A cortical framework for invariant object categorization and recognition

In this paper we present a new model for invariant object categorization and recognition. It is based on explicit multi-scale features: lines, edges and keypoints are extracted from responses of simple, complex and endstopped cells in cortical area V1, and keypoints are used to construct saliency maps for Focus-of-Attention. The model is a functional but dichotomous one, because keypoints are employed to model the “where” data stream, with dynamic routing of features from V1 to higher areas to obtain translation, rotation and size invariance, whereas lines and edges are employed in the “what” stream for object categorization and recognition. Furthermore, both the “where” and “what” pathways are dynamic in that information at coarse scales is employed first, after which information at progressively finer scales is added in order to refine the processes, i.e., both the dynamic feature routing and the categorization level. The construction of group and object templates, which are thought to be available in the prefrontal cortex with “what” and “where” components in PF46d and PF46v, is also illustrated. The model was tested in the framework of an integrated and biologically plausible architecture

Multi-authored monograph

Unmanned aerial vehicles. Perspectives. Management. Power supply : Multi-authored monograph / V. V. Holovenskiy, T. F. Shmelova,Y. M. Shmelev and oth.; Science Editor DSc. (Engineering), T. F. Shmelova. – Warsaw, 2019. – 100 p. - ISBN 978-83-66216-10-5.У монографії аналізуються можливі варіанти енергопостачання та управління безпілотними літальними апаратами. Також розглядається питання прийняття рішення оператором безпілотного літального апарату при управлінні у надзвичайних ситуаціях. Рекомендується для фахівців, аспірантів і студентів за спеціальностями 141 - «Електроенергетика, електротехніка та електромеханіка», 173 - «Авіоніка» та інших суміжних спеціальностей.The monograph analyzes the possible options for energy supply and control of unmanned aerial vehicles. Also, the issue of decision-making by the operator of an unmanned aerial vehicle in the management of emergencies is considered.

Layered Reward Signalling Through Octopamine and Dopamine in Drosophila: A Dissertation

Evaluating our environment by deciding what is beneficial or harmful, pleasant or punishing is a part of our daily lives. Seeking pleasure and avoiding pain is a common trait all mobile organisms exhibit and understanding how rewarding stimuli are represented in the brain remains a major goal of neuroscience. Studying reward learning in the fruit fly, Drosophila melanogaster has enabled us to better understand the complex neural circuit mechanisms involved in reward processing in the brain. By conditioning flies with sugars of differing nutritional properties, we determined that flies trained with sweet but non-nutritive sugars formed robust short-term memory (STM), but not long-term memory (LTM). However, flies conditioned with a sweet and nutritious sugar or a sweet non-nutritious sugar supplemented with a tasteless nutritious compound, formed robust 24 hour LTM. These findings led us to propose a model of parallel reinforcement pathways for appetitive olfactory conditioning in the fly, in which both sweet taste and nutrient value contribute to appetitive long-term memory. We followed this line of research by examining the neural circuitry in the fly brain that represents these parallel reward pathways. We found that the biogenic amine octopamine (OA) only represents the reinforcing effects of sweet taste. Stimulation of OA neurons could replace sugar in olfactory conditioning to form appetitive STM. Surprisingly, implanting memory with OA was dependent on dopamine (DA) signaling, which although being long associated with reward in mammals, was previously linked with punishment in flies. We found that OA-reinforced memory functions through the α-adrenergic OAMB receptor in a novel subset of rewarding DA neurons that innervate the mushroom body (MB). The rewarding population of DA neurons is required for sweet and nutrient reinforced memory suggesting they may integrate both signals to drive appetitive LTM formation. In addition, OA implanted memory requires concurrent modulation of negatively reinforcing DA neurons through the β-adrenergic OCTβ2R receptor. These data provide a new layered reward model in Drosophila in which OA modulates distinct populations of both positive and negative coding DA neurons. Therefore, the reinforcement system in flies is more similar to that of mammals than previously thought

Neuromorphic Engineering Editors' Pick 2021

This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. André van Schaik and Bernabé Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiers’ strong community by recognizing highly deserving authors

