Separate Ensembles Research Articles

Neural activity ramps in frontal cortex signal extended motivation during learning

Learning requires the ability to link actions to outcomes. How motivation facilitates learning is not well understood. We designed a behavioral task in which mice self-initiate trials to learn cue-reward contingencies and found that the anterior cingulate region of the prefrontal cortex (ACC) contains motivation-related signals to maximize rewards. In particular, we found that ACC neural activity was consistently tied to trial initiations where mice seek to leave unrewarded cues to reach reward-associated cues. Notably, this neural signal persisted over consecutive unrewarded cues until reward-associated cues were reached, and was required for learning. To determine how ACC inherits this motivational signal we performed projection-specific photometry recordings from several inputs to ACC during learning. In doing so, we identified a ramp in bulk neural activity in orbitofrontal cortex (OFC)-to-ACC projections as mice received unrewarded cues, which continued ramping across consecutive unrewarded cues, and finally peaked upon reaching a reward-associated cue, thus maintaining an extended motivational state. Cellular resolution imaging of OFC confirmed these neural correlates of motivation, and further delineated separate ensembles of neurons that sequentially tiled the ramp. Together, these results identify a mechanism by which OFC maps out task structure to convey an extended motivational state to ACC to facilitate goal-directed learning.

Neural activity ramps in frontal cortex signal extended motivation during learning.

Learning requires the ability to link actions to outcomes. How motivation facilitates learning is not well understood. We designed a behavioral task in which mice self-initiate trials to learn cue-reward contingencies and found that the anterior cingulate region of the prefrontal cortex (ACC) contains motivation-related signals to maximize rewards. In particular, we found that ACC neural activity was consistently tied to trial initiations where mice seek to leave unrewarded cues to reach reward-associated cues. Notably, this neural signal persisted over consecutive unrewarded cues until reward-associated cues were reached, and was required for learning. To determine how ACC inherits this motivational signal we performed projection-specific photometry recordings from several inputs to ACC during learning. In doing so, we identified a ramp in bulk neural activity in orbitofrontal cortex (OFC)-to-ACC projections as mice received unrewarded cues, which continued ramping across consecutive unrewarded cues, and finally peaked upon reaching a reward-associated cue, thus maintaining an extended motivational state. Cellular resolution imaging of OFC confirmed these neural correlates of motivation, and further delineated separate ensembles of neurons that sequentially tiled the ramp. Together, these results identify a mechanism by which OFC maps out task structure to convey an extended motivational state to ACC to facilitate goal-directed learning.

The combination operation of grouping and ensemble coding for structured biological motion crowds in working memory

Massive studies have explored biological motion (BM) crowds processing for their remarkable social significance, primarily focused on uniformly distributed ones. However, real-world BM crowds often exhibit hierarchical structures rather than uniform arrangements. How such structured BM crowds are processed remains a subject of inquiry. This study investigates the representation of structured BM crowds in working memory (WM), recognizing the pivotal role WM plays in our social interactions involving BM. We propose the group-based ensemble hypothesis and test it through a member identification task. Participants were required to discern whether a presented BM belonged to a prior memory display of eight BM, each with distinct walking directions. Drawing on prominent Gestalt principles as organizational cues, we constructed structured groups within BM crowds by applying proximity and similarity cues in Experiments 1 and 2, respectively. In Experiment 3, we deliberately weakened the visibility of stimuli structures by increasing the similarity between subsets, probing the robustness of results. Consistently, our findings indicate that BM aligned with the mean direction of the subsets was more likely to be recognized as part of the memory stimuli. This suggests that WM inherently organizes structured BM crowds into separate ensembles based on organizational cues. In essence, our results illuminate the simultaneous operation of grouping and ensemble encoding mechanisms for BM crowds within WM.

