An investigation of Hebbian phase sequences as assembly graphs

Hebb proposed that synapses between neurons that fire synchronously are strengthened, forming cell assemblies and phase sequences. The former, on a shorter scale, are ensembles of synchronized cells that function transiently as a closed processing system; the latter, on a larger scale, correspond to the sequential activation of cell assemblies able to represent percepts and behaviors. Nowadays, the recording of large neuronal populations allows for the detection of multiple cell assemblies. Within Hebb's theory, the next logical step is the analysis of phase sequences. Here we detected phase sequences as consecutive assembly activation patterns, and then analyzed their graph attributes in relation to behavior. We investigated action potentials recorded from the adult rat hippocampus and neocortex before, during and after novel object exploration (experimental periods). Within assembly graphs, each assembly corresponded to a node, and each edge corresponded to the temporal sequence of consecutive node activations. The sum of all assembly activations was proportional to firing rates, but the activity of individual assemblies was not. Assembly repertoire was stable across experimental periods, suggesting that novel experience does not create new assemblies in the adult rat. Assembly graph attributes, on the other hand, varied significantly across behavioral states and experimental periods, and were separable enough to correctly classify experimental periods (Naïve Bayes classifier; maximum AUROCs ranging from 0.55 to 0.99) and behavioral states (waking, slow wave sleep, and rapid eye movement sleep; maximum AUROCs ranging from 0.64 to 0.98). Our findings agree with Hebb's view that assemblies correspond to primitive building blocks of representation, nearly unchanged in the adult, while phase sequences are labile across behavioral states and change after novel experience. The results are compatible with a role for phase sequences in behavior and cognition.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Investigação do papel modulador do Haloperidol sobre os níveis de proteínas relacionadas à plasticidade e preferência por objetos novos em camudongos

Dopamine (DA) is known to regulate both sleep and memory formations, while sleep plays a critical role in the consolidation of different types of memories. We believe that pharmacological manipulation of dopaminergic pathways might disrupt the sleep-wake cycle, leading to mnemonic deficits, which can be observed in both behavioral and molecular levels. Therefore, here we investigated how systemic injections of haloperidol (0.3 mg/kg), immediately after training in dark and light periods, affects learning assessed in the novel object preference test (NOPT) in mice. We also investigated the hippocampal levels of the plasticity-related proteins Zif-268, brain-derived neurotrophic factor (BDNF) and phosphorylated Ca2+/calmodulin-dependent protein kinases II (CaMKII-P) in non-exposed (naïve), vehicle-injected controls and haloperidol-treated mice at 3, 6 and 12 hours after training in the light period. Haloperidol administration during the light period led to a subsequent impairment in the NOPT. In contrast, preference was not observed during the dark period neither in mice injected with haloperidol, nor in vehicle-injected animals. A partial increase of CaMKII-P in the hippocampal field CA3 of vehicle-injected mice was detected at 3h. Haloperidol-treated mice showed a significant decrease in the dentate gyrus of CaMKII-P levels at 3, 6 and 12h; of Zif-268 levels at 6h, and of BDNF levels at 12h after training. Since the mnemonic effects of haloperidol were only observed in the light period when animals tend to sleep, we suggest that these effects are related to REM sleep disruption after haloperidol injection

Cinema e educação :interfaces, conceitos e práticas docentes

This project goes beyond the interfacial field of cinema, History and education. We take as our object the epistemological potential of the cinema at the educational scenario, specifically the use of films integrated to the practices of History teachers and educative processes in which we have taken part as a builder. Our objective is to map, initially, the knowledge around this use to search a synthesis and its empirical application. From the methodological point of view, we have made use of different perspectives: (a) interviews with the educator subjects; (b) observation of their practices and formative circumstances; (c) filmic analysis and the relation of the cinema s epistemology with the other areas ones (initially History and further Journalism). Our analysis allowed us to portrait the film such as an epistemological-troubling category, what makes the cinema rather a builder technology and not simply a complementary and illustrative technological resource. Therefore, we have realized that the restriction to the cinema s educational function is linked to the restrictions to the theoretical categories to an only interfacial aspect: historical film as a film which portraits the past (at the historical field) and film on journalism as a film which approaches a single object of Journalism (at the journalistic field). These discussions happen, consequently, at the arena of the nature of cinema s genres (fiction and documentary), which are understood in a naïve way, simpler than its epistemological possibilities, boosted at this research when we analyze the confluence between fiction and reality. The reflections on educative practices and in formation related to the cinema had occurred in three empirical realities: research with professors in performance, practices docent s and accompaniment of students of history. Have to do with our personal career as a teacher and researcher and, when analyzed other practices, have become, unavoidably, the subject and object of this project

