Temporal Firing Patterns Research Articles

Predicting the Occurrence of Forest Fire in the Central-South Region of China

Understanding the spatial and temporal patterns of forest fires, along with the key factors influencing their occurrence, and accurately forecasting these events are crucial for effective forest management. In the Central-South region of China, forest fires pose a significant threat to the ecological system, public safety, and economic stability. This study employs Geographic Information Systems (GISs) and the LightGBM (Light Gradient Boosting Machine) model to identify the determinants of forest fire incidents and develop a predictive model for the likelihood of forest fire occurrences, in addition to proposing a zoning strategy. The purpose of the study is to enhance our understanding of forest fire dynamics in the Central-South region of China and to provide actionable insights for mitigating the risks associated with such disasters. The findings reveal the following: (i) Spatially, fire incidents exhibit significant clustering and autocorrelation, highlighting areas with heightened likelihood. (ii) The Central-South Forest Fire Likelihood Prediction Model demonstrates high accuracy, reliability, and predictive capability, with performance metrics such as accuracy, precision, recall, and F1 scores exceeding 85% and AUC values above 89%, proving its effectiveness in forecasting the likelihood of forest fires and differentiating between fire scenarios. (iii) The likelihood of forest fires in the Central-South region of China varies across regions and seasons, with increased likelihood observed from March to May in specific provinces due to various factors, including weather conditions and leaf litter accumulation. Risks of localized fires are noted from June to August and from September to November in different areas, while certain regions continue to face heightened likelihood from December to February.

Biological Sex Modestly Influences the Impact of Long Duration Bed Rest on Human Sympathetic Neurocirculatory Regulation

Our understanding regarding the impact of simulated microgravity on sympathetic neurocirculatory regulation, and the modulatory effects of biological sex, remain incomplete. Through retrospective analysis of two long-duration bed rest trials, this study investigated the impact of 60 days of -6° head-down bed rest (HDBR) on the transduction of integrated muscle sympathetic nerve activity (MSNA; peroneal microneurography) into mean arterial pressure (MAP; Finometer) in healthy females ( n = 12, 25-40 years) and males ( n = 13, 21-42 years). Signal averaging quantified sympathetic transduction for 12 cardiac cycles (ECG) following integrated MSNA bursts. The transduction of integrated MSNA bursting patterns was studied based on the overall response, temporal burst firing patterns (singlet, doublet, and triplet+), and burst amplitude (binned into quartiles: Q1-Q4). Data are reported as mean (SD) for a 5-minute baseline period pre-HDBR and post-HDBR. Statistical analysis included mixed-effect analyses with post-hoc t-tests. In the pooled analyses, HDBR reduced the overall MAP transduction response to integrated MSNA bursts (pre-HDBR: 1.4 ± 0.5 mmHg, post-HDBR: 1.1 ± 0.4 mmHg; P = 0.003). This stemmed from attenuated MAP transduction responses to small- and medium-sized bursts (i.e., Q1-Q3) but there was no effect of HDBR on the largest bursts (i.e., Q4: pre-HDBR: 2.2 ± 0.9 mmHg, post-HDBR: 2.1 ± 0.9 mmHg; P = 0.59). HDBR did not affect the transduction response to singlets (P = 0.23), but HDBR reduced the MAP transduction responses to doublets and triplets+ (both P < 0.03). HDBR did not affect the reduction in MAP during non-bursting periods (pre-HDBR: -0.5 ± 0.2 mmHg, post-HDBR: -0.4 ± 0.2 mmHg; P = 0.17). When stratifying the analyses by sex, we observed that HDBR reduced the overall MAP transduction response to integrated MSNA bursts more so in males (P < 0.01) than females (P = 0.14). Conversely, HDBR did not affect the MAP reductions following non-bursting periods in males (P = 0.53), but in females the reduction in MAP following non-bursting periods was less after HDBR, particularly for cardiac cycles 5 to 7 (pre-HDBR: -0.8 ± 0.5 mmHg, post-HDBR: -0.5 ± 0.3 mmHg; P = 0.03). These data suggest that 60 days of HDBR attenuates sympathetic neurocirculatory regulation during baseline conditions, with a modest influence of biological sex. This work was supported by the Canadian Space Agency, Centre National d'Etudes Spatiales, European Space Agency, National Aeronautics and Space Administration, Institute of Aerospace Medicine of the German Aerospace Center (DLR), Fond Européen de Développement Régional (FEDER) and the Natural Sciences and Engineering Research Council of Canada (NSERC). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Spatiotemporal dynamics of forest fires in the context of climate change: a review.

