Neural networks engaged in tactile object manipulation: patterns of expression among healthy individuals

Somatosensory object discrimination has been shown to involve widespread cortical and subcortical structures in both cerebral hemispheres. In this study we aimed to identify the networks involved in tactile object manipulation by principal component analysis (PCA) of individual subjects. We expected to find more than one network.

Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks

Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws' texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke.

Specificities of Caenorhabditis elegans and human hairpin binding proteins for the first nucleotide in the histone mRNA hairpin loop.

The 3' ends of animal replication-dependent histone mRNAs are formed by endonucleolytic cleavage of the primary transcripts downstream of a highly conserved RNA hairpin. The hairpin-binding protein (HBP) binds to this RNA element and is involved in histone RNA 3' processing. A minimal RNA-binding domain (RBD) of approximately 73 amino acids that has no similarity with other known RNA-binding motifs was identified in human HBP [Wang Z-F et al., Genes & Dev, 1996, 10:3028-3040]. The primary sequence identity between human and Caenorhabditis elegans RBDs is 55% compared to 38% for the full-length proteins. We analyzed whether differences between C. elegans and human HBP and hairpins are reflected in the specificity of RNA binding. The C. elegans HBP and its RBD recognize only their cognate RNA hairpins, whereas the human HBP or RBD can bind both the mammalian and the C. elegans hairpins. This selectivity of C. elegans HBP is mostly mediated by the first nucleotide in the loop, which is C in C. elegans and U in all other metazoans. By converting amino acids in the human RBD to the corresponding C. elegans residues at places where the latter deviates from the consensus, we could identify two amino acid segments that contribute to selectivity for the first nucleotide of the hairpin loop.

Why Some People Discount More than Others: Baseline Activation in the Dorsal PFC Mediates the Link between COMT Genotype and Impatient Choice

Individuals differ widely in how steeply they discount future rewards. The sources of these stable individual differences in delay discounting (DD) are largely unknown. One candidate is the COMT Val158Met polymorphism, known to modulate prefrontal dopamine levels and affect DD. To identify possible neural mechanisms by which this polymorphism may contribute to stable individual DD differences, we measured 73 participants' neural baseline activation using resting electroencephalogram (EEG). Such neural baseline activation measures are highly heritable and stable over time, thus an ideal endophenotype candidate to explain how genes may influence behavior via individual differences in neural function. After EEG-recording, participants made a series of incentive-compatible intertemporal choices to determine the steepness of their DD. We found that COMT significantly affected DD and that this effect was mediated by baseline activation level in the left dorsal prefrontal cortex (DPFC): (i) COMT had a significant effect on DD such that the number of Val alleles was positively correlated with steeper DD (higher numbers of Val alleles means greater COMT activity and thus lower dopamine levels). (ii) A whole-brain search identified a cluster in left DPFC where baseline activation was correlated with DD; lower activation was associated with steeper DD. (iii) COMT had a significant effect on the baseline activation level in this left DPFC cluster such that a higher number of Val alleles was associated with lower baseline activation. (iv) The effect of COMT on DD was explained by the mediating effect of neural baseline activation in the left DPFC cluster. Our study thus establishes baseline activation level in left DPFC as salient neural signature in the form of an endophenotype that mediates the link between COMT and DD.

The association between implicit alcohol attitudes and drinking behavior is moderated by baseline activation in the lateral prefrontal cortex

OBJECTIVE Intense alcohol consumption is a risk factor for a number of health problems. Dual-process models assume that self-regulatory behavior such as drinking alcohol is guided by both reflective and impulsive processes. Evidence suggests that (a) impulsive processes such as implicit attitudes are more strongly associated with behavior when executive functioning abilities are low, and (b) higher neural baseline activation in the lateral prefrontal cortex (PFC) is associated with better inhibitory control. The present study integrates these 2 strands of research to investigate how individual differences in neural baseline activation in the lateral PFC moderate the association between implicit alcohol attitudes and drinking behavior. METHOD Baseline cortical activation was measured with resting electroencephalography (EEG) in 89 moderate drinkers. In a subsequent behavioral testing session they completed measures of implicit alcohol attitudes and self-reported drinking behavior. RESULTS Implicit alcohol attitudes were related to self-reported alcohol consumption. Most centrally, implicit alcohol attitudes were more strongly associated with drinking behavior in individuals with low as compared with high baseline activation in the right lateral PFC. CONCLUSIONS These findings are in line with predictions made on the basis of dual-process models. They provide further evidence that individual differences in neural baseline activation in the right lateral PFC may contribute to executive functioning abilities such as inhibitory control. Moreover, individuals with strongly positive implicit alcohol attitudes coupled with a low baseline activation in the right lateral PFC may be at greater risk of developing unhealthy drinking patterns than others.

