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PubMed1996

Hippocampal ensemble activity during spatial delayed-nonmatch-to-sample performance in rats.

Multiple-cell recording from specially designed arrays of microwire electrodes allowed analysis of anatomically defined ensemble activity from 10 different locations within the hippocampus of rats (n = 7) performing a two-lever operant version of a spatial delayed-nonmatch-to-sample task (DNMS). Application of population analysis procedures to ensembles of single-neuron activity within the CA1 and CA3 fields revealed firing patterns related to task-relevant events within a DNMS trial. The patterns were extracted via a canonical discriminant analysis in the form of "roots" that represented sources of variance in firing within the ensemble, such as phase of the task (Sample or Nonmatch), spatial position of the lever press response (left or right), and correct versus error trials. Comparison of the ensemble firing on correct versus error trials revealed important insight into ensemble information encoding, such as "miscoding" of the response position and lack of distinct encoding of the response in the Sample phase, which became increasingly vulnerable to error as a function of the duration of delay interval. The extracted discriminant scores were reflective of multiple representations within ensembles and suggested that "conjunctions" of task-relevant features could be represented effectively by small numbers of hippocampal neurons. The findings support the long-held supposition that hippocampal neurons play a critical role in the encoding and retrieval of information in recognition memory tasks.

Animals↗

PubMed1997

Discordance of spatial representation in ensembles of hippocampal place cells.

The extent to which small ensembles of neighboring hippocampal neurons alter their spatial firing patterns concurrently in response to stimulus manipulations was examined in young adult rats as well as in aged rats with and without memory impairment. Recordings from CA1 and CA3 cells were taken as rats performed a spatial radial-maze task that employed prominent distal visual stimuli attached to dark curtains surrounding the maze and local cues on each maze arm provided by inserts with distinctive visual, tactile, and olfactory stimuli. To test the influence of the different stimulus subsets, the distal and local cues were rotated 90 degrees in opposite directions (a Double Rotation). In response to this manipulation, place fields could maintain a fixed position to room coordinates, rotate with either the local or the distal cues, disappear, or new fields could appear. On average 79% of the cells within an ensemble responded in the same way, but only 37% of all ensembles were fully concordant. Typically discordant ensembles had place fields that rotated with one set of cues, whereas the other fields disappeared or new fields appeared. Ensembles in which the place fields rotated in two opposite directions were less frequent in young rats than would be expected by the occurrence of the individual responses, indicating selective competition between directly conflicting representations and ultimate suppression of one. These findings indicate that hippocampal neurons independently encode distinct subsets of the cues in a complex environment, although processing within the hippocampal network may actively reduce the simultaneous representation of conflicting orientation information. This kind of population activity might reflect the higher-order organization of new memories within an established knowledge framework or schema. Concordance was higher in aged memory-impaired rats than in young rats, and the suppression of conflicting representations was absent in these rats. These findings suggest that age-related memory impairment is at least in part associated with a decrease in the scope of information coded and in the coordination of encoded representations.

Animals↗

PubMed2001

Generalized-ensemble algorithms for molecular simulations of biopolymers.

In complex systems with many degrees of freedom such as peptides and proteins, there exists a huge number of local-minimum-energy states. Conventional simulations in the canonical ensemble are of little use, because they tend to get trapped in states of these energy local minima. A simulation in generalized ensemble performs a random walk in potential energy space and can overcome this difficulty. From only one simulation run, one can obtain canonical-ensemble averages of physical quantities as functions of temperature by the single-histogram and/or multiple-histogram reweighting techniques. In this article we review uses of the generalized-ensemble algorithms in biomolecular systems. Three well-known methods, namely, multicanonical algorithm, simulated tempering, and replica-exchange method, are described first. Both Monte Carlo and molecular dynamics versions of the algorithms are given. We then present three new generalized-ensemble algorithms that combine the merits of the above methods. The effectiveness of the methods for molecular simulations in the protein folding problem is tested with short peptide systems.

Algorithms↗

PubMed2004

Comparison of the transition state ensembles for folding of Im7 and Im9 determined using all-atom molecular dynamics simulations with phi value restraints.

