Search results for "oftware"

A Measure of Concurrent Neural Firing Activity Based on Mutual Information

2021

Multiple methods have been developed in an attempt to quantify stimulus-induced neural coordination and to understand internal coordination of neuronal responses by examining the synchronization phenomena in neural discharge patterns. In this work we propose a novel approach to estimate the degree of concomitant firing between two neural units, based on a modified form of mutual information (MI) applied to a two-state representation of the firing activity. The binary profile of each single unit unfolds its discharge activity in time by decomposition into the state of neural quiescence/low activity and state of moderate firing/bursting. Then, the MI computed between the two binary streams is…

Measuring the agreement between brain connectivity networks.

2016

Investigating the level of similarity between two brain networks, resulting from measures of effective connectivity in the brain, can be of interest from many respects. In this study, we propose and test the idea to borrow measures of association used in machine learning to provide a measure of similarity between the structure of (un-weighted) brain connectivity networks. The measures here explored are the accuracy, Cohen's Kappa (K) and Area Under Curve (AUC). We implemented two simulation studies, reproducing two contexts of application that can be particularly interesting for practical applications, namely: i) in methodological studies, performed on surrogate data, aiming at comparing th…

Advanced C++11 Multithreading

2018

Abstract The previous chapter introduced the basic concepts of multithreading using the C++11 threading API starting with basic spawn and join approaches, while finishing with non-trivial synchronization based on mutexes and condition variables. However, the major bottleneck of application performance is usually caused by contention for a shared resource. In case of mutex-based programming all participating threads usually try to acquire the same lock in parallel which effectively serializes the program for lightweight operations such as increment/decrement or updates of a single scalar value. Fortunately, modern CPUs provide dedicated commands that allow for the efficient execution of unin…

Group Metropolis Sampling

2017

Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized with one summary particle and one summary weight. GIS facilitates the design of novel efficient MC techniques. For instance, we present the Group Metropolis Sampling (GMS) algorithm which produces a Markov chain of sets of weighted samples. GMS in general outperforms other multiple try schemes…

Recycling Gibbs sampling

2017

Gibbs sampling is a well-known Markov chain Monte Carlo (MCMC) algorithm, extensively used in signal processing, machine learning and statistics. The key point for the successful application of the Gibbs sampler is the ability to draw samples from the full-conditional probability density functions efficiently. In the general case this is not possible, so in order to speed up the convergence of the chain, it is required to generate auxiliary samples. However, such intermediate information is finally disregarded. In this work, we show that these auxiliary samples can be recycled within the Gibbs estimators, improving their efficiency with no extra cost. Theoretical and exhaustive numerical co…

Translation of Functional Domain Abnormalities from Human to Mouse Motor System

2021

Multi-agent Systems for Estimating Missing Information in Smart Cities

2019

International audience; Smart cities aim at improving the quality of life of citizens. To do this, numerous ad-hoc sensors need to be deployed in a smart city to monitor the environmental state. Even if nowadays sensors are becoming more and more cheap their installation and maintenance costs increase rapidly with their number. This paper makes an inventory of the dimensions required for designing an intelligent system to support smart city initiatives. Then we propose a multi-agent based solution that uses a limited number of sensors to estimate at runtime missing information in smart cities using a limited number of sensors.

Additive noise and multiplicative bias as disclosure limitation techniques for continuous microdata: A simulation study

2004

This paper focuses on a combination of two disclosure limitation techniques, additive noise and multiplicative bias, and studies their efficacy in protecting confidentiality of continuous microdata. A Bayesian intruder model is extensively simulated in order to assess the performance of these disclosure limitation techniques as a function of key parameters like the variability amongst profiles in the original data, the amount of users prior information, the amount of bias and noise introduced in the data. The results of the simulation offer insight into the degree of vulnerability of data on continuous random variables and suggests some guidelines for effective protection measures.

Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics

2012

In this paper we propose a firing statistics based neuronal network burst detection algorithm for neuronal networks exhibiting highly variable action potential dynamics. Electrical activity of neuronal networks is generally analyzed by the occurrences of spikes and bursts both in time and space. Commonly accepted analysis tools employ burst detection algorithms based on predefined criteria. However, maturing neuronal networks, such as those originating from human embryonic stem cells (hESC), exhibit highly variable network structure and time-varying dynamics. To explore the developing burst/spike activities of such networks, we propose a burst detection algorithm which utilizes the firing s…

Quantitative evaluation of muscle synergy models: a single-trial task decoding approach.

2012

Delis, Ioannis | Berret, Bastien | Pozzo, Thierry | Panzeri, Stefano; International audience; ''Muscle synergies, i.e., invariant coordinated activations of groups of muscles, have been proposed as building blocks that the central nervous system (CNS) uses to construct the patterns of muscle activity utilized for executing movements . Several efficient dimensionality reduction algorithms that extract putative synergies from electromyographic (EMG) signals have been developed. Typically, the quality of synergy decompositions is assessed by computing the Variance Accounted For (VAF). Yet, little is known about the extent to which the combination of those synergies en codes task discriminating…