A seamless blended multi‐model ensemble approach to probabilistic medium‐range weather pattern forecasts over the UK

AbstractThis paper describes a new seamless blended multi‐model ensemble configuration of an existing probabilistic medium‐ to extended‐range weather pattern forecasting tool (called Decider) run operationally at the Met Office. In its initial configuration, the tool calculated and presented probabilistic weather pattern forecast information for five individual ensemble forecasting systems, which varied in terms of their number of ensemble members, horizontal resolution, update frequencies and forecast lead time. This resulted in multiple forecasts for the same validity time which varied in terms of forecast skill depending on the lead time in question. This presented challenges for end‐users (e.g., operational meteorologists) in terms of knowing which forecast output is best to use and at which lead time, as well as knowing what to do in situations where forecasts varied between ensembles. To get around these challenges, a new seamless blended multi‐model ensemble configuration has been implemented operationally, comprising of output from five separate ensembles, and provides a single best forecast from day one out to day 45. Objective verification for a set of eight weather pattern groups covering forecasts initialized over a 6‐year period (2017–2022) shows that the seamless blended multi‐model ensemble forecasts are at least as good as, if not better than the best performing individual model.

TRANSLATE: standardized climate projections for Ireland

The TRANSLATE project was established in 2021 by Met Éireann, the Irish national meteorological service, to provide standardized future climate projections for Ireland. This paper outlines the principles and main methods that were used to generate the first set of such projections and presents selected results to the end of the 21st century. Two separate ensembles of dynamically downscaled CMIP5 projections were analyzed. These produce very consistent results, increasing confidence in both, and in the methods used. Future projected fields show plenty of detail (depending on local geography), but the change maps relative to the base period are much smoother, reflecting the global climate change signal. Future forcing uncertainty is represented by 3 different emission scenarios, while model response uncertainty is represented by sub-ensembles corresponding to different climate sensitivities. The resulting matrix of distinct climate ensembles is complemented by ensembles of temperature threshold-based projections, drawn from the same underlying simulations.

Transfer learning and multi-fidelity modeling of laser-driven particle acceleration

Computer models of intense, laser-driven ion acceleration require expensive particle-in-cell simulations that may struggle to capture all the multi-scale, multi-dimensional physics involved at reasonable costs. Explored is an approach to ameliorate this deficiency using a multi-fidelity framework that can incorporate physical trends and phenomena at different levels. As the basis for this study, an ensemble of approximately 8000 1D simulations was generated to buttress separate ensembles of hundreds of higher fidelity 1D and 2D simulations. Using transfer learning with deep neural networks, one can reproduce the results of more complex physics at a much lower cost. The networks trained in this fashion can, in turn, act as surrogate models for the simulations themselves, allowing for quick and efficient exploration of the parameter space of interest. Standard figures-of-merit were used as benchmarks such as the hot electron temperature, peak ion energy, conversion efficiency, and so on. We can rapidly identify and explore under what conditions differing fidelities become an important effect and search for outliers in feature space.

Molybdenum disulfide under extreme conditions: An ab initio study on its melting

Crystalline molybdenum disulfide has become a central actor in the 2D-materials community due to its promising optoelectronic and thermoelectric properties. Despite the extensive work made in investigating these properties, a vast area of knowledge remains unknown on the structure and dynamics of its disordered phases such as liquid and amorphous. Thus, the goal of this work is to investigate the melting of bulk molybdenum disulfide using ab initio molecular dynamics based on density functional theory. We employ the two-phase and Z-methods to evaluate the melting in a number of conditions. Our results at 1 bar reveal that the two-phase procedure is preferred since it predicts a melting point of 2266.92 K that is directly computed using simulations at constant pressure and energy. In contrast, this temperature is indirectly estimated at 2154.01 K with the Z-method using an interpolation of simulations at constant volume and energy. Nevertheless, we find that both methods are complementary as they allow computing different thermodynamic and structural properties. For instance, we estimate a melting heat of 0.67 eV/atom with the two-phase coexistence route, which shows very good agreement with the value of 0.75 eV/atom obtained from the difference of the internal energies of separate crystalline and liquid ensembles at the same conditions of 1 bar and 2266.92 K. In contrast, the Z-method allows us to determine the influence of pressure on the melting temperature, density, and coordination number with a lower computational cost.

Dimensionality reduction and recurrence analysis reveal hidden structures of striatal pathological states.

