nils-herrmann/clean_scirepeval huggingface dataset:
Heidelberg retina tomograph measurements of the optic disc and parapapillary retina for detecting glaucoma analyzed by machine learning classifiers.

To determine whether topographical measurements of the parapapillary region analyzed by machine learning classifiers can detect early to moderate glaucoma better than similarly processed measurements obtained within the disc margin and to improve methods for optimization of machine learning classifier feature selection.



One eye of each of 95 patients with early to moderate glaucomatous visual field damage and of each of 135 normal subjects older than 40 years participating in the longitudinal Diagnostic Innovations in Glaucoma Study (DIGS) were included. Heidelberg Retina Tomograph (HRT; Heidelberg Engineering, Dossenheim, Germany) mean height contour was measured in 36 equal sectors, both along the disc margin and in the parapapillary region (at a mean contour line radius of 1.7 mm). Each sector was evaluated individually and in combination with other sectors. Gaussian support vector machine (SVM) learning classifiers were used to interpret HRT sector measurements along the disc margin and in the parapapillary region, to differentiate between eyes with normal and glaucomatous visual fields and to compare the results with global and regional HRT parameter measurements. The area under the receiver operating characteristic (ROC) curve was used to measure diagnostic performance of the HRT parameters and to evaluate the cross-validation strategies and forward selection and backward elimination optimization techniques that were used to generate the reduced feature sets.



The area under the ROC curve for mean height contour of the 36 sectors along the disc margin was larger than that for the mean height contour in the parapapillary region (0.97 and 0.85, respectively). Of the 36 individual sectors along the disc margin, those in the inferior region between 240 degrees and 300 degrees, had the largest area under the ROC curve (0.85-0.91). With SVM Gaussian techniques, the regional parameters showed the best ability to discriminate between normal eyes and eyes with glaucomatous visual field damage, followed by the global parameters, mean height contour measures along the disc margin, and mean height contour measures in the parapapillary region. The area under the ROC curve was 0.98, 0.94, 0.93, and 0.85, respectively. Cross-validation and optimization techniques demonstrated that good discrimination (99% of peak area under the ROC curve) can be obtained with a reduced number of HRT parameters.



Mean height contour measurements along the disc margin discriminated between normal and glaucomatous eyes better than measurements obtained in the parapapillary region.
Abstract View EXTRACELLULAR POTASSIUM DYNAMICS CONTROLS TRANSITIONS BETWEEN FAST AND SLOW SEIZURE-LIKE OSCILLATIONS

Computational models of cortical networks were used to investigate the effects of extracellular K+ dynamics on seizure-like synchronous oscillatory activity similar to that observed in anesthetized cats in vivo and during LennoxGastaut types of seizures in humans. Active and passive K+ transport through the cell membrane, glial buffering, and diffusion in the extracellular space were included in the model. In a single cell, increase of [K]o caused slow (2-3 Hz) periodic bursting; progressive decrease of [K ]o triggered a switch to fast (~10 Hz) oscillatory firing (fast run). The cell eventually returned to its resting potential. In a network of cortical
Rapid Temporal Modulation of Synchrony by Competition in Cortical Interneuron Networks

The synchrony of neurons in extrastriate visual cortex is modulated by selective attention even when there are only small changes in firing rate (Fries, Reynolds, Rorie, & Desimone, 2001). We used Hodgkin-Huxley type models of cortical neurons to investigate the mechanism by which the degree of synchrony can be modulated independently of changes in firing rates. The synchrony of local networks of model cortical interneurons interacting through GABAA synapses was modulated on a fast timescale by selectively activating a fraction of the interneurons. The activated interneurons became rapidly synchronized and suppressed the activity of the other neurons in the network but only if the network was in a restricted range of balanced synaptic background activity. During stronger background activity, the network did not synchronize, and for weaker background activity, the network synchronized but did not return to an asynchronous state after synchronizing. The inhibitory output of the network blocked the activity of pyramidal neurons during asynchronous network activity, and during synchronous network activity, it enhanced the impact of the stimulus-related activity of pyramidal cells on receiving cortical areas (Salinas & Sejnowski, 2001). Synchrony by competition provides a mechanism for controlling synchrony with minor alterations in rate, which could be useful for information processing. Because traditional methods such as cross-correlation and the spike field coherence require several hundred milliseconds of recordings and cannot measure rapid changes in the degree of synchrony, we introduced a new method to detect rapid changes in the degree of coincidence and precision of spike timing.
Motion detection and prediction through spike-timing dependent plasticity