A ação do coordenador pedagógico e as suas contribuições a vivência compreensiva sob a perspectiva conscienciológica

This work is based on a reflection about my personal and professional background as a teacher and a pedagogical supervisor in two public schools in the municipality of Natal, RN, and how relevant this background is to the development of a proposal of continued formation within the scope of the school, in which a diversity of actionreflection strategies are present. In such contexts, I have identified several of my personality traits as being likely advantages or disadvantages that may contribute or not to the coming up of misunderstanding situations. The self-research experience and the identification of new dimensions of self-evaluation, self-observation and attention serve as a basis to think about the importance of experiencing the understanding within the ambit of the school. The reflections about my actions and those of the teachers bring up the hypothesis that the misunderstandings in the teaching-learning process and in the affective relations are the result of a fragmented, naive and egotistic way of thinking. Thus, they don t contribute to an experience of mutual understanding. That is why there must be an investment on new strategies of self-research and dialogue within the scope of pedagogical meetings that may come to help all educators with the analysis, identification and solving of their own problems as well as the other s. Under this perspective, the question that guides this study assumes the presupposition that the educator can invest in a qualified and meaningful pedagogical formation, either one s own and others , if one has a critical-reflexive overview about oneself and the school s pedagogical process. This research aims to explore, discuss and encourage new reflections about the act of researching in the pedagogical supervisor s role, questioning about the possibility of this action to generate contributions to the process of one s own and other s pedagogical formation within the scope of the school, in a conscientiological perspective, in which the manifestation of the thosenes of the educators are valorized. We approach Paulo Freire (1921-1997), as we see the dialogue as very important to the development of this research work, as well as an encouragement to the consciential dialogue. The empirical research took place from June 4, 2004 to November 11, 2004, with 8 pedagogical meetings and with the participation of 2 pedagogical supervisors and 8 teachers. The application of this new methodology within the scope of pedagogical meetings brought considerable contributions to the interpretation of the elements of the educators thosenes, classified according to the following: uncritical, naïve and critical thoughts; sentiments of assistential or non-assistential affectivity; and actions that may or not make the experience of mutual understanding possible. The action of the pedagogical supervisor and one s contributions to the understanding brought up reflections about new ways of investing in the process of continued formation within the scope of the school

Aplicação de sistemas multi-classificadores no diagnóstico de falhas em motores de indução trifásicos

Equipment maintenance is the major cost factor in industrial plants, it is very important the development of fault predict techniques. Three-phase induction motors are key electrical equipments used in industrial applications mainly because presents low cost and large robustness, however, it isn t protected from other fault types such as shorted winding and broken bars. Several acquisition ways, processing and signal analysis are applied to improve its diagnosis. More efficient techniques use current sensors and its signature analysis. In this dissertation, starting of these sensors, it is to make signal analysis through Park s vector that provides a good visualization capability. Faults data acquisition is an arduous task; in this way, it is developed a methodology for data base construction. Park s transformer is applied into stationary reference for machine modeling of the machine s differential equations solution. Faults detection needs a detailed analysis of variables and its influences that becomes the diagnosis more complex. The tasks of pattern recognition allow that systems are automatically generated, based in patterns and data concepts, in the majority cases undetectable for specialists, helping decision tasks. Classifiers algorithms with diverse learning paradigms: k-Neighborhood, Neural Networks, Decision Trees and Naïves Bayes are used to patterns recognition of machines faults. Multi-classifier systems are used to improve classification errors. It inspected the algorithms homogeneous: Bagging and Boosting and heterogeneous: Vote, Stacking and Stacking C. Results present the effectiveness of constructed model to faults modeling, such as the possibility of using multi-classifiers algorithm on faults classification

Predição de promotores de Bacillus subtilis usando técnicas de aprendizado de máquina