Delineation of the structural properties of transition states is key to deriving models for protein folding. Here we describe the structures of the transition states of the bacterial immunity proteins Im7 and Im9 obtained by all-atom molecular dynamics simulations with phi value restraints derived from protein engineering experiments. This pair of proteins is of special interest because, at pH 7 and 10 degrees C, Im7 folds via an intermediate while Im9 folds with a two-state transition. The structures of the transition states for Im7 and Im9, together with their radii of gyration and distances from the native state, are similar. The typical distance between any two members of the transition state ensemble of both proteins is large, with that of Im9 nearly twice that of Im7. Thus, a broad range of structures make up the transition state ensembles of these proteins. The ensembles satisfy the set of rather low phi values and yet are consistent with high beta(T) values (> 0.85 for both proteins). For both Im7 and Im9 the inter-helical angles are highly variable in the transition state ensembles, although the native contacts between helices I and IV are well conserved. By measuring the distribution of the accessible surface area for each residue we show that the hydrophobic residues that are buried in the native state remain buried in the transition state, corresponding to a hydrophobic collapse to a relatively ordered globule. The data provide new insights into the structural properties of the transition states of these proteins at an atomic level of detail and show that molecular dynamics simulations with phi value restraints can significantly enhance the knowledge of the transition state ensembles (TSE) provided by the experimental phi values alone.

Bacterial Proteins↗

PubMed2006

Ensemble-based signatures of energy propagation in proteins: a new view of an old phenomenon.

The ability of a protein to transmit the energetic effects of binding from one site to another constitutes the underlying basis for allosterism and signal transduction. Despite clear experimental evidence indicating the ability of proteins to transmit the effects of binding, the means by which this propagation is facilitated is not well understood. Using our previously developed ensemble-based description of the equilibrium, we investigated the physical basis of energy propagation and identified several fundamental and general aspects of energetic coupling between residues in a protein. First, partitioning of a conformational ensemble into four distinct sub-ensembles allows for explanation of the range of experimentally observed coupling behaviors (i.e., positive, neutral, and negative coupling between various regions of the protein structure). Second, the relative thermodynamic properties of these four sub-ensembles define the energetic coupling between residues as either positive, neutral, or negative. Third, analysis of the structural and thermodynamic features of the states within each sub-ensemble reveals significant variability. This third result suggests that a quantitative description of energy propagation in proteins requires an understanding of the structural and energetic features of more than just one or a few low-energy states, but also of many high-energy states. Such findings illuminate the difficulty in interpreting energy propagation in proteins in terms of a structural pathway that physically links coupled sites.

Binding Sites↗

PubMed1983

An experimental evaluation of the use of an ensemble average for the calculation of turbulence in pulsatile flow.

A comparison was made between turbulence calculated by subtracting an ensemble average from the instantaneous velocity and calculations made with a high pass digital filter. Velocity was measured with a laser Doppler anemometer in vitro in the region of a normal porcine aortic valve and in patients with a hot film anemometer in the region of normal aortic valves. From the velocity obtained in patients, the absolute turbulence intensity calculated using an ensemble average of 50 beats was nearly twice the turbulence intensity calculated using a digital filter. Individual beats sometimes showed differences of 150% compared to calculations based upon the use of a digital filter. Inspection showed that the ensemble average varied widely from the actual nonfluctuating velocity. Studies in vitro showed less beat to beat variation than occurred in patients. The absolute turbulence intensity measured in vitro, when calculated using an ensemble average, was only 20% greater than calculations using a digital filter. The differences were due primarily to beat-to-beat variations of the nonfluctuating velocity, but these beat-to-beat variations were less prominent than occurred in patients. These observations suggest that ensemble averaging may not be appropriate for the calculation of turbulence, particularly in patients.

Biomedical Engineering↗

PubMed2003

Neural activity in prefrontal cortex during copying geometrical shapes. II. Decoding shape segments from neural ensembles.

We trained two monkeys to draw copies of geometrical shapes (e.g. squares, triangles) using a joystick, and found that several variables describing the arm trajectories were encoded in the activity of individual prefrontal neurons (Averbeck et al. 2003). Copy trajectories were drawn as sequences of segments, identified by the serial order in which they were drawn and the shape that they together produced. Here we use linear discriminant analysis to test how well the segments of copied shapes could be decoded from the neural activity patterns of small ensembles (3-22 neurons) of simultaneously recorded cells in prefrontal cortex. Using this analysis, the proper segment (drawn by the monkey) was correctly decoded from the ensemble activity pattern during the drawing of that segment in 60-80% of the cases when the largest ensembles were considered. The information transmitted by these ensembles, as well as by single neurons, was also calculated. We found that the information transmitted by the ensembles increased on average with the number of neurons they contained. Each neuron conveyed information about multiple segments within the drawing trajectory, suggesting that neurons were 'broadly tuned' across segments and that the neural code of segment was distributed.

Action Potentials↗

PubMed2005

The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming.

