nils-herrmann/clean_scirepeval huggingface dataset:
Region-based motion compensated 3D-wavelet transform coding of video

We present a low bit-rate video compression system that integrates region-based coding with a spatio-temporal wavelet transform. The proposed system is designed for monitoring and video-phone applications. It distinguishes between a moving foreground and a static background, but image segmentation might also be based on other sources. The regions are encoded in separate layers using a chroma-keying technique that allows a controlled lossy recovery of the boundaries. A 3D-wavelet transform is applied to a group of frames of the predictor residual signal. Statistical dependencies of the transform coefficients extracted from different image subbands are captured by conditional probability models. Without the layered coding, the system has a superior performance compared to the H.263 standard for very low bit-rate coding. The layered coding causes a small degradation in visual quality at the same bit-rate.
Unsupervised On-line Learning of Decision Trees for Hierarchical Data Analysis

An adaptive on-line algorithm is proposed to estimate hierarchical data structures for non-stationary data sources. The approach is based on the principle of minimum cross entropy to derive a decision tree for data clustering and it employs a metalearning idea (learning to learn) to adapt to changes in data characteristics. Its efficiency is demonstrated by grouping non-stationary artifical data and by hierarchical segmentation of LANDSAT images.
Multidimensional Scaling by Deterministic Annealing

Multidimensional scaling addresses the problem how proximity data can be faithfully visualized as points in a low-dimensional Euclidian space. The quality of a data embedding is measured by a cost function called stress which compares proximity values with Euclidian distances of the respective points. We present a novel deterministic annealing algorithm to efficiently determine embedding coordinates for this continuous optimization problem. Experimental results demonstrate the superiority of the optimization technique compared to conventional gradient descent methods. Furthermore, we propose a transformation of dissimilarities to reduce the mismatch between a high-dimensional data space and a low-dimensional embedding space.
Regularizing phase-based stereo

Wavelet-based techniques proved to be a promising approach for estimating the disparity between two stereo images. The complex-valued Gabor filter responses reduce the ambiguity of raw image intensities, and their phase differences between left and right image provide a direct measure for the disparities. Experience shows that such phase based measurements are reliable near edges but yield poor results between them. To improve these phase-based disparity estimations, a regularization scheme is proposed which directly compares possible matching pairs. Unreliable regions are filled by using a simple smoothness constraint. In the spirit of Markov random fields, we propose a probabilistic lattice model which describes the complete disparity distribution instead of representing only a single configuration. Experimental results are presented for an artificial image pair generated by computer graphics.
Real-time phase-based stereo for a mobile robot

The performance of a mobile robot crucially depends on the accuracy, duration and reliability of its sensor interpretation. A major source of information are CCD-cameras which provide a detailed view of the robot's environment. This paper presents a real-time stereo algorithm implemented on the mobile robot RHINO of the University of Bonn. The algorithm exploit the phases of wavelet-filtered image pairs to localize edges and to estimate their disparities with subpixel accuracy. The disparities are computed by an initial search for corresponding points within a given interval and a subsequent measurement of phase-differences. The real-time constraints of autonomous object detection and navigation are fulfilled by partially implementing the stereo algorithm on a pipeline computer Datacube. Experimental results on real world scenes under real world conditions demonstrate the stereo algorithm's robustness and suitability for autonomous robot applications.
-y-aminobutyric acid may account for differences between inhibitory responses in the hippocampus and thalamus

We have developed a model of r-aminobutyric acid (GABA)ergic synaptic transmission mediated by GABAA and GABAB receptors, including cooperativity in the guanine nucleotide binding protein (G protein) cascade mediating the activation of K+ channels by GABAB receptors. If the binding of several G proteins is needed to activate the K+ channels, then only a prolonged activation of GABAB receptors evoked detectable currents. This could occur if strong stimuli evoked release in adjacent termipals and the spillover resulted in prolonged activatiod4of the receptors, leading to inhibitory responses similar to those observed in hippocampal slices. The same model also reproduced thalamic GABAB responses to high-frequency bursts of stimuli. In this case, prolonged activation of the receptors was due to highfrequency release conditions. This model provides insights into the function of GABAB receptors in normal and epileptic discharges. Two receptor types, GABAA and GABAB, are responsible for most inhibitory postsynaptic potentials (IPSPs) mediated by the release of y-aminobutyric acid (GABA) from presynaptic terminals. These receptors have characteristic differences in their kinetics (reviewed in refs. 1 and 2); GABAA-mediated currents have a relatively fast time course (time constant, 5-20 ms), whereas GABAB receptors induce much slower changes in the excitability of the cell (time constant, 150-200 ms). They differ as well in their activation. Typically, relatively strong stimulation is needed to evoke GABAB responses, whereas GABAA-mediated currents are evoked even for very low levels of presynaptic stimulation. Miniature GABAA IPSPs also occur spontaneously and are thought to arise from the spontaneous release of GABA from a single vesicle, but they never have a GABAB component (2-4). Physiological data on GABAergic responses show marked differences between thalamic and hippocampal slices. In the thalamus, stimulation of the reticular (RE) nucleus or interneurons induces biphasic GABAergic IPSPs in thalamocortical (TC) cells (5). The ratio between peak GABAA and GABAB currents evoked by RE neurons is insensitive to the intensity of the stimulation (6), but it changes markedly if the discharge of RE cells is enhanced by pharmacological means (6-9). In hippocampal slices, GABAergic currents can be elicited in the dendrites of pyramidal cells by stimulating interneurons in the stratum radiatum. Unlike the situation in thalamic cells, the GABAA/GABAB ratio depends critically on the intensity of the stimulation (10, 11) and on the presence of uptake (3, 12, 13). In this paper, we focus on the activation kinetics of GABAB responses, in which K+ channels are activated through a guanine nucleotide binding protein (G protein) cascade (14, 15). We assume that this activation shows some "cooperativity," in the sense that independent binding of several G-protein subunits is needed to open the K+ channels.
Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements.

