nils-herrmann/clean_scirepeval huggingface dataset:
Inhibition synchronizes sparsely connected cortical neurons within and between columns in realistic network models

Networks of compartmental model neurons were used to investigate the biophysical basis of the synchronization observed between sparsely-connected neurons in neocortex. A model of a single column in layer 5 consisted of 100 model neurons: 80 pyramidal and 20 inhibitory. The pyramidal cells had conductances that caused intrinsic repetitive bursting at different frequencies when driven with the same input. When connected randomly with a connection density of 10%, a single model column displayed synchronous oscillatory action potentials in response to stationary, uncorrelated Poisson spike-train inputs. Synchrony required a high ratio of inhibitory to excitatory synaptic strength; the optimal ratio was 4∶1, within the range observed in cortex. The synchrony was insensitive to variation in amplitudes of postsynaptic potentials and synaptic delay times, even when the mean synaptic delay times were varied over the range 1 to 7 ms. Synchrony was found to be sensitive to the strength of reciprocal inhibition between the inhibitory neurons in one column: Too weak or too strong reciprocal inhibition degraded intra-columnar synchrony. The only parameter that affected the oscillation frequency of the network was the strength of the external driving input which could shift the frequency between 35 to 60 Hz. The same results were obtained using a model column of 1000 neurons with a connection density of 5%, except that the oscillation became more regular.Synchronization between cortical columns was studied in a model consisting of two columns with 100 model neurons each. When connections were made with a density of 3% between the pyramidal cells of each column there was no inter-columnar synchrony and in some cases the columns oscillated 180° out of phase with each other. Only when connections from the pyramidal cells in each column to the inhibitory cells in the other column were added was synchrony between the columns observed. This synchrony was established within one or two cycles of the oscillation and there was on average less than 1 ms phase difference between the two columns. Unlike the intra-columnar synchronization, the inter-columnar synchronization was found to be sensitive to the synaptic delay: A mean delay of greater than 5 ms virtually abolished synchronization between columns.
Probability of Glaucoma Determined from Standard Automated Perimetry and from Optic Disk Topography using Relevance Vector Machine Classifiers

To evaluate relevance vector machine (RVM) as a machine learning classifier for detecting glaucoma from standard automated perimetry (SAP) and from Heidelberg Retina Tomograph (HRT) optic disk topographic measurements. The advantages of RVM over support vector machines are optimized learning and the availability of the probability of a diagnosis.  For evaluation of SAP using RVM, we compared normal eyes (n=189) to glaucoma eyes (n=156). Glaucoma was defined as the presence of glaucomatous optic neurapthy based on evaluation of stereophotographs. The inputs for the RVMs were 52 visual field locations from SAP plus age. For evaluation of HRT optic disk topography using RVM, we compared normal eyes (n=135) to glaucoma eyes (n=95). Glaucoma was defined as repeatable SAP visual field damage. The inputs were 22 global, 84 regional, and 72 sectoral topographic optic disk parameters. Cross validation was used in all training to present to the trained RVMs cases not previously seen by the RVMs.  For SAP, the area (±SD) under the receiver operating characteristic (ROC) curve using RVM was 0.916±0.016. The sensitivity was 76% at a fixed specificity of 90%. For HRT optic disk topography, the area (±SD) under the ROC curve using RVM was 0.939±0.016, and the sensitivity was 83% at a fixed specificity of 90%. For each of the test groups, the mean (±SD) probability of disease in the glaucoma group was significantly higher than the normal group, for SAP: 0.77±0.31 and 0.18±0.21, respectively, and for HRT topography: 0.80±0.31 and 0.13±0.20, respectively.  RVMs performed at the level of the best previously reported machine learning classifiers, including support vector machines and mixture of Gaussian. Compared to these classifiers, RVM has the advantage of providing the probability of diagnosis, which can be used by the clinican in conjunction with risk factor and other diagnostic test information to determine the individual patient's likelihood of having glaucoma.
A large scale Monte Carlo simulator for cellular microphysiology

Biological structures are extremely complex at the cellular level. The MCell project has been highly successful in simulating the microphysiology of systems of modest size, but many larger problems require too much storage and computation time to be simulated on a single workstation. MCell-K, a new parallel variant of MCell, has been implemented using the KeLP framework and is running on NPACl's Blue Horizon. MCell-K not only produces validated results consistent with the serial version of MCell but does so with unprecedented scalability. We have thus found a level of description and a way to simulate cellular systems that can approach the complexity of nature on its own terms. At the heart of MCell is a 3D random walk that models diffusion using a Monte Carlo method. We discuss two challenging issues that arose in parallelizing the diffusion process - detecting time-step termination efficiently and performing parallel diffusion of particles in a biophysically accurate way. We explore the scalability limits of the present parallel algorithm and discuss ways to improve upon these limits.
Confocal scanning laser ophthalmoscopy classifiers and stereophotograph evaluation for prediction of visual field abnormalities in glaucoma-suspect eyes.

