nils-herrmann/clean_scirepeval huggingface dataset:
Shortened Perimetry for Glaucoma With Top 10 Locations Derived by Feature Selection With Machine Learning Classifiers

We propose to simplify and shorten automated perimetry for glaucoma by using the best 10 out of 52 visual field locations of 24-2 standard automated perimetry (SAP).  Feature selection by forward selection and backward elimination with Gaussian kernel Support Vector Machine (SVM) derived the rank of the visual field locations from 3 permutations of the development data (189 normals and 156 glaucomatous optic neuropathy (GON) based on masked photographic assessment). SVM was trained to separate standard fullthreshold normal fields from glaucoma fields for the 3 derived sets of the top 10 locations and for all 52 locations + age. Trained classifiers were tested on an independent data set (158 normals and 161 GON) and compared with each other and to PSD and GHT by ROC area and by sensitivity and specificity in the new data set for threshold values at specificity=0.95 in the original developmental data set.  The top 10 locations had similar sensitivities, specificities, and ROC areas compared to standard 24-2 perimetry (Table). Classification with the 3 sets of the 10 best perimetry locations did not significantly alter correct performance compared to SVM, PSD, and GHT applied to all 52 locations + age. Top 10 feature derivations varied and still performed equally well.  Shortened automated perimetry can simplify the test and reduce time without loss of diagnostic accuracy for a single field test. Comparison best 10 locations (backward selection) v all field locations, PSD, GHT SVM sensitivity SVM specificity SVM ROC area PSD sensitivity PSD specificity GHT sensitivity GHT specificity All locations 0.47 0.94 0.798 0.40 0.97 0.43 0.99 Top 10 permutation1 0.44 0.94 0.786 Top 10 permutation2 0.43 0.94 0.778 Top 10 permutation3 0.45 0.95 0.785
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Combinatorial representation of color in visual cortex

The responses of many neurons in primary visual cortex of monkeys are selective for the color of homogeneous color patches in their receptive fields presented on a neutral gray background. When stimuli were presented on colored backgrounds, chromatic tuning was different in most neurons, and the changes in the tuning curves depended on the chromatic contrast between stimulus and background. Although the response tuning curves and their modulation by the background color do not appear to be separable, the likelihood of the ratio of spike counts can be fit by a product of two terms one of which depends only on the color in the receptive field and the second only on the color in the nonclassical receptive field. This type of separability may be a general mechanism that the cortex uses to combine information from different contexts. Nonclassical receptive field surrounds are found in many cortical neurons and these results suggest that they are an efficient way to combine context information with feature information within a Bayesian framework.
Comparing Confocal Scanning Laser Ophthalmoscopy Measurements of the Optic Nerve Head and Peripapillary Retina for Detecting Glaucoma using Machine Learning Classifiers

To determine whether assessment of Heidelberg Retina Tomograph measurements along the disc margin or in the peripapillary retina, where measurements are often less variable, can improve differentiation between normal eyes and eyes with early to moderate glaucoma using machine learning classifiers.  One eye from 95 patients with early to moderate glaucomatous visual field damage and 135 normal subjects over 40 years of age were included. HRT 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). The sectors were evaluated individually and in combination with other sectors. Linear and Gaussian support vector machine (SVM linear, and SVM Guassian) 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 to global and regional HRT parameter measurements.  The ROC curve for mean height contour of the 36 sectors along the disc margin had a larger area under the ROC curve than mean height contour in the parapapillary region (.97 and .85, respectively). Of the 36 individual sectors along the disc margin, those in the inferior region had the largest area under the ROC curve (.85 to .91). Using SVM techniques, the standard HRT software regional parameters showed the best ability to discriminate between eyes with normal and glaucomatous visual fields, 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 for SVM Gaussian was .98, .94, .93 and .85, respectively.  Global and regional HRT parameters, and mean height contour measurements along the disc margin discriminated better than measurements obtained in the parapapillary region.
Complex Cell-like Direction Selectivity through Spike-Timing Dependent Plasticity

Complex cells in primary visual cortex exhibit highly nonlinear receptive field properties such as phase-invariant direction selectivity and antagonistic interactions between individually excitatory stimuli. Traditional models assume that these properties are governed by the outputs of antecedent simple cells, but these models are at odds with studies showing that complex cells may receive direct inputs from the lateral geniculate nucleus (LGN) or can be driven by stimuli that fail to activate simple cells. Using a biophysically detailed model of recurrently connected cortical neurons, we show that complex cell-like direction selectivity may emerge without antecedent simple ceil inputs, as a consequence of spike-timing dependent synaptic plasticity during visual development. The directionally-selective receptive fields of model neurons, as determined by reverse correlation and 2-bar interaction maps, were similar to those obtained from complex cells in awake monkey primary visual cortex. These results suggest a new interpretation of complex cells as integral components of an adaptive cortical circuit for motion detection and prediction.
Regulation of persistent activity by background inhibition in an in vitro model of a cortical microcircuit.