A pipeline is proposed here to describe different features to study brain microcircuits on a histological scale using multi-scale analyses, including the uniform manifold approximation and projection (UMAP) dimensional reduction technique and modularity algorithm to identify neuronal ensembles, Runs tests to show significant ensembles activation, graph theory to show trajectories between ensembles, and recurrence analyses to describe how regular or chaotic ensembles dynamics are. The data set includes ex-vivo NMDA-activated striatal tissue in control conditions as well as experimental models of disease states: decorticated, dopamine depleted, and L-DOPA-induced dyskinetic rodent samples. The goal was to separate neuronal ensembles that have correlated activity patterns. The pipeline allows for the demonstration of differences between disease states in a brain slice. First, the ensembles were projected in distinctive locations in the UMAP space. Second, graphs revealed functional connectivity between neurons comprising neuronal ensembles. Third, the Runs test detected significant peaks of coactivity within neuronal ensembles. Fourth, significant peaks of coactivity were used to show activity transitions between ensembles, revealing recurrent temporal sequences between them. Fifth, recurrence analysis shows how deterministic, chaotic, or recurrent these circuits are. We found that all revealed circuits had recurrent activity except for the decorticated circuits, which tended to be divergent and chaotic. The Parkinsonian circuits exhibit fewer transitions, becoming rigid and deterministic, exhibiting a predominant temporal sequence that disrupts transitions found in the controls, thus resembling the clinical signs of rigidity and paucity of movements. Dyskinetic circuits display a higher recurrence rate between neuronal ensembles transitions, paralleling clinical findings: enhancement in involuntary movements. These findings confirm that looking at neuronal circuits at the histological scale, recording dozens of neurons simultaneously, can show clear differences between control and diseased striatal states: "fingerprints" of the disease states. Therefore, the present analysis is coherent with previous ones of striatal disease states, showing that data obtained from the tissue are robust. At the same time, it adds heuristic ways to interpret circuitry activity in different states.

Insular cortical circuits as an executive gateway to decipher threat or extinction memory via distinct subcortical pathways

Threat and extinction memories are crucial for organisms’ survival in changing environments. These memories are believed to be encoded by separate ensembles of neurons in the brain, but their whereabouts remain elusive. Using an auditory fear-conditioning and extinction paradigm in male mice, here we discovered that two distinct projection neuron subpopulations in physical proximity within the insular cortex (IC), targeting the central amygdala (CeA) and nucleus accumbens (NAc), respectively, to encode fear and extinction memories. Reciprocal intracortical inhibition of these two IC subpopulations gates the emergence of either fear or extinction memory. Using rabies-virus-assisted tracing, we found IC-NAc projection neurons to be preferentially innervated by intercortical inputs from the orbitofrontal cortex (OFC), specifically enhancing extinction to override fear memory. These results demonstrate that IC serves as an operation node harboring distinct projection neurons that decipher fear or extinction memory under the top-down executive control from OFC.

On the Southern Hemisphere Stratospheric Response to ENSO and Its Impacts on Tropospheric Circulation

Abstract As the leading mode of Pacific variability, El Niño–Southern Oscillation (ENSO) causes vast and widespread climatic impacts, including in the stratosphere. Following discovery of a stratospheric pathway of ENSO to the Northern Hemisphere surface, here we aim to investigate if there is a substantial Southern Hemisphere (SH) stratospheric pathway in relation to austral winter ENSO events. Large stratospheric anomalies connected to ENSO occur on average at high SH latitudes as early as August, peaking at around 10 hPa. An overall colder austral spring Antarctic stratosphere is generally associated with the warm phase of the ENSO cycle, and vice versa. This behavior is robust among reanalysis and six separate model ensembles encompassing two different model frameworks. A stratospheric pathway is identified by separating ENSO events that exhibit a stratospheric anomaly from those that do not and comparing to stratospheric extremes that occur during neutral ENSO years. The tropospheric eddy-driven jet response to the stratospheric ENSO pathway is the most robust in the spring following a La Niña, but extends into summer, and is more zonally symmetric compared to the tropospheric ENSO teleconnection. The magnitude of the stratospheric pathway is weaker compared to the tropospheric pathway and therefore, when it is present, has a secondary role. For context, the magnitude is approximately half that of the eddy-driven jet modulation due to austral spring ozone depletion in the model simulations. This work establishes that the stratospheric circulation acts as an intermediary in coupling ENSO variability to variations in the austral spring and summer tropospheric circulation.

