Parameter Estimation in Multiple Dynamic Synaptic Coupling Model Using Bayesian Point Process State-Space Modeling Framework.

It is of great interest to characterize the spiking activity of individual neurons in a cell ensemble. Many different mechanisms, such as synaptic coupling and the spiking activity of itself and its neighbors, drive a cell's firing properties. Though this is a widely studied modeling problem, there is still room to develop modeling solutions by simplifications embedded in previous models.

Physiological constraints of visual pathway lead to more efficient coding of information in retina.

Nowadays, numerous studies have investigated the modeling of efficient neural encoding processes in the retina of the eye to encode the sensory data. Retina, as the innermost coat of the eye, is the first and the most important area of the visual neural system of mammalians, which is responsible for neural processes. Retina encodes the information of light intensity into a sequence of spikes, and sends them to retinal ganglion cells (RGCs) for further processing.

A Comparison Study of Point-Process Filter and Deep Learning Performance in Estimating Rat Position Using an Ensemble of Place Cells.

The emergence of deep learning techniques has provided new tools for the analysis of complex data in the field of neuroscience. In parallel, advanced statistical approaches like point-process modeling provide powerful tools for analyzing the spiking activity of neural populations. How statistical and machine learning techniques compare when applied to neural data remains largely unclear.

Detection of Pulmonary Nodules in Low-dose Computed Tomography Using Localized Active Contours and Shape Features.

Background: Pulmonary nodules are symptoms of lung cancer. The shape and size of these nodules are used to diagnose lung cancer in computed tomography (CT) images. In the early stages, nodules are very small, and radiologist has to refer to many CT images to diagnose the disease, causing operator mistakes.

Peptide functionalized polyhydroxyalkanoate nanofibrous scaffolds enhance Schwann cells activity.

Interactions between Schwann cells (SCs) and scaffolds are important for tissue development during nerve regeneration, because SCs physiologically assist in directing the growth of regenerating axons. In this study, we prepared electrospun scaffolds combining poly (3-hydroxybutyrate) (PHB) and poly (3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) functionalized with either collagen I, H-Gly-Arg-Gly-Asp-Ser-OH (GRGDS), H-Tyr-Ile-Gly-Ser-Arg-NH2 (YIGSR), or H-Arg-Asn-Ile-Ala-Glu-Ile-Ile-Lys-Asp-Ile-OH (p20) neuromimetic peptides to mimic naturally occurring ECM motifs for nerve regeneration. Cells cultured on fibrous mats presenting these biomolecules showed a significant increase in metabolic activity and proliferation while exhibiting unidirectional orientation along the orientation of the fibers.

Nonrigid image registration in digital subtraction angiography using multilevel B-spline.

We address the problem of motion artifact reduction in digital subtraction angiography (DSA) using image registration techniques. Most of registration algorithms proposed for application in DSA, have been designed for peripheral and cerebral angiography images in which we mainly deal with global rigid motions. These algorithms did not yield good results when applied to coronary angiography images because of complex nonrigid motions that exist in this type of angiography images.

Fabrication, characterization and cellular compatibility of poly(hydroxy alkanoate) composite nanofibrous scaffolds for nerve tissue engineering.

Tissue engineering techniques using a combination of polymeric scaffolds and cells represent a promising approach for nerve regeneration. We fabricated electrospun scaffolds by blending of Poly (3-hydroxybutyrate) (PHB) and Poly (3-hydroxy butyrate-co-3- hydroxyvalerate) (PHBV) in different compositions in order to investigate their potential for the regeneration of the myelinic membrane. The thermal properties of the nanofibrous blends was analyzed by differential scanning calorimetry (DSC), which indicated that the melting and glass temperatures, and crystallization degree of the blends decreased as the PHBV weight ratio increased.

Computer aided measurement of sub-epithelial collagen band in colon biopsies for collagenous colitis diagnosis.

This paper presents a novel computer aided technique for screening of Collagenous Colitis (CC). CC is a type of microscopic colitis mostly characterized by chronic watery diarrhea which is a common feature with a range of other etiologies. Routine paraclinical tests from CC patients such as endoscopic and radiographic studies are usually normal, and diagnosis must be made by biopsy.

Impulse noise cancellation of medical images using wavelet networks and median filters.

This paper presents a new two-stage approach to impulse noise removal for medical images based on wavelet network (WN). The first step is noise detection, in which the so-called gray-level difference and average background difference are considered as the inputs of a WN. Wavelet Network is used as a preprocessing for the second stage.

A Brief Survey of Computational Models of Normal and Epileptic EEG Signals: A Guideline to Model-based Seizure Prediction.

In recent decades, seizure prediction has caused a lot of research in both signal processing and the neuroscience field. The researches have tried to enhance the conventional seizure prediction algorithms such that the rate of the false alarms be appropriately small, so that seizures can be predicted according to clinical standards. To date, none of the proposed algorithms have been sufficiently adequate.