New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM)

LEÃO, Adriano de Castro; DÓRIA NETO, Adrião Duarte; SOUSA, Maria Bernardete Cordeiro de. New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM). Computers in Biology and Medicine, v. 39, p. 853-859, 2009

New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM)

LEÃO, Adriano de Castro; DÓRIA NETO, Adrião Duarte; SOUSA, Maria Bernardete Cordeiro de. New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM). Computers in Biology and Medicine, v. 39, p. 853-859, 2009

The influence of interhemispheric connections on ongoing and evoked orientation preference maps and spiking activity in the cat primary visual cortex

In the primary visual cortex, neurons with similar physiological features are clustered together in columns extending through all six cortical layers. These columns form modular orientation preference maps. Long-range lateral fibers are associated to the structure of orientation maps since they do not connect columns randomly; they rather cluster in regular intervals and interconnect predominantly columns of neurons responding to similar stimulus features. Single orientation preference maps – the joint activation of domains preferring the same orientation - were observed to emerge spontaneously and it was speculated whether this structured ongoing activation could be caused by the underlying patchy lateral connectivity. Since long-range lateral connections share many features, i.e. clustering, orientation selectivity, with visual inter-hemispheric connections (VIC) through the corpus callosum we used the latter as a model for long-range lateral connectivity. In order to address the question of how the lateral connectivity contributes to spontaneously generated maps of one hemisphere we investigated how these maps react to the deactivation of VICs originating from the contralateral hemisphere. To this end, we performed experiments in eight adult cats. We recorded voltage-sensitive dye (VSD) imaging and electrophysiological spiking activity in one brain hemisphere while reversible deactivating the other hemisphere with a cooling technique. In order to compare ongoing activity with evoked activity patterns we first presented oriented gratings as visual stimuli. Gratings had 8 different orientations distributed equally between 0º and 180º. VSD imaged frames obtained during ongoing activity conditions were then compared to the averaged evoked single orientation maps in three different states: baseline, cooling and recovery. Kohonen self-organizing maps were also used as a means of analysis without prior assumption (like the averaged single condition maps) on ongoing activity. We also evaluated if cooling had a differential effect on evoked and ongoing spiking activity of single units. We found that deactivating VICs caused no spatial disruption on the structure of either evoked or ongoing activity maps. The frequency with which a cardinally preferring (0º or 90º) map would emerge, however, decreased significantly for ongoing but not for evoked activity. The same result was found by training self-organizing maps with recorded data as input. Spiking activity of cardinally preferring units also decreased significantly for ongoing when compared to evoked activity. Based on our results we came to the following conclusions: 1) VICs are not a determinant factor of ongoing map structure. Maps continued to be spontaneously generated with the same quality, probably by a combination of ongoing activity from local recurrent connections, thalamocortical loop and feedback connections. 2) VICs account for a cardinal bias in the temporal sequence of ongoing activity patterns, i.e. deactivating VIC decreases the probability of cardinal maps to emerge spontaneously. 3) Inter- and intrahemispheric long-range connections might serve as a grid preparing primary visual cortex for likely junctions in a larger visual environment encompassing the two hemifields.

The influence of interhemispheric connections on ongoing and evoked orientation preference maps and spiking activity in the cat primary visual cortex