24th International Conference on Information Modelling and Knowledge Bases

In the last three decades information modelling and knowledge bases have become essentially important subjects not only in academic communities related to information systems and computer science but also in the business area where information technology is applied. The series of European – Japanese Conference on Information Modelling and Knowledge Bases (EJC) originally started as a co-operation initiative between Japan and Finland in 1982. The practical operations were then organised by professor Ohsuga in Japan and professors Hannu Kangassalo and Hannu Jaakkola in Finland (Nordic countries). Geographical scope has expanded to cover Europe and also other countries. Workshop characteristic - discussion, enough time for presentations and limited number of participants (50) / papers (30) - is typical for the conference. Suggested topics include, but are not limited to: 1. Conceptual modelling: Modelling and specification languages; Domain-specific conceptual modelling; Concepts, concept theories and ontologies; Conceptual modelling of large and heterogeneous systems; Conceptual modelling of spatial, temporal and biological data; Methods for developing, validating and communicating conceptual models. 2. Knowledge and information modelling and discovery: Knowledge discovery, knowledge representation and knowledge management; Advanced data mining and analysis methods; Conceptions of knowledge and information; Modelling information requirements; Intelligent information systems; Information recognition and information modelling. 3. Linguistic modelling: Models of HCI; Information delivery to users; Intelligent informal querying; Linguistic foundation of information and knowledge; Fuzzy linguistic models; Philosophical and linguistic foundations of conceptual models. 4. Cross-cultural communication and social computing: Cross-cultural support systems; Integration, evolution and migration of systems; Collaborative societies; Multicultural web-based software systems; Intercultural collaboration and support systems; Social computing, behavioral modeling and prediction. 5. Environmental modelling and engineering: Environmental information systems (architecture); Spatial, temporal and observational information systems; Large-scale environmental systems; Collaborative knowledge base systems; Agent concepts and conceptualisation; Hazard prediction, prevention and steering systems. 6. Multimedia data modelling and systems: Modelling multimedia information and knowledge; Contentbased multimedia data management; Content-based multimedia retrieval; Privacy and context enhancing technologies; Semantics and pragmatics of multimedia data; Metadata for multimedia information systems. Overall we received 56 submissions. After careful evaluation, 16 papers have been selected as long paper, 17 papers as short papers, 5 papers as position papers, and 3 papers for presentation of perspective challenges. We thank all colleagues for their support of this issue of the EJC conference, especially the program committee, the organising committee, and the programme coordination team. The long and the short papers presented in the conference are revised after the conference and published in the Series of “Frontiers in Artificial Intelligence” by IOS Press (Amsterdam). The books “Information Modelling and Knowledge Bases” are edited by the Editing Committee of the conference. We believe that the conference will be productive and fruitful in the advance of research and application of information modelling and knowledge bases. Bernhard Thalheim Hannu Jaakkola Yasushi Kiyok

Neural patterns of hippocampus and amygdala supporting memory over long timespans

Episodic memory is an imperfect record of events arranged in time and space. When dealing with the storage of memories, the brain is faced with a predicament: it must retain an acceptably faithful facsimile of transpired events while simultaneously permitting inevitable modifications to accommodate learning new information. In this thesis, I first review contemporary theories of how memories can be stored in a neural substrate within the hippocampus, particularly in regards to how they can be arranged in time. Next, using in vivo calcium imaging, I detail how hippocampal “time cell” sequences could support encoding of behavioral events along multiple temporal dimensions. In this study, I trained mice to run in place on a treadmill, thereby measuring single-cell activity in CA1 as a function of time. Neurons in CA1 formed sequences, each cell firing one after another as if forming a scaffold upon which memories can be laid. These sequences were relatively well-preserved over a period of four days, satisfying the first requirement that information must be stored for a memory to persist. Additionally, these sequences also changed over time, which may be revealing a mechanism for how memories can change over time to assimilate new information. In the next experiment, I describe a collaborative project where we used immunohistochemistry, optogenetics, and calcium imaging to investigate the long-term dynamics of a fear memory. After mice initially associated a context with an aversive stimulus, they were placed in the same context over two days where they gradually relearned that the context was harmless. This produced molecular and neurophysiological signatures consistent with memory modification. However, after re-triggering fear, mice reverted to fearful expression with commensurate neural correlates. Using optogenetics, these behaviors could also be reliably suppressed. Finally, I conclude by synthesizing these findings with hippocampal literature on sequence formation and consolidation by proposing a holistic view of how these features can support episodic memory

