An Efficient Partition of Training Data Set Improves Speed and Accuracy of Cascade-correlation Algorithm

This study extends an application of efficient partition algorithm (EPA) for artificial neural network ensemble trained according to Cascade Correlation Algorithm. We show that EPA allows to decrease the number of cases in learning and validated data sets. The predictive ability of the ensemble calculated using the whole data set is not affected and in some cases it is even improved. It is shown that a distribution of cases selected by this method is proportional to the second derivative of the analyzed function.     

Spatiotemporal activity patterns of rat cortical neurons predict responses in a conditioned task

Precise and repeated spike-train timings within and across neurons define spatiotemporal patterns of activity. Although the existence of these patterns in the brain is well established in several species, there has been no direct evidence of their influence on behavioral output. To address this question, up to 15 neurons were recorded simultaneously in the auditory cortex of freely moving rats while animals waited for acoustic cues in a Go/NoGo task. A total of 235 significant patterns were detected during this interval from an analysis of 13 hr of recording involving over 1 million spikes. Of particular interest were 129 (55%) patterns that were significantly associated with the type of response the animal made later, independent of whether the response was that prompted by the cue because the response occurred later and the cue was chosen randomly. Of these behavior-predicting patterns, half (59/129) were associated with an enhanced tendency to go in response to the stimulus, and for 11 patterns of this subset, trials including the pattern were followed by significantly faster reaction time than those lacking the pattern. The remaining behavior-predicting patterns were associated with an enhanced NoGo tendency. Overall mean discharge rates did not vary across trials. Hence, these data demonstrate that particular spatiotemporal patterns predict future behavioral responses. Such presignal activity could form templates for extracting specific sensory information, motor programs prespecifying preference for a particular act, and/or some intermediate, associative brain process.     

Volume learning algorithm significantly improved PLS model for predicting the estrogenic activity of xenoestrogens

Volume learning algorithm (VLA) artificial neural network and partial least squares (PLS) methods were compared using the leave-one-out cross-validation procedure for prediction of relative potency of xenoestrogenic compounds to the estrogen receptor. Using Wilcoxon signed rank test we showed that VLA outperformed PLS by producing models with statistically superior results for a structurally diverse set of compounds comprising eight chemical families. Thus, CoMFA/VLA models are successful in prediction of the endocrine disrupting potential of environmental pollutants and can be effectively applied for testing of prospective chemicals prior their exposure to the environment.     

Prediction of partition coefficient based on atom-type electrotopological state indices

The aim of this study was to determine the efficacy of atom-type electrotopological state indices for estimation of the octanol-water partition coefficient (log P) values in a set of 345 drug compounds or related complex chemical structures. Multilinear regression analysis and artificial neural networks were used to construct models based on molecular weights and atom-type electrotopological state indices. Both multilinear regression and artificial neural networks provide reliable log P estimations. For the same set of parameters, application of neural networks provided better prediction ability for training and test sets. The present study indicates that atom-type electrotopological state indices offer valuable parameters for fast evaluation of octanol-water partition coefficients that can be applied to screen large databases of chemical compounds, such as combinatorial libraries.     

Dynamical cell assemblies in the rat auditory cortex in a reaction-time task

Simultaneous single unit spike trains were recorded in the auditory cortex of freely moving rats performing a complex cognitive task. The experimental paradigm is based on a two-choice task (Go/Nogo) with a two-component (pitch and location) auditory stimulus lasting 500 ms. We report evidence that firstly functional interactions, measured by cross-correlation analysis, between single units in the auditory cortex are dynamically modified in the period preceding the onset of the auditory stimulation, referred to as the 'waiting period'. We secondly observed that spatio-temporal firing patterns both within, and across cell spike trains also tended to appear in the waiting period, several seconds before the actual stimulus delivery. These patterns indicate a very precise repetition of spike discharges separated by long intervals (up to several hundreds of milliseconds). No consistent changes in mean rate were observed. These results suggest that network activity in the auditory cortex is selectively modified in rate independent ways before the actual sensory stimulation. These modifications may reflect participation of recurrent neuronal networks in processes anticipating the expected sensory input.     

Structure-Activity Relationship Modeling and Experimental Validation of the Imidazolium and Pyridinium Based Ionic Liquids as Potential Antibacterials of MDR Acinetobacter baumannii and Staphylococcus aureus

Online Chemical Modeling Environment (OCHEM) was used for QSAR analysis of a set of ionic liquids (ILs) tested against multi-drug resistant (MDR) clinical isolate Acinetobacter baumannii and Staphylococcus aureus strains. The predictive accuracy of regression models has coefficient of determination q² = 0.66 − 0.79 with cross-validation and independent test sets. The models were used to screen a virtual chemical library of ILs, which was designed with targeted activity against MDR Acinetobacter baumannii and Staphylococcus aureus strains. Seven most promising ILs were selected, synthesized, and tested. Three ILs showed high activity against both these MDR clinical isolates.     