In the primary visual cortex, neurons with similar physiological features are clustered together in columns extending through all six cortical layers. These columns form modular orientation preference maps. Long-range lateral fibers are associated to the structure of orientation maps since they do not connect columns randomly; they rather cluster in regular intervals and interconnect predominantly columns of neurons responding to similar stimulus features. Single orientation preference maps – the joint activation of domains preferring the same orientation - were observed to emerge spontaneously and it was speculated whether this structured ongoing activation could be caused by the underlying patchy lateral connectivity. Since long-range lateral connections share many features, i.e. clustering, orientation selectivity, with visual inter-hemispheric connections (VIC) through the corpus callosum we used the latter as a model for long-range lateral connectivity. In order to address the question of how the lateral connectivity contributes to spontaneously generated maps of one hemisphere we investigated how these maps react to the deactivation of VICs originating from the contralateral hemisphere. To this end, we performed experiments in eight adult cats. We recorded voltage-sensitive dye (VSD) imaging and electrophysiological spiking activity in one brain hemisphere while reversible deactivating the other hemisphere with a cooling technique. In order to compare ongoing activity with evoked activity patterns we first presented oriented gratings as visual stimuli. Gratings had 8 different orientations distributed equally between 0º and 180º. VSD imaged frames obtained during ongoing activity conditions were then compared to the averaged evoked single orientation maps in three different states: baseline, cooling and recovery. Kohonen self-organizing maps were also used as a means of analysis without prior assumption (like the averaged single condition maps) on ongoing activity. We also evaluated if cooling had a differential effect on evoked and ongoing spiking activity of single units. We found that deactivating VICs caused no spatial disruption on the structure of either evoked or ongoing activity maps. The frequency with which a cardinally preferring (0º or 90º) map would emerge, however, decreased significantly for ongoing but not for evoked activity. The same result was found by training self-organizing maps with recorded data as input. Spiking activity of cardinally preferring units also decreased significantly for ongoing when compared to evoked activity. Based on our results we came to the following conclusions: 1) VICs are not a determinant factor of ongoing map structure. Maps continued to be spontaneously generated with the same quality, probably by a combination of ongoing activity from local recurrent connections, thalamocortical loop and feedback connections. 2) VICs account for a cardinal bias in the temporal sequence of ongoing activity patterns, i.e. deactivating VIC decreases the probability of cardinal maps to emerge spontaneously. 3) Inter- and intrahemispheric long-range connections might serve as a grid preparing primary visual cortex for likely junctions in a larger visual environment encompassing the two hemifields.

Processamento Inteligente de Sinais de Pressão e Temperatura Adquiridos Através de Sensores Permanentes em Poços de Petróleo

Originally aimed at operational objectives, the continuous measurement of well bottomhole pressure and temperature, recorded by permanent downhole gauges (PDG), finds vast applicability in reservoir management. It contributes for the monitoring of well performance and makes it possible to estimate reservoir parameters on the long term. However, notwithstanding its unquestionable value, data from PDG is characterized by a large noise content. Moreover, the presence of outliers within valid signal measurements seems to be a major problem as well. In this work, the initial treatment of PDG signals is addressed, based on curve smoothing, self-organizing maps and the discrete wavelet transform. Additionally, a system based on the coupling of fuzzy clustering with feed-forward neural networks is proposed for transient detection. The obtained results were considered quite satisfactory for offshore wells and matched real requisites for utilization

Emprego de redes neurais artificiais supervisionadas e não supervisionadas no estudo de parâmetros reológicos de excipientes farmacêuticos sólidos

In this paper artificial neural network (ANN) based on supervised and unsupervised algorithms were investigated for use in the study of rheological parameters of solid pharmaceutical excipients, in order to develop computational tools for manufacturing solid dosage forms. Among four supervised neural networks investigated, the best learning performance was achieved by a feedfoward multilayer perceptron whose architectures was composed by eight neurons in the input layer, sixteen neurons in the hidden layer and one neuron in the output layer. Learning and predictive performance relative to repose angle was poor while to Carr index and Hausner ratio (CI and HR, respectively) showed very good fitting capacity and learning, therefore HR and CI were considered suitable descriptors for the next stage of development of supervised ANNs. Clustering capacity was evaluated for five unsupervised strategies. Network based on purely unsupervised competitive strategies, classic "Winner-Take-All", "Frequency-Sensitive Competitive Learning" and "Rival-Penalize Competitive Learning" (WTA, FSCL and RPCL, respectively) were able to perform clustering from database, however this classification was very poor, showing severe classification errors by grouping data with conflicting properties into the same cluster or even the same neuron. On the other hand it could not be established what was the criteria adopted by the neural network for those clustering. Self-Organizing Maps (SOM) and Neural Gas (NG) networks showed better clustering capacity. Both have recognized the two major groupings of data corresponding to lactose (LAC) and cellulose (CEL). However, SOM showed some errors in classify data from minority excipients, magnesium stearate (EMG) , talc (TLC) and attapulgite (ATP). NG network in turn performed a very consistent classification of data and solve the misclassification of SOM, being the most appropriate network for classifying data of the study. The use of NG network in pharmaceutical technology was still unpublished. NG therefore has great potential for use in the development of software for use in automated classification systems of pharmaceutical powders and as a new tool for mining and clustering data in drug development