OBJECT: The classification of cancer based on gene expression data is one of the most important procedures in bioinformatics. In order to obtain highly accurate results, ensemble approaches have been applied when classifying DNA microarray data. Diversity is very important in these ensemble approaches, but it is difficult to apply conventional diversity measures when there are only a few training samples available. Key issues that need to be addressed under such circumstances are the development of a new ensemble approach that can enhance the successful classification of these datasets. MATERIALS AND METHODS: An effective ensemble approach that does use diversity in genetic programming is proposed. This diversity is measured by comparing the structure of the classification rules instead of output-based diversity estimating. RESULTS: Experiments performed on common gene expression datasets (such as lymphoma cancer dataset, lung cancer dataset and ovarian cancer dataset) demonstrate the performance of the proposed method in relation to the conventional approaches. CONCLUSION: Diversity measured by comparing the structure of the classification rules obtained by genetic programming is useful to improve the performance of the ensemble classifier.

Artificial Intelligence↗

PubMed2004

Theoretical analysis of drug release into a finite medium from sphere ensembles with various size and concentration distributions.

Release kinetics for heterogeneous sphere ensembles with a dissolved drug, i.e., initial drug loading below or equal to the drug solubility in the matrix, in a finite external medium was modeled with consideration of heterogeneity among and within spheres. Numerical solutions were obtained using the finite element method for sphere ensemble with normal or log-normal distribution of particle size or initial drug loading among spheres. Exact series solutions were derived for ensembles with various initial loading distributions within spheres, namely linear, quadratic, sigmoidal and uniform distribution, using their mean or average radii. Simplified solutions retaining only one term of the series for non-uniform distributions and three terms for uniform distribution were suggested because of their good approximation to the exact solution. The results of finite element analysis showed that the release rate of an ensemble decreased with increasing standard deviation of particle size. Using weight-average radii in the exact solution gave a prediction of release profile closer to that from the actual size distribution than using mean radii. The three non-uniform loading patterns within spheres all showed reduced initial burst and release rate, leading to more steady release rates than uniform loading, among which the sigmoidal distribution offered the best near-zero order release. Non-uniform initial loading among spheres seemed to have insignificant influence on the release profiles. The volume ratio of liquid to a sphere ensemble played an important role in release kinetics. The derived analytical solutions are applicable to multiple spheres or a single sphere in a finite medium or in a perfect sink.

Algorithms↗

PubMed2005

The native energy landscape for interleukin-1beta. Modulation of the population ensemble through native-state topology.

A minimalist Go-model, with no energetic frustration in the native conformation, has been shown to describe accurately the folding pathway of the beta-trefoil protein, interleukin-1beta (IL-1beta). While it appears that these models successfully model transition states and intermediates between the unfolded and native ensembles, it is unclear how accurately they capture smaller, yet biologically relevant, structural changes within the native ensemble after energetic perturbation. Here, we address the following questions. Can a simple Go-model of interleukin-1beta, based on native topology, describe changes in structural properties of the native ensemble as the protein stability is changed? Or is it necessary to include a more explicit representation of atoms, electrostatic, hydrogen bonding, and van der Waals forces to describe these changes? The native ensemble of IL-1beta was characterized using a variety of experimental probes under native (0 M NaCl, guanidine hydrochloride (Gdn-HCl)), moderately destabilized (0 M NaCl, 0.8 M Gdn-HCl), and in moderate salt concentration (0.8 M NaCl, 0 M Gdn-HCl). Heteronuclear (1)H-(15)N nuclear Overhauser effect spectroscopy (NOESY) and heteronuclear single quantum correlation (HSQC) NMR spectra confirmed that the beta-trefoil global fold was largely intact under these three conditions. However, 25 of the 153 residues throughout the chain did demonstrate (13)C and (1)H-(15)N chemical shifts when perturbed with 0.8 M NaCl or Gdn-HCl. Despite large differences in protection factors from solvent hydrogen-deuterium exchange for all residues between stable (0 M Gdn-HCl) and destabilized (0.8 M Gdn-HCl) IL-1beta, no difference in steady-state (15)N-(1)H NOE enhancements were measured. Thus, the chemical shifts correlate with a global but limited increase in residue flexibility in the presence of Gdn-HCl. Minimalist simulations highlight the regions of greatest position shift between native and 0.8 M Gdn-HCl, which were determined experimentally. This correlation demonstrates that structural changes within the native ensemble of IL-1beta are, at least partially, governed by the principle of minimal energetic frustration.

Amides↗

PubMed2004

Generalized-ensemble algorithms: enhanced sampling techniques for Monte Carlo and molecular dynamics simulations.