To classify healthy and glaucomatous eyes using relevance vector machine (RVM) and support vector machine (SVM) learning classifiers trained on retinal nerve fiber layer (RNFL) thickness measurements obtained by scanning laser polarimetry (SLP).



Seventy-two eyes of 72 healthy control subjects (average age = 64.3 +/- 8.8 years, visual field mean deviation = -0.71 +/- 1.2 dB) and 92 eyes of 92 patients with glaucoma (average age = 66.9 +/- 8.9 years, visual field mean deviation = -5.32 +/- 4.0 dB) were imaged with SLP with variable corneal compensation (GDx VCC; Laser Diagnostic Technologies, San Diego, CA). RVM and SVM learning classifiers were trained and tested on SLP-determined RNFL thickness measurements from 14 standard parameters and 64 sectors (approximately 5.6 degrees each) obtained in the circumpapillary area under the instrument-defined measurement ellipse (total 78 parameters). Ten-fold cross-validation was used to train and test RVM and SVM classifiers on unique subsets of the full 164-eye data set and areas under the receiver operating characteristic (AUROC) curve for the classification of eyes in the test set were generated. AUROC curve results from RVM and SVM were compared to those for 14 SLP software-generated global and regional RNFL thickness parameters. Also reported was the AUROC curve for the GDx VCC software-generated nerve fiber indicator (NFI).



The AUROC curves for RVM and SVM were 0.90 and 0.91, respectively, and increased to 0.93 and 0.94 when the training sets were optimized with sequential forward and backward selection (resulting in reduced dimensional data sets). AUROC curves for optimized RVM and SVM were significantly larger than those for all individual SLP parameters. The AUROC curve for the NFI was 0.87.



Results from RVM and SVM trained on SLP RNFL thickness measurements are similar and provide accurate classification of glaucomatous and healthy eyes. RVM may be preferable to SVM, because it provides a Bayesian-derived probability of glaucoma as an output. These results suggest that these machine learning classifiers show good potential for glaucoma diagnosis.
Homeostatic synaptic plasticity can explain post-traumatic epileptogenesis in chronically isolated neocortex.

Chronically isolated neocortex develops chronic hyperexcitability and focal epileptogenesis in a period of days to weeks. The mechanisms operating in this model of post-traumatic epileptogenesis are not well understood. We hypothesized that the spontaneous burst discharges recorded in chronically isolated neocortex result from homeostatic plasticity (a mechanism generally assumed to stabilize neuronal activity) induced by low neuronal activity after deafferentation. To test this hypothesis we constructed computer models of neocortex incorporating a biologically based homeostatic plasticity rule that operates to maintain firing rates. After deafferentation, homeostatic upregulation of excitatory synapses on pyramidal cells, either with or without concurrent downregulation of inhibitory synapses or upregulation of intrinsic excitability, initiated slowly repeating burst discharges that closely resembled the epileptiform burst discharges recorded in chronically isolated neocortex. These burst discharges lasted a few hundred ms, propagated at 1-3 cm/s and consisted of large (10-15 mV) intracellular depolarizations topped by a small number of action potentials. Our results support a role for homeostatic synaptic plasticity as a novel mechanism of post-traumatic epileptogenesis.
View RELIABILITY OF MODEL V 1 CELL RESPONSES TO NATURAL THALAMIC STIMULUS INPUTS

