627 research outputs found
STSimM: A new tool for evaluating neuron model performance and detecting spike trains similarity
No full textBackground: In computational neuroscience, performance measures are essential for quantitatively assessing the predictive power of neuron models, while similarity measures are used to estimate the level of synchrony between two or more spike trains. Most of the measures proposed in the literature require setting an appropriate time-scale and often neglect silent periods. New method: Four time-scale adaptive performance and similarity measures are proposed and implemented in the STSimM (Spike Trains Similarity Measures) Python tool. These measures are designed to accurately capture both the precise timing of individual spikes and shared periods of inactivity among spike trains. Results: The proposed ST-measures demonstrate enhanced sensitivity over Spike-contrast and SPIKE-distance in detecting spike train similarity, aligning closely with SPIKE-synchronization. Correlations among all similarity measures were observed in Poisson datasets, whereas in vivo-like synaptic stimulations showed correlations only between ST-measures and SPIKE-synchronization. Comparison of existing method: The STSimM measures are compared with SPIKE-distance, SPIKE-synchronization and Spike-contrast using four spike train datasets with varying similarity levels. Conclusion: ST-measures appear more suitable for detecting both the precise timing of single spikes and shared periods of inactivity among spike trains compared to those considered in this work. Their flexibility originates from two primary factors: firstly, the inclusion of four key measures — ST-Accuracy, ST-Precision, ST-Recall, ST-Fscore — capable of discerning similarity levels across neuronal activity, whether interleaved with silent periods or solely focusing on spike timing accuracy; secondly, the integration of three model parameters that govern both precise spike detection and the weighting of silent periods
A multiscale approach to automatic and unsupervised retinal vessel segmentation using Self-Organizing Maps
No full textIn this paper an automatic unsupervised method for retinal vessel segmentation is described. Self-Organizing Map, modified Fuzzy C-Means, STAPLE algorithms and majority voting strategy were adopted to identify a segmentation of the retinal vessels. The performance of the proposed method was evaluated on the DRIVE database
Modeling realistic synaptic inputs of CA1 hippocampal pyramidal neurons and interneurons via Adaptive Generalized Leaky Integrate-and-Fire models
No full text: Computational models of brain regions are crucial for understanding neuronal network dynamics and the emergence of cognitive functions. However, current supercomputing limitations hinder the implementation of large networks with millions of morphological and biophysical accurate neurons. Consequently, research has focused on simplified spiking neuron models, ranging from the computationally fast Leaky Integrate and Fire (LIF) linear models to more sophisticated non-linear implementations like Adaptive Exponential (AdEX) and Izhikevic models, through Generalized Leaky Integrate and Fire (GLIF) approaches. However, in almost all cases, these models are tuned (and can be validated) only under constant current injections and they may not, in general, also reproduce experimental findings under variable currents. This study introduces an Adaptive GLIF (A-GLIF) approach that addresses this limitation by incorporating a new set of update rules. The extended A-GLIF model successfully reproduces both constant and variable current inputs, and it was validated against the results obtained using a biophysical accurate model neuron. This enhancement provides researchers with a tool to optimize spiking neuron models using classic experimental traces under constant current injections, reliably predicting responses to synaptic inputs, which can be confidently used for large-scale network implementations
Graph-Based Minimal Path Tracking in the Skeleton of the Retinal Vascular Network
No full textThis paper presents a semi-automatic framework for minimal path tracking in the skeleton of the retinal vascular network. The method is based on the graph structure of the vessel network. The vascular network is represented based on the skeleton of the available segmented vessels and using an undirected graph. Significant points on the skeleton are considered nodes of the graph, while the edge of the graph is represented by the vessel segment linking two neighboring
nodes. The graph is represented then in the form of a connectivity matrix, using a novel method for defining vertex connectivity. Dijkstra and Floyd-Warshall algorithms
are applied for detection of minimal paths within the graph. The major contribution of this work is the accurate detection of significant points and the novel definition of vertex connectivity based on a new neighborhood system adopted. The qualitative performance of our method evaluated on the
publicly available DRIVE database shows useful results for further purposes
Analysis of low-correlated spatial gene expression patterns: A clustering approach in the mouse brain data hosted in the Allen Brain Atlas
No full textThe Allen Brain Atlas (ABA) provides a similar gene expression dataset by genome-scale mapping of the C57BL/6J mouse brain. In this study, the authors describe a method to extract the spatial information of gene expression patterns across a set of 1047 genes. The genes were chosen from among the 4104 genes having the lowest Pearson correlation coefficient used to compare the expression patterns across voxels in a single hemisphere for available coronal and sagittal volumes. The set of genes analysed in this study is the one discarded in the article by Bohland et al., which was considered to be of a lower consistency, not a reliable dataset. Following a normalisation task with a global and local approach, voxels were clustered using hierarchical and partitioning clustering techniques. Cluster analysis and a validation method based on entropy and purity were performed. They analyse the resulting clusters of the mouse brain for different number of groups and compared them with a classically-defined anatomical reference atlas. The high degree of correspondence between clusters and anatomical regions highlights how gene expression patterns with a low Pearson correlation coefficient between sagittal and coronal sections can accurately identify different neuroanatomical regions
Human Visual Perception and Retinal Diseases
No full textRetinal diseases are causing alterations of
the visual perception leading sometimes to blindness. For this reason, early detection and diagnosis of retinal pathologies is very important. Using digital image processing
techniques, retinal images may be analyzed quickly and computer-assisted diagnosis systems may be developed in order to help the ophthalmologists to make a diagnosis.