ВИЗНАЧЕННЯ ЗОН ОХОРОНИ ДЛЯ РІЗНИХ ТИПІВ ПАМ’ЯТОК ТА ОБ’ЄКТІВ КУЛЬТУРНОЇ СПАДЩИНИ

Only part of the historical heritage that has a legal status as a "monument", is on the protection of the Ukrainian state. Because now the process of creating a national geospatial database is underway in Ukraine, it is necessary to determine the actual protection zones for all these monuments. The method of determining protection zones for cultural monuments, which is enshrined in Ukrainian law now, doesn't include all types of monuments. It applies only to architectural monuments, including separate buildings and ensembles. After the repeal of the decree that applied to all types of monuments in 2002, the method of determining protection zones for the historical monuments, archeological monuments, objects of monumental art, objects of science and technology, historical landscapes, and historical gardens isn't defined. It is recommended to expand the range of the protected architectural heritage by including in protection zones all objects of valuable historical buildings in the historical cities. Thus, it is proposed to link the protection zones not only with separate monuments and ensembles but also with the most valuable areas of historic cities. In order not to provoke subjectivity and misusage, it is necessary to fix in the legal field the method of defining protection zones for those types of monuments that are not currently described. There is a possibility to partly use old practice when the protection zones define as a circle around the base point of the monuments. It can use for single small objects which sizes aren't more than 3х3 m. But for all other objects, the protection zones should be defined around the territory of the monuments. They should consider the specific requirements for the preservation of specific types of monuments, such as the possible boundaries of mass graves that cannot be established visually, or the possible shifting of land layers when establishing the boundaries of archaeological sites. It is also necessary to consider the conditions of the visual perception of the monuments, which depend on the environment in which these monuments are located. A separate legal issue is to define protection zones for monuments that have been included in the protection lists before the setting of modern rules for the description of cultural heritage sites. The methodology for defining protection zones should take into account the existence of such errors in the descriptions and clearly define the procedure at these rates.

Fatigue risk management based on self-reported fatigue: Expanding a biomathematical model of fatigue-related performance deficits to also predict subjective sleepiness

Biomathematical models of fatigue can be used to predict neurobehavioral deficits during sleep/wake or work/rest schedules. Current models make predictions for objective performance deficits and/or subjective sleepiness, but known differences in the temporal dynamics of objective versus subjective outcomes have not been addressed. We expanded a biomathematical model of fatigue previously developed to predict objective performance deficits as measured on the Psychomotor Vigilance Test (PVT) to also predict subjective sleepiness as self-reported on the Karolinska Sleepiness Scale (KSS). Four model parameters were re-estimated to capture the distinct dynamics of the KSS and account for the scale difference between KSS and PVT. Two separate ensembles of datasets – drawn from laboratory studies of sleep deprivation, sleep restriction, simulated night work, napping, and recovery sleep – were used for calibration and subsequent validation of the model for subjective sleepiness. The expanded model was found to exhibit high prediction accuracy for subjective sleepiness, while retaining high prediction accuracy for objective performance deficits. Application of the validated model to an example scenario based on cargo aviation operations revealed divergence between predictions for objective and subjective outcomes, with subjective sleepiness substantially underestimating accumulating objective impairment, which has important real-world implications. In safety-sensitive operations such as commercial aviation, where self-ratings of sleepiness are used as part of fatigue risk management, the systematic differences in the temporal dynamics of objective versus subjective measures of functional impairment point to a potentially significant risk evaluation sensitivity gap. The expanded biomathematical model of fatigue presented here provides a useful quantitative tool to bridge this previously unrecognized gap.

