Network constraints on learnability of probabilistic motor sequences

Human learners are adept at grasping the complex relationships underlying incoming sequential input. In the present work, we formalize complex relationships as graph structures derived from temporal associations in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties inherent to those graph structures. Participants performed a probabilistic motor sequence task in which the order of button presses was determined by the traversal of graphs with modular, lattice-like, or random organization. Graph nodes each represented a unique button press and edges represented a transition between button presses. Results indicate that learning, indexed here by participants' response times, was strongly mediated by the graph's meso-scale organization, with modular graphs being associated with shorter response times than random and lattice graphs. Moreover, variations in a node's number of connections (degree) and a node's role in mediating long-distance communication (betweenness centrality) impacted graph learning, even after accounting for level of practice on that node. These results demonstrate that the graph architecture underlying temporal sequences of stimuli fundamentally constrains learning, and moreover that tools from network science provide a valuable framework for assessing how learners encode complex, temporally structured information.Comment: 29 pages, 4 figure

Controlling Complex Systems Dynamics without Prior Model

International audienceControlling complex systems imposes to deal with high dynamics, non-linearity and multiple interdependencies. To handle these dif¿culties we can either build analytic models of the process to control, or enable the controller to learn how the process behaves. Adaptive Multi-Agent Systems (AMAS) are able to learn and adapt themselves to their environment thanks to the cooperative self-organization of their agents. A change in the organization of the agents results in a change of the emergent function. Thus we assume that AMAS are a good alternative for complex systems control, reuniting learning, adaptivity, robustness and genericity. The problem of control leads to a speci¿c architecture presented in this paper

A Multi-Agent Architecture for An Intelligent Web-Based Educational System

An intelligent educational system must constitute an adaptive system built on multi-agent system architecture. The multi-agent architecture component provides self-organization, self-direction, and other control functionalities that are crucially important for an educational system. On the other hand, the adaptiveness of the system is necessary to provide customization, diversification, and interactional functionalities. Therefore, an educational system architecture that integrates multi-agent functionality [50] with adaptiveness can offer the learner the required independent learning experience. An educational system architecture is a complex structure with an intricate hierarchal organization where the functional components of the system undergo sophisticated and unpredictable internal interactions to perform its function. Hence, the system architecture must constitute adaptive and autonomous agents differentiated according to their functions, called multi-agent systems (MASs). The research paper proposes an adaptive hierarchal multi-agent educational system (AHMAES) [51] as an alternative to the traditional education delivery method. The document explains the various architectural characteristics of an adaptive multi-agent educational system and critically analyzes the system’s factors for software quality attributes

Integrating reinforcement-learning, accumulator models and motor-primitives to study action selection and researching in monkeys.

This paper presents a model of brain systems underlying reaching in monkeys based on the idea that complex behaviors are built on the basis of a repertoire of motor primitives organized around specific goals (in this case, arm\u27s postures). The architecture of the system is based on an actor-critic reinforcement-learning model, enhanced with an accumulator model for action selection, capable of selecting sensorimotor primitives so as to accomplish a discrimination reaching task that has been used in physiological studies of monkeys\u27 premotor cortex. The results show that the proposed architecture is a first important step towards the construction of a biologically plausible integrated motor-primitive based model of the hierarchical organization of mammals\u27 sensorimotor systems

MEMSORN: Self-organization of an inhomogeneous memristive hardware for sequence learning

Learning is a fundamental component for creating intelligent machines. Biological intelligence orchestrates synaptic and neuronal learning at multiple time-scales to self-organize populations of neurons for solving complex tasks. Inspired by this, we design and experimentally demonstrate an adaptive hardware architecture Memristive Self-organizing Spiking Recurrent Neural Network (MEMSORN). MEMSORN incorporates resistive memory (RRAM) in its synapses and neurons which configure their state based on Hebbian and Homeostatic plasticity respectively. For the first time, we derive these plasticity rules directly from the statistical measurements of our fabricated RRAM-based neurons and synapses. These “technologically plausible” learning rules exploit the intrinsic variability of the devices and improve the accuracy of the network on a sequence learning task by 30%. Finally, we compare the performance of MEMSORN to a fully-randomly set-up recurrent network on the same task, showing that self-organization improves the accuracy by more than 15%. This work demonstrates the importance of the device-circuit-algorithm co-design approach for implementing brain-inspired computing hardware

