This paper demonstrates that porous 'sandwich' structures can provide an effective route for the design and optimisation of piezoelectric materials for energy harvesting applications, which is becoming an increasingly important technology for self -powered wireless networks and sensors. A numerical model is presented that accounts for the complex poling distribution throughout a layered ferroelectric and helps to develop a detailed understanding of the relationship between the geometry of the porous structure and the poling characteristics of porous ferroelectric materials, with good agreement with experimental data. Novel layered barium titanate ceramics were fabricated whereby dense outer layers surround a highly porous sandwich layer, and for specific layer geometries an unusual condition was achieved where the longitudinal piezoelectric strain coefficients (d(33)) increased as the thickness of the porous layer and total porosity level of the layered structure increased. The permittivity (epsilon(T)(33)) decreased with increasing thickness and increasing porosity level of the porous layer due to the presence of a low permittivity air phase. These two factors in combination led to an increase in the longitudinal energy harvesting figure of merit, d(33)(2)/epsilon(T)(33), for the layered structure, with a maximum of 3.74 pm(2)/N when the relative thickness of the porous layer was 0.52 and the porosity within this layer was similar to 60 vol%. This harvesting performance of these novel structures is much larger than both dense barium titanate (1.40 pm(2)/N) and barium titanate with randomly distributed porosity at the same 60% volume fraction (2.75 pm(2)/N). (C) 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Recordings made directly from the nervous system are a key tool in experimental electrophysiology and the development of bioelectronic medicines. Analysis of these recordings involves the identification of signals from individual neurons, a process known as spike sorting. A critical and limiting feature of spike sorting is the need to align individual spikes in time. However, electrophysiological recordings are made in extremely noisy environments that seriously limit the performance of the spike-alignment process. We present a new centroid-based method and demonstrate its effectiveness using deterministic models of nerve signals. We show that spike alignment in the presence of noise is possible with a 30 dB reduction in minimum SNR compared with the conventional methods. We present a mathematical analysis of the centroid method, characterizing its fundamental operation and performance. Furthermore, we show that the centroid method lends itself particularly well to hardware realization, and we present results from a low-power implementation that operates on an FPGA, consuming ten times less power than conventional techniques - an important property for implanted devices. Our centroid method enables the accurate alignment of spikes in sub-0 dB SNR recordings and has the potential to enable the analysis of spikes in a wider range of environments than has been previously possible. Our method thus has the potential to influence significantly the design of electrophysiological recording systems in the future.

A series of 10 lectures on various aspects of Cognitive Linguistics as these relate to matters of language teaching and learning. Topics addressed include the role of categorization, the nature of rules, the encyclopaedic scope of semantics, spatial expressions, metaphor and metonymy, nouns and nominals, tense and aspect, and the theoretical status of the phoneme.

The perception-action cycle is the circular flow of information that takes place between the organism and its environment in the course of a sensory-guided sequence of behaviour towards a goal. Each action causes changes in the environment that are analyzed bottom-up through the perceptual hierarchy and lead to the processing of further action, top-down through the executive hierarchy, toward motor effectors. These actions cause new changes that are analyzed and lead to new action, and so the cycle continues. The Perception-action cycle: Models, architectures and hardware book provides focused and easily accessible reviews of various aspects of the perception-action cycle. It is an unparalleled resource of information that will be an invaluable companion to anyone in constructing and developing models, algorithms and hardware implementations of autonomous machines empowered with cognitive capabilities. The book is divided into three main parts. In the first part, leading computational neuroscientists present brain-inspired models of perception, attention, cognitive control, decision making, conflict resolution and monitoring, knowledge representation and reasoning, learning and memory, planning and action, and consciousness grounded on experimental data. In the second part, architectures, algorithms, and systems with cognitive capabilities and minimal guidance from the brain, are discussed. These architectures, algorithms, and systems are inspired from the areas of cognitive science, computer vision, robotics, information theory, machine learning, computer agents and artificial intelligence. In the third part, the analysis, design and implementation of hardware systems with robust cognitive abilities from the areas of mechatronics, sensing technology, sensor fusion, smart sensor networks, control rules, controllability, stability, model/knowledge representation, and reasoning are discussed.