We describe a possible mechanism for the formation of direction- and velocity-selective cells in visual cortex through spike-timing dependent learning. We contrast the case where only feedforward excitation and inhibition signals are provided to visual neurons with the case where both feedforward and feedback signals are provided. In the feedforward-only case, neurons become selective for a broad range of velocities centered around the training velocity. However, we show that direction selectivity in this case is strongly dependent on delayed feedforward inhibition and in contrast to experimental results, becomes dramatically weaker when inhibition is reduced. When feedback connections are introduced, direction selectivity becomes much more robust due to predictive delays encoded in recurrent activity. Direction selectivity persists in the face of decreasing inhibition in a manner similar to experimental findings. The model predicts that direction-selective cells should exhibit anticipatory activity due to recurrent excitation and suggests a pivotal role for spike-timing dependent plasticity in shaping cortical circuits for visual motion detection and prediction.
Influence of ionic conductances on spike timing reliability of cortical neurons for suprathreshold rhythmic inputs.

Spike timing reliability of neuronal responses depends on the frequency content of the input. We investigate how intrinsic properties of cortical neurons affect spike timing reliability in response to rhythmic inputs of suprathreshold mean. Analyzing reliability of conductance-based cortical model neurons on the basis of a correlation measure, we show two aspects of how ionic conductances influence spike timing reliability. First, they set the preferred frequency for spike timing reliability, which in accordance with the resonance effect of spike timing reliability is well approximated by the firing rate of a neuron in response to the DC component in the input. We demonstrate that a slow potassium current can modulate the spike timing frequency preference over a broad range of frequencies. This result is confirmed experimentally by dynamic-clamp recordings from rat prefrontal cortical neurons in vitro. Second, we provide evidence that ionic conductances also influence spike timing beyond changes in preferred frequency. Cells with the same DC firing rate exhibit more reliable spike timing at the preferred frequency and its harmonics if the slow potassium current is larger and its kinetics are faster, whereas a larger persistent sodium current impairs reliability. We predict that potassium channels are an efficient target for neuromodulators that can tune spike timing reliability to a given rhythmic input.
Electroencephalographic Brain Dynamics Following Manually Responded Visual Targets

Scalp-recorded electroencephalographic (EEG) signals produced by partial synchronization of cortical field activity mix locally synchronous electrical activities of many cortical areas. Analysis of event-related EEG signals typically assumes that poststimulus potentials emerge out of a flat baseline. Signals associated with a particular type of cognitive event are then assessed by averaging data from each scalp channel across trials, producing averaged event-related potentials (ERPs). ERP averaging, however, filters out much of the information about cortical dynamics available in the unaveraged data trials. Here, we studied the dynamics of cortical electrical activity while subjects detected and manually responded to visual targets, viewing signals retained in ERP averages not as responses of an otherwise silent system but as resulting from event-related alterations in ongoing EEG processes. We applied infomax independent component analysis to parse the dynamics of the unaveraged 31-channel EEG signals into maximally independent processes, then clustered the resulting processes across subjects by similarities in their scalp maps and activity power spectra, identifying nine classes of EEG processes with distinct spatial distributions and event-related dynamics. Coupled two-cycle postmotor theta bursts followed button presses in frontal midline and somatomotor clusters, while the broad postmotor “P300” positivity summed distinct contributions from several classes of frontal, parietal, and occipital processes. The observed event-related changes in local field activities, within and between cortical areas, may serve to modulate the strength of spike-based communication between cortical areas to update attention, expectancy, memory, and motor preparation during and after target recognition and speeded responding.
REFINED MONTE CARLO MODELS OF NMDA RECEPTOR SIGNALING

Optical measurements of synaptically activated calcium transients in single dendritic spines revealed that only a low number of NMDA receptors (NMDARs) open at the peak of synaptic transmission (Nimchinsky EA et al, J Neurosci. 2004). Moreover, variability in the calcium transient amplitudes was much lower than expected from such a small number of open NMDARs. Here we investigate various models that might reconcile the observed large dynamic range of NMDAR mediated synaptic transmission and small variability in calcium responses with previous observations of a lack of saturation of NMDARs. We use MCell, the Monte-Carlo cell simulator, to model the consequences of local clustering of NMDARs. A model of calcium-dependent NMDAR inactivation is implemented which results in variance dampening effects with increased inactivation at high ranges of calcium input. Other hypothetical phenomena such as receptor cooperativity and spillover are also investigated. The effects on the calculated variance are analyzed and compared.
Discovering Spike Patterns in Neuronal Responses

