Deficits in sensory prediction are related to delusional ideation in healthy individuals.

Motor control strongly relies on neural processes that predict the sensory consequences of self-generated actions. Previous research has demonstrated deficits in such sensory-predictive processes in schizophrenic patients and these low-level deficits are thought to contribute to the emergence of delusions of control. Here, we examined the extent to which individual differences in sensory prediction are associated with a tendency towards delusional ideation in healthy participants. We used a force-matching task to quantify sensory-predictive processes, and administered questionnaires to assess schizotypy and delusion-like thinking. Individuals with higher levels of delusional ideation showed more accurate force matching suggesting that such thinking is associated with a reduced tendency to predict and attenuate the sensory consequences of self-generated actions. These results suggest that deficits in sensory prediction in schizophrenia are not simply consequences of the deluded state and are not related to neuroleptic medication. Rather they appear to be stable, trait-like characteristics of an individual, a finding that has important implications for our understanding of the neurocognitive basis of delusions.

Integrating vertically aligned carbon nanotubes on micromechanical structures

The integration of high yield, uniform and preferential growth of vertically aligned carbon nanotubes (VACNT) on low stress micromechanical structures was analyzed. A combination of electron-beam crosslinked surface micromachining and direct current plasma enhanced chemical vapor deposition of electric field aligned carbon nanotubes was used for the analysis. The selective placement of high yield and uniform VACNTs on a partially suspended Ni/SiO2/Ti microstructure was also demonstrated.

Optimum design of 1.4KV trench IGBTs - The next generation of high power switching devices

The Trench Insulated Gate Bipolar Transistor (IGBT) is the most promising structure for the next generation of power semiconductor devices with wide applications ranging from motor control (1-4 kV) to HVDC (6.5 kV). Here we present for the first time an optimum design of a 1.4kV Trench IGBT using a new, fully integrated optimisation system comprising process and device simulators and the RSM optimiser. The use of this new TCAD system has contributed largely to realizing devices with characteristics far superior to the previous DMOS generation of IGBTs. Full experimental results on 1.4kV Trench IGBTs which are in excellent agreement with the TCAD predictions are reported.

Modeling the development of pronunciation in infant speech acquisition.

Pronunciation is an important part of speech acquisition, but little attention has been given to the mechanism or mechanisms by which it develops. Speech sound qualities, for example, have just been assumed to develop by simple imitation. In most accounts this is then assumed to be by acoustic matching, with the infant comparing his output to that of his caregiver. There are theoretical and empirical problems with both of these assumptions, and we present a computational model- Elija-that does not learn to pronounce speech sounds this way. Elija starts by exploring the sound making capabilities of his vocal apparatus. Then he uses the natural responses he gets from a caregiver to learn equivalence relations between his vocal actions and his caregiver's speech. We show that Elija progresses from a babbling stage to learning the names of objects. This demonstrates the viability of a non-imitative mechanism in learning to pronounce.

Vertically aligned carbon nanotubes growth using self-assembled Ni nanoparticles produced by ion implantation

An alternative method for seeding catalyst nanoparticles for carbon nanotubes and nanowires growth is presented. Ni nanoparticles are formed inside a 450 nm SiO2 film on (100) Si wafers through the implantation of Ni ions at fluences of 7.5×1015 and 1.7×1016 ions.cm-2 and post-annealing treatments at 700, 900 and 1100°C. After exposed to the surface by HF dip etching, the Ni nanoparticles are used as catalyst for the growth of vertically aligned carbon nanotubes by direct current plasma enhanced chemical vapor deposition. © 2007 Materials Research Society.

Increasing the open-circuit voltage of photoprotein-based photoelectrochemical cells by manipulation of the vacuum potential of the electrolytes.

The innately highly efficient light-powered separation of charge that underpins natural photosynthesis can be exploited for applications in photoelectrochemistry by coupling nanoscale protein photoreaction centers to man-made electrodes. Planar photoelectrochemical cells employing purple bacterial reaction centers have been constructed that produce a direct current under continuous illumination and an alternating current in response to discontinuous illumination. The present work explored the basis of the open-circuit voltage (V(OC)) produced by such cells with reaction center/antenna (RC-LH1) proteins as the photovoltaic component. It was established that an up to ~30-fold increase in V(OC) could be achieved by simple manipulation of the electrolyte connecting the protein to the counter electrode, with an approximately linear relationship being observed between the vacuum potential of the electrolyte and the resulting V(OC). We conclude that the V(OC) of such a cell is dependent on the potential difference between the electrolyte and the photo-oxidized bacteriochlorophylls in the reaction center. The steady-state short-circuit current (J(SC)) obtained under continuous illumination also varied with different electrolytes by a factor of ~6-fold. The findings demonstrate a simple way to boost the voltage output of such protein-based cells into the hundreds of millivolts range typical of dye-sensitized and polymer-blend solar cells, while maintaining or improving the J(SC). Possible strategies for further increasing the V(OC) of such protein-based photoelectrochemical cells through protein engineering are discussed.