Forest fires are sudden, destructive, hazardous, and challenging to manage and rescue, earning them a place on UNESCO's list of the world's eight major natural disasters. Currently, amid global warming, all countries worldwide have entered a period of high forest fire incidence. Due to global warming, the frequency of forest fires has accelerated, the likelihood of large fires has increased, and the spatial and temporal dynamics of forest fires have shown different trends. Therefore, the impact of climate change on the spatiotemporal dynamics of forest fires has become a hot issue in the field of forest fire research in recent years. Therefore, it is of great significance and necessity to conduct a review of the research in this area. This review delves into the interactions and impacts between climate change and the spatiotemporal dynamics of forest fires. To address this issue, scholars have mainly adopted the following research methods: first, statistical analysis methods, second, the establishment of spatiotemporal prediction models for meteorology and forest fires, and third, the coupling of climate models with forest fire risk forecasting models. The statistical analysis method relies on the analysis of historical meteorological and fire-related data to study the effects of climate change and meteorological factors on fire occurrence. Meanwhile, forest fire prediction models utilize technical tools such as remote sensing. These models synthesize historical meteorological and fire-related data, incorporating key meteorological factors such as temperature, rainfall, relative humidity, and wind. The models revealed the spatial and temporal distribution patterns of fires, identified key drivers, and explored the interactions between climate change and forest fire dynamics, culminating in the construction of predictive models. With the deepening of the study, the coupling of climate models and fire risk ranking systems became a trend in the prediction of forest fire risk trends. Moreover, as the climate warms, the increased frequency of extreme weather events like heatwaves, droughts, snow and ice storms, and El Niño-Southern Oscillation (ENSO) has accelerated forest fire occurrences and raised the risk of major fires. This review offers valuable technical insights by comprehensively analyzing the spatial and temporal characteristics of forest fires, elucidating key meteorological drivers, and exploring potential mechanisms. These insights serve as a scientific foundation for preventive measures and effective forest fire management. In the face of a changing climate, this synthesis contributes to the development of informed strategies to mitigate the escalating threat of forest fires.

Spatial and temporal patterns of forest fires in the Central Monte: relationships with regional climate

BackgroundNatural and anthropogenic wildfires burn large areas of arid and semi-arid forests with significant socio-economic and environmental impacts. Fire regimes are controlled by climate, vegetation type, and anthropogenic factors such as ignition sources and human-induced disturbances. Projections of climate and land-use change suggest that these controlling factors will change, altering fire regimes in the near future. In the southern Central Monte, Mendoza, Argentina, the factors that modulate the fire temporal and spatial variability are poorly understood. We reconstructed the fire history of southeast of Mendoza from 1984 to 2023 and investigated the relationships between fire extent and climate variability at seasonal and interannual scales. Burned areas were determined using Google Earth Engine by processing Landsat 5-TM, Landsat 7-ETM+ , and Landsat 8-OLI-TIRS sensor imagery.ResultsThe region exhibited high spatial and temporal variability in fire occurrence, being a mosaic of areas with different fire histories and recovery times. Between 1985 and 2023, fire recurrence ranged from sites unburned to sites with up to 14 fires. The occurrence of large fires was strongly favored by a combination of a year with abundant spring–early summer precipitation, which favors fuel accumulation, followed by a year of low spring–early summer precipitation. Precipitation and burnt area showed a very pronounced 6–7 year cycle, suggesting a dominant climatic control on fire occurrence.ConclusionsFire distribution in southeastern Mendoza forests is not homogeneous, resulting in a mosaic of patches with different fire histories. This heterogeneity may be related to vegetation patterns and land use. The temporal variability of fires is strongly influenced by climate variability, which would promote fuel production and subsequent drying. Large fires are concentrated in periods of high interannual precipitation variability. Climate change scenarios predict an increase in temperature and precipitation variability in the region, suggesting future changes in fire dynamics. Our results contribute to the development of fire guidelines for southeastern Mendoza forests, focusing on periods of wet years followed by dry years that favor fire occurrence and spread.