When a cortical neuron is repeatedly injected with the same fluctuating current stimulus (frozen noise) the timing of the spikes is highly precise from trial to trial and the spike pattern appears to be unique. We show here that the same repeated stimulus can produce more than one reliable temporal pattern of spikes. A new method is introduced to find these patterns in raw multitrial data and is tested on surrogate data sets. Using it, multiple coexisting spike patterns were discovered in pyramidal cells recorded from rat prefrontal cortex in vitro, in data obtained in vivo from the middle temporal area of the monkey (Buracas et al., 1998) and from the cat lateral geniculate nucleus (Reinagel and Reid, 2002). The spike patterns lasted from a few tens of milliseconds in vitro to several seconds in vivo. We conclude that the prestimulus history of a neuron may influence the precise timing of the spikes in response to a stimulus over a wide range of time scales.
Assignment of Multiplicative Mixtures in Natural Images

In the analysis of natural images, Gaussian scale mixtures (GSM) have been used to account for the statistics of filter responses, and to inspire hierarchical cortical representational learning schemes. GSMs pose a critical assignment problem, working out which filter responses were generated by a common multiplicative factor. We present a new approach to solving this assignment problem through a probabilistic extension to the basic GSM, and show how to perform inference in the model using Gibbs sampling. We demonstrate the efficacy of the approach on both synthetic and image data.
Support Vector Machine Analysis of VCC Scanning Laser Polarimetry RNFL Thickness Measurements

To compare Gaussian support vector machine (G–SVM) analysis of RNFL thickness measurements to standard software generated parameter measurements obtained with variable corneal compensated scanning laser polarimetry (VCC SLP), for differentiating between glaucomatous and non–glaucomatous eyes.  Using VCC SLP, we imaged one eye from each of 87 glaucoma patients (defined as having repeatable achromatic visual field defects) and 67 healthy subjects of similar age, and expressed RNFL thickness as measurements from 64 sectors in the peripapillary area under the instrument defined measurement ellipse. Using these 64 measurements as G–SVM input, we constructed an ROC curve for classification of eyes using cross–validation and compared this curve to curves generated using the VCC GDx software–generated "Nerve Fiber Indicator" (NFI, an SVM including the 64 parameters included in our data set and 28 more), average RNFL thickness, superior average RNFL thickness, and inferior average RNFL thickness. Sensitivities at 0.96, 0.90, and 0.85 specificity also were compared.  ROC curve areas were similar for G–SVM and NFI. ROC curve areas for G–SVM and NFI were significantly larger than for average RNFL thickness, superior average RNFL thickness, and inferior average RNFL thickness. Sensitivities at all specificities were somewhat higher for G–SVM than for NFI, and were considerably higher than for other parameters. Support vector machine analysis of VCC GDx parameters improves diagnostic precision compared to standard RNFL thickness parameters. Good discrimination between glaucoma and healthy eyes is possible with a G–SVM trained on a reduced data set.
Diagnosing glaucoma from frequency doubling technology perimetry using supervised machine learning classifiers

To determine if machine learning classifiers can learn to interpret frequency doubling technology (FDT) perimetry and to compare their performance with the global indices for both FDT II Matrix pattern 24–2 and FDT pattern N30.  Support vector machine with Gaussian kernels (SVMg) and mixture of Gaussian (MoG) classifiers were trained and tested using cross validation on the numerical values of absolute sensitivity plus age from one FDT 24–2 test each from 104 normal and 81 eyes with glaucomatous optic neuropathy (GON) or one FDT N30 test each from 201 normal and 293 eyes with GON. GON was determined by masked consensus reviews of simultaneous stereophotos. For I± = 0.05, a Bonferroni adjustment for 6 comparisons (P <= 0.008) was used to compare the areas under the ROC curves for the machine classifiers and global indices.  For FDT 24–2, the areas under the ROC curves for SVMg, MoG, MD, and PSD were 0.840 Â± 0.029, 0.785 Â± 0.033, 0.703 Â± 0.039, and 0.712 Â± 0.038 (mean Â± SE), respectively. At a specificity of 0.90, SVMg, MoG, MD, and PSD yielded sensitivities of 0.580, 0.457, 0.321, and 0.296. Based on presence of GON vs. healthy, SVMg had a specificity and sensitivity of 82% and 72%, respectively; MoG of 89% and 46%. For FDT N30, the areas under the ROC curves for SVMg, MoG, MD, and PSD were 0.802 Â± 0.019, 0.795 Â± 0.020, 0.727 Â± 0.023, and 0.686 Â± 0.024. At a specificity of 0.90, SVMg, MoG, MD, and PSD yielded sensitivities of 0.529, 0.532, 0.386, and 0.334. SVMg had a specificity and sensitivity of 78% and 68%, respectively; MoG of 67% and 76%. For both FDT 24–2 and N30, the areas under the ROC curves were greater in both SVMg and MoG than in MD or PSD; SVMg and MoG were not significantly different.  Supervised machine learning classifiers are capable of discriminating between normal and GON eyes using FDT results as well as, if not better than, the testsâ|*128*|TM global indices. This is consistent with the superior performance of SVMg and MoG for a variety of visual function (SAP, SWAP) and imaging (HRT) data.
MODEL FOR ION MOTION IN DENDRITIC SPINES