Strong zero-field Förster resonances in K-Rb Rydberg systems

We study resonant dipole-dipole coupling and the associated van der Waals energy shifts in Rydberg excited atomic rubidium and potassium and investigate F\"orster resonances between interspecies pair states. A comprehensive survey over experimentally accessible pair state combinations reveals multiple candidates with small F\"orster defects. We crucially identify the existence of an ultrastrong, "low" electric field K-Rb F\"orster resonance with a extremely large zero-field crossover distance exceeding 100 $\mu$m between the van der Waals regime and the resonant regime. This resonance allows for a strong interaction over a wide range of distances and by investigating its dependence on the strength and orientation of external fields we show this to be largely isotropic. As a result, the resonance offers a highly favorable setting for studying long-range resonant excitation transfer and entanglement generation between atomic ensembles in a flexible geometry. The two-species K-Rb system establishes a unique way of realizing a Rydberg single-photon optical transistor with a high-input photon rate and we specifically investigate an experimental scheme with two separate ensembles.

Benchmarking and parameter sensitivity of physiological and vegetation dynamics using the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) at Barro Colorado Island, Panama

Abstract. Plant functional traits determine vegetation responses to environmental variation, but variation in trait values is large, even within a single site. Likewise, uncertainty in how these traits map to Earth system feedbacks is large. We use a vegetation demographic model (VDM), the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), to explore parameter sensitivity of model predictions, and comparison to observations, at a tropical forest site: Barro Colorado Island in Panama. We define a single 12-dimensional distribution of plant trait variation, derived primarily from observations in Panama, and define plant functional types (PFTs) as random draws from this distribution. We compare several model ensembles, where individual ensemble members vary only in the plant traits that define PFTs, and separate ensembles differ from each other based on either model structural assumptions or non-trait, ecosystem-level parameters, which include (a) the number of competing PFTs present in any simulation and (b) parameters that govern disturbance and height-based light competition. While single-PFT simulations are roughly consistent with observations of productivity at Barro Colorado Island, increasing the number of competing PFTs strongly shifts model predictions towards higher productivity and biomass forests. Different ecosystem variables show greater sensitivity than others to the number of competing PFTs, with the predictions that are most dominated by large trees, such as biomass, being the most sensitive. Changing disturbance and height-sorting parameters, i.e., the rules of competitive trait filtering, shifts regimes of dominance or coexistence between early- and late-successional PFTs in the model. Increases to the extent or severity of disturbance, or to the degree of determinism in height-based light competition, all act to shift the community towards early-successional PFTs. In turn, these shifts in competitive outcomes alter predictions of ecosystem states and fluxes, with more early-successional-dominated forests having lower biomass. It is thus crucial to differentiate between plant traits, which are under competitive pressure in VDMs, from those model parameters that are not and to better understand the relationships between these two types of model parameters to quantify sources of uncertainty in VDMs.

Experimental simultaneous readout of the real and imaginary parts of the weak value

The weak value, the average result of a weak measurement, has proven useful for probing quantum and classical systems. Examples include the amplification of small signals, investigating quantum paradoxes, and elucidating fundamental quantum phenomena such as geometric phase. A key characteristic of the weak value is that it can be complex, in contrast to a standard expectation value. However, typically only either the real or imaginary component of the weak value is determined in a given experimental setup. Weak measurements can be used to, in a sense, simultaneously measure non-commuting observables. This principle was used in the direct measurement of the quantum wavefunction. However, the wavefunction's real and imaginary components, given by a weak value, are determined in different setups or on separate ensembles of systems, putting the procedure's directness in question. To address these issues, we introduce and experimentally demonstrate a general method to simultaneously read out both components of the weak value in a single experimental apparatus. In particular, we directly measure the polarization state of an ensemble of photons using weak measurement. With our method, each photon contributes to both the real and imaginary parts of the weak-value average. On a fundamental level, this suggests that the full complex weak value is a characteristic of each photon measured.

Assessing the impact of a multimetric calibration procedure on modelling performance in a headwater catchment in Mau Forest, Kenya

Study regionNyangores catchment, Mara catchment, Kenya. Study focusHydrologic models are widely used tools in watershed management to assist in decision making by representing catchment functions under alternative scenarios. This study focused on the evaluation of the 5 segment Flow Duration Curve based calibration procedure (5FDC) for the period 1975–1978 using the Soil Water Assessment Tool (SWAT). The Normal Calibration Procedure (NCP) based on the hydrograph was compared to the 5FDC. Two separate ensembles each containing 10 Pareto calibration runs were obtained individually from each method and evaluated based on their goodness-of-fit. A final Combined Procedure (CP), which was an intersection of 5FDC and NCP was executed. With this supplementary analysis, the parameter distribution and water component balance were analyzed and compared. New hydrological insights for the regionThe comparison of 5FDC and NCP shows that the 5FDC provides a better representation of the low and mid-level section of the flow duration curve as compared to the NCP, which greatly overestimates these flows. This method also closely matches the observed runoff ratios. This indicates that the 5FDC calibration may be well suited for water resource applications focused on low flows. The CP combines the advantages of both procedures by improving parameter identifiability, leading to better representation of high and low flows.