One of the most important goals of bioinformatics is the ability to identify genes in uncharacterized DNA sequences on world wide database. Gene expression on prokaryotes initiates when the RNA-polymerase enzyme interacts with DNA regions called promoters. In these regions are located the main regulatory elements of the transcription process. Despite the improvement of in vitro techniques for molecular biology analysis, characterizing and identifying a great number of promoters on a genome is a complex task. Nevertheless, the main drawback is the absence of a large set of promoters to identify conserved patterns among the species. Hence, a in silico method to predict them on any species is a challenge. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. In this work, we present an empirical comparison of Machine Learning (ML) techniques such as Na¨ýve Bayes, Decision Trees, Support Vector Machines and Neural Networks, Voted Perceptron, PART, k-NN and and ensemble approaches (Bagging and Boosting) to the task of predicting Bacillus subtilis. In order to do so, we first built two data set of promoter and nonpromoter sequences for B. subtilis and a hybrid one. In order to evaluate of ML methods a cross-validation procedure is applied. Good results were obtained with methods of ML like SVM and Naïve Bayes using B. subtilis. However, we have not reached good results on hybrid database

Aplicação de técnicas de aprendizado de máquina no reconhecimento de classes estruturais de proteínas

Nowadays, classifying proteins in structural classes, which concerns the inference of patterns in their 3D conformation, is one of the most important open problems in Molecular Biology. The main reason for this is that the function of a protein is intrinsically related to its spatial conformation. However, such conformations are very difficult to be obtained experimentally in laboratory. Thus, this problem has drawn the attention of many researchers in Bioinformatics. Considering the great difference between the number of protein sequences already known and the number of three-dimensional structures determined experimentally, the demand of automated techniques for structural classification of proteins is very high. In this context, computational tools, especially Machine Learning (ML) techniques, have become essential to deal with this problem. In this work, ML techniques are used in the recognition of protein structural classes: Decision Trees, k-Nearest Neighbor, Naive Bayes, Support Vector Machine and Neural Networks. These methods have been chosen because they represent different paradigms of learning and have been widely used in the Bioinfornmatics literature. Aiming to obtain an improvment in the performance of these techniques (individual classifiers), homogeneous (Bagging and Boosting) and heterogeneous (Voting, Stacking and StackingC) multiclassification systems are used. Moreover, since the protein database used in this work presents the problem of imbalanced classes, artificial techniques for class balance (Undersampling Random, Tomek Links, CNN, NCL and OSS) are used to minimize such a problem. In order to evaluate the ML methods, a cross-validation procedure is applied, where the accuracy of the classifiers is measured using the mean of classification error rate, on independent test sets. These means are compared, two by two, by the hypothesis test aiming to evaluate if there is, statistically, a significant difference between them. With respect to the results obtained with the individual classifiers, Support Vector Machine presented the best accuracy. In terms of the multi-classification systems (homogeneous and heterogeneous), they showed, in general, a superior or similar performance when compared to the one achieved by the individual classifiers used - especially Boosting with Decision Tree and the StackingC with Linear Regression as meta classifier. The Voting method, despite of its simplicity, has shown to be adequate for solving the problem presented in this work. The techniques for class balance, on the other hand, have not produced a significant improvement in the global classification error. Nevertheless, the use of such techniques did improve the classification error for the minority class. In this context, the NCL technique has shown to be more appropriated

Estudo de técnicas para o tratamento alternativo de efluentes oleosos oriundos da industrialização da castanha de caju

