scholarly journals Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor components analysis

2017 ◽  
Author(s):  
Alex H. Williams ◽  
Tony Hyun Kim ◽  
Forea Wang ◽  
Saurabh Vyas ◽  
Stephen I. Ryu ◽  
...  
Keyword(s):  
Large Scale ◽  
Cognitive State ◽  
Neural Recordings ◽  
Formidable Challenge ◽  
Learned Behaviors ◽  
Maze Navigation ◽  
Machine Interface ◽  
Components Analysis ◽  
Low Dimensional

AbstractPerceptions, thoughts and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it remains a formidable challenge to extract unbiased and interpretable descriptions of how rapid single-trial circuit dynamics change slowly over many trials to mediate learning. We demonstrate a simple tensor components analysis (TCA) can meet this challenge by extracting three interconnected low dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state. We demonstrate the broad applicability of TCA by revealing insights into diverse datasets derived from artificial neural networks, large-scale calcium imaging of rodent prefrontal cortex during maze navigation, and multielectrode recordings of macaque motor cortex during brain machine interface learning.

scholarly journals Tracking single units in chronic, large scale, neural recordings for brain machine interface applications

2014 ◽  
Vol 7 ◽  
Author(s):  
Ahmed Eleryan ◽  
Mukta Vaidya ◽  
Joshua Southerland ◽  
Islam S. Badreldin ◽  
Karthikeyan Balasubramanian ◽  
...  
Keyword(s):  
Large Scale ◽  
Single Units ◽  
Brain Machine Interface ◽  
Neural Recordings ◽  
Machine Interface

scholarly journals Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience

2018 ◽  
Author(s):  
Emily L. Mackevicius ◽  
Andrew H. Bahle ◽  
Alex H. Williams ◽  
Shijie Gu ◽  
Natalia I. Denissenko ◽  
...  
Keyword(s):  
Large Scale ◽  
Temporal Structure ◽  
Simulated Data ◽  
Salient Feature ◽  
High Dimensional ◽  
Neural Data ◽  
Neural Recordings ◽  
Reduction Techniques ◽  
Low Dimensional ◽  
Neural Sequences

AbstractIdentifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here we describe a software toolbox—called seqNMF—with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral data sets. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs.

scholarly journals Stability Analysis and Optimization of Semi-Explicit Predictor–Corrector Methods

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2463
Author(s):  
Aleksandra Tutueva ◽  
Denis Butusov
Keyword(s):  
Numerical Stability ◽  
Large Scale ◽  
Integration Step ◽  
Variable Integration ◽  
Predicted Values ◽  
Low Dimensional ◽  
Three Body ◽  
Systems Simulation ◽  
Predictor Corrector

The increasing complexity of advanced devices and systems increases the scale of mathematical models used in computer simulations. Multiparametric analysis and study on long-term time intervals of large-scale systems are computationally expensive. Therefore, efficient numerical methods are required to reduce time costs. Recently, semi-explicit and semi-implicit Adams–Bashforth–Moulton methods have been proposed, showing great computational efficiency in low-dimensional systems simulation. In this study, we examine the numerical stability of these methods by plotting stability regions. We explicitly show that semi-explicit methods possess higher numerical stability than the conventional predictor–corrector algorithms. The second contribution of the reported research is a novel algorithm to generate an optimized finite-difference scheme of semi-explicit and semi-implicit Adams–Bashforth–Moulton methods without redundant computation of predicted values that are not used for correction. The experimental part of the study includes the numerical simulation of the three-body problem and a network of coupled oscillators with a fixed and variable integration step and finely confirms the theoretical findings.

scholarly journals Recent progress in perovskite solar cells: the perovskite layer

2020 ◽  
Vol 11 ◽  
pp. 51-60 ◽  
Author(s):  
Xianfeng Dai ◽  
Ke Xu ◽  
Fanan Wei
Keyword(s):  
Solar Cells ◽  
High Speed ◽  
Large Scale ◽  
Recent Progress ◽  
High Efficiency ◽  
Low Cost ◽  
Perovskite Solar Cells ◽  
Perovskite Layer ◽  
Low Dimensional