Ageing, Adaption and Accessibility: Time for the Inclusive Revolution!

Information and Communication Technology (ICT) is becoming increasingly central to many people’s lives, making it possible to be connected in any place at any time, be unceasingly and instantly informed, and benefit from greater economic and educational opportunities. With all the benefits afforded by these new-found capabilities, however, come potential drawbacks. A plethora of new PCs, laptops, tablets, smartphones, Bluetooth, the internet, Wi-Fi (the list goes on) expect us to know or be able to guess, what, where and when to connect, click, double-click, tap, flick, scroll, in order to realise these benefits, and to have the physical and cognitive capability to do all these things. One of the groups most affected by this increase in high-demand technology is older people. They do not understand and use technology in the same way that younger generations do, because they grew up in the simpler electro-mechanical era and embedded that particular model of the world in their minds. Any consequential difficulty in familiarising themselves with modern ICT and effectively applying it to their needs can also be exacerbated by age-related changes in vision, motor control and cognitive functioning. Such challenges lead to digital exclusion. Much has been written about this topic over the years, usually by academics from the area of inclusive product design. The issue is complex and it is fair to say that no one researcher has the whole picture. It is difficult to understand and adequately address the issue of digital exclusion among the older generation without looking across disciplines and at industry’s and government’s understanding, motivation and efforts toward resolving this important problem. To do otherwise is to risk misunderstanding the true impact that ICT has and could have on people’s lives across all generations. In this European year of Active Ageing and Solidarity between Generations and as the British government is moving forward with its Digital by Default initiative as part of a wider objective to make ICT accessible to as many people as possible by 2015, the Engineering Design Centre (EDC) at the University of Cambridge collaborated with BT to produce a book of thought pieces to address, and where appropriate redress, these important and long-standing issues. “Ageing, Adaption and Accessibility: Time for the Inclusive Revolution!” brings together opinions and insights from twenty one prominent thought leaders from government, industry and academia regarding the problems, opportunities and strategies for combating digital exclusion among senior citizens. The contributing experts were selected as individuals, rather than representatives of organisations, to provide the broadest possible range of perspectives. They are renowned in their respective fields and their opinions are formed not only from their own work, but also from the contributions of others in their area. Their views were elicited through conversations conducted by the editors of this book who then drafted the thought pieces to be edited and approved by the experts. We hope that this unique collection of thought pieces will give you a broader perspective on ageing, people’s adaption to the ever changing world of technology and insights into better ways of designing digital devices and services for the older population.

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

Robotics and neuroscience: A rhythmic interaction

At the crossing between motor control neuroscience and robotics system theory, the paper presents a rhythmic experiment that is amenable both to handy laboratory implementation and simple mathematical modeling. The experiment is based on an impact juggling task, requiring the coordination of two upper-limb effectors and some phase-locking with the trajectories of one or several juggled objects. We describe the experiment, its implementation and the mathematical model used for the analysis. Our underlying research focuses on the role of sensory feedback in rhythmic tasks. In a robotic implementation of our experiment, we study the minimum feedback that is required to achieve robust control. A limited source of feedback, measuring only the impact times, is shown to give promising results. A second field of investigation concerns the human behavior in the same impact juggling task. We study how a variation of the tempo induces a transition between two distinct control strategies with different sensory feedback requirements. Analogies and differences between the robotic and human behaviors are obviously of high relevance in such a flexible setup. © 2008 Elsevier Ltd. All rights reserved.

