scholarly journals CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains

2009 ◽  
Vol 29 (1-2) ◽  
pp. 327-350 ◽  
Author(s):  
Benjamin Staude ◽  
Stefan Rotter ◽  
Sonja Grün
Keyword(s):  
Spike Trains ◽  
Higher Order ◽  
Massively Parallel

scholarly journals Testing for higher-order correlations in massively parallel spike trains

2007 ◽  
Vol 8 (S2) ◽  
Author(s):  
Benjamin Staude ◽  
Stefan Rotter ◽  
Sonja Grün
Keyword(s):  
Spike Trains ◽  
Higher Order ◽  
Massively Parallel

Massively parallel structured direct solver for equations describing time-harmonic qP-polarized waves in TTI media

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. T69-T82 ◽  
Author(s):  
Shen Wang ◽  
Jianlin Xia ◽  
Maarten V. de Hoop ◽  
Xiaoye S. Li
Keyword(s):  
Finite Difference ◽  
Higher Order ◽  
Difference Schemes ◽  
Massively Parallel ◽  
Finite Difference Schemes ◽  
Variable Order ◽  
Nested Dissection ◽  
Partial Differential ◽  
Polarized Waves ◽  
Time Harmonic

We considered the discretization and approximate solutions of equations describing time-harmonic qP-polarized waves in 3D inhomogeneous anisotropic media. The anisotropy comprises general (tilted) transversely isotropic symmetries. We are concerned with solving these equations for a large number of different sources. We considered higher-order partial differential equations and variable-order finite-difference schemes to accommodate anisotropy on the one hand and allow higher-order accuracy — to control sampling rates for relatively high frequencies — on the other hand. We made use of a nested dissection based domain decomposition in a massively parallel multifrontal solver combined with hierarchically semiseparable matrix compression techniques. The higher-order partial differential operators and the variable-order finite-difference schemes require the introduction of separators with variable thickness in the nested dissection; the development of these and their integration with the multifrontal solver is the main focus of our study. The algorithm that we developed is a powerful tool for anisotropic full-waveform inversion.

scholarly journals Efficient Identification of Assembly Neurons within Massively Parallel Spike Trains

2010 ◽  
Vol 2010 ◽  
pp. 1-18 ◽  
Author(s):  
Denise Berger ◽  
Christian Borgelt ◽  
Sebastien Louis ◽  
Abigail Morrison ◽  
Sonja Grün
Keyword(s):  
Computation Time ◽  
Spike Trains ◽  
Correlation Structure ◽  
Massively Parallel ◽  
Data Sets ◽  
Combinatorial Explosion ◽  
Pattern Complexity ◽  
Large Numbers ◽  
The Individual ◽  
Assembly Activity

The chance of detecting assembly activity is expected to increase if the spiking activities of large numbers of neurons are recorded simultaneously. Although such massively parallel recordings are now becoming available, methods able to analyze such data for spike correlation are still rare, as a combinatorial explosion often makes it infeasible to extend methods developed for smaller data sets. By evaluating pattern complexity distributions the existence of correlated groups can be detected, but their member neurons cannot be identified. In this contribution, we present approaches to actually identify the individual neurons involved in assemblies. Our results may complement other methods and also provide a way to reduce data sets to the “relevant” neurons, thus allowing us to carry out a refined analysis of the detailed correlation structure due to reduced computation time.

scholarly journals ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains

2016 ◽  
Vol 12 (7) ◽  
pp. e1004939 ◽  
Author(s):  
Emiliano Torre ◽  
Carlos Canova ◽  
Michael Denker ◽  
George Gerstein ◽  
Moritz Helias ◽  
...  
Keyword(s):  
Spike Trains ◽  
Massively Parallel

scholarly journals Theory of the Snowflake Plot and Its Relations to Higher-Order Analysis Methods

2005 ◽  
Vol 17 (7) ◽  
pp. 1456-1479 ◽  
Author(s):  
Gabriela Czanner ◽  
Sonja Grün ◽  
Satish Iyengar
Keyword(s):  
Cross Correlation ◽  
Spike Trains ◽  
Higher Order ◽  
Scatter Plot ◽  
Coincidence Detector ◽  
Order Analysis ◽  
Analysis Methods ◽  
Null Case ◽  
Joint Histogram ◽  
Quantitative Properties

The snowflake plot is a scatter plot that displays relative timings of three neurons. It has had rather limited use since its introduction by Perkel, Gerstein, Smith, and Tatton (1975), in part because its triangular coordinates are unfamiliar and its theoretical properties are not well studied. In this letter, we study certain quantitative properties of this plot: we use projections to relate the snowflake plot to the cross-correlation histogram and the spike-triggered joint histogram, study the sampling properties of the plot for the null case of independent spike trains, study a simulation of a coincidence detector, and describe the extension of this plot to more than three neurons.

scholarly journals Generation of Synthetic Spike Trains with Defined Pairwise Correlations

2007 ◽  
Vol 19 (7) ◽  
pp. 1720-1738 ◽  
Author(s):  
Ernst Niebur
Keyword(s):  
Synaptic Input ◽  
Cross Correlation ◽  
Spike Trains ◽  
Higher Order ◽  
Neural Systems ◽  
Theoretical Understanding ◽  
Pairwise Correlation ◽  
Technological Advances ◽  
Pairwise Correlations ◽  
Novel Algorithm

Recent technological advances as well as progress in theoretical understanding of neural systems have created a need for synthetic spike trains with controlled mean rate and pairwise cross-correlation. This report introduces and analyzes a novel algorithm for the generation of discretized spike trains with arbitrary mean rates and controlled cross correlation. Pairs of spike trains with any pairwise correlation can be generated, and higher-order correlations are compatible with common synaptic input. Relations between allowable mean rates and correlations within a population are discussed. The algorithm is highly efficient, its complexity increasing linearly with the number of spike trains generated and therefore inversely with the number of cross-correlated pairs.

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