,latitude,year+id_gc&ϕgr=fyear+llongitude,latitude+llongitude,latitude,year+id_gc where ygr—of number of individuals of a given grid cells g in year r, pg—Poisson-Gamma distribution, s—spline, gc—id of grid cell.
The differences between the two models were tested using likelihood ratio tests.
Furthermore, we also calculated annual population growth rate (λ) which is an exponential model and ensures the estimate of statistical growth of the population per year26 . ny2=ny1×λ(y2-y1) where ny2—number of individuals in year 2, while ny1 means number of individuals in the year preceding ny2.Based on the next GAMM we also estimated the species density. Response variable were expressed as the quotient of the population size in a given grid cell in year and the population trend obtained from the model described above. This variable was implemented in GAMM with Poisson-Gamma distribution where as predictor interactive and additive effect longitude and latitude and year were used. Based on this model we created predictive maps of the spatial density distribution.
Hadad E., Kosicki J.Z, & Yosef R. (2023). Population trends of striped hyena (Hyaena hyaena) in Israel for the past five decades. Scientific Reports, 13, 3982.
Publication 2023
cDNA Library Gamma Rays Gastrin-Secreting Cells Grid Cells Microtubule-Associated Proteins
2
Characterizing Neighborhood Diversity: A Multidimensional Approach

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2023
Childbirth Ethnicity Grid Cells Minority Groups Radius
3
Mobility Patterns in Stockholm during COVID-19

