A hierarchy of phase transitions in optimal neuronal coding:from binary to M-ary discrete optimal codes

We have investigated how optimal coding for neural systems changes with the time available for decoding. Optimization was in terms of maximizing information transmission. We have estimated the parameters for Poisson neurons that optimize Shannon transinformation with the assumption of rate coding. We observed a hierarchy of phase transitions from binary coding, for small decoding times, toward discrete (M-ary) coding with two, three and more quantization levels for larger decoding times. We postulate that the presence of subpopulations with specific neural characteristics could be a signiture of an optimal population coding scheme and we use the mammalian auditory system as an example.

A neuromodulação do córtex pré-frontal dorsolateral na percepção de tempo em contexto neutro ou emocionalmente ativo

The time perception is critical for environmental adaptation in humans and other species. The temporal processing, has evolved through different neural systems, each responsible for processing different time scales. Among the most studied scales is that spans the arrangement of seconds to minutes. Evidence suggests that the dorsolateral prefrontal (DLPFC) cortex has relationship with the time perception scale of seconds. However, it is unclear whether the deficit of time perception in patients with brain injuries or even "reversible lesions" caused by transcranial magnetic stimulation (TMS) in this region, whether by disruption of other cognitive processes (such as attention and working memory) or the time perception itself. Studies also link the region of DLPFC in emotional regulation and specifically the judgment and emotional anticipation. Given this, our objective was to study the role of the dorsolateral prefrontal cortex in the time perception intervals of active and emotionally neutral stimuli, from the effects of cortical modulation by transcranial direct current stimulation (tDCS), through the cortical excitation (anodic current), inhibition (cathode current) and control (sham) using the ranges of 4 and 8 seconds. Our results showed that there is an underestimation when the picture was presented by 8 seconds, with the anodic current in the right DLPFC, there is an underestimation and with cathodic current in the left DLPFC, there is an overestimation of the time reproduction with neutral ones. The cathodic current over the left DLPFC leads to an inverse effect of neutral ones, an underestimation of time with negative pictures. Positive or negative pictures improved estimates for 8 second and positive pictures inhibited the effect of tDCS in DLPFC in estimating time to 4 seconds. With this work, we conclude that the DLPFC plays a key role in the o time perception and largely corresponds to the stages of memory and decision on the internal clock model. The left hemisphere participates in the perception of time in both active and emotionally neutral contexts, and we can conclude that the ETCC and an effective method to study the cortical functions in the time perception in terms of cause and effect.

Decoding Spontaneous Emotional States in the Human Brain.

Pattern classification of human brain activity provides unique insight into the neural underpinnings of diverse mental states. These multivariate tools have recently been used within the field of affective neuroscience to classify distributed patterns of brain activation evoked during emotion induction procedures. Here we assess whether neural models developed to discriminate among distinct emotion categories exhibit predictive validity in the absence of exteroceptive emotional stimulation. In two experiments, we show that spontaneous fluctuations in human resting-state brain activity can be decoded into categories of experience delineating unique emotional states that exhibit spatiotemporal coherence, covary with individual differences in mood and personality traits, and predict on-line, self-reported feelings. These findings validate objective, brain-based models of emotion and show how emotional states dynamically emerge from the activity of separable neural systems.

Association between the oxytocin receptor (OXTR) gene and mesolimbic responses to rewards.

BACKGROUND: There has been significant progress in identifying genes that confer risk for autism spectrum disorders (ASDs). However, the heterogeneity of symptom presentation in ASDs impedes the detection of ASD risk genes. One approach to understanding genetic influences on ASD symptom expression is to evaluate relations between variants of ASD candidate genes and neural endophenotypes in unaffected samples. Allelic variations in the oxytocin receptor (OXTR) gene confer small but significant risk for ASDs for which the underlying mechanisms may involve associations between variability in oxytocin signaling pathways and neural response to rewards. The purpose of this preliminary study was to investigate the influence of allelic variability in the OXTR gene on neural responses to monetary rewards in healthy adults using functional magnetic resonance imaging (fMRI). METHODS: The moderating effects of three single nucleotide polymorphisms (SNPs) (rs1042778, rs2268493 and rs237887) of the OXTR gene on mesolimbic responses to rewards were evaluated using a monetary incentive delay fMRI task. RESULTS: T homozygotes of the rs2268493 SNP demonstrated relatively decreased activation in mesolimbic reward circuitry (including the nucleus accumbens, amygdala, insula, thalamus and prefrontal cortical regions) during the anticipation of rewards but not during the outcome phase of the task. Allelic variation of the rs1042778 and rs237887 SNPs did not moderate mesolimbic activation during either reward anticipation or outcomes. CONCLUSIONS: This preliminary study suggests that the OXTR SNP rs2268493, which has been previously identified as an ASD risk gene, moderates mesolimbic responses during reward anticipation. Given previous findings of decreased mesolimbic activation during reward anticipation in ASD, the present results suggest that OXTR may confer ASD risk via influences on the neural systems that support reward anticipation.

Modulare Logistiksysteme in der Prozessindustrie unter Einbeziehung von Industrie 4.0

Zur Gewährleistung einer kundenindividuellen, flexiblen Produktion, welche auch kleine Losegrößen kostengünstig produzieren kann, werden zukünftig Fabriken der Prozessindustrie nicht nur modular sondern darüber hinaus mobil sein. Zur Erreichung dieses Zieles werden neben Produktionselemente, auch sämtliche Logistikkomponenten in Containern zu Modulen zusammengefasst. Hierfür ist es notwendig Lager, aber auch komplexe Verpackungsmaschinen, zu konzipieren welche sich zusammen mit Produktionsmodulen, nach dem Plug-and-Produce-Prinzip, zu kompletten Produktionslinien verknüpfen lassen. Die Steuerung und Organisation erfolgt nach den Gedanken von Industrie 4.0.