A flame surface density approach to large-eddy simulation of premixed turbulent combustion

The flame surface density approach to the modeling of premixed turbulent combustion is well established in the context of Reynolds-averaged simulations. For the future, it is necessary to consider large-eddy simulation (LES), which is likely to offer major advantages in terms of physical accuracy, particularly for unsteady combustion problems. LES relies on spatial filtering for the removal of unresolved phenomena whose characteristic length scales are smaller than the computational grid scale. Thus, there is a need for soundly based physical modeling at the subgrid scales. The aim of this paper is to explore the usefulness of the flame surface density concept as a basis for LES modeling of premixed turbulent combustion. A transport equation for the filtered flame surface density is presented, and models are proposed for unclosed terms. Comparison with Reynolds-averaged modeling is shown to reveal some interesting similarities and differences. These were exploited together with known physics and statistical results from experiment and from direct numerical stimulation in order to gain insight and refine the modeling. The model has been implemented in a combustion LES code together with standard models for scalar and momentum transport. Computational results were obtained for a simple three-dimensional flame propagation test problem, and the relative importance of contributing terms in the modeled equation for flame surface density was assessed. Straining and curvature are shown to have a major influence at both the resolved and subgrid levels.

Resonance based multi-gaited robot locomotion

In order to understand the underlying mechanisms of animals' agility, dexterity and efficiency in motor control, there has been an increasing interest in the study of gait patterns in biological and artificial legged systems. This paper presents a novel approach to the study of gait patterns which makes use of intrinsic mechanical dynamics of robotic systems. Each of these robots consists of a U-shape elastic beam and exploits free vibration to generate different gait patterns. We developed a conceptual model for these robots, and through simulation and real-world experiments, we show three distinct mechanisms for generating four different gait patterns in these robots. © 2012 IEEE.

Learning legged locomotion

Legged locomotion of biological systems can be viewed as a self-organizing process of highly complex system-environment interactions. Walking behavior is, for example, generated from the interactions between many mechanical components (e.g., physical interactions between feet and ground, skeletons and muscle-tendon systems), and distributed informational processes (e.g., sensory information processing, sensory-motor control in central nervous system, and reflexes) [21]. An interesting aspect of legged locomotion study lies in the fact that there are multiple levels of self-organization processes (at the levels of mechanical dynamics, sensory-motor control, and learning). Previously, the self-organization of mechanical dynamics was nicely demonstrated by the so-called Passive Dynamic Walkers (PDWs; [18]). The PDW is a purely mechanical structure consisting of body, thigh, and shank limbs that are connected by passive joints. When placed on a shallow slope, it exhibits natural bipedal walking dynamics by converting potential to kinetic energy without any actuation. An important contribution of these case studies is that, if designed properly, mechanical dynamics can generate a relatively complex locomotion dynamics, on the one hand, and the mechanical dynamics induces self-stability against small disturbances without any explicit control of motors, on the other. The basic principle of the mechanical self-stability appears to be fairly general that there are several different physics models that exhibit similar characteristics in different kinds of behaviors (e.g., hopping, running, and swimming; [2, 4, 9, 16, 19]), and a number of robotic platforms have been developed based on them [1, 8, 13, 22]. © 2009 Springer London.

Enlarging regions of stable running with segmented legs

In human and animal running spring-like leg behavior is found, and similar concepts have been demonstrated by various robotic systems in the past. In general, a spring-mass model provides self-stabilizing characteristics against external perturbations originated in leg-ground interactions and motor control. Although most of these systems made use of linear spring-like legs. The question addressed in this paper is the influence of leg segmentation (i.e. the use of rotational joint and two limb-segments) to the self-stability of running, as it appears to be a common design principle in nature. This paper shows that, with the leg segmentation, the system is able to perform self-stable running behavior in significantly broader ranges of running speed and control parameters (e.g. control of angle of attack at touchdown, and adjustment of spring stiffness) by exploiting a nonlinear relationship between leg force and leg compression. The concept is investigated by using a two-segment leg model and a robotic platform, which demonstrate the plausibility in the real world. ©2008 IEEE.

AI in locomotion: Challenges and perspectives of underactuated robots

This article discusses the issues of adaptive autonomous navigation as a challenge of artificial intelligence. We argue that, in order to enhance the dexterity and adaptivity in robot navigation, we need to take into account the decentralized mechanisms which exploit physical system-environment interactions. In this paper, by introducing a few underactuated locomotion systems, we explain (1) how mechanical body structures are related to motor control in locomotion behavior, (2) how a simple computational control process can generate complex locomotion behavior, and (3) how a motor control architecture can exploit the body dynamics through a learning process. Based on the case studies, we discuss the challenges and perspectives toward a new framework of adaptive robot control. © Springer-Verlag Berlin Heidelberg 2007.