We combined in vitro intracellular recording from prefrontal cortical neurons with simulated synaptic activity of a layer 5 prefrontal microcircuit using a dynamic clamp. During simulated in vivo background conditions, the cell responded to a brief depolarization with a sequence of spikes that outlasted the depolarization, mimicking the activity of a cell recorded during the delay period of a working memory task in the behaving monkey. The onset of sustained activity depended on the number of action potentials elicited by the cue-like depolarization. Too few spikes failed to provide enough NMDA drive to elicit sustained reverberations; too many spikes activated a slow intrinsic hyperpolarization current that prevented spiking; an intermediate number of spikes produced sustained activity. When high dopamine levels were simulated by depolarizing the cell and by increasing the amount of NMDA current, the cell exhibited spontaneous 'up-states' that terminated by the activation of a slow intrinsic hyperpolarizing current. The firing rate during the delay period could be effectively modulated by the standard deviation of the inhibitory background synaptic noise without significant changes in the background firing rate before cue onset. These results suggest that the balance between fast feedback inhibition and slower AMPA and NMDA feedback excitation is critical in initiating persistent activity and that the maintenance of persistent activity may be regulated by the amount of correlated background inhibition.
Self–organizing neural systems based on predictive learning

The ability to predict future events based on the past is an important attribute of organisms that engage in adaptive behaviour. One prominent computational method for learning to predict is called temporal–difference (TD) learning. It is so named because it uses the difference between successive predictions to learn to predict correctly. TD learning is well suited to modelling the biological phenomenon of conditioning, wherein an organism learns to predict a reward even though the reward may occur later in time. We review a model for conditioning in bees based on TD learning. The model illustrates how the TD–learning algorithm allows an organism to learn an appropriate sequence of actions leading up to a reward, based solely on reinforcement signals. The second part of the paper describes how TD learning can be used at the cellular level to model the recently discovered phenomenon of spike–timing–dependent plasticity. Using a biophysical model of a neocortical neuron, we demonstrate that the shape of the spike–timing–dependent learning windows found in biology can be interpreted as a form of TD learning occurring at the cellular level. We conclude by showing that such spike–based TD–learning mechanisms can produce direction selectivity in visual–motion–sensitive cells and can endow recurrent neocortical circuits with the powerful ability to predict their inputs at the millisecond time–scale.
Abstract View INTERACTION OF SUB-THRESHOLD OSCILLATIONS WITH SYNAPTIC INPUT IN THE CORTEX

We recorded from layer II/III pyramidal neurons and interneurons in the slices of the mouse (P28 to P35) visual cortex and evoked the subthreshold oscillations. We then used small negative current pulses and extracellularlly evoked IPSPs to perturb the oscillations. The change in phase and power of the oscillation as well as the change in amplitude, voltage integral and slope of the IPSPs was recorded as a function of power, median voltage and phase of the oscillation at the time of the pulse or IPSP. A phaseshift, which lasted for several hundred miliseconds, was induced by pulses or IPSPs at all phases . We also observed a phase-dependent increase in power, with the largest increase occurring when the pulse/IPSP occurred near the trough of the oscillation.
Assessing Validity of Visual Field Clustering Schemes for Standard Perimetry Using Machine Learning Classifiers

To evaluate the validity of 4 different visual field clustering schemes used to discriminate glaucomatous from healthy eyes by assessing their performance with machine learning classifiers.  Commonly used schemes for clustering 24-2 visual field locations ("maps") were compared Garway-Heath et al., Weber et al., Glaucoma Hemifield Test Sectors (GHTs) and GHT Difference (GHTd) maps. Glaucoma patients (156 eyes/156 patients) had IOP>23 mmHg and glaucomatous optic neuropathy by masked stereophotograph review. Normal controls (189 eyes/189 participants) had no history of elevated IOP and normal optic discs. Raw thresholds from standard perimetry Humphrey full-threshold field locations were averaged within each cluster ("cluster thresholds"), for all clusters within each map. Four types of statistical and machine classifiers [Linear Discriminant Function (LDF), constrained Mixture of Gaussian (cMoG) and Support Vector Machines (SVM linear; SVM gaussian)] were trained separately on each of the "maps" using cluster thresholds plus age,.and tested using cross-validation techniques.  Areas under the ROC curves ranged from 0.77±0.02 (GHTd with SVMl and LDF) to 0.93±0.01 (Garway-Heath with cMoG). Sensitivities for detection of GON at a specificity of 96%, for example, generally ranged from 0.50 (Garway-Heath with LDF) to 0.75 (Garway-Heath with cMoG), although GHTd with LDF and SVMl were lower with sensitivities of 0.35 and 0.33 respectively.  All maps appear to aggregate the visual field locations with similar effectiveness. None of the maps reduce performance compared to a full set of 52 field locations and no single map outperformed the others. This suggests that the choice of map for comparison of the visual field with structural locations on the optic disc is open and determined by the needs of a particular study. 1 Goldbaum et. al.(2002), IOVS, 43, 162-9.
Towards Automatic Recognition of Spontaneous Facial Actions