Perturbations in Blood Phosphatidylcholine and Sphingomyelin Fatty Acid Composition in a Sample Population of Cigarette Smokers

Docosahexaenoic (DHA) and arachidonic acids (AA) are polyunsaturated fatty acids (PUFAs), major components of brain tissue and neural systems, and the precursors of a number of biologically active metabolites with functions in inflammation resolution, neuroprotection and other actions. As PUFAs are highly susceptible to peroxidation, we hypothesised whether cigarette smokers would present altered PUFAs levels in plasma and erythrocyte phospholipids. Adult males from Indian, Sri-Lankan or Bangladeshi genetic backgrounds who reported smoking between 20 and 60 cigarettes per week were recruited. The control group consisted of matched non-smokers. A blood sample was taken, plasma and erythrocyte total lipids were extracted, phospholipids were separated by thin layer chromatography, and the fatty acid content analysed by gas chromatography. In smokers, dihomo-gamma-linolenic acid, the AA precursor, was significantly reduced in plasma and erythrocyte phosphatidylcholine. AA and DHA were significantly reduced in erythrocyte sphingomyelin. Relatively short term smoking has affected the fatty acid composition of plasma and erythrocyte phospholipids with functions in neural tissue composition, cell signalling, cell growth, intracellular trafficking, neuroprotection and inflammation, in a relatively young population. As lipid peroxidation is pivotal in the pathogenesis of atherosclerosis and neurodegenerative diseases such as Alzheimer disease, early effects of smoking may be relevant for the development of such conditions.

[In search of] Digital biomarkers for objective pain assessment

Pain is a highly complex phenomenon involving intricate neural systems, whose interactions with other physiological mechanisms are not fully understood. Standard pain assessment methods, relying on verbal communication, often fail to provide reliable and accurate information, which poses a critical challenge in the clinical context. In the era of ubiquitous and inexpensive physiological monitoring, coupled with the advancement of artificial intelligence, these new tools appear as the natural candidates to be tested to address such a challenge. This thesis aims to conduct experimental research to develop digital biomarkers for pain assessment. After providing an overview of the state-of-the-art regarding pain neurophysiology and assessment tools, methods for appropriately conditioning physiological signals and controlling confounding factors are presented. The thesis focuses on three different pain conditions: cancer pain, chronic low back pain, and pain experienced by patients undergoing neurorehabilitation. The approach presented in this thesis has shown promise, but further studies are needed to confirm and strengthen these results. Prior to developing any models, a preliminary signal quality check is essential, along with the inclusion of personal and health information in the models to limit their confounding effects. A multimodal approach is preferred for better performance, although unimodal analysis has revealed interesting aspects of the pain experience. This approach can enrich the routine clinical pain assessment procedure by enabling pain to be monitored when and where it is actually experienced, and without the involvement of explicit communication,. This would improve the characterization of the pain experience, aid in antalgic therapy personalization, and bring timely relief, with the ultimate goal of improving the quality of life of patients suffering from pain.

Modular non-computational-connectionist-hybrid neural network approach to robotic systems

Spiking neural networks are usually limited in their applications due to their complex mathematical models and the lack of intuitive learning algorithms. In this paper, a simpler, novel neural network derived from a leaky integrate and fire neuron model, the ‘cavalcade’ neuron, is presented. A simulation for the neural network has been developed and two basic learning algorithms implemented within the environment. These algorithms successfully learn some basic temporal and instantaneous problems. Inspiration for neural network structures from these experiments are then taken and applied to process sensor information so as to successfully control a mobile robot.

Harmonic identification using parallel neural networks in single-phase systems

In this paper, artificial neural networks are employed in a novel approach to identify harmonic components of single-phase nonlinear load currents, whose amplitude and phase angle are subject to unpredictable changes, even in steady-state. The first six harmonic current components are identified through the variation analysis of waveform characteristics. The effectiveness of this method is tested by applying it to the model of a single-phase active power filter, dedicated to the selective compensation of harmonic current drained by an AC controller. Simulation and experimental results are presented to validate the proposed approach. (C) 2010 Elsevier B. V. All rights reserved.

Towards modular and coordinated manufacturing systems oriented to services

Nowadays, there is a trend for industry reorganization in geographically dispersed systems, carried out of their activities with autonomy. These systems must maintain coordinated relationship among themselves in order to assure an expected performance of the overall system. Thus, a manufacturing system is proposed, based on ""web services"" to assure an effective orchestration of services in order to produce final products. In addition, it considers special functions, such as teleoperation and remote monitoring, users` online request, among others. Considering the proposed system as discrete event system (DES), techniques derived from Petri nets (PN), including the Production Flow Schema (PFS), can be used in a PFS/PN approach for modeling. The system is approached in different levels of abstraction: a conceptual model which is obtained by applying the PFS technique and a functional model which is obtained by applying PN. Finally, a particular example of the proposed system is presented.

Abstract computation in schizophrenia detection through artificial neural network based systems

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

Application of neural network to hybrid systems with binary inputs

Boolean input systems are in common used in the electric industry. Power supplies include such systems and the power converter represents these. For instance, in power electronics, the control variable are the switching ON and OFF of components as thyristors or transistors. The purpose of this paper is to use neural network (NN) to control continuous systems with Boolean inputs. This method is based on classification of system variations associated with input configurations. The classical supervised backpropagation algorithm is used to train the networks. The training of the artificial neural network and the control of Boolean input systems are presented. The design procedure of control systems is implemented on a nonlinear system. We apply those results to control an electrical system composed of an induction machine and its power converter.