A framework for preparing a stochastic nonlinear integrate-and-fire model for integrated information theory.

This paper presents a framework for spiking neural networks to be prepared for the Integrated Information Theory (IIT) analysis, using a stochastic nonlinear integrate-and-fire model. The model includes the crucial dynamics of the all-or-none law and after-spike refractoriness. The noise is modelled as an additive term in the system's equations. By preparing the model for the IIT analysis, it is meant to determine the length of the analysis time-window and the transition probability distributions required for the IIT 3.0. To this end, a system of differential equations is proposed to estimate the time evolution of the system's mean and covariance. Assuming the binary Fired/Silent activity as the possible states of each neuron, an algorithm is proposed to calculate the required probability distributions. As long as the Fired/Silent probabilities are only concerned, the Gaussian density assumption with the estimated moments is a reasonable estimate. The synaptic inputs are treated as random variables with low variances to avoid the costs of conditioning on the system's past activities. The Monte-Carlo simulation is used to validate the estimation methods. To increase the reliability of the inductive inference behind the Monte-Carlo method, various stimulation protocols are applied to evoke the dynamics of the equations.

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems.

OBJECTIVE: Considering the importance and the near-future development of noninvasive brain-machine interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-based systems in which EEG signals are used. APPROACH: The paper is divided into two main parts. In the first part, a wide range of different types of the base and combinatorial classifiers including boosting and bagging classifiers and evolutionary algorithms are reviewed and investigated. In the second part, these classifiers and evolutionary algorithms are assessed and compared based on two types of relatively widely used BMI systems, sensory motor rhythm-BMI and event-related potentials-BMI. Moreover, in the second part, some of the improved evolutionary algorithms as well as bi-objective algorithms are experimentally assessed and compared. MAIN RESULTS: In this study two databases are used, and cross-validation accuracy (CVA) and stability to data volume (SDV) are considered as the evaluation criteria for the classifiers. According to the experimental results on both databases, regarding the base classifiers, linear discriminant analysis and support vector machines with respect to CVA evaluation metric, and naive Bayes with respect to SDV demonstrated the best performances. Among the combinatorial classifiers, four classifiers, Bagg-DT (bagging decision tree), LogitBoost, and GentleBoost with respect to CVA, and Bagging-LR (bagging logistic regression) and AdaBoost (adaptive boosting) with respect to SDV had the best performances. Finally, regarding the evolutionary algorithms, single-objective invasive weed optimization (IWO) and bi-objective nondominated sorting IWO algorithms demonstrated the best performances. SIGNIFICANCE: We present a general survey on the base and the combinatorial classification methods for EEG signals (sensory motor rhythm and event-related potentials) as well as their optimization methods through the evolutionary algorithms. In addition, experimental and statistical significance tests are carried out to study the applicability and effectiveness of the reviewed methods.

The cardiac effects of carbon nanotubes in rat.

INTRODUCTION: Carbon nanotubes (CNTs) are novel candidates in nanotechnology with a variety of increasing applications in medicine and biology. Therefore the investigation of nanomaterials' biocompatibility can be an important topic. The aim of present study was to investigate the CNTs impact on cardiac heart rate among rats. METHODS: Electrocardiogram (ECG) signals were recorded before and after injection of CNTs on a group with six rats. The heart rate variability (HRV) analysis was used for signals analysis. The rhythm-to-rhythm (RR) intervals in HRV method were computed and features of signals in time and frequency domains were extracted before and after injection. RESULTS: RESULTS of the HRV analysis showed that CNTs increased the heart rate but generally these nanomaterials did not cause serious problem in autonomic nervous system (ANS) normal activities. CONCLUSION: Injection of CNTs in rats resulted in increase of heart rate. The reason of phenomenon is that multiwall CNTs may block potassium channels. The suppressed and inhibited IK and potassium channels lead to increase of heart rate.

Design and construction of an optical computed tomography scanner for polymer gel dosimetry application.

Polymer gel dosimeter is the only accurate three dimensional (3D) dosimeter that can measure the absorbed dose distribution in a perfect 3D setting. Gel dosimetry by using optical computed tomography (OCT) has been promoted by several researches. In the current study, we designed and constructed a prototype OCT system for gel dosimetry. First, the electrical system for optical scanning of the gel container using a Helium-Neon laser and a photocell was designed and constructed. Then, the mechanical part for two rotational and translational motions was designed and step motors were assembled to it. The data coming from photocell was grabbed by the home-built interface and sent to a personal computer. Data processing was carried out using MATLAB software. To calibrate the system and tune up the functionality of it, different objects was designed and scanned. Furthermore, the spatial and contrast resolution of the system was determined. The system was able to scan the gel dosimeter container with a diameter up to 11 cm inside the water phantom. The standard deviation of the pixels within water flask image was considered as the criteria for image uniformity. The uniformity of the system was about ±0.05%. The spatial resolution of the system was approximately 1 mm and contrast resolution was about 0.2%. Our primary results showed that this system is able to obtain two-dimensional, cross-sectional images from polymer gel samples.

A new fast nonlinear modeling of soft tissue for surgical simulation.

In this paper, soft tissue is modeled by a mass-spring-damper system and tissue deformations under the compression of surgical instruments are simulated. For this purpose, soft tissue confined in a cubic plastic mold is studied using a nonlinear viscoelastic model. Displacements resulting from probe insertions are measured and modeled for use in robotic surgery. Data is collected on bovine sirloin using the Instron hardness tester. The model's dynamic equations are obtained in the form of ordinary differential equations. The external force is considered as the input and the resulting deformation as the output of the model. Simulation results are compared with laboratory findings, and the nonlinear model's unknown parameters are estimated. The threshold force and displacement before the tearing of the soft tissue are respectively determined by analyzing the force-time and displacement-time diagrams obtained for the test samples.

A review on creatinine measurement techniques.

This paper reviews the entire recent global tendency for creatinine measurement. Creatinine biosensors involve complex relationships between biology and micro-mechatronics to which the blood is subjected. Comparison between new and old methods shows that new techniques (e.g. Molecular Imprinted Polymers based algorithms) are better than old methods (e.g. Elisa) in terms of stability and linear range. All methods and their details for serum, plasma, urine and blood samples are surveyed. They are categorized into five main algorithms: optical, electrochemical, impedometrical, Ion Selective Field-Effect Transistor (ISFET) based technique and chromatography. Response time, detection limit, linear range and selectivity of reported sensors are discussed. Potentiometric measurement technique has the lowest response time of 4-10 s and the lowest detection limit of 0.28 nmol L(-1) belongs to chromatographic technique. Comparison between various techniques of measurements indicates that the best selectivity belongs to MIP based and chromatographic techniques.