Computer assisted neurophysiology by a distributed Java program

The mere appearance of a tubular, epithelially-covered, bilateral structure, no matter how minuscule, on the anteroventral nasal septum of tetrapods, is generally called the vomeronasal organ (of Jacobson). However, considering the functionality of this chemosensory structure, the presence of a non-cilated (microvillar) neuroepithelium (and not just any odd type of epithelium) encased in a variously shaped vomeronasal cartilage, along with vomeronasal nerve bundles and above all an accessory olfactory bulb connected to the limbic vomeronasal amygdala, are the absolute essential neurostructural characteristics and anatomic requirement for a functional VNO and the accessory olfactory system in any tetrapod. The distribution of the vomeronasal organ is reported here in two mammalian orders: Chiroptera and Primates. An impressive data pool on the vomeronasal organ of bats is now available, pointing to the fact that at this time bats may be the only group in which this organ system is extremely variable, ranging from total absence (even in the embryo) to spectacular development with numerous intervening stages in different chiropteran species. Of the eighteen bat families, only one family of New World leaf-nosed bats, family Phyllostomidae, exhibits functional vomeronasal organs. The vespertilionid bat Miniopterus, and the mormoopid bat Pteronotus, present exceptions to this rule. Among Primates, very few species have been rigorously studied. As a result, developmental variability of the vomeronasal organ is almost unknown; either the vomeronasal organ is well developed (such as in New World monkeys) or absent (as in Old World monkeys and great apes) in the adult. The concept whether adult humans or embryonic and fetal forms are endowed with this so-called sixth sense, is a controversial one and is under intense study in our laboratory and by others. The general phylogenetic implications based on our cladistic analysis of bats are that the vomeronasal organ complex has evolved several times. Among the prosimians and platyrrhine primates, the organ is well developed, although to a varying degree. Among catarrhine primates, its loss has occurred only once, as it is generally absent in the adult forms.     

Associative Neural Network

An associative neural network (ASNN) is a combination of an ensemble of the feed-forward neural networks and the K-nearest neighbor technique. The introduced network uses correlation between ensemble responses as a measure of distance among the analyzed cases for the nearest neighbor technique and provides an improved prediction by the bias correction of the neural network ensemble both for function approximation and classification. Actually, the proposed method corrects a bias of a global model for a considered data case by analyzing the biases of its nearest neighbors determined in the space of calculated models. An associative neural network has a memory that can coincide with the training set. If new data become available the network can provide a reasonable approximation of such data without a need to retrain the neural network ensemble. Applications of ASNN for prediction of lipophilicity of chemical compounds and classification of UCI letter and satellite data set are presented. The developed algorithm is available on-line at http://www.virtuallaboratory.org/lab/asnn.     

An Enhancement of Generalization Ability in Cascade Correlation Algorithm by Avoidance of Overfitting/Overtraining Problem

The current study investigates a method for avoidance of an overfitting/overtraining problem in Artificial Neural Network (ANN) based on a combination of two algorithms: Early Stopping and Ensemble averaging (ESE). We show that ESE provides an improvement of the prediction ability of ANN trained according to Cascade Correlation Algorithm. A simple algorithm to estimate the generalization ability of the method according to the Leave-One-Out technique is proposed and discussed. In the accompanying paper the problem of optimal selection of training cases is considered for accelerated learning of the ESE method.     

Pharmaceutical fingerprinting in phase space. 2. Pattern recognition.

The current study introduces an approach for pattern recognition of drug manufacturers according to their HPLC trace impurity data. This method considers signals in phase space and accounts for two different types of noise: additive and perturbative. The pharmaceutical fingerprints are estimated as mean trajectories of HPLC trace impurity data and are used as reference models for recognition of new data by the minimal length classifier. The chromatographic trace organic impurity patterns collected from six different manufacturers of L-tryptophan are analyzed as an example. The prediction ability of the new method tested using three different cross-validation procedures remains about 95% even if the number of available data in the training sets decreases by 5 times. The accuracy of prediction in phase space is superior compared to results calculated using a Window Preprocessing method and artificial neural networks. The difference in performance between new and previous methods becomes more significant under particular conditions that are more adequate for practical application of the method. In addition, the current approach enables simple and comprehensive interpretation of the calculated results.