An Event based Prediction Suffix Tree

This article introduces the Event based Prediction Suffix Tree (EPST), a biologically inspired, event-based prediction algorithm. The EPST learns a model online based on the statistics of an event based input and can make predictions over multiple overlapping patterns. The EPST uses a representation specific to event based data, defined as a portion of the power set of event subsequences within a short context window. It is explainable, and possesses many promising properties such as fault tolerance, resistance to event noise, as well as the capability for one-shot learning. The computational features of the EPST are examined in a synthetic data prediction task with additive event noise, event jitter, and dropout. The resulting algorithm outputs predicted projections for the near term future of the signal, which may be applied to tasks such as event based anomaly detection or pattern recognition

Analogue VLSI building blocks for an electronic auditory pathway

This thesis gives an overview of my work over the last four years on the development of analogue electronic building blocks for the auditory pathway, and their application to some models of processing in the auditory brainstem. The anatomy and physiology of the human ear is presented, and is decomposed into three key elements, i.e., the basilar membrane 'band-pass' filters, the transduction into a neural signal performed by the inner hair cells, and the mechanical feedback introduced by the outer hair cells. An electronic model for the first two of these elements is presented and measurement results are shown to compare these circuits with their biological counterparts. The remaining part of the human auditory pathway consists of several groups of different types of spiking neurons. Since the main part of signal processing in the auditory pathway is performed by these different types of spiking neurons, a good spiking neuron model is essential. The electrophysiology and anatomy needed to understand the basics of the spiking behaviour of these neurons is presented. If we want to model large groups of spiking neurons, the neuron circuit also needs to be small. I propose a circuit that is simple and small, yet capable of emulating several types of neurons found in the Cochlear Nucleus, as shown by chip measurements. With these electronic building blocks I can start to model neural architectures in the brain that extract certain signal characteristics, and these models will operate in real time. Although only few of such architectures have been identified to date, once the types of neurons in these brain circuits. and their inter-connectivity is known, it is then fairly straightforward to create an analogue VLSI model. I present two examples, both based on periodicity detection, since this is a domain where spike based operation seems especially useful. The first example uses synchronized activity on auditory nerve fibres from two positions along the basilar membrane to obtain a high frequency selectivity and a representation of the sound which is independent of intensity. This model is completely hypothetical, since no evidence has been found in the brain for its existence, but it provides an excellent introduction into the periodicity detecting power of spike based computation. The second example is a model of amplitude modulation sensitivity in the inferior colliculus. This model can be used for example to extract the modulation frequency of an amplitude modulated sound, or to extract the fundamental frequency of a harmonic complex. These sounds are of special interest because many natural sounds such as animal calls or speech fail into this category. This model has a much stronger biological basis, since neurons have been found in the inferior colliculus that react to amplitude modulation in a similar way as the model

Neuromorphic Audio–Visual Sensor Fusion on a Sound-Localizing Robot

This paper presents the first robotic system featuring audio–visual (AV) sensor fusion with neuromorphic sensors. We combine a pair of silicon cochleae and a silicon retina on a robotic platform to allow the robot to learn sound localization through self motion and visual feedback, using an adaptive ITD-based sound localization algorithm. After training, the robot can localize sound sources (white or pink noise) in a reverberant environment with an RMS error of 4–5° in azimuth. We also investigate the AV source binding problem and an experiment is conducted to test the effectiveness of matching an audio event with a corresponding visual event based on their onset time. Despite the simplicity of this method and a large number of false visual events in the background, a correct match can be made 75% of the time during the experiment