To determine whether Heidelberg Retina Tomograph (HRT; Heidelberg Engineering, Dossenheim, Germany) classification techniques and investigational support vector machine (SVM) analyses can detect optic disc abnormalities in glaucoma-suspect eyes before the development of visual field abnormalities.



Glaucoma-suspect eyes (n = 226) were classified as converts or nonconverts based on the development of repeatable (either two or three consecutive) standard automated perimetry (SAP)-detected abnormalities over the course of the study (mean follow-up, approximately 4.5 years). Hazard ratios for development of SAP abnormalities were calculated based on baseline classification results, follow-up time, and end point status (convert, nonconvert). Classification techniques applied were HRT classification (HRTC), Moorfields Regression Analysis, forward-selection optimized SVM (SVM fwd) and backward elimination-optimized SVM (SVM back) analysis of HRT data, and stereophotograph assessment.



Univariate analyses indicated that all classification techniques were predictors of the development of two repeatable abnormal SAP results, with hazards ratios (95% confidence interval [CI]) ranging from 1.32 (1.00-1.75) for HRTC to 2.0 (1.48-2.76) for stereophotograph assessment (all P < or = 0.05). Only SVM (SVM fwd and SVM back) analysis of HRT data and stereophotograph assessment were univariate predictors of the development of three repeatable abnormal SAP results, with hazard ratios (95% CI) ranging from 1.73 (1.16-2.82) for SVM fwd to 1.82 (1.19-3.12) for SVM back (both P < 0.007). Multivariate analyses including each classification technique individually in a model with age, baseline SAP pattern standard deviation [PSD], and baseline IOP indicated that all classification techniques except HRTC (P = 0.06) were predictors of the development of two repeatable abnormal SAP results with hazards ratios ranging from 1.30 (0.99, 1.73) for HRTC to 1.90 (1.37, 2.69) for stereophotograph assessment. Only SVM (SVM fwd and SVM back) analysis of HRT data and stereophotograph assessment were significant predictors of the development of three repeatable abnormal SAP results in multivariate analyses; hazard ratios of 1.57 (1.03, 2.59) and 1.70 (1.18, 2.51), respectively. SAP PSD was a significant predictor of two repeatable abnormal SAP results in multivariate models with all classification techniques, with hazard ratios ranging from 3.31 (1.39, 7.89) to 4.70 (2.02, 10.93) per 1-dB increase.



HRT classifications techniques and stereophotograph assessment can detect optic disc topography abnormalities in glaucoma-suspect eyes before the development of SAP abnormalities. These data support strongly the importance of optic disc examination for early glaucoma diagnosis.
SIMULATION-DRIVEN DYNAMIC CLAMPING OF NEURONS

We describe an experimentation environment that enables large-scale numerical simulations of neural microphysiology to be fed back onto living neurons in-vitro via dynamic wholecell patch clamping - in effect making living neurons and simulated neurons part of the same neural circuit. Owing to high computational demands, the experimental testbed will be dispersed over a local area network comprising several high performance computing resources. Parallel execution, including feedback between the simulation components, will be managed by the Tarragon, a programming model and run time library that supports asynchronous data driven execution. Tarragon's execution model matches the underlying dynamics of Monte Carlo simulation of diffusive processes and it masks the long network latencies entailed in coupled dispersed simulations. We discuss Tarragon and show how its data driven execution model can be used to dynamically feed back the results of a neural circuit simulation onto living cells in order to better understand the underlying signaling pathways between and within living cells.
Abstract View GENERATION OF 6-14 HZ BURST DISCHARGES IN MODELS OF THE THALAMIC RETICULAR NUCLEUS INCORPORATING SYNAPTIC AND ELECTRICAL COUPLING