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2023
COVID 19 Grid Cells
4
Estimating Air Quality Impacts of Vehicle Electrification
To illustrate the application of EASIUR-HR, we examined the distribution
of air quality impacts of passenger vehicle electrification across
race/ethnic groups. Passenger vehicles emit substantial amounts of
primary PM2.5 and NOx in vehicle
exhaust, emissions that are eliminated if gasoline vehicles are replaced
by electric vehicles. However, this switch will increase electricity
demand, potentially increasing emissions of primary PM2.5, NOx, and SO2 depending on
the mix of sources for electricity generation. We modeled both of
these effects using EASIUR-HR to predict changes in concentrations
of passenger vehicle exhaust primary PM2.5 and base EASIUR
to predict changes in PM2.5 concentrations due to changes
in vehicle exhaust NOx and all electricity
generation emissions. We present two bounding cases for national vehicle
electrification: vehicle electrification under the current electricity
grid (EV-CUR) using emissions factors from 2018 power plant data and
vehicle electrification under an all-renewable grid (EV-REN), assuming
no air pollutant emissions from electricity generation.
We use
the EPA’s MOtor Vehicle Emission Simulator (MOVES)8 to generate county-level vehicle exhaust emissions
of primary PM2.5 and NOx from
2019 and then allocated them to the base EASIUR grid using a population-weighted
average. After estimating emissions on the base EASIUR grid, we allocated
the emissions in each base EASIUR grid cell to the 300 m grid
EASIUR-HR operates on using spatial surrogates. MOVES automotive emissions
are classified by three road types: off-network, unrestricted access
roads, and restricted access roads. We used population density as
a surrogate to assign off-network emissions to the 300 m grid
and road length by road type as a surrogate for unrestricted and restricted
access roads using 2018 road network data from OpenStreetMap.27 We classify motorways, trunk roads, and primary
roads as restricted access roads and all other roads as unrestricted
access. We allocate the grid cell emissions by road type to the 300 m
EASIUR-HR grid and then add up contributions from each road type to
get a total estimate of vehicle exhaust primary PM2.5 emissions
at a 300 m resolution across the country.
To estimate
changes in power plant (electricity generating unit,
or EGU) air pollutant emissions to meet the demands of electric vehicles
under EV-CUR, we follow the methodology of Holland et al.,19 (link) estimating emissions factors econometrically.
We regress hourly SO2, NOx,
and PM2.5 emissions at the plant level against hourly electricity
demand within each North American Electric Reliability Corporation
(NERC) region in the same interconnect as the plant using emissions
and load from the year 2019.4 ,6 ,7 Unlike Holland et al., we do not include hour-of-day effects in
our regression; this assumption precludes the use of nonconstant vehicle
charging profiles. The regression coefficients of emissions against
regional load provide marginal emissions factors, which we then apply
to an annual estimated increase in load due to electric vehicle charging,
proportional to total VMT using an estimated vehicle efficiency of
100 MPGe. For EV-REN, we assume that there were no additional emissions
associated with producing the electricity for electric vehicles. We
also performed sensitivity analyses to dispatch model31 (link) and our assumed vehicle efficiency.
Gentry B.M., Robinson A.L, & Adams P.J. (2023). EASIUR-HR: A Model To Evaluate Exposure Inequality Caused by Ground-Level Sources of Primary Fine Particulate Matter. Environmental Science & Technology, 57(9), 3817-3824.
Publication 2023
A 300 Air Pollutants Electricity Enzyme Multiplied Immunoassay Technique Ethnic Groups Grid Cells Hypersensitivity North American People Plants
5
Coastal Phytoplankton Bloom Trends and Climate Change
To assess the impacts of climate change on long-term trends in coastal phytoplankton blooms, we correlated the annual mean bloom frequency and the associated SST and SST gradient in various coastal current systems for grid cells with significant changes in bloom frequency (Fig. 3c). The SST and SST gradient were averaged over the growth window within a year, assuming that the changes within the growth window, either in water temperatures or ocean circulations, play more important roles in the bloom trends compared to other seasons32 (link).
We determined the growth window of phytoplankton blooms for each 1° × 1° grid cell (Extended Data Fig. 9a) using the following method: first, we estimated the proportion of cumulative bloom-affected pixels within the grid cells for a year. Second, a generalized additive model72 was used to determine the shape of the phenological curves (Extended Data Fig. 9b), where a log link function and a cubic cyclic regression spline smoother were applied73 ,74 (link). Third, the timing of maximum bloom-affected areas (TMBAA) was then determined by identifying the inflection point on the bloom growth curve (Extended Data Fig. 9c). To facilitate comparisons across Northern and Southern Hemispheres, the year in the Southern Hemisphere was shifted forward by 183 days (Extended Data Fig. 9c). We characterized the similarity of the bloom growth curve between different grid cells and grouped them into three distinct clusters using a fuzzy c-means cluster analysis method75 ,76 (link). We found uniform distributions of the clusters over large geographic areas. Cluster I is mainly distributed in mid-low latitudes (<45° N and <30° S), where the maximum bloom-affected areas were expected in the early period of the year. Cluster II was mostly found in higher latitudes, with bloom developments (quasi-) synchronized with increases in SST. Cluster III was detected along the coastlines, where the bloom-affected areas increase throughout the entire year. In practice, the growth window for clusters I and III was set as the entire year, and that for cluster II was set from day 150 to day 270 within the year. We further found that the TMBAA for cluster II showed small changes over the entire period (Extended Data Fig. 9d), indicating relatively stable phenological cycles for those phytoplankton blooms32 (link),77 (link).
Dai Y., Yang S., Zhao D., Hu C., Xu W., Anderson D.M., Li Y., Song X.P., Boyce D.G., Gibson L., Zheng C, & Feng L. (2023). Coastal phytoplankton blooms expand and intensify in the 21st century. Nature, 615(7951), 280-284.
Publication 2023
Blood Circulation Climate Change Cuboid Bone Grid Cells Phytoplankton

Top products related to «Grid Cells»