Low-power transcutaneous current stimulator for wearable applications

BACKGROUND: Peripheral neuropathic desensitization associated with aging, diabetes, alcoholism and HIV/AIDS, affects tens of millions of people worldwide, and there is little or no treatment available to improve sensory function. Recent studies that apply imperceptible continuous vibration or electrical stimulation have shown promise in improving sensitivity in both diseased and healthy participants. This class of interventions only has an effect during application, necessitating the design of a wearable device for everyday use. We present a circuit that allows for a low-power, low-cost and small form factor implementation of a current stimulator for the continuous application of subthreshold currents. RESULTS: This circuit acts as a voltage-to-current converter and has been tested to drive + 1 to - 1 mA into a 60 k[Formula: see text] load from DC to 1 kHz. Driving a 60 k[Formula: see text] load with a 2 mA peak-to-peak 1 kHz sinusoid, the circuit draws less than 21 mA from a 9 V source. The minimum operating current of the circuit is less than 12 mA. Voltage compliance is ± 60 V with just 1.02 mA drawn by the high voltage current drive circuitry. The circuit was implemented as a compact 46 mm × 21 mm two-layer PCB highlighting its potential for use in a body-worn device. CONCLUSIONS: No design to the best of our knowledge presents comparably low quiescent power with such high voltage compliance. This makes the design uniquely appropriate for low-power transcutaneous current stimulation in wearable applications. Further development of driving and instrumentation circuitry is recommended

A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems

It has been widely recognized that closed-loop neuroprosthetic systems achieve more favourable outcomes for users then equivalent open-loop devices. Improved performance of tasks, better usability and greater embodiment have all been reported in systems utilizing some form of feedback. However the interdisciplinary work on neuroprosthetic systems can lead to miscommunication due to similarities in well established nomenclature in different fields. Here we present a review of control strategies in existing experimental, investigational and clinical neuroprosthetic systems in order to establish a baseline and promote a common understanding of different feedback modes and closed loop controllers. The first section provides a brief discussion of feedback control and control theory. The second section reviews the control strategies of recent Brain Machine Interfaces, neuromodulatory implants, neuroprosthetic systems and assistive neurorobotic devices. The final section examines the different approaches to feedback in current neuroprosthetic and neurorobotic systems

AER Auditory Filtering and CPG for Robot Control

Address-Event-Representation (AER) is a communication protocol for transferring asynchronous events between VLSI chips, originally developed for bio-inspired processing systems (for example, image processing). The event information in an AER system is transferred using a highspeed digital parallel bus. This paper presents an experiment using AER for sensing, processing and finally actuating a Robot. The AER output of a silicon cochlea is processed by an AER filter implemented on a FPGA to produce rhythmic walking in a humanoid robot (Redbot). We have implemented both the AER rhythm detector and the Central Pattern Generator (CPG) on a Spartan II FPGA which is part of a USB-AER platform developed by some of the authors.Commission of the European Communities IST-2001-34124 (CAVIAR)Comisión Interministerial de Ciencia y Tecnología TIC-2003-08164-C03-0

Non-invasive Electronic Biosensor Circuits and Systems

An aging population has lead to increased demand for health-care and an interest in moving health care services from the hospital to the home to reduce the burden on society. One enabling technology is comfortable monitoring and sensing of bio-signals. Sensors can be embedded in objects that people interact with daily such as a computer, chair, bed, toilet, car, telephone or any portable personal electronic device. Moreover, the relatively recent and wide availability of microelectronics that provide the capabilities of embedded software, open access wireless protocols and long battery life has led many research groups to develop wearable, wireless bio-sensor systems that are worn on the body and integrated into clothing. These systems are capable of interaction with other devices that are nowadays commonly in our possession such as a mobile phone, laptop, PDA or smart multifunctional MP3 player. The development of systems for wireless bio-medical long term monitoring is leading to personal monitoring, not just for medical reasons, but also for enhancing personal awareness and monitoring self-performance, as with sports-monitoring for athletes. These developments also provide a foundation for the Brain Computer Interface (BCI) that aims to directly monitor brain signals in order to control or manipulate external objects. This provides a new communication channel to the brain that does not require activation of muscles and nerves. This innovative and exciting research field is in need of reliable and easy to use long term recording systems (EEG). In particular we highlight the development and broad applications of our own circuits for wearable bio-potential sensor systems enabled by the use of an amplifier circuit with sufficiently high impedance to allow the use of passive dry electrodes which overcome the significant barrier of gel based contacts