A computational model for electrodiffusion is important for calculating membrane potentials as well as computing ionic concentrations. This is especially true in small compartments, such as dendritic spines on pyramidal cells, where cable models of electrical conduction fail. Preliminary results are presented from a realistic computer model of calcium dynamics and signal transduction. The algorithm uses MCell, a program that uses highly optimized Monte Carlo algorithms to track the stochastic behavior of discrete molecules coupled to a finite-volume method where evaluation of the electric profiles take place. A three-dimensional finite-volume grid will be constructed using NWGrid, a computational package used for mesh generation. The Nernst-Planck equation was solved to study the motion of ions in an electric field, incorporating experimental electrophysiological data to model the voltage-dependent ion channels. The ultimate goal was to study the function and importance of a variety of signaling pathways in dendrites. Support Contributed By: Sloan-Swartz Foundation for Theoretical Neurobiology, NIH P01 NS044306, NIH R01 GM068630
A MONTE CARLO MODEL OF EXTRACELLULAR CALCIUM DYNAMICS IN THE CEREBELLAR GLOMERULUS

Cerebellar glomerulus (CG) is a complex structure comprised of a mossy fiber axon terminal (MFt) that synapses onto 20-50 granule cell (GrC) dendrites. CG is ensheathed by an astrocyte. Our past simulations under conditions of complete ensheathment showed that the concentration of extracellular calcium ([Ca++]o) is a function of the activity of GrC dendrites (GrCD) a result that supported our original hypothesis that [Ca++]o dynamics may play a role in the information coding in the cerebellar glomerulus. However, when the ensheathment is incomplete, [Ca++]o does not show significant changes from baseline. We simulated virtual glia to quantify how changes in size of holes and glial wrapping of GrCD trunks influence the rate of re-equilibration of [Ca++]o. Preliminary results show that the rate of re-equilibration is proportional to the size of the holes and inversely proportional to the extent of GrCD wrapping. Further, our recent simulations with complete ensheathment show that [Ca++]o is related to the activity of both MFt and GrCDs, which suggests a need for a mechanism whereby the GrCs can compare the current [Ca++]o to the activity of the MFt. Finally, within the glomerulus, GrCDs form adhesion points with each other that have been recently shown to contain NMDARs. We speculate that these adhesion points may allow GrCs to sense each other's individual activity by detecting local changes in [Ca++]o. This may suggest a more complex situation, whereby GrCs may be sensing both the average activity of all GrCs as well as the more local activities of their immediate neighbors. We are performing simulations to measure the separability of such global and local signals. These hypotheses will also be explored in an accurately reconstructed glomerulus using EM tomography.
Perceived luminance depends on temporal context

Brightness—the perception of an object's luminance—arises from complex and poorly understood interactions at several levels of processing. It is well known that the brightness of an object depends on its spatial context, which can include perceptual organization, scene interpretation, three-dimensional interpretation, shadows, and other high-level percepts. Here we present a new class of illusion in which temporal relations with spatially neighbouring objects can modulate a target object's brightness. When compared with a nearby patch of constant luminance, a brief flash appears brighter with increasing onset asynchrony. Simultaneous contrast, retinal effects, masking, apparent motion and attentional effects cannot account for this illusory enhancement of brightness. This temporal context effect indicates that two parallel streams—one adapting and one non-adapting—encode brightness in the visual cortex.
The Virtual Instrument: Support for Grid-Enabled Mcell Simulations

Ensembles of widely distributed, heterogeneous resources, or Grids, have emerged as popular platforms for largescale scientific applications. In this paper we present the Virtual Instrument project, which provides an integrated application execution environment that enables end-users to run and interact with running scientific simulations on Grids. This work is performed in the specific context of MCell, a computational biology application. While MCell provides the basis for running simulations, its capabilities are currently limited in terms of scale, ease-of-use, and interactivity. These limitations preclude usage scenarios that are critical for scientific advances. Our goal is to create a scientific “Virtual Instrument” from MCell by allowing its users to transparently access Grid resources while being able to steer running simulations. In this paper, we motivate the Virtual Instrument project and discuss a number of relevant issues and accomplishments in the area of Grid software development and application scheduling. We then describe our software design and report on the current implementation. We verify and evaluate our design via experiments with MCell on a real-world Grid testbed.