scholarly journals A simple generative model of the mouse mesoscale connectome

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Sid Henriksen ◽  
Rich Pang ◽  
Mark Wronkiewicz
Keyword(s):  
Mouse Brain ◽  
Network Model ◽  
Generative Model ◽  
Data Sets ◽  
Neural Connectivity ◽  
Developmental Processes ◽  
Technological Advances ◽  
Connectivity Patterns ◽  
Organizational Principles

Recent technological advances now allow for the collection of vast data sets detailing the intricate neural connectivity patterns of various organisms. Oh et al. (2014) recently published the most complete description of the mouse mesoscale connectome acquired to date. Here we give an in-depth characterization of this connectome and propose a generative network model which utilizes two elemental organizational principles: proximal attachment ‒ outgoing connections are more likely to attach to nearby nodes than to distant ones, and source growth ‒ nodes with many outgoing connections are likely to form new outgoing connections. We show that this model captures essential principles governing network organization at the mesoscale level in the mouse brain and is consistent with biologically plausible developmental processes.

scholarly journals Implantable Optrode Array for Optogenetic Modulation and Electrical Neural Recording

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 725
Author(s):  
Saeyeong Jeon ◽  
Youjin Lee ◽  
Daeho Ryu ◽  
Yoon Kyung Cho ◽  
Yena Lee ◽  
...  
Keyword(s):  
Light Emitting Diode ◽  
Neural Recording ◽  
Electrophysiological Recording ◽  
Light Emitting ◽  
Technological Advances ◽  
Neuroscience Research ◽  
Cell Type Specific ◽  
Novel Design

During the last decade, optogenetics has become an essential tool for neuroscience research due to its unrivaled feature of cell-type-specific neuromodulation. There have been several technological advances in light delivery devices. Among them, the combination of optogenetics and electrophysiology provides an opportunity for facilitating optogenetic approaches. In this study, a novel design of an optrode array was proposed for realizing optical modulation and electrophysiological recording. A 4 × 4 optrode array and five-channel recording electrodes were assembled as a disposable part, while a reusable part comprised an LED (light-emitting diode) source and a power line. After the characterization of the intensity of the light delivered at the fiber tips, in vivo animal experiment was performed with transgenic mice expressing channelrhodopsin, showing the effectiveness of optical activation and neural recording.

scholarly journals Characterization of the Subventricular-Thalamo-Cortical Circuit in the NP-C Mouse Brain, and New Insights Regarding Treatment

2019 ◽  
Vol 27 (8) ◽  
pp. 1507-1526 ◽  
Author(s):  
Min Hee Park ◽  
Byung Jo Choi ◽  
Min Seock Jeong ◽  
Ju Youn Lee ◽  
In Kyung Jung ◽  
...  
Keyword(s):  
Mouse Brain ◽  
Cortical Circuit

Anti-Geometry and NeutroGeometry Characterization of Non-Euclidean Data

2021 ◽  
pp. 24-33
Author(s):  
Prem Kumar Singh ◽  
Keyword(s):  
Euclidean Geometry ◽  
Current Paper ◽  
Data Sets ◽  
Knowledge Processing ◽  
Non Euclidean Geometry ◽  
Precise Representation

Recently, a problem is addressed while dealing with fourth dimensional or non-Euclidean data sets. These are the data sets does not follow one of the postulates established by Euclid specially the parallel postulates. In this case, the precise representation of these data sets is major issues for knowledge processing tasks. Hence, the current paper tried to introduce some non-Euclidean geometry or Anti-Geometry methods and its examples for various applications.

scholarly journals Irradiance Variability Quantification and Small-Scale Averaging in Space and Time: A Short Review

Atmosphere ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 264 ◽  
Author(s):  
Gerald Lohmann
Keyword(s):  
Short Review ◽  
Power Grids ◽  
Small Scale ◽  
Stable Operation ◽  
Data Sets ◽  
Spatial Smoothing ◽  
Spatial And Temporal Scales ◽  
Output Variability ◽  
Temporal Averaging

The ongoing world-wide increase of installed photovoltaic (PV) power attracts notice to weather-induced PV power output variability. Understanding the underlying spatiotemporal volatility of solar radiation is essential to the successful outlining and stable operation of future power grids. This paper concisely reviews recent advances in the characterization of irradiance variability, with an emphasis on small spatial and temporal scales (respectively less than about 10 km and 1 min), for which comprehensive data sets have recently become available. Special attention is given to studies dealing with the quantification of variability using such unique data, the analysis and modeling of spatial smoothing, and the evaluation of temporal averaging.

scholarly journals Bayesian Classification of Microbial Communities Based on 16S rRNA Metagenomic Data