A novel interface for hybrid mock circulations to evaluate ventricular assist devices

This paper presents a novel mock circulation for the evaluation of ventricular assist devices (VADs), which is based on a hardware-in-the-loop concept. A numerical model of the human blood circulation runs in real time and computes instantaneous pressure, volume, and flow rate values. The VAD to be tested is connected to a numerical-hydraulic interface, which allows the interaction between the VAD and the numerical model of the circulation. The numerical-hydraulic interface consists of two pressure-controlled reservoirs, which apply the computed pressure values from the model to the VAD, and a flow probe to feed the resulting VAD flow rate back to the model. Experimental results are provided to show the proper interaction between a numerical model of the circulation and a mixed-flow blood pump.

Evaluating the Impact of the Number of Access Points in Mobile Robots Localization Using Artificial Neural Networks

Localization is information of fundamental importance to carry out various tasks in the mobile robotic area. The exact degree of precision required in the localization depends on the nature of the task. The GPS provides global position estimation but is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras are commonly used for position estimation, but these require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of vision. Currently, Wireless Networks (WN) are widely available in indoor environments and can allow efficient global localization that requires relatively low computing resources. However, the inherent instability in the wireless signal prevents it from being used for very accurate position estimation. The growth in the number of Access Points (AP) increases the overlap signals areas and this could be a useful means of improving the precision of the localization. In this paper we evaluate the impact of the number of Access Points in mobile nodes localization using Artificial Neural Networks (ANN). We use three to eight APs as a source signal and show how the ANNs learn and generalize the data. Added to this, we evaluate the robustness of the ANNs and evaluate a heuristic to try to decrease the error in the localization. In order to validate our approach several ANNs topologies have been evaluated in experimental tests that were conducted with a mobile node in an indoor space.

Real-time closed-loop electrophysiology: towards new frontiers in in vitro investigations in the neurosciences

Reflected at any level of organization of the central nervous system, most of the processes ranging from ion channels to neuronal networks occur in a closed loop, where the input to the system depends on its output. In contrast, most in vitro preparations and experimental protocols operate autonomously, and do not depend on the output of the studied system. Thanks to the progress in digital signal processing and real-time computing, it is now possible to artificially close the loop and investigate biophysical processes and mechanisms under increased realism. In this contribution, we review some of the most relevant examples of a new trend in in vitro electrophysiology, ranging from the use of dynamic-clamp to multi-electrode distributed feedback stimulation. We are convinced these represents the beginning of new frontiers for the in vitro investigation of the brain, promising to open the still existing borders between theoretical and experimental approaches while taking advantage of cutting edge technologies.

Matching Recall and Storage in Sequence Learning with Spiking Neural Networks

Storing and recalling spiking sequences is a general problem the brain needs to solve. It is, however, unclear what type of biologically plausible learning rule is suited to learn a wide class of spatiotemporal activity patterns in a robust way. Here we consider a recurrent network of stochastic spiking neurons composed of both visible and hidden neurons. We derive a generic learning rule that is matched to the neural dynamics by minimizing an upper bound on the Kullback–Leibler divergence from the target distribution to the model distribution. The derived learning rule is consistent with spike-timing dependent plasticity in that a presynaptic spike preceding a postsynaptic spike elicits potentiation while otherwise depression emerges. Furthermore, the learning rule for synapses that target visible neurons can be matched to the recently proposed voltage-triplet rule. The learning rule for synapses that target hidden neurons is modulated by a global factor, which shares properties with astrocytes and gives rise to testable predictions.