An evaluation project was conducted on the technique of treatment for effluent oil which is the deriving process to improve cashews. During the evaluation the following techniques were developed: advanced processes of humid oxidation, oxidative processes, processes of biological treatment and processes of adsorption. The assays had been carried through in kinetic models, with an evaluation of the quality of the process by means of determining the chemical demand of oxygen (defined as a technique of control by means of comparative study between the available techniques). The results demonstrated that the natural biodegradation of the effluent ones is limited, as result using the present natural flora in the effluent one revealed impracticable for an application in the industrial systems, independent of the evaluation environment (with or without the oxygen presence). The job of specific microorganisms for the oily composite degradation developed the viability technique of this route, the acceptable levels of inclusion in effluent system of treatment of the improvement of the cashew being highly good with reasonable levels of removal of CDO. However, the use combined with other techniques of daily pay-treatment for these effluent ones revealed to still be more efficient for the context of the treatment of effluent and discarding in receiving bodies in acceptable standards for resolution CONAMA 357/2005. While the significant generation of solid residues the process of adsorption with agroindustrial residues (in special the chitosan) is a technical viable alternative, however, when applied only for the treatment of the effluent ones for discarding in bodies of water, the economic viability is harmed and minimized ambient profits. Though, it was proven that if used for ends of I reuse, the viability is equalized and justifies the investments. There was a study of the photochemistry process which have are applicable to the treatment of the effluent ones, having resulted more satisfactory than those gotten for the UV-Peroxide techniques. There was different result on the one waited for the use of catalyses used in the process of Photo. The catalyses contained the mixing oxide base of Cerium and Manganese, incorporated of Potassium promoters this had presented the best results in the decomposition of the involved pollutants. Having itself an agreed form the gotten photochemistry daily paytreatment resulted, then after disinfection with chlorine the characteristics next the portability to the water were guarantee. The job of the humid oxidation presented significant results in the removal of pollutants; however, its high cost alone is made possible for job in projects of reuses, areas of low scarcity and of raised costs with the capitation/acquisition of the water, in special, for use for industrial and potable use. The route with better economic conditions and techniques for the job in the treatment of the effluent ones of the improvement of the cashew possesses the sequence to follow: conventional process of separation water-oil, photochemistry process and finally, the complementary biological treatment

Oscilações Teta no hipocampo do rato durante tarefa de tomada de decisão espacial

The processing of spatial and episodic information during memory tasks depends on hippocampal theta oscillations. In the present study, I investigated the relationship between theta power and choice selection during spatial decision-making. I recorded local field potentials from the CA1 region of rats retrieving reward locations in a 4-arm maze. In trained but not in naïve animals, I observed a significant increase in theta power during decision-making, which could not be explained by changes in locomotion speed. Furthermore, a Bayesian decoder based on theta power predicted choice outcomes in speed-matched trials. The decoding time course revealed that performance increased above chance before the decision moment exclusively for theta power, remaining flat for other frequency bands. These results occurred for trained animals, but no significant prediction could be made for naïve animals. Altogether, the data support a mnemonic function of theta rhythm during spatial decision-making, indicating that these oscillations correlate with the retrieval of memories required for successful decisions

Uma Análise de métodos de distriubuição de atributos em comitês de classificadores

The objective of the researches in artificial intelligence is to qualify the computer to execute functions that are performed by humans using knowledge and reasoning. This work was developed in the area of machine learning, that it s the study branch of artificial intelligence, being related to the project and development of algorithms and techniques capable to allow the computational learning. The objective of this work is analyzing a feature selection method for ensemble systems. The proposed method is inserted into the filter approach of feature selection method, it s using the variance and Spearman correlation to rank the feature and using the reward and punishment strategies to measure the feature importance for the identification of the classes. For each ensemble, several different configuration were used, which varied from hybrid (homogeneous) to non-hybrid (heterogeneous) structures of ensemble. They were submitted to five combining methods (voting, sum, sum weight, multiLayer Perceptron and naïve Bayes) which were applied in six distinct database (real and artificial). The classifiers applied during the experiments were k- nearest neighbor, multiLayer Perceptron, naïve Bayes and decision tree. Finally, the performance of ensemble was analyzed comparatively, using none feature selection method, using a filter approach (original) feature selection method and the proposed method. To do this comparison, a statistical test was applied, which demonstrate that there was a significant improvement in the precision of the ensembles

Uma investigação das sequências de fase Hebbianas descritas como grafos de assembleias neuronais