The reticular (RE) nucleus of the thalamus is thought to be the site of sleep spindle initiation. Previous models of spindle oscillations in the isolated RE nucleus were based on the ability of RE cells to generate low-threshold rebound burst discharges in response to hyperpolarizing GABAergic synaptic currents. These models require a relatively hyperpolarized GABA-A reversal potential of around -80 mV. However, the chloride reversal potential in RE cells was recently found to be more depolarized around -70 mV, which does not support strong rebound bursting. Here we examined the dynamics of 1-D networks of Hodgkin-Huxley-type RE cells that have local synaptic connectivity and RE cells hyperpolarized below the GABA-A reversal potential. We found that stimulation of a few RE cells evoked depolarizing GABAergic postsynaptic potentials in neighboring RE cells that could lead to single full-blown low-threshold bursts. These burst discharges propagated in a novel, saltatory manner skipping groups of RE cells at a frequency of 10-15 Hz. The exact frequency depended on the resting membrane potential, the strength of the low-threshold (T-) current and GABA-A synaptic conductance. We also investigated 1-D networks with both synaptic and electrical coupling. With strong electrical coupling two modes of propagation were observed depending on the parameters. The first mode was similar to that of the model without gap junctions, consisting of locally synchronized burst discharges that propagated at 11-14 Hz. The second mode consisted of a local, gap-junction-mediated propagation of burst discharges that skipped groups of RE cells and repeated at 6-8 Hz. These results suggest that GABA-A-mediated excitation in the RE nucleus can robustly initiate sequences of sleep spindle oscillations. Support Contributed By: NIH
Unraveling Spatio-temporal Dynamics in fMRI Recordings Using Complex ICA

Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data is commonly carried out under the assumption that each source may be represented as a spatially fixed pattern of activation, which leads to the instantaneous mixing model. To allow modeling patterns of spatio-temporal dynamics, in particular, the flow of oxygenated blood, we have developed a convolutive ICA approach: spatial complex ICA applied to frequency-domain fMRI data. In several frequency-bands, we identify components pertaining to activity in primary visual cortex (V1) and blood supply vessels. One such component, obtained in the 0.10-Hz band, is analyzed in detail and found to likely reflect flow of oxygenated blood in V1.
EFFECTS OF A SYNAPTIC LEARNING RULE ON THE SPARSENESS OF ODOR REPRESENTATIONS IN A MODEL OF THE LOCUST OLFACTORY SYSTEM

Recordings from the mushroom body demonstrate high specificity in Kenyon cell (KC) responses during odor processing. We explored the possibility that a form of plasticity may control and tune synaptic weights to the mushroom body to provide specificity of KCs responses to familiar or meaningful odors. To test our hypothesis, we compared two potential mechanisms: (1) spike-rate dependent plasticity (SRDP) learning mechanism; and (2) spike-timing dependent plasticity (STDP), both in a network model that included a one-dimensional layer of simplified KCs each receiving N partially overlapping inputs with realistic spike trains. The input to the KCs was characterized by slow temporal patterns and transient synchronization between spike trains in different afferents, as observed experimentally. In the model, mechanisms 1 and 2 yielded similar patterns of spiking in KCs, which usually included a small proportion of odor-specific neurons, independent of the initial (pre-training) synaptic weights to the KCs. However, at high initial weights greater specificity was attained in the network with the STDP-based learning rule. When the networks were trained by a set of multiple odors, the STDP network responded to any single odor from the set with firing patterns that were highly similar to those in the network trained by this odor alone. The SRDP network could reproduce this behavior only partially. The finding indicates that STDP preserves the network s ability to respond with different patterns for different odors from a training set. The study suggests a possible role for synaptic plasticity in enabling efficient decoding of afferent temporal patterns and in favoring sparsening of odor representations, as observed in the locust mushroom body. Support Contributed By: the Swiss NSF, the NIH and the HHMI.
Interactions between membrane conductances underlying thalamocortical slow-wave oscillations.

Neurons of the central nervous system display a broad spectrum of intrinsic electrophysiological properties that are absent in the traditional "integrate-and-fire" model. A network of neurons with these properties interacting through synaptic receptors with many time scales can produce complex patterns of activity that cannot be intuitively predicted. Computational methods, tightly linked to experimental data, provide insights into the dynamics of neural networks. We review this approach for the case of bursting neurons of the thalamus, with a focus on thalamic and thalamocortical slow-wave oscillations. At the single-cell level, intrinsic bursting or oscillations can be explained by interactions between calcium- and voltage-dependent channels. At the network level, the genesis of oscillations, their initiation, propagation, termination, and large-scale synchrony can be explained by interactions between neurons with a variety of intrinsic cellular properties through different types of synaptic receptors. These interactions can be altered by neuromodulators, which can dramatically shift the large-scale behavior of the network, and can also be disrupted in many ways, resulting in pathological patterns of activity, such as seizures. We suggest a coherent framework that accounts for a large body of experimental data at the ion-channel, single-cell, and network levels. This framework suggests physiological roles for the highly synchronized oscillations of slow-wave sleep.
Communication in Neuronal Networks

Brains perform with remarkable efficiency, are capable of prodigious computation, and are marvels of communication. We are beginning to understand some of the geometric, biophysical, and energy constraints that have governed the evolution of cortical networks. To operate efficiently within these constraints, nature has optimized the structure and function of cortical networks with design principles similar to those used in electronic networks. The brain also exploits the adaptability of biological systems to reconfigure in response to changing needs.
CONSISTENCY OF INFOMAX ICA DECOMPOSITION OF FUNCTIONAL BRAIN IMAGING DATA