In complex systems with many degrees of freedom such as spin glass and biomolecular systems, conventional simulations in canonical ensemble suffer from the quasi-ergodicity problem. A simulation in generalized ensemble performs a random walk in potential energy space and overcomes this difficulty. From only one simulation run, one can obtain canonical ensemble averages of physical quantities as functions of temperature by the single-histogram and/or multiple-histogram reweighting techniques. In this article we review the generalized ensemble algorithms. Three well-known methods, namely, multicanonical algorithm (MUCA), simulated tempering (ST), and replica-exchange method (REM), are described first. Both Monte Carlo (MC) and molecular dynamics (MD) versions of the algorithms are given. We then present five new generalized-ensemble algorithms which are extensions of the above methods.

Algorithms↗

PubMed1999

Independent component analyses for quantifying neuronal ensemble interactions.

The goal of this study was to compare how multivariate statistical methods for dimension reduction account for correlations between simultaneously recorded neurons. Here, we describe applications of principal component analysis (PCA) and independent component analysis (ICA) (Cardoso J-F, Souloumiac A. IEE-Proc F 1993;140:362-70; Hyvarinen A, Oja E. Neural Comput 1997;9:1483-92; Lee TW, Girolami M, Sejnowski TJ. Neural Comp 1999;11:417-41) to neuronal ensemble data. Simulated ensembles of neurons were used to compare how well the methods above could account for correlated neuronal firing. The simulations showed that 'population vectors' defined by PCA were broadly distributed over the neuronal ensembles; thus, PCA was unable to identify independent groupings of neurons that shared common sources of input. By contrast, the ICA methods were all able to identify groupings of neurons that emerged due to correlated firing. This result suggests that correlated neuronal firing is reflected in higher-order correlations between neurons and not simply in the neurons' covariance. To assess the significance of these methods for real neuronal ensembles, we analyzed data from populations of neurons recorded in the motor cortex of rats trained to perform a reaction-time task. Scores for PCA and ICA were reconstructed on a bin-by-bin basis for single trials. These data were then used to train an artificial neural network to discriminate between single trials with either short or long reaction-times. Classifications based on scores from the ICA-based methods were significantly better than those based on PCA. For example, scores for components defined with an ICA-based method, extended ICA (Lee et al., 1999), classified more trials correctly (80.58+/-1.25%) than PCA (73.14+/-0.84%) for an ensemble of 26 neurons recorded in the motor cortex (ANOVA: P < 0.005). This result suggests that behaviorally relevant information is represented in correlated neuronal firing and can be best detected when higher-order correlations between neurons are taken into account.

Action Potentials↗

PubMed1998

The information transmitted by ensembles of primary spindle afferents is diminished when ketamine is used as a pre-anaesthetic.

The effect of pre-anaesthetic ketamine on ensemble coding of different stimuli consisting of muscle stretches of various amplitudes was studied for ensembles of simultaneously recorded primary muscle spindle afferents (MSAs). The experiments were conducted on 8 alpha-chloralose anaesthetised cats. Three of the cats received a pre-anaesthetic dose of ketamine (25 mg/kg) injected subcutaneously (ketamine group), while the remaining five animals did not (non-ketamine group). Data for ensemble coding were collected both before and after cutting the ventral root. A method based on principal component analysis and algorithms was used to quantify stimulus discrimination and an ANOVA tested differences between groups as well as differences due to ventral root cutting. When the fusimotor supply was intact, a general trend of an increase in the ability to discriminate stimuli with increasing ensemble size was observed for both groups, however, this trend was significantly greater for the non-ketamine group as compared to the ketamine group. When the ventral root was cut, the discrimination pattern for the non-ketamine group decreased significantly (as compared to before ventral root cutting), however, no change occurred for the ketamine group. Consequently, no difference in discrimination pattern was detected between groups after ventral root cutting. The reduction in information transmitted by ensembles of primary MSAs when ketamine is used as a pre-anaesthetic may suggest that ketamine elicits an adverse affect on the fusimotor system.

Afferent Pathways↗

PubMed2003

Electrode configuration influences action potential initiation site and ensemble stochastic response properties.

