scholarly journals Maximum entropy models reveal the correlation structure in cortical neural activity during wakefulness and sleep

2018 ◽  
Author(s):  
Trang-Anh Nghiem ◽  
Bartosz Telenczuk ◽  
Olivier Marre ◽  
Alain Destexhe ◽  
Ulisse Ferrari
Keyword(s):  
Maximum Entropy ◽  
Neural Activity ◽  
Large Data ◽  
Inhibitory Neurons ◽  
Brain State ◽  
Data Sets ◽  
Multielectrode Arrays ◽  
Maximum Entropy Models ◽  
Inhibitory Population

Maximum Entropy models can be inferred from large data-sets to uncover how local interactions generate collective dynamics. Here, we employ such models to investigate the characteristics of neurons recorded by multielectrode arrays in the cortex of human and monkey throughout states of wakefulness and sleep. Taking advantage of the separation of excitatory and inhibitory types, we construct a model including this distinction. By comparing the performances of Maximum Entropy models at predicting neural activity in wakefulness and deep sleep, we identify the dominant interactions between neurons in each brain state. We find that during wakefulness, dominant functional interactions are pairwise while during sleep, interactions are population-wide. In particular, inhibitory neurons are shown to be strongly tuned to the inhibitory population. This shows that Maximum Entropy models can be useful to analyze data-sets with excitatory and inhibitory neurons, and can reveal the role of inhibitory neurons in organizing coherent dynamics in cerebral cortex.

Influence and Information Flow in Online Social Networks

2017 ◽  
Vol 9 (4) ◽  
pp. 1-17
Author(s):  
Afrand Agah ◽  
Mehran Asadi
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Large Data ◽  
Marketing Strategies ◽  
Large Data Sets ◽  
Divide And Conquer ◽  
Data Sets ◽  
Divide And Conquer Algorithm ◽  
Flow Of Information

This article introduces a new method to discover the role of influential people in online social networks and presents an algorithm that recognizes influential users to reach a target in the network, in order to provide a strategic advantage for organizations to direct the scope of their digital marketing strategies. Social links among friends play an important role in dictating their behavior in online social networks, these social links determine the flow of information in form of wall posts via shares, likes, re-tweets, mentions, etc., which determines the influence of a node. This article initially identities the correlated nodes in large data sets using customized divide-and-conquer algorithm and then measures the influence of each of these nodes using a linear function. Furthermore, the empirical results show that users who have the highest influence are those whose total number of friends are closer to the total number of friends of each node divided by the total number of nodes in the network.

Influence and Information Flow in Online Social Networks

2021 ◽  
pp. 502-520
Author(s):  
Afrand Agah ◽  
Mehran Asadi
Keyword(s):  
Social Networks ◽  
Information Flow ◽  
Online Social Networks ◽  
Large Data ◽  
Marketing Strategies ◽  
Divide And Conquer ◽  
Data Sets ◽  
Divide And Conquer Algorithm ◽  
Flow Of Information

This article introduces a new method to discover the role of influential people in online social networks and presents an algorithm that recognizes influential users to reach a target in the network, in order to provide a strategic advantage for organizations to direct the scope of their digital marketing strategies. Social links among friends play an important role in dictating their behavior in online social networks, these social links determine the flow of information in form of wall posts via shares, likes, re-tweets, mentions, etc., which determines the influence of a node. This article initially identities the correlated nodes in large data sets using customized divide-and-conquer algorithm and then measures the influence of each of these nodes using a linear function. Furthermore, the empirical results show that users who have the highest influence are those whose total number of friends are closer to the total number of friends of each node divided by the total number of nodes in the network.

scholarly journals 0074 Inhibitory Neurons in the Dorsomedial Medulla Promote REM Sleep

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A30-A30
Author(s):  
J Stucynski ◽  
A Schott ◽  
J Baik ◽  
J Hong ◽  
F Weber ◽  
...  
Keyword(s):  
Rem Sleep ◽  
Nrem Sleep ◽  
Inhibitory Neurons ◽  
Brain State ◽  
Sleep Regulation ◽  
Optogenetic Stimulation ◽  
Electrophysiological Recordings ◽  
Stimulation Of