Perovskite solar cells (PSCs) are set to be game changing components in next-generation photovoltaic technology due to their high efficiency and low cost. In this article, recent progress in the development of perovskite layers, which are the basis of PSCs, is reviewed. Achievements in the fabrication of high-quality perovskite films by various methods and techniques are introduced. The reported works demonstrate that the power conversion efficiency of the perovskite layers depends largely on their morphology and the crystalline quality. Furthermore, recent achievements concerning the scalability of perovskite films are presented. These developments aim at manufacturing large-scale perovskite solar modules at high speed. Moreover, it is shown that the development of low-dimensional perovskites plays an important role in improving the long-term ambient stability of PSCs. Finally, these latest advancements can enhance the competitiveness of PSCs in photovoltaics, paving the way for their commercialization. In the closing section of this review, some future critical challenges are outlined, and the prospect of commercialization of PSCs is presented.

scholarly journals Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Emily L Mackevicius ◽  
Andrew H Bahle ◽  
Alex H Williams ◽  
Shijie Gu ◽  
Natalia I Denisenko ◽  
...  
Keyword(s):  
Large Scale ◽  
Temporal Structure ◽  
Simulated Data ◽  
Salient Feature ◽  
High Dimensional ◽  
Neural Data ◽  
Neural Recordings ◽  
Reduction Techniques ◽  
Low Dimensional ◽  
Neural Sequences

Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here, we describe a software toolbox—called seqNMF—with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral data sets. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs.

Evolution of Large-Scale Coronal Structures

1994 ◽  
Vol 144 ◽  
pp. 29-33
Author(s):  
P. Ambrož
Keyword(s):  
Magnetic Field ◽  
Field Line ◽  
Large Scale ◽  
Coronal Magnetic Field ◽  
Source Surface ◽  
Solar Cycles ◽  
Characteristic Relationship ◽  
The Magnetic Field ◽  
Coronal Structures

AbstractThe large-scale coronal structures observed during the sporadically visible solar eclipses were compared with the numerically extrapolated field-line structures of coronal magnetic field. A characteristic relationship between the observed structures of coronal plasma and the magnetic field line configurations was determined. The long-term evolution of large scale coronal structures inferred from photospheric magnetic observations in the course of 11- and 22-year solar cycles is described.Some known parameters, such as the source surface radius, or coronal rotation rate are discussed and actually interpreted. A relation between the large-scale photospheric magnetic field evolution and the coronal structure rearrangement is demonstrated.

Computer Analyses in Preventive Health Research

1967 ◽  
Vol 06 (01) ◽  
pp. 8-14 ◽  
Author(s):  
M. F. Collen
Keyword(s):  
Medical Research ◽  
Preventive Medicine ◽  
Health Research ◽  
Large Scale ◽  
Preventive Health ◽  
New Era ◽  
Large Numbers ◽  
Normal Test ◽  
Computer Analyses

The utilization of an automated multitest laboratory as a data acquisition center and of a computer for trie data processing and analysis permits large scale preventive medical research previously not feasible. Normal test values are easily generated for the particular population studied. Long-term epidemiological research on large numbers of persons becomes practical. It is our belief that the advent of automation and computers has introduced a new era of preventive medicine.

(A review) Atlas of especially protected natural areas of Saint Petersburg / Ed. in chief V. N. Khramtsov, T. V. Kovaleva, N. Yu. Natsvaladze. St. Petersburg, 2013. 176 p.

2014 ◽  
pp. 124-129
Author(s):  
Z. V. Karamysheva
Keyword(s):  
Protected Areas ◽  
Large Scale ◽  
Natural Areas ◽  
Natural Protected Areas ◽  
Saint Petersburg ◽  
Natural Processes ◽  
Original Illustrations ◽  
Vegetation Maps ◽  
Protected Natural Areas

The review contains detailed description of the «Atlas of especially protected natural areas of Saint Petersburg» published in 2013. This publication presents the results of long-term studies of 12 natural protected areas made by a large research team in the years from 2002 to 2013 (see References). The Atlas contains a large number of the historical maps, new satellite images, the original illustrations, detailed texts on the nature of protected areas, summary tables of rare species of vascular plants, fungi and vertebrates recorded in these areas. Special attention is paid to the principles of thematic large-scale mapping. The landscape maps, the vegetation maps as well as the maps of natural processes in landscapes are included. Reviewed Atlas deserves the highest praise.

Sign in / Sign up

Export Citation Format

Share Document