Neurobiological mechanisms for language, symbols and concepts: Clues from brain-constrained deep neural networks

Neural networks are successfully used to imitate and model cognitive processes. However, to provide clues about the neurobiological mechanisms enabling human cognition, these models need to mimic the structure and function of real brains. Brain-constrained networks differ from classic neural networks by implementing brain similarities at different scales, ranging from the micro- and mesoscopic levels of neuronal function, local neuronal links and circuit interaction to large-scale anatomical structure and between-area connectivity. This review shows how brain-constrained neural networks can be applied to study in silico the formation of mechanisms for symbol and concept processing and to work towards neurobiological explanations of specifically human cognitive abilities. These include verbal working memory and learning of large vocabularies of symbols, semantic binding carried by specific areas of cortex, attention focusing and modulation driven by symbol type, and the acquisition of concrete and abstract concepts partly influenced by symbols. Neuronal assembly activity in the networks is analyzed to deliver putative mechanistic correlates of higher cognitive processes and to develop candidate explanations founded in established neurobiological principles

Agoric computation: trust and cyber-physical systems

In the past two decades advances in miniaturisation and economies of scale have led to the emergence of billions of connected components that have provided both a spur and a blueprint for the development of smart products acting in specialised environments which are uniquely identifiable, localisable, and capable of autonomy. Adopting the computational perspective of multi-agent systems (MAS) as a technological abstraction married with the engineering perspective of cyber-physical systems (CPS) has provided fertile ground for designing, developing and deploying software applications in smart automated context such as manufacturing, power grids, avionics, healthcare and logistics, capable of being decentralised, intelligent, reconfigurable, modular, flexible, robust, adaptive and responsive. Current agent technologies are, however, ill suited for information-based environments, making it difficult to formalise and implement multiagent systems based on inherently dynamical functional concepts such as trust and reliability, which present special challenges when scaling from small to large systems of agents. To overcome such challenges, it is useful to adopt a unified approach which we term agoric computation, integrating logical, mathematical and programming concepts towards the development of agent-based solutions based on recursive, compositional principles, where smaller systems feed via directed information flows into larger hierarchical systems that define their global environment. Considering information as an integral part of the environment naturally defines a web of operations where components of a systems are wired in some way and each set of inputs and outputs are allowed to carry some value. These operations are stateless abstractions and procedures that act on some stateful cells that cumulate partial information, and it is possible to compose such abstractions into higher-level ones, using a publish-and-subscribe interaction model that keeps track of update messages between abstractions and values in the data. In this thesis we review the logical and mathematical basis of such abstractions and take steps towards the software implementation of agoric modelling as a framework for simulation and verification of the reliability of increasingly complex systems, and report on experimental results related to a few select applications, such as stigmergic interaction in mobile robotics, integrating raw data into agent perceptions, trust and trustworthiness in orchestrated open systems, computing the epistemic cost of trust when reasoning in networks of agents seeded with contradictory information, and trust models for distributed ledgers in the Internet of Things (IoT); and provide a roadmap for future developments of our research

Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)

