Use of adaptive network burst detection methods for multielectrode array data and the generation of artificial spike patterns for method evaluation.

OBJECTIVE: Multielectrode arrays are an informative extracellular recording technology that enables the analysis of cultured neuronal networks and network bursts (NBs) are a dominant feature observed in these recordings. This paper focuses on the validation of NB detection methods on different network activity patterns and developing a detection method that performs robustly across a wide variety of activity patterns. APPROACH: A firing rate based approach was used to generate artificial spike timestamps where NBs were introduced as episodes where the probability of spiking increases. Variations in firing and bursting characteristics were also included. In addition, an improved methodology of detecting NBs is proposed, based on time-binned average firing rates and time overlaps of single channel bursts. The robustness of the proposed method was compared against three existing algorithms using simulated, publicly available and newly acquired data. MAIN RESULTS: A range of activity patterns were generated by changing simulation variables that correspond to NB duration (40-2200 ms), intervals (0.3-16 s), firing rates (0.1-1 spikes s(-1)), local burst percentage (0%-90%), number of channels in local bursts (20-40) as well as the number of tonic and frequently-bursting channels. By extracting simulation parameters directly from real data, we generated synthetic data that closely resemble activity of mouse and rat cortical cultures at native and chemically perturbed states. In 50 simulated data sets with randomly selected parameter values, the improved NB detection method performed better (ascertained by the f-measure) than three existing methods (p < 0.005). The improved method was also able to detect clustered, long-tailed and short-frequent NBs on real data. SIGNIFICANCE: This work presents an objective method of assessing the applicability of NB detection methods for different neuronal activity patterns. Furthermore, it proposes an improved NB detection method that can be used robustly across a range of data types.

Use of local field potentials of dissociated cultures grown on multi-electrode arrays for pharmacological assays.

In vitro Multi-Electrode Arrays (MEA) are an extracellular recording technology that enables the analysis of networks of neurons in vitro. Neurons in culture exhibit a range of behavioral dynamics, which can be measured in terms of individual action potentials, network-wide synchronized firing and a host of other features that characterize network activity. MEA data analysis was historically focused on high frequency spike data forgoing the low frequency content of the signal. In this study, we use local field potentials, which are low frequency components of MEA signals, to differentiate between two types of antiepileptic drugs (p<;0.0001) with different mechanisms of action.

Fast splice site detection using information content and feature reduction.

BACKGROUND: Accurate identification of splice sites in DNA sequences plays a key role in the prediction of gene structure in eukaryotes. Already many computational methods have been proposed for the detection of splice sites and some of them showed high prediction accuracy. However, most of these methods are limited in terms of their long computation time when applied to whole genome sequence data. RESULTS: In this paper we propose a hybrid algorithm which combines several effective and informative input features with the state of the art support vector machine (SVM). To obtain the input features we employ information content method based on Shannon's information theory, Shapiro's score scheme, and Markovian probabilities. We also use a feature elimination scheme to reduce the less informative features from the input data. CONCLUSION: In this study we propose a new feature based splice site detection method that shows improved acceptor and donor splice site detection in DNA sequences when the performance is compared with various state of the art and well known methods.

Splice site identification using probabilistic parameters and SVM classification.

BACKGROUND: Recent advances and automation in DNA sequencing technology has created a vast amount of DNA sequence data. This increasing growth of sequence data demands better and efficient analysis methods. Identifying genes in this newly accumulated data is an important issue in bioinformatics, and it requires the prediction of the complete gene structure. Accurate identification of splice sites in DNA sequences plays one of the central roles of gene structural prediction in eukaryotes. Effective detection of splice sites requires the knowledge of characteristics, dependencies, and relationship of nucleotides in the splice site surrounding region. A higher-order Markov model is generally regarded as a useful technique for modeling higher-order dependencies. However, their implementation requires estimating a large number of parameters, which is computationally expensive. RESULTS: The proposed method for splice site detection consists of two stages: a first order Markov model (MM1) is used in the first stage and a support vector machine (SVM) with polynomial kernel is used in the second stage. The MM1 serves as a pre-processing step for the SVM and takes DNA sequences as its input. It models the compositional features and dependencies of nucleotides in terms of probabilistic parameters around splice site regions. The probabilistic parameters are then fed into the SVM, which combines them nonlinearly to predict splice sites. When the proposed MM1-SVM model is compared with other existing standard splice site detection methods, it shows a superior performance in all the cases. CONCLUSION: We proposed an effective pre-processing scheme for the SVM and applied it for the identification of splice sites. This is a simple yet effective splice site detection method, which shows a better classification accuracy and computational speed than some other more complex methods.

Dynamic self-organizing maps with controlled growth for knowledge discovery.

The growing self-organizing map (GSOM) has been presented as an extended version of the self-organizing map (SOM), which has significant advantages for knowledge discovery applications. In this paper, the GSOM algorithm is presented in detail and the effect of a spread factor, which can be used to measure and control the spread of the GSOM, is investigated. The spread factor is independent of the dimensionality of the data and as such can be used as a controlling measure for generating maps with different dimensionality, which can then be compared and analyzed with better accuracy. The spread factor is also presented as a method of achieving hierarchical clustering of a data set with the GSOM. Such hierarchical clustering allows the data analyst to identify significant and interesting clusters at a higher level of the hierarchy, and as such continue with finer clustering of only the interesting clusters. Therefore, only a small map is created in the beginning with a low spread factor, which can be generated for even a very large data set. Further analysis is conducted on selected sections of the data and as such of smaller volume. Therefore, this method facilitates the analysis of even very large data sets.

Fuzzy neural networks: between functional equivalence and applicability.

Research in fuzzy neural networks, which started from application oriented fuzzy system tuning, then moving to the automatic generation of fuzzy systems from data, is reaching a more mature stage, especially after the proof of functional equivalence of certain fuzzy models and neural networks. It is essential that the applicability of such developments is explored emphasizing the directions that research should follow. It can be shown that the nearest prototype classifier is functionally equivalent to an alternative fuzzy classifier model. Efficient, hardware friendly training algorithms are developed for dynamic generation of an optimum number of nearest prototypes for neural classifiers which enable the generation of fuzzy systems in real time. These systems are tested with complex applications showing the simulation results.