Charles Darwin (1872/1998) was the first to fully recognize that facial expression is one of the most powerful and immediate means for human beings to communicate their emotions, intentions, and opinions to each other. In addition to providing information about affective state, facial expressions also provide information about cognitive state, such as interest, boredom, confusion, and stress, and conversational signals with information about speech emphasis and syntax. Facial expressions also contain information about whether an expression of emotion is posed or felt (Ekman, 2001; Frank, Ekman, & Friesen, 1993). In order to objectively measure the richness and complexity of facial expressions, behavioral scientists have found it necessary to develop objective coding standards. The Facial Action Coding System (FACS) from Ekman and Friesen (1978) is arguably the most comprehensive and influential of such standards. FACS is based on the anatomy of the human face, and codes expressions in terms of component movements, called " action units " (AUs). Ekman and Friesen defined 46 AUs to describe each independent movement of the face. FACS measures all visible facial muscle movements, including head and eye movements, and not just those presumed to be related to emotion. When learning FACS, a coder is trained to identify the characteristic pattern of bulges, wrinkles, and movements for each facial AU. The AUs approximate individual facial muscle movements but there is not always a 1:1 correspondence. FACS has been used to verify the physiological presence of emotion in a number of studies, with high (over 75%) agreement Because it is comprehensive, FACS also allows for the discovery of new patterns related to emotional or situational states. For example, using FACS Ekman et al (1990) and Davidson et al (1990) found that smiles which featured both orbicularis oculi (AU6), as well as zygomatic major action (AU12), were correlated with self-reports of enjoyment, as well as different patterns of brain activity, whereas smiles that featured only zygomatic major (AU12) were not. Subsequent research demonstrated that the presence of smiles that involve the orbicularis oculi (hereafter " enjoyment smiles ") on the part of a person who has survived the death of their romantic partner predicts successful coping with that traumatic loss (Bonnano & Keltner, 1997). Other work has shown a similar pattern. For example, infants show enjoyment smiles to the presence of their mothers, but not to strangers (Fox & Davidson, 1988). Mothers do not show as many enjoyment …
Abstract View MOTION BIASES POSITION JUDGMENTS: A UNIFIED EXPLANATION FOR THE FEATURE-FLASH, FLASH-LAG, FLASH-DRAG, AND FROHLICH

We propose that several superficially distinct illusions can be explained by a single mechanism. Several illusions share a common feature: when asked to localize a moving object at instant t, observers mislocalize the object in the direction of motion that occurs in the ~80 ms following t (Eagleman & Sejnowski, 2000). We propose this mislocalization occurs because motion signals from area MT feed back to bias position judgments in V1. To unify several illusions, we present a combination of new psychophysical results and modeling. First, we address the feature-flash illusion, in which a sudden color change of a moving bar is mislocalized to a later point in the trajectory. While it has been suggested that the illusion results from delayed feature binding (Cai & Schlag, 2001), we instead present 4 experiments demonstrating that motion signals shift localization of the feature-flash. We further show that motion signals can differentially bias individual edges of objects in transformational motion, such that a flashed object can have illusory size changes along any of its dimensions. Motion biasing offers a unifying explanation for 3 other illusions: (1) the Flash-Lag effect results from shifted localization of the moving object in the 80 ms following the flash. (2) In the Frohlich effect, the perceived starting position is shifted in the direction of later motion. (3) In the Flash-Drag illusion, motion signals can bias stationary objects -here the timestamping flash is itself mislocalized. We present a computational model in which motion biasing results from area MT feedback to V1. Biasing of position judgments is a strategy by which the visual system uses motion information from a small window of time after an event to shift delayed, judged positions closer to their true locations in the outside world. In this way, several phenomena classified as motion illusions expose a sensible evolutionary adaptation that allows us to perceive a better representation of the present.
Having your voxels and timing them too