The configuration of intracochlear electrodes used to electrically stimulate the auditory nerve influences the ensemble fiber response. For example, monopolar stimulation produces lower thresholds and greater spread of excitation than does bipolar stimulation. We used two approaches to investigate how the ensemble of auditory-nerve fibers responds to stimulation delivered by different electrode configurations. As the electrically evoked compound action potential (ECAP) reflects the ensemble response of the nerve, we used its morphology and changes with stimulus level to assess issues related to site-of-excitation and fiber recruitment. In our first approach, feline ECAPs were obtained using a nucleus-style banded electrode array. ECAP latency functions indicated that bipolar stimulation can initiate action potentials at more peripheral sites than does monopolar stimulation. We observed double-peaked ECAPs with bipolar and tripolar stimulation, suggesting excitation of both peripheral and central neural processes. Finally, we observed in some cases a tendency for monopolar stimulation to produce wider ECAP potentials, consistent with the notion that monopolar stimulation excites a broader spatial extent of the fiber population. In our second approach, we applied a simple model to published surveys of single-fiber responses to provide insight into the stochastic properties of the ensemble response. Our results suggest that broader recruitment of fiber activity produced by monopolar stimulation results in a population response with more probabilistic response characteristics and ensemble spike jitter. These observations and our ECAP results are consistent with reports of perceptual advantages attributed to monopolar or other less-focused modes of stimulation.

Animals↗

PubMed2003

Clustering ensembles of neural network models.

We show that large ensembles of (neural network) models, obtained e.g. in bootstrapping or sampling from (Bayesian) probability distributions, can be effectively summarized by a relatively small number of representative models. In some cases this summary may even yield better function estimates. We present a method to find representative models through clustering based on the models' outputs on a data set. We apply the method on an ensemble of neural network models obtained from bootstrapping on the Boston housing data, and use the results to discuss bootstrapping in terms of bias and variance. A parallel application is the prediction of newspaper sales, where we learn a series of parallel tasks. The results indicate that it is not necessary to store all samples in the ensembles: a small number of representative models generally matches, or even surpasses, the performance of the full ensemble. The clustered representation of the ensemble obtained thus is much better suitable for qualitative analysis, and will be shown to yield new insights into the data.

Algorithms↗

PubMed1999

Ensemble learning via negative correlation.

This paper presents a learning approach, i.e. negative correlation learning, for neural network ensembles. Unlike previous learning approaches for neural network ensembles, negative correlation learning attempts to train individual networks in an ensemble and combines them in the same learning process. In negative correlation learning, all the individual networks in the ensemble are trained simultaneously and interactively through the correlation penalty terms in their error functions. Rather than producing unbiased individual networks whose errors are uncorrelated, negative correlation learning can create negatively correlated networks to encourage specialisation and cooperation among the individual networks. Empirical studies have been carried out to show why and how negative correlation learning works. The experimental results show that negative correlation learning can produce neural network ensembles with good generalisation ability.

Journal Article↗

PubMed2005

Constructing support vector machine ensembles for cancer classification based on proteomic profiling.

In this study, we present a constructive algorithm for training cooperative support vector machine ensembles (CSVMEs). CSVME combines ensemble architecture design with cooperative training for individual SVMs in ensembles. Unlike most previous studies on training ensembles, CSVME puts emphasis on both accuracy and collaboration among individual SVMs in an ensemble. A group of SVMs selected on the basis of recursive classifier elimination is used in CSVME, and the number of the individual SVMs selected to construct CSVME is determined by 10-fold cross-validation. This kind of SVME has been tested on two ovarian cancer datasets previously obtained by proteomic mass spectrometry. By combining several individual SVMs, the proposed method achieves better performance than the SVME of all base SVMs.

Algorithms↗

PubMed2001

A tandem column ensemble with an atmospheric pressure junction-point vent for high-speed GC with selective control of peak-pair separation.

A series-coupled (tandem) ensemble of two capillary GC columns using different stationary phases and a pneumatically actuated low-volume valve connecting the column junction point to an atmospheric-pressure vent line is used to adjust the ensemble separation of selected pairs of target compounds. The valve is normally closed, and the pressure at the column junction point assumes the value that would occur in the absence of any other connections. The valve can be opened for brief periods of time, thus producing pulses of atmospheric pressure at the column junction point. If a component pair is separated by the first column but coelutes from the column ensemble, the ensemble separation can be increased if a pulse occurs when one of the components has migrated across the column junction but the second component is still on the first column. All of the mixture components that are on the same column during the time that the valve is open (pulse duration) will be shifted to either larger or smaller retention times, but the pattern of peaks (elution order) for these components from the column ensemble will be relatively unaffected by the pressure pulse. Multiple pulses can be used to enhance the separation of different component pairs, which sequentially reach the column junction point. Performance of the valve-operated system is described. Time-of-flight mass spectrometry with time-array detection is used to examine the effects of pulse duration on the separation achieved for different component pairs.

Journal Article↗