Self-organization of an inhomogeneous memristive hardware for sequence learning

Learning is a fundamental component of creating intelligent machines. Biological intelligence orchestrates synaptic and neuronal learning at multiple time scales to self-organize populations of neurons for solving complex tasks. Inspired by this, we design and experimentally demonstrate an adaptive hardware architecture Memristive Self-organizing Spiking Recurrent Neural Network (MEMSORN). MEMSORN incorporates resistive memory (RRAM) in its synapses and neurons which configure their state based on Hebbian and Homeostatic plasticity respectively. For the first time, we derive these plasticity rules directly from the statistical measurements of our fabricated RRAM-based neurons and synapses. These "technologically plausible” learning rules exploit the intrinsic variability of the devices and improve the accuracy of the network on a sequence learning task by 30%. Finally, we compare the performance of MEMSORN to a fully-randomly-set-up spiking recurrent network on the same task, showing that self-organization improves the accuracy by more than 15%. This work demonstrates the importance of the device-circuit-algorithm co-design approach for implementing brain-inspired computing hardware

Preface to the Special Issue on Immersive Environments: Challenges, Research and New Developments

Immersion could be defined as a subjective impression of participating in a comprehensive, realistic experience [1]. However, immersion should not be treated as a unique property, as it is achieved from a complex interaction of representational fidelity and learner interaction, holding a dependency on other aspects of the environment [2, 3]. The use of immersive environments which create a feeling of ?presence? naturally allows for more complex social interactions and designed experiences [4]. A particular use of these technologies is in educational settings, where they can enhance learning experiences, foster participation, collaboration, creativity and engagement; creating huge opportunities for integration and research. Effective immersive learning experiences can be created with multiple media using myriad techniques and employing a wealth of knowledge that spans many disciplines. This includes but is not limited to computer science, user experience and media design, the learning sciences, architecture, game development, artificial intelligence, biology, medicine, and thousands of disciplinary and occupational content areas wherein immersive learning and training may be relevant. The Immersive Learning Research Network (iLRN) is ?an international organization of developers, educators, and research professionals collaborating to develop the scientific, technical, and applied potential of immersive learning? [5]. The vision of the network is to develop a comprehensive research and outreach agenda that encompasses the breadth and scope of learning potentialities, affordances and challenges of immersive learning environments. The first international conference iLRN 2015 held in Prague, Czech Republic, attracted a number of high-quality contributions. As a follow-up, this special issue was organised as an open call to seek a wider set of contributions from the research community, including extended versions of iLRN 2015 best papers

Understanding Organizational Traps in Implementing Service-Oriented Architecture

One of the major objectives of adopting service-oriented architecture (SOA) is to enhance the IS agility of organizations and improve IT-business alignment. In practice the contradictory experiences about SOA implementation turn out to be a paradox: why many organizations failed to meet their expectations about SOA implementation efforts, while others succeeded? Contrast to prior research on SOA, this study adopts the process perspective and provides plausible theoretical explanations for the SOA implementation paradox. Specifically, the study uses multiple case-study methods to develop a system dynamics model which highlights the feedback loops and time delay during the SOA implementation process. The results reveal the dynamic characteristics of learning curve of SOA implementation and two organizational traps (technology learning trap and implementation effectiveness trap) associated with SOA implementation. Technology learning trap refers to the situation that the less learning in using the technology, the more difficult and complex the technology is perceived. Implementation effectiveness trap refers to the situation in which the organization may misperceive the inappropriateness of SOA when SOA implementation is temporally less effective and perceived benefits of SOA are delayed. The theory of the organizational traps can be generalized to a broad context of innovative IS implementation. Further, the theoretical causes of the traps are investigated. Finally, the research implication of this study and connections with existing literature on IS and organization are discussed