The two major noninvasive functional human brain imaging modalities developed during the last part of the twentieth century, high-density scalp EEG (electroencephalogram) and fMRI (functional magnetic resonance imaging), appear from a logical viewpoint to be largely complementary. BOLD (blood oxygen level difference) signals can have a spatial resolution of less than 1 cm3, meaning time series of blood oxygenation level differences can be recorded from many more than 10,000 spatially and structurally identifiable brain regions (voxels). However, changes in blood oxygenation are inherently slow, much slower than the firing of individual neurons (circa 1 ms) or tile frequencies at which neural networks tend to synchronize (1-200 Hz or more). EEG signals, on the other hand, can be recorded at sampling rates of a kHz or more per channel, and can thus easily and accurately record cortical potentials throughout their frequency range, if they reflect a sufficient amount or density of synchronous activity within cortex that the summed local fields reach the scalp without canceling one another. It therefore seems easy to argue, as many researchers have, that by concurrently recording EEG and fMRI BOLD signals, researchers could acquire functional brain activity data with both high spatial and high temporal resolution.
The Computational Self

Your brain is never at rest. Shifting patterns of activity course through your brain at night as you review the events of the day and plan the next day before falling asleep. Rumination is a reflection of the Self that is not directly driven by sensory stimuli. When we record from single neurons in the brain, we discover that even in the absence of sensory stimulation, neurons are continuously active. This is called maintained, or spontaneous, activity, and although it is well documented, it has not been as well studied. Most experiments are designed to look for signals that are elicited by sensory stimuli above the background, without mentioning whether the background has changed too, as it often does. New methods have been developed recently that allow us to study the brain's spontaneous activity and to explore how it might provide clues to the origin and nature of the Self.
The Line-Motion Illusion Can Be Reversed by Motion Signals after the Line Disappears

In the line-motion illusion, a briefly flashed line appears to propagate from the locus of attention, despite being physically presented on the screen all at once. It has been proposed that the illusion reflects low-level visual information processing that occurs faster at the locus of attention (Hikosaka et al 1993 Vision Research 33 1219–1240; Perception 22 517–526). Such an explanation implicitly embeds the assumption that speeding or slowing of neural signals will map directly onto perceptual timing. This ‘online’ hypothesis presupposes that signals which arrive first are perceived first. However, other evidence suggests that events in a window of time after the disappearance of a visual stimulus can influence the brain's interpretation of that stimulus (Eagleman and Sejnowski 2000 Science 287 2036–2038; 289 1107a; 290 1051a; 2002 Trends in Neuroscience 25 293). If the online hypothesis were true, we should expect that events occurring after the flashing of the line would not change the illusion. Consistent with our hypothesis that awareness is an a posteriori reconstruction, we demonstrate that the perceived direction of illusory line-motion can be reversed by manipulating stimuli after the line has disappeared.
DEPLETION OF EXTRACELLULAR CALCIUM IN THE CEREBELLAR GLOMERULUS

In the cerebellar glomerulus, a mossy fiber (MF) axon terminal is wrapped by 30-50 granule cell (GC) dendrites, and is enclosed by a glial sheath. We explored the hypothesis that the concentration of extracellular calcium ([Ca++]o) in the glomerulus can change as a function of the amount of GC activity. If this occurs, it suggests a novel learning rule for weight changes at the MF-GC synapses, one in which mutual information between the input and output layers is maximized. To explore whether [Ca++]o would change based on GC activity, we have performed simulations with MCell, a Monte Carlo simulator of molecular biophysics. A model glomerulus was constructed from tessellated surfaces. The space between these membranes contained diffusing Ca++ ions. The membranes were populated with Ca++ channels whose parameters were varied in simulations within biologically reasonable ranges. Our simulations quantify the rate at which Ca++ is depleted in the glomerulus as a function of the firing rate of the GCs and MFs. Given a constant firing rate of both the MF and GCs, [Ca++]o levels reach new steady state with a time constatnt of ~100-150 ms. MF activity contributes to the rate at which [Ca++]o levels reach the new steady state, but not to the average level of [Ca++]o at steady state. Thus, the level of Ca++ at steady state is primarily a function of the GC activity. The simulations also show that the magnitude and duration of [Ca++]o depletion is highly sensitive to the completeness of ensheathment by the glial cell. In order to measure the extent of ensheathment, we have begun electron microscopic studies of the glial sheath. In summary, our simulations explore the range in which [Ca++]o levels in the glomerulus will reflect a sum of post-synaptic activity; in this range, novel learning rules based on information-theoretic principles can be realized.
Variational Bayesian Learning of ICA with Missing Data

Missing data are common in real-world data sets and are a problem for many estimation techniques. We have developed a variational Bayesian method to perform independent component analysis (ICA) on high-dimensional data containing missing entries. Missing data are handled naturally in the Bayesian framework by integrating the generative density model. Modeling the distributions of the independent sources with mixture of gaussians allows sources to be estimated with different kurtosis and skewness. Unlike the maximum likelihood approach, the variational Bayesian method automatically determines the dimensionality of the data and yields an accurate density model for the observed data without overfitting problems. The technique is also extended to the clusters of ICA and supervised classification framework.