Ten spatial infomax ICA decompositions were performed on two fMRI data sets collected from the same subject. The maximallyindependent spatial components were then tested across decompositions for one-to-one correspondences. Matching independent component maps by mutual information alone proved ineffective. Matching component map pairs by correlating their z-transformed voxel map weights demonstrated that the top 100 components were stably reproduced in each of the ten decompositions. Infomax ICA therefore provided a stable decomposition of fMRI data into spatially independent components.
Spatiochromatic Receptive Field Properties Derived from Information-Theoretic Analyses of Cone Mosaic Responses to Natural Scenes

Neurons in the early stages of processing in the primate visual system efficiently encode natural scenes. In previous studies of the chromatic properties of natural images, the inputs were sampled on a regular array, with complete color information at every location. However, in the retina cone photoreceptors with different spectral sensitivities are arranged in a mosaic. We used an unsupervised neural network model to analyze the statistical structure of retinal cone mosaic responses to calibrated color natural images. The second-order statistical dependencies derived from the covariance matrix of the sensory signals were removed in the first stage of processing. These decorrelating filters were similar to type I receptive fields in parvo- or konio-cellular LGN in both spatial and chromatic characteristics. In the subsequent stage, the decorrelated signals were linearly transformed to make the output as statistically independent as possible, using independent component analysis. The independent component filters showed luminance selectivity with simple-cell-like receptive fields, or had strong color selectivity with large, often double-opponent, receptive fields, both of which were found in the primary visual cortex (V1). These results show that the form and color channels of the early visual system can be derived from the statistics of sensory signals.
Dictionary Learning Algorithms for Sparse Representation

Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave (CSC) negative log priors. Such priors are appropriate for obtaining sparse representations of environmental signals within an appropriately chosen (environmentally matched) dictionary. The elements of the dictionary can be interpreted as concepts, features, or words capable of succinct expression of events encountered in the environment (the source of the measured signals). This is a generalization of vector quantization in that one is interested in a description involving a few dictionary entries (the proverbial 25 words or less), but not necessarily as succinct as one entry. To learn an environmentally adapted dictionary capable of concise expression of signals generated by the environment, we develop algorithms that iterate between a representative set of sparse representations found by variants of FOCUSS and an update of the dictionary using these sparse representations. Experiments were performed using synthetic data and natural images. For complete dictionaries, we demonstrate that our algorithms have improved performance over other independent component analysis (ICA) methods, measured in terms of signal-to-noise ratios of separated sources. In the overcomplete case, we show that the true underlying dictionary and sparse sources can be accurately recovered. In tests with natural images, learned overcomplete dictionaries are shown to have higher coding efficiency than complete dictionaries; that is, images encoded with an overcomplete dictionary have both higher compression (fewer bits per pixel) and higher accuracy (lower mean square error).
A biologically inspired computational model of the Block Copying Task

We present in this paper a biologically inspired model of the Basal Ganglia which deals with Block Copying as a sequence learning task. By breaking a relatively complex task into simpler operations with well-defined skills, an approach which is termed as a skill-based machine design is used in the device of computational models to complete such tasks. Basal Ganglia are critically involved in sensorimotor control. From the learning aspects, Actor-Critic architectures have been proposed to model the Basal Ganglia and Temporal difference has been proposed as a learning algorithm. The model is implemented and simulation results are presented to show the capability of our model to successfully complete the task.
Independent Sources of Quantal Variability at Single Glutamatergic Synapses

Variability in the size of single postsynaptic responses is a feature of most central neurons, although the source of this variability is not completely understood. The dominant source of variability could be either intersynaptic or intrasynaptic. To quantitatively examine this question, a biophysically realistic model of an idealized central axospinous synapse was used to assess mechanisms underlying synaptic variability measurements. Three independent sources of variability were considered: stochasticity of postsynaptic receptors (“channel noise”), variations of glutamate concentration in the synaptic cleft (Δq), and differences in the potency of vesicles released from different locations on the active zone [release-location dependence (RLD)]. As expected, channel noise was small (8% of the total variance) and Δq was the dominant source of variability (58% of total variance). Surprisingly, RLD accounted for a significant amount of variability (36%). Our simulations show that potency of release sites decreased with a length constant of ∼100 nm, and that receptors were not activated by release events >300 nm away, which is consistent with the observation that single active zones are rarely >300 nm. RLD also predicts that the manner in which receptors are added or removed from synapses can dramatically affect the nature of the synaptic response, with increasing receptor density being more efficient than merely increasing synaptic area. Saturation levels and synaptic geometry were also important in determining the size and shape of the distribution of amplitudes recorded at different synapses.