In this paper we described shortly two computer-assisted systems for the detection of retinal landmarks (optic disc and vasculature) together with a brief introduction to the human visual system and to some alterations of the visual perception caused by retinal diseases
Automated Detection of Optic Disc Location in Retinal Images
No full textThis contribution presents an automated method to locate the optic disc in color fundus images. The method uses texture descriptors and a regression based method in order to determine the best circle that fits the optic disc. The best circle is chosen from a set of circles determined with an innovative
method, not using the Hough transform as past approaches. An evaluation of the proposed method has been done using a database of 40 images. On this data set, our method achieved 95% success rate for the localization of the optic disc and 70% success rate for the identification of the optic disc contour (as a circle)
Stable Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and a Modified Fuzzy C-Means Clustering
No full textIn this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. Three features are extracted from the tested image. The features are scaled down by a factor of 2 and mapped into a Self-Organizing Map. A modified Fuzzy C-Means clustering algorithm is used to divide the neuron units of the map in 2 classes. The entire image is again input for the Self-Organizing Map and the class of each pixel will be the class of its best matching unit in the Self-Organizing Map. Finally, the vessel network is post-processed using a hill climbing strategy on the connected components of the segmented image.
The experimental evaluation on the DRIVE database shows accurate extraction of vessels network and a good agreement between our segmentation and the ground truth. The mean accuracy, 0.9482 with a standard deviation of 0.0075, is outperforming the manual segmentation rates obtained by other widely used unsupervised methods. A good kappa value of 0.6565 is comparable with state-of-the-art supervised or unsupervised approaches
The effect of superficial hydrophobic treatments to increase the frost durability of concrete evaluated by means of acoustic emission and CDF test
No full textReinforced concrete structures (RCS) exposed to freezing phenomena in the presence of de-icing salts are known to be subjected to the corrosion of embedded reinforcements. The penetration of aggressive substances inside concrete is related to water suction through capillary action. In order to slow down this action, concrete surface can be treated by applying hydrophobic solutions in the admixture or onto the surface. The superficial layer which is formed can modify the water contact angle and hence hinder the water penetration.
This research work is focused on the durability of concrete with or without hydrophobic treatments, and exposed to freeze-thaw cycles. Four different concrete mixtures were prepared at the same aggregate/cement ratio by varying the water/binder ratios (0.50, 0.55, 0.60, 0.65). Specimens were studied in terms of mechanical and stability properties. Flexural and compressive strength tests and CDF-test after exposure of 28-days cured concrete specimens in presence of de-icing salts were performed. Acoustic emission measurements are still going on. Results obtained highlight that the application of the surface treatment with hydrophobic admixture is an important parameter for increasing the resistance of concrete to frost phenomena
Approximated overlap error for the evaluation of feature descriptors on 3D scenes
No full textThis paper presents a new framework to evaluate feature descriptors on 3D datasets. The proposed method employs the approximated overlap error in order to conform with the reference planar evaluation case of the Oxford dataset based on the overlap error. The method takes into account not only the keypoint centre but also the feature shape and it does not require complex data setups, depth maps or an accurate camera calibration. Only a ground-truth fundamental matrix should be computed, so that the dataset can be freely extended by adding further images. The proposed approach is robust to false positives occurring in the evaluation process, which do not introduce any relevant changes in the results, so that the framework can be used unsupervised. Furthermore, the method has no loss in recall, which can be unsuitable for testing descriptors. The proposed evaluation compares on the SIFT and GLOH descriptors, used as references, and the recent state-of-the-art LIOP and MROGH descriptors, so that further insight on their behaviour in 3D scenes is provided as contribution too
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