Chir pine forest and pre-monsoon drought determine spatial, and temporal patterns of forest fires in Uttarakhand Himalaya.

The online version contains supplementary material available at 10.1007/s42965-023-00306-9.

Multiproxy Approach to Reconstruct the Fire History of Araucaria araucana Forests in the Nahuelbuta Coastal Range, Chile

Multiproxy reconstructions of fire regimes in forest ecosystems can provide a clearer understanding of past fire activity and circumvent some limitations of single proxy reconstructions. While inferring fire history from scars in trees is the most precise method to reconstruct temporal fire patterns, this method is limited in Araucaria araucana forests by rot after fire injuries, successive fires that destroy the evidence and the prohibition of sample extraction from living Araucaria trees. In this context, dendrochemical studies in Araucaria trees and charcoal analysis from sediment cores can complement and extend the time perspective of the fire history in the relictual Araucaria-Nothofagus forests of the coastal range. We used dendrochemical, fire scar and charcoal records from the Nahuelbuta Coastal Range (37.8° S; 73° W) spanning the last 1000 years to reconstruct the fire history. The results indicate that periods with higher fire activity occurred between 1400 and 1650 AD. Long-term changes in the fire regime are related to increased climate variability over the last 1000 years, and especially with the arrival of settlers to the area after 1860 CE. The most severe fire events in the Nothofagus and Araucaria forests occurred when suitable fire-prone conditions were superimposed with high human densities.

Forest Fire Patterns and Lightning-Caused Forest Fire Detection in Heilongjiang Province of China Using Satellite Data

Large forest fires can cause significant damage to forest ecosystems and threaten human life and property. Heilongjiang Province is a major forested area in China with the highest number and concentration of lightning-caused forest fires in the country. This study examined the spatial and temporal distribution patterns of forest fires in Heilongjiang Province, as well as the ability of satellite remote sensing to detect these fires using VIIRS 375 m fire point data, ground history forest fire point data, and land cover dataset. The study also investigated the occurrence patterns of lightning-caused forest fires and the factors affecting satellite identification of these fires through case studies. Results show that April has the highest annual number of forest fires, with 77.6% of forest fires being caused by lightning. However, less than 30% of forest fires can be effectively detected by satellites, and lightning-caused forest fires account for less than 15% of all fires. There is a significant negative correlation between the two. Lightning-caused forest fires are concentrated in the Daxing’an Mountains between May and July, and are difficult to monitor by satellites due to cloud cover and lack of satellite transit. Overall, the trend observed in the number of forest fire pixels that are monitored by satellite remote sensing systems is generally indicative of the trends in the actual number of forest fires. However, lightning-caused forest fires are the primary cause of forest fires in Heilongjiang Province, and satellite remote sensing is relatively weak in monitoring these fires due to weather conditions and the timing of satellite transit.

