Statistical significance of sequential firing patterns in multi-neuronal spike trains

Sequential firings with fixed time delays are frequently observed in simultaneous recordings from multiple neurons. Such temporal patterns are potentially indicative of underlying microcircuits and it is important to know when a repeatedly occurring pattern is statistically significant. These sequences are typically identified through correlation counts. In this paper we present a method for assessing the significance of such correlations. We specify the null hypothesis in terms of a bound on the conditional probabilities that characterize the influence of one neuron on another. This method of testing significance is more general than the currently available methods since under our null hypothesis we do not assume that the spiking processes of different neurons are independent. The structure of our null hypothesis also allows us to rank order the detected patterns. We demonstrate our method on simulated spike trains.

Statistical significance of episodes with general partial orders

Frequent episode discovery is one of the methods used for temporal pattern discovery in sequential data. An episode is a partially ordered set of nodes with each node associated with an event type. For more than a decade, algorithms existed for episode discovery only when the associated partial order is total (serial episode) or trivial (parallel episode). Recently, the literature has seen algorithms for discovering episodes with general partial orders. In frequent pattern mining, the threshold beyond which a pattern is inferred to be interesting is typically user-defined and arbitrary. One way of addressing this issue in the pattern mining literature has been based on the framework of statistical hypothesis testing. This paper presents a method of assessing statistical significance of episode patterns with general partial orders. A method is proposed to calculate thresholds, on the non-overlapped frequency, beyond which an episode pattern would be inferred to be statistically significant. The method is first explained for the case of injective episodes with general partial orders. An injective episode is one where event-types are not allowed to repeat. Later it is pointed out how the method can be extended to the class of all episodes. The significance threshold calculations for general partial order episodes proposed here also generalize the existing significance results for serial episodes. Through simulations studies, the usefulness of these statistical thresholds in pruning uninteresting patterns is illustrated. (C) 2014 Elsevier Inc. All rights reserved.

Conditional Probability-Based Significance Tests for Sequential Patterns in Multineuronal Spike Trains

We consider the problem of detecting statistically significant sequential patterns in multineuronal spike trains. These patterns are characterized by ordered sequences of spikes from different neurons with specific delays between spikes. We have previously proposed a data-mining scheme to efficiently discover such patterns, which occur often enough in the data. Here we propose a method to determine the statistical significance of such repeating patterns. The novelty of our approach is that we use a compound null hypothesis that not only includes models of independent neurons but also models where neurons have weak dependencies. The strength of interaction among the neurons is represented in terms of certain pair-wise conditional probabilities. We specify our null hypothesis by putting an upper bound on all such conditional probabilities. We construct a probabilistic model that captures the counting process and use this to derive a test of significance for rejecting such a compound null hypothesis. The structure of our null hypothesis also allows us to rank-order different significant patterns. We illustrate the effectiveness of our approach using spike trains generated with a simulator.

Spectral analysis of noise in the neonatal intensive care unit

Objective To perform spectral analysis of noise generated by equipments and activities in a level III neonatal intensive care unit (NICU) and measure the real time sequential hourly noise levels over a 15 day period. Methods Noise generated in the NICU by individual equipments and activities were recorded with a digital spectral sound analyzer to perform spectral analysis over 0.5–8 KHz. Sequential hourly noise level measurements in all the rooms of the NICU were done for 15 days using a digital sound pressure level meter. Independent sample t test and one way ANOVA were used to examine the statistical significance of the results. The study has a 90% power to detect at least 4 dB differences from the recommended maximum of 50 dB with 95 % confidence. Results The mean noise levels in the ventilator room and stable room were 19.99 dB (A) sound pressure level (SPL) and 11.81 dB (A) SPL higher than the maximum recommended of 50 dB (A) respectively (p < 0.001). The equipments generated 19.11 dB SPL higher than the recommended norms in 1–8 KHz spectrum. The activities generated 21.49 dB SPL higher than the recommended norms in 1–8 KHz spectrum (p< 0.001). The ventilator and nebulisers produced excess noise of 8.5 dB SPL at the 0.5 KHz spectrum.Conclusion Noise level in the NICU is unacceptably high. Spectral analysis of equipment and activity noise have shown noise predominantly in the 1–8 KHz spectrum. These levels warrant immediate implementation of noise reduction protocols as a standard of care in the NICU.

Feasibility study of the measurement of Higgs pair creation at a photon linear collider

We studied the feasibility of the measurement of Higgs pair creation at a photon linear collider. From the sensitivity to the anomalous self-coupling of the Higgs boson, the optimum gamma gamma collision energy was found to be around 270 GeV for a Higgs mass of 120 GeV/c(2). We found that large backgrounds such as gamma gamma -> W+W-, ZZ, and b (b) over barb (b) over bar can be suppressed if correct assignment of tracks to parent partons is achieved and Higgs pair events can be observed with a statistical significance of similar to 5 sigma by operating the photon linear collider for 5 years.

Spectral analysis of Cauvery river flows

A detalied study of the maonthly Convery river flows at the krishna raja sagara (KRS) reservoir is carried out by using the techniques of spectral analysis. The correlogram and power spectrum ate platted and used to identify the peridiocities inherent in the monthly inflows. The statistical significance of these periodicities is tested. Apart from the periodiocities at 12 months and 6 months, a significant of periodicity of 4 month was also observed in the monthly inflows. The analysis prepares ground for developing an appropriate stochastic model for the item series of the monthly flows.

Structural, thermal and quantum chemical studies of p-coumaric and caffeic acids

Two hydroxycinnamic acids viz., p-coumaric, and caffeic acids have been extracted and purified from Parthenium hysterophorus, subsequently characterized by elemental analysis, FT-IR, NMR, single crystal X-ray crystallography. The optimized structures of these acids were calculated in terms of density functional theory by Gaussian 09. The validation of experimental and theoretically obtained data for structural parameters such as bond lengths and bond angles has have been carried out to analyze the statistical significance by curve fitting analysis and the values of correlation coefficient found to be 0.985, 0.992, and 0.984, 0.975 in p-coumaric, and caffeic acids, respectively. The calculated HOMO and LUMO energies show the eventual charge transfer interaction within the molecule. Thermal studies were also carried out by thermogravimetry (TG), differential thermogravimetric analysis (DTA), and derivative thermogravimetry (DTG). (C) 2014 Elsevier B.V. All rights reserved.