This both accessible and exhaustive book will help to improve modeling of attention and to inspire innovations in industry. It introduces the study of attention and focuses on attention modeling, addr

This paper presents results from a pilot experiment in which the technique of velocity selective recording (VSR) was used to identify naturally occurring electroneurogram (ENG) signals within the intact nerve of a rat. Signals were acquired using a set of electrodes placed along the length of the nerve, formed from simple wire hooks. This basic form of recording has already been applied in-vivo to the analysis of electrically excited compound action potentials (CAPs) in both pig and frog, however, this method has never before been used to identify naturally occurring neural signals. Results in this paper highlight challenges which must be overcome in order for the transition to be made from electrically evoked potentials to naturally occurring signals.

This paper describes an integrated circuit (ASIC) implementing the core functionality for the technique of velocity selective recording (VSR) of ENG in which multiple neural signals are matched and summed to identify excited axon populations in terms of velocity. Delay matching is achieved using multiple sample-and-hold blocks arranged to realize a matching range between 10-100 μs for eight input channels (80 μs-800 μs total delay) as well as signal summation. The system laid out in 0.35 μm CMOS technology occupies 0.78 mm 2 core area and consumes 30 μW of power from a 3 V supply. A buffer driver stage is added which consumes 150 μW. Simulated results are provided to confirm that the velocity spectrum is successfully extracted using the proposed system.

This paper describes improvements to the theory of velocity selective recording (VSR) of neural signals. Action potentials are classified and differentiated based on their conduction velocities which can be calculated from concurrent neural recordings taking at different locations on a nerve. Existing work has focussed primarily on electrically evoked compound action potentials (CAPs) where only a single evoked response per velocity is recorded. This paper extends the theory of VSR to naturally occurring neural signals recorded from rat and attempts to identify the level of activity (firing rates) within particular velocity ranges.

Multi-electrode cuffs (MECs) have been proposed as a means for extracting additional information about the velocity and direction of nerve signals from multi-electrode recordings. This paper discusses certain aspects of the implementation of a system for velocity selective recording (VSR) where multiple neural signals are matched and summed to identify excited axon populations in terms of velocity. The approach outlined in the paper involves the replacement of the digital signal processing stages of a standard delay-matched VSR system with analogue switched-capacitor (SC) delay lines which promises significant savings in both size and power consumption. The system specifications are derived and two circuits, each composed of low-noise preamplifiers connecting to a 2nd rank SC gain stage, are evaluated. One of the systems provides a single-ended SC stage whereas the other system is fully differential. Both approaches are shown to provide the low-noise, low-power operation, practically identical channel gains and sample delay range required for VSR. Measured results obtained from chips fabricated in 0.8 μ m CMOS technology are reported.

In this paper, we describe a brief survey of observational learning, with particular emphasis on how this could impact on the use of observational learning in robots. We present a set of simulations of a neural model which fits recent experimental data and such that it leads to the basic idea that observational learning uses simulations of internal models to represent the observed activity, so allowing for efficient learning of the observed actions. We conclude with a set of recommendations as to how observational learning might most efficiently be used in developing and training robots for their variety of tasks.

We show aspects of brain processing on how visual perception, recognition, attention, cognitive control, value attribution, decision-making, affordances and action can be melded together in a coherent manner in a cognitive control architecture of the perception–action cycle for visually guided reaching and grasping of objects by a robot or an agent. The work is based on the notion that separate visuomotor channels are activated in parallel by specific visual inputs and are continuously modulated by attention and reward, which control a robot’s/agent’s action repertoire. The suggested visual apparatus allows the robot/agent to recognize both the object’s shape and location, extract affordances and formulate motor plans for reaching and grasping. A focus-of-attention signal plays an instrumental role in selecting the correct object in its corresponding location as well as selects the most appropriate arm reaching and hand grasping configuration from a list of other configurations based on the success of previous experiences. The cognitive control architecture consists of a number of neurocomputational mechanisms heavily supported by experimental brain evidence: spatial saliency, object selectivity, invariance to object transformations, focus of attention, resonance, motor priming, spatial-to-joint direction transformation and volitional scaling of movement.