Como se fora brincadeira de roda: a ciranda da ludopoiese para uma educação musical humanescente

The present work concerns an auto-ethnographic study based on life experiences and reflections of an educator at Escola Viva Preschool and Elementary-Middle School, located in the city center of Natal, Rio Grande do Norte. As a cognitive model of operation, we use the metaphor of the Circle Dance. The objective of this study is to identify, interpret and describe the ludopoetics that are achieved through a Musical Education program, which we denominate, Humanescent. The data of this investigation was derived from the music making by Preschool and Elementary-Middle School students at Escola Viva during 2007, 2008 and 2009, from which 20 learners were selected to form the corpus, along with the description and interpretation of photos of their experiences and sand tray scenes. We justify the methodological systemization of the research based on our own pedagogical practice, which supports Musical Education in the schools based on the principals of Embodiment, Autopoesis and Flow. The methodological systemization was developed through an Action Research model and on the concepts of Systemic Development, with the goal of re-reading the context investigated through the structuring of categories of Ludopoesis: Self-esteem, Self-territory, Self-connectivity, Self-realization and Selfworth. We used an observant-participant research approach with regard to the perception of emergent knowledge, the surroundings, the experience lived and the contextual and vibration of the circumstances. Besides this, we used projection to interpret the experiences lived, in the form of drawings, short poems, letters or sand tray scenes as symbolic interpretations of experience. In the unfolding of the Ludopoetic Process (Selfesteem, Self-territory, Self-connectivity, Self-realization and Selfworth) we draw conclusions about the relevance of the ludic musical experience, which foments the formation of the self based on music learning, and which is demonstrated in the Embodiment of the learners. In the auto-formative process (of learners and educators) we observe the importance of pedagogical work based on Musical Humanescent Education that gives value to the music making path to the construction of music and performance in play, creativity, and sensibility. The experience of making music in a playful way allows for organization of the self and its autonomous production in the joy of living within a ludopoetic process. These findings highlight the educator as in a permanent state of selfformation, which generates moments of flow. However, in Musical Humanescent Education, music is learned collectively, doing a circle dance, experiencing love, fostering an expansion of the creative spirit, and giving recognition to playfulness as a necessary condition for education and to the value of music made with the true nature and sensibilities of the educators

Algoritmo SOM com estrutura hierárquica e dinâmica aplicado a compressão de imagens

ln this work the implementation of the SOM (Self Organizing Maps) algorithm or Kohonen neural network is presented in the form of hierarchical structures, applied to the compression of images. The main objective of this approach is to develop an Hierarchical SOM algorithm with static structure and another one with dynamic structure to generate codebooks (books of codes) in the process of the image Vector Quantization (VQ), reducing the time of processing and obtaining a good rate of compression of images with a minimum degradation of the quality in relation to the original image. Both self-organizing neural networks developed here, were denominated HSOM, for static case, and DHSOM, for the dynamic case. ln the first form, the hierarchical structure is previously defined and in the later this structure grows in an automatic way in agreement with heuristic rules that explore the data of the training group without use of external parameters. For the network, the heuristic mIes determine the dynamics of growth, the pruning of ramifications criteria, the flexibility and the size of children maps. The LBO (Linde-Buzo-Oray) algorithm or K-means, one ofthe more used algorithms to develop codebook for Vector Quantization, was used together with the algorithm of Kohonen in its basic form, that is, not hierarchical, as a reference to compare the performance of the algorithms here proposed. A performance analysis between the two hierarchical structures is also accomplished in this work. The efficiency of the proposed processing is verified by the reduction in the complexity computational compared to the traditional algorithms, as well as, through the quantitative analysis of the images reconstructed in function of the parameters: (PSNR) peak signal-to-noise ratio and (MSE) medium squared error