Forest fire pattern and vulnerability mapping using deep learning in Nepal

BackgroundIn the last two decades, Nepal has experienced an increase in both forest fire frequency and area, but very little is known about its spatiotemporal dimension. A limited number of studies have researched the extent, timing, causative parameters, and vulnerability factors regarding forest fire in Nepal. Our study analyzed forest fire trends and patterns in Nepal for the last two decades and analyzed forest fire-vulnerability risk based on historical incidents across the country.ResultsWe analyzed the spatial and temporal patterns of forest fires and the extent of burned area using the Mann-Kendall trend test and two machine-learning approaches maximum entropy (MaxEnt), and deep neural network (DNN). More than 78% of the forest fire burned area was recorded between March and May. The total burned area has increased over the years since 2001 by 0.6% annually. The forest fire-vulnerability risk obtained from both approaches was categorized into four classes—very high, high, low, and very low.ConclusionsAlthough burned area obtained from both models was comparable, the DNN slightly outperformed the MaxEnt model. DNN uses a complex structure of algorithms modeled on the human brain that enables the processing of the complex relationship between input and output dataset, making DNN-based models recommended over MaxEnt. These findings can be very useful for initiating and implementing the most suitable forest management intervention.

Satellite-Based Analysis of Spatiotemporal Wildfire Pattern in the Mongolian Plateau

Burned area is a critical input to biomass burning carbon emissions algorithms and for understanding variability in fire activity due to climate change. This study presents the spatial and temporal patterns of wildland fires in the Mongolian Plateau (MP) using Collection 6 NASA MCD64A1 500 m global Burned Area product from 2001 to 2021. Both inter- and intra-annual fire trends and variations in two subregions, Mongolia and China’s Inner Mongolia, were analyzed. The results indicated that an average area of 1.3 × 104 km2 was consumed by fire per year in the MP. The fire season has two peaks: spring (March, April, and May) and autumn (September, October, and December). The profiles of the burnt area for each subregion exhibit distinct seasonality. The majority of wildfires occurred in the northeastern and southwestern regions of the MP, on the border between Mongolia and China. There were 2.7 × 104 km2 of land burned by wildfires in the MP from 2001 to 2021, 57% of which occurred in spring. Dornod aimag (province) of Mongolia is the most fire-prone region, accounting for 51% of the total burned area in the MP, followed by Hulunbuir, at 17%, Sukhbaatar, at 9%, and Khentii at 8%. The changing patterns of spatiotemporal patterns of fire in the MP were analyzed by using a spatiotemporal cube analysis tool, ArcGIS Pro 3.0.2. The results suggested that fires showed a decreasing trend overall in the MP from 2001 to 2021. Fires in the southern region of Dornod aimag and eastern parts of Great Xing’an Mountain showed a sporadic increasing trend. Therefore, these areas should be priorities for future fire protection for both Mongolia and China.

Achieving Brazil's Deforestation Target Will Reduce Fire and Deliver Air Quality and Public Health Benefits.

Climate, deforestation, and forest fires are closely coupled in the Amazon, but models of fire that include these interactions are lacking. We trained machine learning models on temperature, rainfall, deforestation, land-use, and fire data to show that spatial and temporal patterns of fire in the Amazon are strongly modified by deforestation. We find that fire count across the Brazilian Amazon increases by 0.44 percentage points for each percentage point increase in deforestation rate. We used the model to predict that the increased deforestation rate in the Brazilian Amazon from 2013 to 2020 caused a 42% increase in fire counts in 2020. We predict that if Brazil had achieved the deforestation target under the National Policy on Climate Change, there would have been 32% fewer fire counts across the Brazilian Amazon in 2020. Using a regional chemistry-climate model and exposure-response associations, we estimate that the improved air quality due to reduced smoke emission under this scenario would have resulted in 2,300 fewer deaths due to reduced exposure to fine particulate matter. Our analysis demonstrates the air quality and public health benefits that would accrue from reducing deforestation in the Brazilian Amazon.

Bifurcations in the Firing of Neuronal Population Caused by a Small Difference in Pulse Parameters During Sustained Stimulations in Rat Hippocampus in Vivo.