Abstract Introduction The neural circuits controlling rapid eye movement (REM) sleep, and in particular the role of the medulla in regulating this brain state, remains an active area of study. Previous electrophysiological recordings in the dorsomedial medulla (DM) and electrical stimulation experiments suggested an important role of this area in the control of REM sleep. However the identity of the involved neurons and their precise role in REM sleep regulation are still unclear. Methods The properties of DM GAD2 neurons in mice were investigated through stereotaxic injection of CRE-dependent viruses in conjunction with implantation of electrodes for electroencephalogram (EEG) and electromyogram (EMG) recordings and optic fibers. Experiments included in vivo calcium imaging (fiber photometry) across sleep and wake states, optogenetic stimulation of cell bodies, chemogenetic excitation and suppression (DREADDs), and connectivity mapping using viral tracing and optogenetics. Results Imaging the calcium activity of DM GAD2 neurons in vivo indicates that these neurons are most active during REM sleep. Optogenetic stimulation of DM GAD2 neurons reliably triggered transitions into REM sleep from NREM sleep. Consistent with this, chemogenetic activation of DM GAD2 neurons increased the amount of REM sleep while inhibition suppressed its occurrence and enhanced NREM sleep. Anatomical tracing revealed that DM GAD2 neurons project to several areas involved in sleep / wake regulation including the wake-promoting locus coeruleus (LC) and the REM sleep-suppressing ventrolateral periaquaductal gray (vlPAG). Optogenetic activation of axonal projections from DM to LC, and DM to vlPAG was sufficient to induce REM sleep. Conclusion These experiments demonstrate that DM inhibitory neurons expressing GAD2 powerfully promote initiation of REM sleep in mice. These findings further characterize the dorsomedial medulla as a critical structure involved in REM sleep regulation and inform future investigations of the REM sleep circuitry. Support R01 HL149133

scholarly journals ON PRIVACY CLASSIFICATION IN UBIQUITOUS COMPUTING SYSTEMS

2014 ◽  
pp. 26-35
Author(s):  
Dan Cvrcek ◽  
Vaclav Matyas ◽  
Marek Kumpost
Keyword(s):  
Ubiquitous Computing ◽  
Formal Model ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Computing Systems ◽  
Common Criteria ◽  
Trust Models ◽  
Ubiquitous Computing Environments

Many papers and articles attempt to define or even quantify privacy, typically with a major focus on anonymity. A related research exercise in the area of evidence-based trust models for ubiquitous computing environments has given us an impulse to take a closer look at the definition(s) of privacy in the Common Criteria, which we then transcribed in a bit more formal manner. This led us to a further review of unlinkability, and revision of another semi-formal model allowing for expression of anonymity and unlinkability – the Freiburg Privacy Diamond. We propose new means of describing (obviously only observable) characteristics of a system to reflect the role of contexts for profiling – and linking – users with actions in a system. We believe this approach should allow for evaluating privacy in large data sets.

scholarly journals Role of Pre-processing Phase in Document Clustering Technique for Gurmukhi Script

2020 ◽  
Vol 9 (3) ◽  
pp. 3216-3220
Keyword(s):  
Significant Role ◽  
Document Clustering ◽  
Large Data ◽  
Data Sets ◽  
Similar Data ◽  
Clustering Technique ◽  
Two Phases ◽  
Data Objects ◽  
Gurmukhi Script

Document clustering plays a central role in knowledge discovery and data mining by representing large data-sets into a certain number of data objects called clusters. Each cluster consists similar data objects in such a way that data objects in the same cluster are more similar and dissimilar to the data objects of other clusters. Document clustering technique for Gurmukhi script consists two phases namely: 1) Pre-processing phase 2) Processing phase. This paper concentrates pre-processing phase of document clustering technique for Gurmukhi script. The purpose of pre-processing phase is to convert unstructured text into structured text format. Various sub-phases of pre-processing phase are: segmentation, tokenization, removal of stop words, stemming, and normalization. The purpose of this paper is to present the significant role of pre-processing phase in an overall performance of document clustering technique for Gurmukhi script. The experimental results represent the significant role of pre-processing phase in terms of performance regarding assignment of data objects to the relevant clusters as well as in creation of meaningful cluster title list. .

scholarly journals Big Data System for Medical Images Analysis

2020 ◽  
Author(s):  
Janusz Bobulski ◽  
Mariusz Kubanek
Keyword(s):  
Big Data ◽  
Medical Images ◽  
Processing System ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Computer Data ◽  
Fast Processing

Big Data in medicine includes possibly fast processing of large data sets, both current and historical in purpose supporting the diagnosis and therapy of patients' diseases. Support systems for these activities may include pre-programmed rules based on data obtained from the interview medical and automatic analysis of test results diagnostic results will lead to classification of observations to a specific disease entity. The current revolution using Big Data significantly expands the role of computer science in achieving these goals, which is why we propose a Big Data computer data processing system using artificial intelligence to analyze and process medical images.

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