2018 ◽  
Author(s):  
Arghavan Bahadorinejad ◽  
Ivan Ivanov ◽  
Johanna W Lampe ◽  
Meredith AJ Hullar ◽  
Robert S Chapkin ◽  
...  
Keyword(s):  
16S Rrna ◽  
Sample Size ◽  
Microbial Communities ◽  
State Of The Art ◽  
Metagenomic Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Sample Data

AbstractWe propose a Bayesian method for the classification of 16S rRNA metagenomic profiles of bacterial abundance, by introducing a Poisson-Dirichlet-Multinomial hierarchical model for the sequencing data, constructing a prior distribution from sample data, calculating the posterior distribution in closed form; and deriving an Optimal Bayesian Classifier (OBC). The proposed algorithm is compared to state-of-the-art classification methods for 16S rRNA metagenomic data, including Random Forests and the phylogeny-based Metaphyl algorithm, for varying sample size, classification difficulty, and dimensionality (number of OTUs), using both synthetic and real metagenomic data sets. The results demonstrate that the proposed OBC method, with either noninformative or constructed priors, is competitive or superior to the other methods. In particular, in the case where the ratio of sample size to dimensionality is small, it was observed that the proposed method can vastly outperform the others.Author summaryRecent studies have highlighted the interplay between host genetics, gut microbes, and colorectal tumor initiation/progression. The characterization of microbial communities using metagenomic profiling has therefore received renewed interest. In this paper, we propose a method for classification, i.e., prediction of different outcomes, based on 16S rRNA metagenomic data. The proposed method employs a Bayesian approach, which is suitable for data sets with small ration of number of available instances to the dimensionality. Results using both synthetic and real metagenomic data show that the proposed method can outperform other state-of-the-art metagenomic classification algorithms.

scholarly journals Monte Carlo and Reconstruction Membership Inference Attacks against Generative Models

2019 ◽  
Vol 2019 (4) ◽  
pp. 232-249 ◽  
Author(s):  
Benjamin Hilprecht ◽  
Martin Härterich ◽  
Daniel Bernau
Keyword(s):  
Data Privacy ◽  
Information Leakage ◽  
Generative Models ◽  
Generative Model ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Data Sets ◽  
Success Rates ◽  
Model Quality ◽  
Type Formalization

Abstract We present two information leakage attacks that outperform previous work on membership inference against generative models. The first attack allows membership inference without assumptions on the type of the generative model. Contrary to previous evaluation metrics for generative models, like Kernel Density Estimation, it only considers samples of the model which are close to training data records. The second attack specifically targets Variational Autoencoders, achieving high membership inference accuracy. Furthermore, previous work mostly considers membership inference adversaries who perform single record membership inference. We argue for considering regulatory actors who perform set membership inference to identify the use of specific datasets for training. The attacks are evaluated on two generative model architectures, Generative Adversarial Networks (GANs) and Variational Autoen-coders (VAEs), trained on standard image datasets. Our results show that the two attacks yield success rates superior to previous work on most data sets while at the same time having only very mild assumptions. We envision the two attacks in combination with the membership inference attack type formalization as especially useful. For example, to enforce data privacy standards and automatically assessing model quality in machine learning as a service setups. In practice, our work motivates the use of GANs since they prove less vulnerable against information leakage attacks while producing detailed samples.

scholarly journals Phylogenetic analysis of large molecular data sets

2017 ◽  
pp. 99
Author(s):  
Pamela S. Soltis ◽  
Douglas E. Soltis
Keyword(s):  
Large Data ◽  
Molecular Data ◽  
Global Optimum ◽  
Large Data Sets ◽  
Phylogeny Reconstruction ◽  
Local Optimum ◽  
Data Sets ◽  
Additional Consideration ◽  
Technological Advances ◽  
The Difference

Technological advances in molecular biology have greatly increased the speed and efficiency of DNA sequencing, making it possible to construct large molecular data sets for phylogeny reconstruction relatively quickly. Despite their potential for improving our understanding of phylogeny, these large data sets also provide many challenges. In this paper, we discuss several of these challenges, including 1) the failure of a search to find the most parsimonious trees (the local optimum) in a reasonable amount of time, 2) the difference between a local optimum and the global optimum, and 3) the existence of multiple classes (islands) of most parsimonious trees. We also discuss possible strategies to improve the' likelihood of finding the most parsimonious tree(s) and present two examples from our work on angiosperm phylogeny. We conclude with a discussion of two alternatives to analyses of entire large data sets, the exemplar approach and compartmentalization, and suggest that additional consideration must be given to issues of data analysis for large data sets, whether morphological or molecular.

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