The bifurcation of neuronal firing is one of important nonlinear phenomena in the nervous system and is characterized by a significant change in the rate or temporal pattern of neuronal firing on responding to a small disturbance from external inputs. Previous studies have reported firing bifurcations for individual neurons, not for a population of neurons. We hypothesized that the integrated firing of a neuronal population could also show a bifurcation behavior that should be important in certain situations such as deep brain stimulations. The hypothesis was verified by experiments of rat hippocampus in vivo. Stimulation sequences of paired-pulses with two different inter-pulse-intervals (IPIs) or with two different pulse intensities were applied on the alveus of hippocampal CA1 region in anaesthetized rats. The amplitude and area of antidromic population spike (APS) were used as indices to evaluate the differences in the responses of neuronal population to the different pulses in stimulations. During sustained paired-pulse stimulations with a high mean pulse frequency such as ∼130 Hz, a small difference of only a few percent in the two IPIs or in the two intensities was able to generate a sequence of evoked APSs with a substantial bifurcation in their amplitudes and areas. Small differences in the excitatory inputs can cause nonlinearly enlarged differences in the induced firing of neuronal populations. The novel dynamics and bifurcation of neuronal responses to electrical stimulations provide important clues for developing new paradigms to extend neural stimulations to treat more diseases.

Temporal responses of bumblebee gustatory neurons to sugars

SummaryThe sense of taste permits the recognition of valuable nutrients and the avoidance of potential toxins. Previously, we found that bumblebees have a specialized mechanism for sensing sugars whereby two gustatory receptor neurons (GRNs) within the galeal sensilla of the bees’ mouthparts exhibit bursts of spikes. Here, we show that the temporal firing patterns of these GRNs separate sugars into four distinct groups that correlate with sugar nutritional value and palatability. We also identified a third GRN that responded to stimulation with relatively high concentrations of fructose, sucrose, and maltose. Sugars that were nonmetabolizable or toxic suppressed the responses of bursting GRNs to sucrose. These abilities to encode information about sugar value are a refinement to the bumblebee’s sense of sweet taste that could be an adaptation that enables precise calculations of the nature and nutritional value of floral nectar.

Fire Risk Assessment and Warning Based on Hierarchical Density-Based Spatial Clustering Algorithm and Grey Relational Analysis

Understanding the spatial and temporal distribution patterns of fire is of ecological, social and economic importance. The purpose of this study is to examine the spatial distribution of high fire risk using machine learning algorithms and early warning weather in high-risk areas. Take the satellite monitored fire point data in Yunnan Province during 2015–2019 as an example. The spatial distribution law of high-density parts is found using hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm, and the correlation degree analysis of high-density fire areas based on clustering and annual mean meteorological factors is carried out using grey correlation analysis (GCA) method. Results illustrates that within five years, fires frequently occurred in the seven regions of Wenshan Zhuang and Miao Autonomous Prefecture, Honghe Hani and Yi Autonomous Prefecture, Lijiang City, Pu’er City, and Xishuangbanna Dai Autonomous Prefecture. Among them, the fire in Lijiang had the greatest relationship with precipitation, Pu’er and Xishuangbanna had the greatest correlation with temperature, and Honghe Hani and Wenshan Zhuang were most affected by wind speed. This article acclaims fire prevention in key periods, key areas, key weather and reinforces the protection of transmission lines in the risk area.

Spatial and Temporal Patterns of Fire on Saipan, CNMI

Sediment core studies from Saipan suggest that fires did not play a prominent role in the disturbance regime of the Mariana Islands and have increased in frequency since human settlement around 4,000 years ago. On Saipan fires are understood to interrupt the pattern of succession leading to the degradation of native limestone forests, the proliferation of grasslands and the eventual creation of badlands. Little baseline data regarding the spatial and temporal patterns of fire on Saipan exist to create effective Fire Management Plans. This project uses Landsat 8 images from April 2013 to July 2020 and the Normalized Burn Ratio to identify historic fires to evaluate patterns that will inform on effective fire management policies. Over the study period we detected 1,608 ha of burnt land, in four specific hotspots. Of the area burned, 40% were in grasslands, 31% in evergreen forests, and 21% in scrub-shrub. 41% of all hectares that burned more than once throughout the study period were grasslands, indicating that this was the landcover type most vulnerable to repeat burn events. We also found a strong seasonal trend, with the average amount of burnt land detected in the dry season 280% higher than the average amount of burnt land detected in the wet season. Finally, both total precipitation and the absence of precipitation were highly correlated to the amount of burn area identified (P < .05). The information elucidated through this study will be used by local agencies to implement management plans geared toward controlling wildfires.