EDIT: an Educational Design Intelligence Tool for supporting design decisions

Designing for learning is a complex task and considered one of the most fundamental activities of teaching practitioners. A well-balanced teaching system ensures that all aspects of teaching, from the intended learning outcomes, the teaching and learning activities used, and the assessment tasks are all associated and aligned to each other (Biggs, 1996). This guarantees appropriate and therefore effective student engagement. The design and promotion of constructively aligned teaching practices has been supported to some degree by the development of software tools that attempt to support teaching practitioners in the design process and assist them in the development of more informed design decisions. Despite the potential of the existing tools, these tools have several limitations in respect of the support and guidance provided and cannot be adapted according to how the design pattern works in practice. Therefore; there is a real need to incorporate an intelligent metric system that enables intelligent design decisions to be made not only theoretically according to pedagogical theories but also practically based on good design practices according to high levels of satisfaction scores. To overcome the limitations of existing design tools, this research explores machine learning techniques; in particular artificial neural networks as an innovative approach for building an Educational Intelligence Design Tool EDIT that supports teaching practitioners to measure, align, and edit their teaching designs based on good design practices and on the pedagogic theory of constructive alignment. Student satisfaction scores are utilized as indicators of good design practice to identify meaningful alignment ranges for the main components of Tepper's metric (2006). It is suggested that modules designed within those ranges will be well-formed and constructively aligned and potentially yield higher student satisfaction. On this basis, the research had developed a substantial module design database with 519 design patterns spanning 476 modules from the STEM discipline. This is considered the first substantial database compared to the state-of-the-art Learning Design Support Environment (LDSE)(Laurillard, 2011), which includes 122 design patterns available. In order to have a neural-based framework for EDIT, a neural auto-encoder was incorporated to act as an auto-associative memory that learns on the basis of exposure to sets of 'good' design patterns. 519 generated design patterns were coded as input criteria and introduced to the designed neural network with feed-forward multilayer perceptron architecture using the IV hyperbolic tangent function and back-propagation training algorithm for learning the desired task. After successful training (88%), the testing phase was followed by presenting 102 new patterns (associated with low student satisfaction) to the network where higher pattern errors were generated suggesting substantial design changes to input patterns had been generated by the network. The findings of the research are significant in showing the degree of changes for the test patterns (before) and (after) and evaluating the relationships between the core features of module designs and overall student satisfaction. T-test analysis results show statistically significant differences in the test set (before) and (after) in case of the alignment score between learning outcomes and learning objectives (V1) and the alignment score between learning objectives and teaching activities (V2), whereas no statistically significant difference is seen in the alignment score between learning outcomes and assessment tasks (V3). The network gives an average improvement of 0.9, 1.5, and 0.5 in the alignment scores of V1, V2, and V3, respectively. This resulted in increasing the average of satisfaction scores from 3.3 to 3.8. Accordingly, positive correlation with different degrees between student satisfaction and the alignment scores were suggested as a result of applying the network proposal changes. EDIT, with its data‐orientated and adaptive approach to design, reveals orthodox practices whilst revealing some unexpected incongruity between alignment theory and design practice. For example, as expected, increasing the amount of questioning, interaction and group‐based activity effects higher levels of student satisfaction even though misalignment may be present. However, the model is relatively ambivalent towards the alignment of learning outcomes and learning objectives suggesting there is some confusion between practitioners as to how these are related. Also, this confusion appears to persist when defining session learning objectives for different types of teaching, learning and assessment tasks in that the activities themselves appear to be at a higher cognitive level according to Bloom's Taxonomy than the respective learning objectives (resulting in positive misalignment)

Physics based supervised and unsupervised learning of graph structure

Graphs are central tools to aid our understanding of biological, physical, and social systems. Graphs also play a key role in representing and understanding the visual world around us, 3D-shapes and 2D-images alike. In this dissertation, I propose the use of physical or natural phenomenon to understand graph structure. I investigate four phenomenon or laws in nature: (1) Brownian motion, (2) Gauss\u27s law, (3) feedback loops, and (3) neural synapses, to discover patterns in graphs

Research Methods for the Digital Humanities

In holistic Digital Humanities studies of information infrastructure, we cannot rely solely on the selection of any given techniques from various disciplines. In addition to selecting our research methods pragmatically, for their relative efficacy at answering a part of a research question, we must also attend to the way in which those methods complement or contradict one another. In my study on West African network backbone infrastructure, I use the tools of different humanities, social-sciences, and computer science disciplines depending not only on the type of information that they help glean, but also on how they can build upon one another as I move through the phases of the study. Just as the architecture of information infrastructure includes discrete “layers” of machines, processes, human activity, and concepts, so too does the study of that architecture allow for multiple layers of abstraction and assumption, each a useful part of a unified, interdisciplinary approach

Quantitative Differences in the Conversational Performance of People with Severe Expressive Aphasia Using Three Types of Visual Screen Displays on Speech Generating Devices

This multiple single-subject research study measured quantitative differences in communication success, communicator roles and act functions during dyadic conversational interactions between six people with severe aphasia and their peer communication partners across three conditions involving a type of augmentative communication intervention, speech generating devices (SGDs). Researchers assessed these variables across four conditions involving the message display of the SGD: no display (Condition A), visual scenes (contextual photographic) display (Condition B), Traditional Grid Display (Condition C), while participants engage in conversational story telling. This study is important because technology is currently being developed to assist people with aphasia to access messages stored on an SGD by activating photographic representations that access a set of spoken messages that are related to the photo. This contrasts with a more traditional method of representing messages, in which decontextual line drawings associated with individual concepts are displayed on the screen. Results from this study indicate that interactions between peer communication partners and people with aphasia can and do benefit from external, symbolic representation of messages on AAC devices. However, an unexpected finding was that given too much contextual information as with visual scenes, peer communication partners can deduce the content and context of the story, thereby being more apt to dominate the conversation than they are with no display