1
Vitrobot mark 4 by Thermo Fisher Scientific
Sourced in United States, Germany, Netherlands, United Kingdom, Czechia, Israel
The Vitrobot Mark IV is a cryo-electron microscopy sample preparation instrument designed to produce high-quality vitrified specimens for analysis. It automates the process of blotting and plunge-freezing samples in liquid ethane, ensuring consistent and reproducible sample preparation.
2
Tecnai t12 by Thermo Fisher Scientific
Sourced in United States
The Tecnai T12 is a transmission electron microscope (TEM) designed and manufactured by Thermo Fisher Scientific. It is a high-resolution imaging tool used for the analysis of various materials and samples at the nanoscale level.
3
Magellan xhr sem by Thermo Fisher Scientific
Sourced in United States
The Magellan XHR SEM is a high-resolution scanning electron microscope designed for advanced materials analysis. Its core function is to provide high-resolution imaging and analytical capabilities for a wide range of samples, enabling users to investigate the properties and characteristics of materials at the nanoscale level.
4
Opti mem by Thermo Fisher Scientific
Sourced in United States, United Kingdom, Germany, China, Japan, Canada, France, Switzerland, Italy, Australia, Belgium, Spain, Denmark, Ireland, Netherlands, Holy See (Vatican City State), Israel
Opti-MEM is a cell culture medium designed to support the growth and maintenance of a variety of cell lines. It is a serum-reduced formulation that helps to reduce the amount of serum required for cell culture, while still providing the necessary nutrients and growth factors for cell proliferation.
5
Genios pro microplate reader by Tecan
Sourced in Switzerland, Germany, Austria
The GENios Pro microplate reader is a versatile laboratory instrument designed for various absorbance-based applications. It is capable of performing multi-mode detection, including absorbance, fluorescence, and luminescence measurements, across a wide range of microplates.
6
Beta 5014i by Thermo Fisher Scientific
The Beta 5014i is a laboratory instrument designed for the analysis of alpha and beta radioactivity. It features high-performance detection capabilities and can be used for a variety of applications in the field of radiochemistry and environmental monitoring.
7
Lysotracker red dnd 99 by Thermo Fisher Scientific
Sourced in United States, United Kingdom, Germany, Japan, France, China, Spain
LysoTracker Red DND-99 is a fluorescent dye that selectively stains acidic organelles, such as lysosomes, in live cells. It can be used to visualize and monitor the distribution and activity of lysosomes within the cellular environment.
8
Gold conjugated goat anti rabbit antibodies by Jackson ImmunoResearch
Sourced in United States
Gold-conjugated goat anti-rabbit antibodies are secondary antibodies produced in goats and conjugated with gold particles. They are designed to detect and bind to primary rabbit antibodies in various immunoassays and microscopy techniques.
9
Axio observer z1 by Zeiss
Sourced in Germany, United States, Japan, United Kingdom, Switzerland, France, Sweden, Canada, China, Morocco, Austria, Lithuania, Denmark
The Axio Observer Z1 is a high-performance inverted microscope designed for a wide range of applications in life sciences and materials research. It offers exceptional image quality and a versatile platform for various imaging techniques, including fluorescence, phase contrast, and brightfield microscopy.
10
Jeol7401 field emission scanning electron microscope by JEOL
Sourced in Japan
The JEOL7401 is a field emission scanning electron microscope. It provides high-resolution imaging of small-scale samples.

More about "Grid Cells"

Grid cells are a specialized type of neuron found in the entorhinal cortex, a region of the brain critical for spatial navigation and memory.
These neurons exhibit a unique firing pattern, forming a hexagonal grid-like structure that serves as a cognitive map of the surrounding environment.
Grid cells work in tandem with other spatial cells, such as place cells and head direction cells, to help animals and humans orient themselves and navigate through complex spatial settings.
By providing a metric for distance and direction, grid cells contribute to the brain's ability to encode the geometrical structure of the external world.
Understanding the function and properties of grid cells is a crucial area of research in neuroscience, with potential applications in the development of more efficient navigation systems and the study of spatial cognition dysfunctions associated with neurological disorders.
Researchers utilize various advanced techniques, such as Vitrobot Mark IV for vitrification, Tecnai T12 for electron microscopy, Magellan XHR SEM for high-resolution imaging, and Opti-MEM for cell culture, to investigate the mechanisms and characteristics of these remarkable neurons.
Additionally, tools like the GENios Pro microplate reader, Beta 5014i centrifuge, and LysoTracker Red DND-99 dye can be employed to analyze cellular processes and signaling pathways related to grid cell function.
Specialized imaging techniques, such as those available on the Axio Observer Z1 microscope and the JEOL7401 Field Emission Scanning Electron Microscope, enable researchers to visualize the intricate structure and interactions of grid cells in great detail.
By leveraging these cutting-edge technologies and innovative research platforms like PubCompare.ai, scientists can optimize their protocols, enhance reproducibility, and gain deeper insights into the remarkable world of grid cells and their role in spatial cognition.