An analysis of Spatio–temporal patterns of fires in an Iranian city

Exploring Spatio–temporal patterns of fire incidents provides important information to help develop strategies to prevent and mitigate fire risk based on geographical knowledge. This study aims to map and analyse the Spatio–temporal patterns of urban fire incidents. Fire incident data were obtained from Ardabil Municipality Fire Department and Emergency Services and analysed using radial shape charts, kernel density estimation and average nearest neighbour to quantify spatial and Spatio–temporal patterns of fires. The results show that the Spatio–temporal fire patterns vary, depending on time, their types and causes. Interestingly, results indicate that fires are most likely to occur on Tuesdays and Thursdays and during summer. The study provides evidence to enhance decision-making on resource allocation in terms of establishing new fire stations and deploying an additional workforce to more vulnerable localities, or to formulate prevention strategies, such as education campaigns, to mitigate fire risk.

Biophysical controls over fire regime properties in Central Portugal

The concept of fire regime can be used to describe, with different degrees of complexity, the spatial and temporal patterns of fires and their effects within a given area and over a given period. In this work, we explore the relations between fire regime and a set of potential biophysical controls at a local scale, for 972 civil parishes in central Portugal. The fire regime was characterized with reference to a 44-year period (1975–2018) using three properties: cumulative percentage of parish area burned, area-weighted total number of wildfires, and the Gini concentration index of burned area over time. Potential control variables included topography, seasonal temperature and rainfall, and land use/land cover type and patch fragmentation. Ordinal logistic regression was used to model the relations between the fire regime properties and the potential control factors. Results show that the fire regime properties have important spatial contrasts within the study area, and that land use/land cover distribution, spring rainfall and summer temperatures are the major controls over their variability. The percentage of each parish occupied by shrubland and spontaneous herbaceous vegetation is the single most important factor influencing cumulative percentage of parish area burned and the Gini concentration index of burned area, whereas spring rainfall is the foremost factor regarding area-weighted total number of wildfires. Along with the role of spring rainfall in promoting fuel availability later in the year, our results highlight the importance of the speed of regrowth of shrubland and spontaneous herbaceous vegetation after burning, pointing out the need of tailoring fuel management strategies to the properties of each parish.

Analysis of how the spatial and temporal patterns of fire and their bioclimatic and anthropogenic drivers vary across the Amazon rainforest in El Niño and non-El Niño years.

In the past two decades, Amazon rainforest countries (Brazil, Bolivia, Colombia, Ecuador, Guyana, Peru and Venezuela) have experienced a substantial increase in fire frequency due to the changes in the patterns of different anthropogenic and climatic drivers. This study examines how both fire dynamics and bioclimatic factors varied based on the season (wet season and dry season) El Niño years across the different countries and ecosystems within the Amazon rainforest. Data from publicly available databases on forest fires (Global Fire Atlas) and bioclimatic, topographic and anthropogenic variables were employed in the analysis. Linear mixed-effect models discovered that year type (El Niño vs. non-El Niño), seasonality (dry vs. wet), land cover and forest strata (in terms of canopy cover and intactness) and their interactions varied across the Amazonian countries (and the different ecosystems) under consideration. A machine learning model, Multivariate Adaptive Regression Spline (MARS), was utilized to determine the relative importance of climatic, topographic, forest structure and human modification variables on fire dynamics across wet and dry seasons, both in El Niño and non-El Niño years. The findings of this study make clear that declining precipitation and increased temperatures have strong impact on fire dynamics (size, duration, expansion and speed) for El Niño years. El Niño years also saw greater fire sizes and speeds as compared to non-El Niño years. Dense and relatively undisturbed forests were found to have the lowest fire activity and increased human impact on a landscape was associated with exacerbated fire dynamics, especially in the El Niño years. Additionally, the presence of grass-dominated ecosystems such as grasslands also acted as a driver of fire in both El Niño and non-El Niño years. Hence, from a conservation perspective, increased interventions during the El Niño periods should be considered.