We previously showed that combinations of correlated firing, bursting, and spatial grouping of synapses are candidate mechanisms for explaining the reliability of spike timing in cat V1 neurons despite the presence of large amounts of noise from background synaptic inputs, the highly stochastic nature of synaptic release, and the fact that only about one tenth of the synapses in a layer 4 smooth stellate cell come directly from LGN. Here, we enhanced the original model to include active channels in the dendritic tree of the reconstructed cat V1 stellate neuron, and tested it with natural thalamic stimulus inputs recorded from in vivo experiments. These inputs activated inhibitory and probabilistic excitatory synapses, some of which had short-term use-dependent facilitation and depression. We corroborated the previous finding that reliable firing patterns could be obtained by conditioning pre-synaptic LGN inputs with either low levels of temporal correlation or with the addition of bursts. However, the effect of spatial grouping on synaptic spike time reliability was greatly dependent on the distribution and characteristics of dendritic active channels. These results place morphological and physiological constraints on information flow from the LGN to V1 and highlight interactions between spatial distribution, timing of excitatory and inhibitory inputs, and the effects of active channels. Support Contributed By: Howard Hughes Medical Institute and NSF-IGERT
Evidence for Ectopic Neurotransmission at a Neuronal Synapse

Neurotransmitter release is well known to occur at specialized synaptic regions that include presynaptic active zones and postsynaptic densities. At cholinergic synapses in the chick ciliary ganglion, however, membrane formations and physiological measurements suggest that release distant from postsynaptic densities can activate the predominantly extrasynaptic α7 nicotinic receptor subtype. We explored such ectopic neurotransmission with a novel model synapse that combines Monte Carlo simulations with high-resolution serial electron microscopic tomography. Simulated synaptic activity is consistent with experimental recordings of miniature excitatory postsynaptic currents only when ectopic transmission is included in the model, broadening the possibilities for mechanisms of neuronal communication.
Maintenance and termination of neocortical oscillations by dynamic modulation of intrinsic and synaptic excitability.

Mechanisms underlying seizure cessation remain elusive. The Lennox-Gastaut syndrome, a severe childhood epileptic disorder, is characterized by episodes of seizure with alternating epochs of spike-wave and fast run discharges. In a detailed computational model that incorporates extracellular potassium dynamics, we studied the dynamics of these state transitions between slow and fast oscillations. We show that dynamic modulation of synaptic transmission can cause termination of paroxysmal activity. An activity-dependent shift in the balance between synaptic excitation and inhibition towards more excitation caused seizure termination by favoring the slow oscillatory state, which permits recovery of baseline extracellular potassium concentration. We found that slow synaptic depression and change in chloride reversal potential can have similar effects on the seizure dynamics. Our results indicate a novel role for synaptic dynamics during epileptic neural activity patterns.
Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects.

Clustering by unsupervised learning with machine learning classifiers was shown to segment clusters of patterns in standard automated perimetry (SAP) for glaucoma in previous publications. In this study, unsupervised learning by independent component analysis decomposed SAP field patterns into axes, and the information represented by these axes was evaluated.



SAP fields were used that were obtained with the Humphrey Visual Field Analyzer (Carl Zeiss Meditec, Dublin, CA) from 189 normal eyes and 156 eyes with glaucomatous optic neuropathy (GON) determined by masked review with stereoscopic optic disc photographs. The variational Bayesian independent component analysis mixture model (vB-ICA-mm) partitioned the SAP fields into the most informative number of clusters. Simultaneously, the model learned an optimal number of maximally independent axes for each cluster.



The most informative number of clusters in the SAP set was two. vB-ICA-mm placed 68.6% of the eyes with GON in a cluster labeled G and 98.4% of the eyes with normal optic discs in a cluster labeled N. Cluster G optimally contained six axes. Post hoc analysis of patterns generated at -1 SD and +2 SD from the cluster G mean on the six axes revealed defects similar to those identified by experts as indicative of glaucoma. SAP fields associated with an axis showed increasing severity, as they were located farther in the positive direction from the cluster G mean.



vB-ICA-mm represented the SAP fields with patterns that were meaningful for glaucoma experts. This process also captured severity in the patterns uncovered. These findings should validate vB-ICA-mm as a data-mining technique for new and unfamiliar complex tests.
Membrane bistability in thalamic reticular neurons during spindle oscillations.