Hebb proposed that synapses between neurons that fire synchronously are strengthened, forming cell assemblies and phase sequences. The former, on a shorter scale, are ensembles of synchronized cells that function transiently as a closed processing system; the latter, on a larger scale, correspond to the sequential activation of cell assemblies able to represent percepts and behaviors. Nowadays, the recording of large neuronal populations allows for the detection of multiple cell assemblies. Within Hebb’s theory, the next logical step is the analysis of phase sequences. Here we detected phase sequences as consecutive assembly activation patterns, and then analyzed their graph attributes in relation to behavior. We investigated action potentials recorded from the adult rat hippocampus and neocortex before, during and after novel object exploration (experimental periods). Within assembly graphs, each assembly corresponded to a node, and each edge corresponded to the temporal sequence of consecutive node activations. The sum of all assembly activations was proportional to firing rates, but the activity of individual assemblies was not. Assembly repertoire was stable across experimental periods, suggesting that novel experience does not create new assemblies in the adult rat. Assembly graph attributes, on the other hand, varied significantly across behavioral states and experimental periods, and were separable enough to correctly classify experimental periods (Naïve Bayes classifier; maximum AUROCs ranging from 0.55 to 0.99) and behavioral states (waking, slow wave sleep, and rapid eye movement sleep; maximum AUROCs ranging from 0.64 to 0.98). Our findings agree with Hebb’s view that neuronal assemblies correspond to primitive building blocks of representation, nearly unchanged in 10 the adult, while phase sequences are labile across behavioral states and change after novel experience. The results are compatible with a role for phase sequences in behavior and cognition

Finding the way home: movement of butterflies in non-familiar habitats

Paisagens naturais tem sido afetadas dramaticamente pela perda de habitat e fragmentação, os quais transformam paisagens contínuas em manchas de habitat circundadas por áreas de não-habitat (matrizes). Essas matrizes, inóspitas ou não, afetam incontáveis processos ecológicos, como a dispersão. Uma das formas de compreender o efeito da matriz no processo de dispersão é estudando o alcance da percepção do habitat dos animais, o qual é definido como o alcance (distância) em que um animal pode perceber elementos da paisagem. Essa percepção está diretamente relacionada ao sucesso de chegada à um novo habitat, enquanto o animal navega por uma matriz. Nós avaliamos a percepção do habitat em Heliconius erato, uma borboleta tropical. Entretanto nós também estávamos interessados em avaliar o efeito da idade das borboletas e do tipo de matriz na percepção do habitat. Consequentemente, nós criamos borboletas em laboratório e pareamos com borboletas coletadas na floresta durante um experimento de soltura. Para determinar o alcance da percepção, nós realizamos solturas em duas diferentes matrizes (coqueiral e campo em regeneração) à três distâncias da floresta (0,30 e 100 metros) e medimos o ângulo final alcançado pelas borboletas. Nós encontramos que (I) borboletas soltas na borda se orientaram fortemente para a floresta; (II) quanto maior a distância de soltura, menor a habilidade de perceber o habitat e (III) há interação entre matriz e idade da borboleta. Borboletas inexperientes se orientaram melhor no campo aberto (alcance da percepção: 30-100 metros) e experientes se orientaram melhor no coqueiral (alcance da percepção entre 30-100 metros).

An investigation of Hebbian phase sequences as assembly graphs

Hebb proposed that synapses between neurons that fire synchronously are strengthened, forming cell assemblies and phase sequences. The former, on a shorter scale, are ensembles of synchronized cells that function transiently as a closed processing system; the latter, on a larger scale, correspond to the sequential activation of cell assemblies able to represent percepts and behaviors. Nowadays, the recording of large neuronal populations allows for the detection of multiple cell assemblies. Within Hebb's theory, the next logical step is the analysis of phase sequences. Here we detected phase sequences as consecutive assembly activation patterns, and then analyzed their graph attributes in relation to behavior. We investigated action potentials recorded from the adult rat hippocampus and neocortex before, during and after novel object exploration (experimental periods). Within assembly graphs, each assembly corresponded to a node, and each edge corresponded to the temporal sequence of consecutive node activations. The sum of all assembly activations was proportional to firing rates, but the activity of individual assemblies was not. Assembly repertoire was stable across experimental periods, suggesting that novel experience does not create new assemblies in the adult rat. Assembly graph attributes, on the other hand, varied significantly across behavioral states and experimental periods, and were separable enough to correctly classify experimental periods (Naïve Bayes classifier; maximum AUROCs ranging from 0.55 to 0.99) and behavioral states (waking, slow wave sleep, and rapid eye movement sleep; maximum AUROCs ranging from 0.64 to 0.98). Our findings agree with Hebb's view that assemblies correspond to primitive building blocks of representation, nearly unchanged in the adult, while phase sequences are labile across behavioral states and change after novel experience. The results are compatible with a role for phase sequences in behavior and cognition.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model