Medial entorhinal cortex lesions produce delay-dependent disruptions in memory for elapsed time

Our memory for time is a fundamental ability that we use to judge the duration of events, put our experiences into a temporal context, and decide when to initiate actions. The medial entorhinal cortex (MEC), with its direct projections to the hippocampus, has been proposed to be the key source of temporal information for hippocampal time cells. However, the behavioral relevance of such temporal firing patterns remains unclear, as most of the paradigms used for the study of temporal processing and time cells are either spatial tasks or tasks for which MEC function is not required. In this study, we asked whether the MEC is necessary for rats to perform a time duration discrimination task (TDD), in which rats were trained to discriminate between 10-s and 20-s delay intervals. After reaching a 90% performance criterion, the rats were assigned to receive an excitotoxic MEC-lesion or sham-lesion surgery. We found that after recovering from surgery, rats with MEC lesions were impaired on the TDD task in comparison to rats with sham lesions, failing to return to criterion performance. Their impairment, however, was specific to the longer, 20-s delay trials. These results indicate that time processing is dependent on MEC neural computations only for delays that exceed 10 s, perhaps because long-term memory resources are needed to keep track of longer time intervals.

Burst activity plays no role in the field-to-field variability and rate remapping of grid cells.

Grid cells in rodent medial entorhinal cortex are thought to play a key role for spatial navigation. When the animal is freely moving in an open arena the firing fields of each grid cell tend to form a highly regular, hexagonal lattice spanning the environment. However, firing rates vary from field to field and change under contextual modifications, whereas the field locations shift at most by a small amount under such "rate remapping." The observed differences in firing rate could reflect overall activity changes or changes in the detailed spike-train statistics. As these two alternatives imply distinct neural coding schemes, we investigated whether temporal firing patterns vary from field to field and whether they change under rate remapping. Focusing on short time scales, we found that the proportion of bursts compared to all discharge events is similar in all firing fields of a given grid cell and does not change under rate remapping. For each cell, mean firing rates with bursts are proportional to mean firing rates without bursts. However, this ratio varies across cells. Additionally, we looked at how rate remapping relates to entorhinal theta-frequency oscillations. Theta-phase coding was preserved despite firing-rate changes from rate remapping but we did not observe differences between the first and second half of the theta cycle, as had been reported for CA1. Our results indicate that both, the heterogeneity between firing fields and rate remapping, are not due to altered firing patterns on short time scales but reflect location-specific changes at the firing-rate level.

REV-ERB in GABAergic neurons controls diurnal hepatic insulin sensitivity.

SUMMARY PARAGRAPHSystemic insulin sensitivity shows diurnal rhythm with a peak at wakening1,2. The molecular mechanism underlying such a temporal pattern is unclear. Here we demonstrate that nuclear receptors Rev-erbα/β in the GABAergic neurons in the suprachiasmatic nucleus (SCNGABA) control the diurnal rhythm of insulin-mediated suppression of hepatic glucose production in mice, without affecting diurnal eating or locomotor behaviors under the regular light-dark cycles. Rev-erb regulates the rhythmic expression of genes involved in neurotransmission in the SCN and modulates the oscillatory firing activity of SCNGABA neurons. Chemogenetic stimulation of SCNGABA neurons at wakening causes glucose intolerance, while restoration of the temporal pattern of either SCNGABA neuron firing or Rev-erb expression rescues the time-dependent glucose metabolic phenotype due to Rev-erb depletion. The elevated blood glucose level after awakening is a defining feature of the extended dawn phenomenon (DP) in diabetes patients3,4. Type 2 diabetes (T2D) patients with the extended DP display a differential temporal pattern of Rev-erb gene expression compared to T2D patients without DP. These findings provide mechanistic insights into how the central circadian clock regulates the diurnal rhythm of hepatic insulin sensitivity, which has implications in the extended DP in T2D.