Estratégias baseadas em aprendizado para coordenação de uma frota de robôs em tarefas cooperativas

In multi-robot systems, both control architecture and work strategy represent a challenge for researchers. It is important to have a robust architecture that can be easily adapted to requirement changes. It is also important that work strategy allows robots to complete tasks efficiently, considering that robots interact directly in environments with humans. In this context, this work explores two approaches for robot soccer team coordination for cooperative tasks development. Both approaches are based on a combination of imitation learning and reinforcement learning. Thus, in the first approach was developed a control architecture, a fuzzy inference engine for recognizing situations in robot soccer games, a software for narration of robot soccer games based on the inference engine and the implementation of learning by imitation from observation and analysis of others robotic teams. Moreover, state abstraction was efficiently implemented in reinforcement learning applied to the robot soccer standard problem. Finally, reinforcement learning was implemented in a form where actions are explored only in some states (for example, states where an specialist robot system used them) differently to the traditional form, where actions have to be tested in all states. In the second approach reinforcement learning was implemented with function approximation, for which an algorithm called RBF-Sarsa($lambda$) was created. In both approaches batch reinforcement learning algorithms were implemented and imitation learning was used as a seed for reinforcement learning. Moreover, learning from robotic teams controlled by humans was explored. The proposal in this work had revealed efficient in the robot soccer standard problem and, when implemented in other robotics systems, they will allow that these robotics systems can efficiently and effectively develop assigned tasks. These approaches will give high adaptation capabilities to requirements and environment changes.

Contribuições a técnicas de agrupamento e visualização de dados multivariados utilizando mapas auto-organizáveis

Self-organizing maps (SOM) are artificial neural networks widely used in the data mining field, mainly because they constitute a dimensionality reduction technique given the fixed grid of neurons associated with the network. In order to properly the partition and visualize the SOM network, the various methods available in the literature must be applied in a post-processing stage, that consists of inferring, through its neurons, relevant characteristics of the data set. In general, such processing applied to the network neurons, instead of the entire database, reduces the computational costs due to vector quantization. This work proposes a post-processing of the SOM neurons in the input and output spaces, combining visualization techniques with algorithms based on gravitational forces and the search for the shortest path with the greatest reward. Such methods take into account the connection strength between neighbouring neurons and characteristics of pattern density and distances among neurons, both associated with the position that the neurons occupy in the data space after training the network. Thus, the goal consists of defining more clearly the arrangement of the clusters present in the data. Experiments were carried out so as to evaluate the proposed methods using various artificially generated data sets, as well as real world data sets. The results obtained were compared with those from a number of well-known methods existent in the literature

Estudo comportamental, hormonal e histopatológico de animais com perda de peso progressina (SEP) e da variável peso associada a uma nova classificação ontogenética em Callithrix jacchus

