A roadmap to integrate astrocytes into Systems Neuroscience.

Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease

Discovering phase and causal dependencies on manufacturing processes

Discovering phase and causal dependencies on manufacturing processes. Keyword machine learning, causality, Industry 4.

Robotic learning of force-based industrial manipulation tasks

Even with the rapid technological advancements, robots are still not the most comfortable machines to work with. Firstly, due to the separation of the robot and human workspace which imposes an additional financial burden. Secondly, due to the significant re-programming cost in case of changing products, especially in Small and Medium-sized Enterprises (SMEs). Therefore, there is a significant need to reduce the programming efforts required to enable robots to perform various tasks while sharing the same space with a human operator. Hence, the robot must be equipped with a cognitive and perceptual capabilities that facilitate human-robot interaction. Humans use their various senses to perform tasks such as vision, smell and taste. One sensethat plays a significant role in human activity is ’touch’ or ’force’. For example, holding a cup of tea, or making fine adjustments while inserting a key requires haptic information to achieve the task successfully. In all these examples, force and torque data are crucial for the successful completion of the activity. Also, this information implicitly conveys data about contact force, object stiffness, and many others. Hence, a deep understanding of the execution of such events can bridge the gap between humans and robots. This thesis is being directed to equip an industrial robot with the ability to deal with force perceptions and then learn force-based tasks using Learning from Demonstration (LfD).To learn force-based tasks using LfD, it is essential to extract task-relevant features from the force information. Then, knowledge must be extracted and encoded form the task-relevant features. Hence, the captured skills can be reproduced in a new scenario. In this thesis, these elements of LfD were achieved using different approaches based on the demonstrated task. In this thesis, four robotics problems were addressed using LfD framework. The first challenge was to filter out robots’ internal forces (irrelevant signals) using data-driven approach. The second robotics challenge was the recognition of the Contact State (CS) during assembly tasks. To tackle this challenge, a symbolic based approach was proposed, in which a force/torque signals; during demonstrated assembly, the task was encoded as a sequence of symbols. The third challenge was to learn a human-robot co-manipulation task based on LfD. In this case, an ensemble machine learning approach was proposed to capture such a skill. The last challenge in this thesis, was to learn an assembly task by demonstration with the presents of parts geometrical variation. Hence, a new learning approach based on Artificial Potential Field (APF) to learn a Peg-in-Hole (PiH) assembly task which includes no-contact and contact phases. To sum up, this thesis focuses on the use of data-driven approaches to learning force based task in an industrial context. Hence, different machine learning approaches were implemented, developed and evaluated in different scenarios. Then, the performance of these approaches was compared with mathematical modelling based approaches.</div

Models, Simulations, and the Reduction of Complexity

Modern science is a model-building activity. But how are models contructed? How are they related to theories and data? How do they explain complex scientific phenomena, and which role do computer simulations play? To address these questions which are highly relevant to scientists as well as to philosophers of science, 8 leading natural, engineering and social scientists reflect upon their modeling work, and 8 philosophers provide a commentary

Integrating Constraint-led and Step-Game approaches to develop sport performance: a season-long action-research study of a youth volleyball team.

A presente dissertação procura examinar a influência da combinação de uma abordagem ecológica (i.e., abordagem guiada por constrangimentos) com uma abordagem construtivista (i.e., abordagem progressiva ao jogo), através de um desenho de investigação-ação no qual o investigador assumiu o duplo papel de treinador-investigador, no desenvolvimento da performance desportiva de jovens jogadoras de voleibol ao longo de uma época competitiva. O carácter cíclico e interventivo da investigação-ação possibilitou a monitorização sistemática e contextualizada de variáveis de processo (i.e., conhecimento tático) e de produto (i.e., tendências de sincronização coletivas em ambiente competitivo), facilitando, portanto, a inter-relação entre a informação proveniente do treino e da competição. Ademais, dada a natureza interpretativa deste projeto de investigação, foi possível compreender o impacto que o uso de diferentes estratégicas pedagógicas característica de ambas as abordagens (p.e., aumento complexidade tática via manipulação de constrangimentos, questionamento) tiveram no desenvolvimento da performance desportiva das jogadoras, em cada momento da época. A evolução do processo ensino-aprendizagem foi captada através de um diário reflexivo, notas de campo, reuniões de grupo focal, e documentação dos planos de treinos e de jogos. As coordenadas posicionais das jogadoras foram obtidas através do software TACTO, e utilizadas para aferir acerca do desenvolvimento das tendências de sincronização coletivas pelo cálculo do método de fase-cluster. O uso combinado das duas abordagens, revelou-se benéfico para a evolução do conhecimento tático, bem como para o desenvolvimento das tendências de sincronização coletivas em competição. Em particular, o aumento da complexidade tática: (i) induziu um progressivo aumento do conhecimento tático (i.e., consciência tática, atenção focal, e pensamento estratégico); (ii) atuou como ruído, promovendo diminuição da sincronia da equipa a curto-prazo, mas um reaumento a longo-prazo. PALAVRAS-CHAVE: PEDAGOGIA DO DESPORTO, ANÁLISE DA PERFORMANCE, INVESTIGAÇÃO-AÇÃO, ABORDAGEM CENTRADA NO AMBIENTE E NO JOGADOR, VOLEIBOL.The aim of the present thesis was to examine the influence of combining an ecological approach (i.e., constraints-led approach) with an constructivist approach (i.e., step-game approach), using an insider action-research design where the researcher assumed the dual role of coach-researcher, on the development of sport performance in youth female volleyball players over a competitive season. The cyclical and interventive nature of the action-research design allowed for the systematic and contextualised monitoring of process variables (i.e., tactical knowledge) and product variables (i.e., collective synchronisation tendencies within competitive environment), thus facilitating the interplay between information from training and competition. Moreover, because of the interpretative nature of this research project, it was possible to comprehend the impact of using different pedagogical strategies with characteristics of both approaches (e.g., increasing tactical complexity via constraints manipulation, questioning) on the development of players' sport performance at each moment of the season. The evolution of the coaching-learning process was captured using a reflexive diary, field notes, focus group interviews, and by documenting training and game plans. The players' positional coordinates were collected using TACTO software and used to measure the development of collective synchronisation tendencies via the cluster-phase method calculation. The combined use of both approaches had benefits for evolving tactical knowledge, and for the development of collective synchronisation tendencies in competition. In particular, the increasing of tactical complexity: (i) induced a progressive enhancement of tactical knowledge (i.e., tactical awareness, attentional focus, and strategical thinking); and (ii) acted as noise, causing a decrease in team synchrony in the short-term, but a re-achievement in the long-term

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

Models, Simulations, and the Reduction of Complexity

Modern science is a model-building activity. But how are models contructed? How are they related to theories and data? How do they explain complex scientific phenomena, and which role do computer simulations play? To address these questions which are highly relevant to scientists as well as to philosophers of science, 8 leading natural, engineering and social scientists reflect upon their modeling work, and 8 philosophers provide a commentary