Ensemble method for dengue prediction.

BackgroundIn the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico) during four dengue seasons: 1) peak height (i.e., maximum weekly number of cases during a transmission season; 2) peak week (i.e., week in which the maximum weekly number of cases occurred); and 3) total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date.MethodsOur approach used ensemble models created by combining three disparate types of component models: 1) two-dimensional Method of Analogues models incorporating both dengue and climate data; 2) additive seasonal Holt-Winters models with and without wavelet smoothing; and 3) simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations.Principal findingsOur ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week.ConclusionsThe Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.

Perceived Average Orientation Reflects Effective Gist of the Surface

The human ability to represent ensemble visual information, such as average orientation and size, has been suggested as the foundation of gist perception. To effectively summarize different groups of objects into the gist of a scene, observers should form ensembles separately for different groups, even when objects have similar visual features across groups. We hypothesized that the visual system utilizes perceptual groups characterized by spatial configuration and represents separate ensembles for different groups. Therefore, participants could not integrate ensembles of different perceptual groups on a task basis. We asked participants to determine the average orientation of visual elements comprising a surface with a contour situated inside. Although participants were asked to estimate the average orientation of all the elements, they ignored orientation signals embedded in the contour. This constraint may help the visual system to keep the visual features of occluding objects separate from those of the occluded objects.

Projections of Southern Hemisphere atmospheric circulation interannual variability

An analysis is made of the coherent patterns, or modes, of interannual variability of Southern Hemisphere 500 hPa geopotential height field under current and projected climate change scenarios. Using three separate multi-model ensembles (MMEs) of coupled model intercomparison project phase 5 (CMIP5) models, the interannual variability of the seasonal mean is separated into components related to (1) intraseasonal processes; (2) slowly-varying internal dynamics; and (3) the slowly-varying response to external changes in radiative forcing. In the CMIP5 RCP8.5 and RCP4.5 experiments, there is very little change in the twenty-first century in the intraseasonal component modes, related to the Southern annular mode (SAM) and mid-latitude wave processes. The leading three slowly-varying internal component modes are related to SAM, the El Nino–Southern oscillation (ENSO), and the South Pacific wave (SPW). Structural changes in the slow-internal SAM and ENSO modes do not exceed a qualitative estimate of the spatial sampling error, but there is a consistent increase in the ENSO-related variance. Changes in the SPW mode exceed the sampling error threshold, but cannot be further attributed. Changes in the dominant slowly-varying external mode are related to projected changes in radiative forcing. They reflect thermal expansion of the tropical troposphere and associated changes in the Hadley Cell circulation. Changes in the externally-forced associated variance in the RCP8.5 experiment are an order of magnitude greater than for the internal components, indicating that the SH seasonal mean circulation will be even more dominated by a SAM-like annular structure. Across the three MMEs, there is convergence in the projected response in the slow-external component.

Artificial association of pre-stored information to generate a qualitatively new memory.

Memory is thought to be stored in the brain as anensemble of cells activated during learning. Although optical stimulation of a cell ensemble triggers the retrieval of the corresponding memory, it is unclear how the association of information occurs at the cell ensemble level. Using optogenetic stimulation without any sensory input in mice, we found that an artificial association between stored, non-related contextual, and fear information was generated through the synchronous activation of distinct cell ensembles corresponding to the stored information. This artificial association shared characteristics with physiologically associated memories, such as N-methyl-D-aspartate receptor activity and protein synthesis dependence. These findings suggest that the association of information is achieved through the synchronous activity of distinct cell ensembles. This mechanism may underlie memory updating by incorporating novel information into pre-existing networks to form qualitatively new memories.