Proceedings of the CPSL 202

Thinking- Skins

Under the guiding concept of a thinking skin, the research project examines the transferability of cyber-physical systems to the application field of façades. It thereby opens up potential increases in the performance of automated and adaptive façade systems and provides a conceptual framework for further research and development of intelligent building envelopes in the current age of digital transformation. The project is characterized by the influence of digital architectural design methods and the associated computational processing of information in the design process. The possible establishment of relationships and dependencies in an architecture understood as a system, in particular, are the starting point for the conducted investigation. With the available automation technologies, the possibility of movable building constructions, and existing computer-based control systems, the technical preconditions for the realisation of complex and active buildings exist today. Against this background, dynamic and responsive constructions that allow adaptations in the operation of the building are a current topic in architecture. In the application field of the building envelope, the need for such designs is evident, particularly with regards to the concrete field of adaptive façades. In its mediating role, the façade is confronted with the dynamic influences of the external microclimate of a building and the changing comfort demands of the indoor climate. The objective in the application of adaptive façades is to increase building efficiency by balancing dynamic influencing factors and requirements. Façade features are diverse and with the increasing integration of building services, both the scope of fulfilled façade functions and the complexity of today’s façades increase. One challenge is the coordination of adaptive functions to ensure effective reactions of the façade as a complete system. The ThinkingSkins research project identifies cyber-physical systems as a possible solution to this challenge. This involves the close integration of physical systems with their digital control. Important features are the decentralized organization of individual system constituents and their cooperation via an exchange of information. Developments in recent decades, such as the miniaturisation of computer technology and the availability of the Internet, have established the technical basis required for these developments. Cyber-physical systems are already employed in many fields of application. Examples are decentralized energy supply, or transportation systems with autonomous vehicles. The influence is particularly evident in the transformation of the industrial sector to Industry 4.0, where formerly mechatronic production plants are networked into intelligent technical systems with the aim of achieving higher and more flexible productivity. In the ThinkingSkins research project it is assumed that the implementation of cyber-physical systems based on the role model of cooperating production plants in IIndustry 4.0 can contribute to an increase in the performance of façades. Accordingly, the research work investigates a possible transfer of cyber-physical systems to the application field of building envelopes along the research question: How can cyber-physical systems be applied to façades, in order to enable coordinated adaptations of networked individual façade functions? To answer this question, four partial studies are carried out, which build upon each other. The first study is based on a literature review, in which the understanding and the state-of-the-art development of intelligent façade systems is examined in comparison to the exemplary field of application of cyber-physical systems in the manufacturing industry. In the following partial study, a second literature search identifies façade functions that can be considered as components of a cyber-physical façade due to their adaptive feasibility and their effect on the façade performance. For the evaluation of the adaptive capabilities, characteristics of their automated and adaptive implementation are assigned to the identified façade functions. The resulting superposition matrix serves as an organizational tool for the third investigation of the actual conditions in construction practice. In a multiple case study, realized façade projects in Germany are examined with regard to their degree of automation and adaptivity. The investigation includes interviews with experts involved in the projects as well as field studies on site. Finally, an experimental examination of the technical feasibility of cyber-physical façade systems is carried out through the development of a prototype. In the sense of an internet of façade functions, the automated adaptive façade functions ventilation, sun protection as well as heating and cooling are implemented in decentrally organized modules. They are connected to a digital twin and can exchange data with each other via a communication protocol. The research project shows that the application field of façades has not yet been exploited for the implementation of cyber-physical systems. With the automation technologies used in building practice, however, many technical preconditions for the development of cyber-physical façade systems already exist. Many features of such a system are successfully implemented within the study by the development of a prototype. The research project therefore comes to the conclusion that the application of cyber-physical systems to the façade is possible and offers a promising potential for the effective use of automation technologies. Due to the lack of artificial intelligence and machine learning strategies, the project does not achieve the goal of developing a façade in the sense of a true ThinkingSkin as the title indicates. A milestone is achieved by the close integration of the physical façade system with a decentralized and integrated control system. In this sense, the researched cyber-physical implementation of façades represents a conceptual framework for the realisation of corresponding systems in building practice, and a pioneer for further research of ThinkingSkins

ThinkingSkins:

New technologies and automation concepts emerge in the digitalization of our environment. This is, for example, reflected by intelligent production systems in Industry 4.0. A core aspect of such systems is their cyber-physical implementation, which aims to increase productivity and flexibility through embedded computing capacities and the cooperation of decentrally networked production plants. This development stage of automation has not yet been achieved in the current state-of-the-art of façades. Being responsible for the execution of adaptive measures, façade automation is part of hierarchically and centrally organised Building Automation Systems (BAS). The research project ThinkingSkins is guided by the hypothesis that, aiming at an enhanced overall building performance, façades can be implemented as cyber-physical systems. Accordingly, it addresses the research question: How can cyber-physical systems be applied to façades, in order to enable coordinated adaptations of networked individual façade functions? The question is approached in four partial investigations. First, a comprehensive understanding of intelligent systems in both application fields, façades and Industry 4.0, is elaborated by a literature review. Subsequently, relevant façade functions are identified by a second literature review in a superposition matrix, which also incorporates characteristics for a detailed assessment of each function’s adaptive capacities. The third investigation focuses on existing conditions in building practice by means of a multiple case study analysis. Finally, the technical feasibility of façades implemented as cyber-physical systems is investigated by developing a prototype. The research project identifies the possibility and promising potential of cyberphysical façades. As result, the doctoral dissertation provides a conceptual framework for the implementation of such systems in building practice and for further research