The thalamic reticular (RE) nucleus is a major source of inhibition in the thalamus. It plays a crucial role in regulating the excitability of thalamocortical networks and in generating some sleep rhythms. Current-clamp intracellular recordings of RE neurons in cats under barbiturate anesthesia revealed the presence of membrane bistability in approximately 20% of neurons. Bistability consisted of two alternate membrane potentials, separated by approximately 17-20 mV. While non-bistable (common) RE neurons fired rhythmic spike-bursts during spindles, bistable RE neurons fired tonically, with burst modulation, throughout spindle sequences. Bistability was strongly voltage dependent and only expressed under resting conditions (i.e. no current injection). The transition from the silent to the active state was a regenerative event that could be activated by brief depolarization, whereas brief hyperpolarizations could switch the membrane potential from the active to the silent state. These effects outlasted the current pulses. Corticothalamic stimulation could also switch the membrane potential from silent to active states. Addition of QX-314 in the recording micropipette either abolished or disrupted membrane bistability, suggesting I(Na(p)) to be responsible for its generation. Thalamocortical cells presented various patterns of spindling that reflected the membrane bistability in RE neurons. Finally, experimental data and computer simulations predicted a role for RE neurons' membrane bistability in inducing various patterns of spindling in target thalamocortical cells. We conclude that membrane bistability of RE neurons is an intrinsic property, likely generated by I(Na(p)) and modulated by cortical influences, as well as a factor that determines different patterns of spindle rhythms in thalamocortical neurons.
Improved rejection of artifacts from EEG data using high-order statistics and independent component analysis

While it is now generally accepted that independent component analysis (ICA) is a good tool for isolating both artifacts and cognition-related processes in EEG data, there is little definite proof that data preprocessed using ICA is more effective than artifact rejection on raw channel data, especially when more subtle signal processing methods are used to detect artifacts. Here we applied five statistical signal processing methods for detecting artifactual data epochs from either the raw data containing simulated artifacts or from the ICA decomposition of these data, and tested their performance for different sizes of introduced artifacts. The most efficient rejection method used threshold limits applied to the single trial data spectra. We show that for this or other methods ICA preprocessing can improve the detection of data epochs containing eye, muscle, and electrical artifacts by 10-20%.
Underlying Thalamocortical Slow-Wave Oscillations Interactions Between Membrane Conductances

, June 25, 2004; 304 (5679): 1926-1929. Science G. Buzsaki and A. Draguhn Neuronal Oscillations in Cortical Networks [PDF]  [ , November 1, 2004; 11 (6): 686-696. Learn. Mem. S. Ribeiro and M. A.L. Nicolelis Reverberation, storage, and postsynaptic propagation of memories during sleep [PDF]  [ , May 11, 2005; 25 (19): 4844-4855.  Wu and E. Perez-Reyes Epilepsy Variants of CACNA1H, a T-Type Calcium Channel Functional Characterization and Neuronal Modeling of the Effects of Childhood Absence [PDF]  [ , June 15, 2005; 565 (3): 765-781. J. Physiol. P. A Rhodes and R. Llinas A model of thalamocortical relay cells
A Bayesian Framework for Tilt Perception and Confidence

The misjudgement of tilt in images lies at the heart of entertaining visual illusions and rigorous perceptual psychophysics. A wealth of findings has attracted many mechanistic models, but few clear computational principles. We adopt a Bayesian approach to perceptual tilt estimation, showing how a smoothness prior offers a powerful way of addressing much confusing data. In particular, we faithfully model recent results showing that confidence in estimation can be systematically affected by the same aspects of images that affect bias. Confidence is central to Bayesian modeling approaches, and is applicable in many other perceptual domains.
Machine learning approaches for phenotype-genotype mapping: predicting heterozygous mutations in the CYP21B gene from steroid profiles.

Non-linear relations between multiple biochemical parameters are the basis for the diagnosis of many diseases. Traditional linear analytical methods are not reliable predictors. Novel nonlinear techniques are increasingly used to improve the diagnostic accuracy of automated data interpretation. This has been exemplified in particular for the classification and diagnostic prediction of cancers based on expression profiling data. Our objective was to predict the genotype from complex biochemical data by comparing the performance of experienced clinicians to traditional linear analysis, and to novel non-linear analytical methods.



As a model, we used a well-defined set of interconnected data consisting of unstimulated serum levels of steroid intermediates assessed in 54 subjects heterozygous for a mutation of the 21-hydroxylase gene (CYP21B) and in 43 healthy controls.



The genetic alteration was predicted from the pattern of steroid levels with an accuracy of 39% by clinicians and of 64% by linear analysis. In contrast, non-linear analysis, such as self-organizing artificial neural networks, support vector machines, and nearest neighbour classifiers, allowed for higher accuracy up to 83%.



The successful application of these non-linear adaptive methods to capture specific biochemical problems may have generalized implications for biochemical testing in many areas. Nonlinear analytical techniques such as neural networks, support vector machines, and nearest neighbour classifiers may serve as an important adjunct to the decision process of a human investigator not 'trained' in a specific complex clinical or laboratory setting and may aid them to classify the problem more directly.