The use of non-human primates in scientific research has contributed significantly to the biomedical area and, in the case of Callithrix jacchus, has provided important evidence on physiological mechanisms that help explain its biology, making the species a valuable experimental model in different pathologies. However, raising non-human primates in captivity for long periods of time is accompanied by behavioral disorders and chronic diseases, as well as progressive weight loss in most of the animals. The Primatology Center of the Universidade Federal do Rio Grande do Norte (UFRN) has housed a colony of C. jacchus for nearly 30 years and during this period these animals have been weighed systematically to detect possible alterations in their clinical conditions. This procedure has generated a volume of data on the weight of animals at different age ranges. These data are of great importance in the study of this variable from different perspectives. Accordingly, this paper presents three studies using weight data collected over 15 years (1985-2000) as a way of verifying the health status and development of the animals. The first study produced the first article, which describes the histopathological findings of animals with probable diagnosis of permanent wasting marmoset syndrome (WMS). All the animals were carriers of trematode parasites (Platynosomum spp) and had obstruction in the hepatobiliary system; it is suggested that this agent is one of the etiological factors of the syndrome. In the second article, the analysis focused on comparing environmental profile and cortisol levels between the animals with normal weight curve evolution and those with WMS. We observed a marked decrease in locomotion, increased use of lower cage extracts and hypocortisolemia. The latter is likely associated to an adaptation of the mechanisms that make up the hypothalamus-hypophysis-adrenal axis, as observed in other mammals under conditions of chronic malnutrition. Finally, in the third study, the animals with weight alterations were excluded from the sample and, using computational tools (K-means and SOM) in a non-supervised way, we suggest found new ontogenetic development classes for C. jacchus. These were redimensioned from five to eight classes: infant I, infant II, infant III, juvenile I, juvenile II, sub-adult, young adult and elderly adult, in order to provide a more suitable classification for more detailed studies that require better control over the animal development

Processamento Inteligente de Sinais de Pressão e Temperatura Adquiridos Através de Sensores Permanentes em Poços de Petróleo

Originally aimed at operational objectives, the continuous measurement of well bottomhole pressure and temperature, recorded by permanent downhole gauges (PDG), finds vast applicability in reservoir management. It contributes for the monitoring of well performance and makes it possible to estimate reservoir parameters on the long term. However, notwithstanding its unquestionable value, data from PDG is characterized by a large noise content. Moreover, the presence of outliers within valid signal measurements seems to be a major problem as well. In this work, the initial treatment of PDG signals is addressed, based on curve smoothing, self-organizing maps and the discrete wavelet transform. Additionally, a system based on the coupling of fuzzy clustering with feed-forward neural networks is proposed for transient detection. The obtained results were considered quite satisfactory for offshore wells and matched real requisites for utilization

A survey of control architectures for autonomous mobile robots

MEDEIROS, Adelardo A. D.A survey of control architectures for autonomous mobile robots. J. Braz. Comp. Soc., Campinas, v. 4, n. 3, abr. 1998 .Disponível em: Acesso: 27 set. 2010.

A survey of control architectures for autonomous mobile robots

MEDEIROS, Adelardo A. D.A survey of control architectures for autonomous mobile robots. J. Braz. Comp. Soc., Campinas, v. 4, n. 3, abr. 1998 .Disponível em: Acesso: 27 set. 2010.

Mapeamento robótico 2,5-D com representação em grade de ocupação-elevação

This work introduces a new method for environment mapping with three-dimensional information from visual information for robotic accurate navigation. Many approaches of 3D mapping using occupancy grid typically requires high computacional effort to both build and store the map. We introduce an 2.5-D occupancy-elevation grid mapping, which is a discrete mapping approach, where each cell stores the occupancy probability, the height of the terrain at current place in the environment and the variance of this height. This 2.5-dimensional representation allows that a mobile robot to know whether a place in the environment is occupied by an obstacle and the height of this obstacle, thus, it can decide if is possible to traverse the obstacle. Sensorial informations necessary to construct the map is provided by a stereo vision system, which has been modeled with a robust probabilistic approach, considering the noise present in the stereo processing. The resulting maps favors the execution of tasks like decision making in the autonomous navigation, exploration, localization and path planning. Experiments carried out with a real mobile robots demonstrates that this proposed approach yields useful maps for robot autonomous navigation