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available

An agent-based approach to model farmers' land use cover change intentions

Land Use and Cover Change (LUCC) occurs as a consequence of both natural and human activities, causing impacts on biophysical and agricultural resources. In enlarged urban regions, the major changes are those that occur from agriculture to urban uses. Urban uses compete with rural ones due among others, to population growth and housing demand. This competition and the rapid nature of change can lead to fragmented and scattered land use development generating new challenges, for example, concerning food security, soil and biodiversity preservation, among others. Landowners play a key role in LUCC. In peri-urban contexts, three interrelated key actors are pre-eminent in LUCC complex process: 1) investors or developers, who are waiting to take advantage of urban development to obtain the highest profit margin. They rely on population growth, housing demand and spatial planning strategies; 2) farmers, who are affected by urban development and intend to capitalise on their investment, or farmers who own property for amenity and lifestyle values; 3) and at a broader scale, land use planners/ decision-makers. Farmers’ participation in the real estate market as buyers, sellers or developers and in the land renting market has major implications for LUCC because they have the capacity for financial investment and to control future agricultural land use. Several studies have analysed farmer decision-making processes in peri-urban regions. These studies identified agricultural areas as the most vulnerable to changes, and where farmers are presented with the choice of maintaining their agricultural activities and maximising the production potential of their crops or selling their farmland to land investors. Also, some evaluate the behavioural response of peri-urban farmers to urban development, and income from agricultural production, agritourism, and off-farm employment. Uncertainty about future land profits is a major motivator for decisions to transform farmland into urban development. Thus, LUCC occurs when the value of expected urban development rents exceeds the value of agricultural ones. Some studies have considered two main approaches in analysing farmer decisions: how drivers influence farmer’s decisions; and how their decisions influence LUCC. To analyse farmers’ decisions is to acknowledge the present and future trends and their potential spatial impacts. Simulation models, using cellular automata (CA), artificial neural networks (ANN) or agent-based systems (ABM) are commonly used. This PhD research aims to propose a model to understand the agricultural land-use change in a peri-urban context. We seek to understand how human drivers (e.g., demographic, economic, planning) and biophysical drivers can affect farmer’s intentions regarding the future agricultural land and model those intentions. This study presents an exploratory analysis aimed at understanding the complex dynamics of LUCC based on farmers’ intentions when they are faced with four scenarios with the time horizon of 2025: the A0 scenario – based on current demographic, social and economic trends and investigating what happens if conditions are maintained (BAU); the A1 scenario – based on a regional food security; the A2 scenario – based on climate change; and the B0 scenario – based on farming under urban pressure, and investigating what happens if people start to move to rural areas. These scenarios were selected because of the early urbanisation of the study area, as a consequence of economic, social and demographic development; and because of the interest in preserving and maintaining agriculture as an essential resource. Also, Torres Vedras represents one of the leading suppliers of agricultural goods (mainly fresh fruits, vegetables, and wine) in Portugal. To model LUCC a CA-Markov, an ANN-multilayer perceptron, and an ABM approach were applied. Our results suggest that significant LUCC will occur depending on farmers’ intentions in different scenarios. The highlights are: (1) the highest growth in permanently irrigated land in the A1 scenario; (2) the most significant drop in non-irrigated arable land, and the highest growth in the forest and semi-natural areas in the A2 scenario; and (3) the greatest urban growth was recognised in the B0 scenario. To verify if the fitting simulations performed well, statistical analysis to measure agreement and quantity-allocation disagreements and a participatory workshop with local stakeholders to validate the achieved results were applied. These outcomes could provide decision-makers with the capacity to observe different possible futures in ‘what if’ scenarios, allowing them to anticipate future uncertainties, and consequently allowing them the possibility to choose the more desirable future

Circuit motifs for sensory integration, learning, and the initiation of adaptive behavior in Drosophila

Goal-directed behavior is crucial for survival in complex, dynamic environments. It requires the detection of relevant sensory stimuli and the formation of separable neuronal representations. Learning the contingencies of these sensory stimuli with innately positive or negative valent stimuli (reinforcement) forms associations, allowing the former to cue the latter. This yields cue-based predictions to upgrade the behavioral repertoire from reactive to anticipatory. In this thesis, the Trias of sensory integration, learning of contingencies, and the initiation of anticipatory behavior are studied in the framework of the fruit fly Drosophila olfactory pathway and mushroom body, a higher-order brain center for integrating sensory input and coincidence detection using computational network models representing the mushroom body architecture with varying degrees of abstraction. Additionally, simulations of larval locomotion were employed to investigate how the output of the mushroom body relates to behavior and to foster comparability with animal experiments. We showed that inhibitory feedback within the mushroom body produces sparse stimulus representations, increasing the separability of different sensory stimuli. This separability reduced reinforcement generalization in learning experiments through the decreased overlap of stimulus representations. Furthermore, we showed that feedback from the valence-signaling output to the reinforcement-signaling dopaminergic neurons that innervate the mushroom body could explain experimentally observed temporal dynamics of the formation of associations between sensory cues and reinforcement. This supports the hypothesis that dopaminergic neurons encode the difference between predicted and received reinforcement, which in turn drives the learning process. These dopaminergic neurons have also been argued to convey an indirect reinforcement signal in second-order learning experiments. A new sensory cue is paired with an already established one that activates dopaminergic neurons due to its association with the reinforcement. We demonstrated how different pathways for feedforward or feedback input from the mushroom body’s intrinsic or output neurons can provide an indirect reinforcement signal to the dopaminergic neurons. Any direct or indirect association of sensory cues with reinforcement yielded a reinforcement expectation, biasing the fly’s behavioral response towards the approach or avoidance of the respective sensory cue. We then showed that the simulated locomotory behavior of individual animals in a virtual environment depends on the biasing output of the mushroom body. In conclusion, our results contribute to understanding the implementation of mechanisms for separable stimulus representations, postulated key features of associative learning, and the link between MB output and adaptive behavior in the mushroom body and confirm their explanatory power for animal behavior

Making a stronger case for comparative research to investigate the behavioral and neurological bases of three-dimensional navigation

The rich diversity of avian natural history provides exciting possibilities for comparative research aimed at understanding three-dimensional navigation. We propose some hypotheses relating differences in natural history to potential behavioral and neurological adaptations possessed by contrasting bird species. This comparative approach may offer unique insights into some of the important questions raised by Jeffery et al