Cepstrum based unsupervised spike classification

In this research, we study the effect of feature selection in the spike detection and sorting accuracy.We introduce a new feature representation for neural spikes from multichannel recordings. The features selection plays a significant role in analyzing the response of brain neurons. The more precise selection of features leads to a more accurate spike sorting, which can group spikes more precisely into clusters based on the similarity of spikes. Proper spike sorting will enable the association between spikes and neurons. Different with other threshold-based methods, the cepstrum of spike signals is employed in our method to select the candidates of spike features. To choose the best features among different candidates, the Kolmogorov-Smirnov (KS) test is utilized. Then, we rely on the superparamagnetic method to cluster the neural spikes based on KS features. Simulation results demonstrate that the proposed method not only achieve more accurate clustering results but also reduce computational burden, which implies that it can be applied into real-time spike analysis.

Spike sorting using hidden markov models

In this paper, hidden Markov models (HMM) is studied for spike sorting. We notice that HMM state sequences have capability to represent spikes precisely and concisely. We build a HMM for spikes, where HMM states respect spike significant shape variations. Four shape variations are introduced: silence, going up, going down and peak. They constitute every spike with an underlying probabilistic dependence that is modelled by HMM. Based on this representation, spikes sorting becomes a classification problem of compact HMM state sequences. In addition, we enhance the method by defining HMM on extracted Cepstrum features, which improves the accuracy of spike sorting. Simulation results demonstrate the effectiveness of the proposed method as well as the efficiency.

The French consul's wife : memoirs of Céleste de Chabrillan in gold-rush Australia

A former Parisian courtesan, bare-back-rider, and polka dancer, Céleste de Chabrillan scandalized Melbourne when she arrived in 1854. Her vivid account of years spent in diplomatic circles and on the goldfields reveals her great energy and will.

Spike sorting using locality preserving projection with gap statistics and landmark-based spectral clustering

Understanding neural functions requires knowledge from analysing electrophysiological data. The process of assigning spikes of a multichannel signal into clusters, called spike sorting, is one of the important problems in such analysis. There have been various automated spike sorting techniques with both advantages and disadvantages regarding accuracy and computational costs. Therefore, developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice.

Neural spike representation using Cepstrum

Neural spikes define the human brain function. An accurate extraction of spike features leads to better understanding of brain functionality. The main challenge of feature extraction is to mitigate the effect of strong background noises. To address this problem, we introduce a new feature representation for neural spikes based on Cepstrum of multichannel recordings. Simulation results indicated that the proposed method is more robust than the existing Haar wavelet method.

Optimal feature subset selection for neuron spike sorting using the genetic algorithm

It is crucial for a neuron spike sorting algorithm to cluster data from different neurons efficiently. In this study, the search capability of the Genetic Algorithm (GA) is exploited for identifying the optimal feature subset for neuron spike sorting with a clustering algorithm. Two important objectives of the optimization process are considered: to reduce the number of features and increase the clustering performance. Specifically, we employ a binary GA with the silhouette evaluation criterion as the fitness function for neuron spike sorting using the Super-Paramagnetic Clustering (SPC) algorithm. The clustering results of SPC with and without the GA-based feature selector are evaluated using benchmark synthetic neuron spike data sets. The outcome indicates the usefulness of the GA in identifying a smaller feature set with improved clustering performance.

Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches

The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.

Use of sneaker rush and symptoms occurrence of skeletal muscle (SME) in Ballerina

Introduction: Technical literature shows high frequencies of injuries occurring in classical ballet dancers; however, only limited information about the permanent effects of chronic diseases are mentioned. Objective: To compare the presence of MSD among dancers who wear pointe shoes and those who do not. Methods: The research was conducted at the 27th Festival of Joinville in Santa Catarina. The study had the participation of 111 dancers, 88 of whom wore pointe shoes while 23 did not. Specific procedures were used to obtain information related to MSD and foot injuries caused by dancing. Results: The most affected parts were the knees (29.7% with pointe shoes versus 39% without), spine (26.4% with pointe shoes versus 22% without), and ankle/foot (20% with pointe shoes versus 12.2% without). Through odds ratio and respective confidence intervals (IC95%), the study identified protection factor in the knees (0.24; CI95% - 0.09-0.64) and legs (0.11; CI95% - 0.02-0.65) for dancers who wear pointe shoes. It was found that the risk of injuries in specific structures of the foot is significantly higher among those dancers. In this case, the appearance of bunions (9.74; CI95% - 1.25-75,99), calluses on the toes (3.46; CI95% - 1.29-9.27) and the association of the three (4.47; CI95% - 1.69-11.83) were those that showed an increased risk factor compared to dancers who do not stand en pointe. Conclusion: The use of pointe shoes in elite Brazilian dancers was associated to lower occurrence of MSD in the knee and leg, however it was strongly associated to foot injuries.

Molecular and phylogenetic analysis of bovine coronavirus based on the spike glycoprotein gene

Bovine coronavirus has been associated with diarrhoea in newborn calves, winter dysentery in adult cattle and respiratory tract infections in calves and feedlot cattle. In Cuba, the presence of BCoV was first reported in 2006. Since then, sporadic outbreaks have continued to occur. This study was aimed at deepening the knowledge of the evolution, molecular markers of virulence and epidemiology of BCoV in Cuba. A total of 30 samples collected between 2009 and 2011 were used for PCR amplification and direct sequencing of partial or full S gene. Sequence comparison and phylogenetic studies were conducted using partial or complete S gene sequences as phylogenetic markers. All Cuban bovine coronavirus sequences were located in a single cluster supported by 100% bootstrap and 1.00 posterior probability values. The Cuban bovine coronavirus sequences were also clustered with the USA BCoV strains corresponding to the GenBank accession numbers EF424621 and EF424623, suggesting a common origin for these viruses. This phylogenetic cluster was also the only group of sequences in which no recombination events were detected. Of the 45 amino acid changes found in the Cuban strains, four were unique. (C) 2012 Elsevier B.V. All rights reserved.

Avian coronavirus Spike Glycoprotein Ectodomain Shows a Low Codon Adaptation to Gallus gallus with Virus-Exclusive Codons in Strategic Amino Acids Positions

This is a study on the Avian coronavirus IBV and chicken host-relationship from the codon usage point of view based on fifty-nine non-redundant IBV S1 sequences (nt 1-507) from strains detected worldwide and chicken tissue-specific protein genes sequences from IBV-replicating sites. The effective number of codons (ENC) values ranged from 36 to 47.8, indicating a high-to-moderate codon usage bias. The highest IBV codon adaptation index (CAI) value was 0.7, indicating a distant virus versus host synonymous codons usage. The ENC x GC3 % curve indicates that both mutational pressure and natural selection are the driving forces on codon usage pattern in S1. The low CAI values agree with a low S protein expression and considering that S protein is a determinant for attachment and neutralization, this could be a further mechanism besides mRNA transcription attenuation for a low expression of this protein leading to an immune camouflage.

Learning Spike-Based Population Codes by Reward and Population Feedback

We investigate a recently proposed model for decision learning in a population of spiking neurons where synaptic plasticity is modulated by a population signal in addition to reward feedback. For the basic model, binary population decision making based on spike/no-spike coding, a detailed computational analysis is given about how learning performance depends on population size and task complexity. Next, we extend the basic model to n-ary decision making and show that it can also be used in conjunction with other population codes such as rate or even latency coding.