Current PhD projects

» email: James.Wright@westernsydney.edu.au

James' research focuses on interfaces with the somatosensory system, and attempts to bridge the gap between the biological nervous system and the electronic device. Many challenges exist at the neural interface, but the potential benefits of successfully negotiating these will be techniques that can be used in the restoration of sensation to stroke patients, improved assistive technologies for the elderly and disabled and new methods for the teleoperation of remote devices. Concentrating on the peripheral nervous system, we are seeking to extract a control signal from the efferent pathway suitable for directing an assistive device or prosthetic, and to return feedback from the device via the afferent pathway. This research includes materials engineering of electrodes and interfaces, signal processing to decode and encode sensory and control information, neurobiology and anatomy of the peripheral nervous system,  and computer science and electrical engineering for the construction of the device.

Ying Xu 

» email: Ying.Xu@westernsydney.edu.au

Ying's project encompasses developing a neuromorphic integrated circuit (IC) and system to perform real-time auditory signal recognition and localization tasks simultaneously. This system will model the auditory periphery and mimic the neurobiological architecture present in the human nervous system. In this system, an auditory model will be built to convert input sound into frequency time auditory spectrogram. Furthermore, electronic neural systems for classifying auditory spectrograms and localizing the sound source will also be implemented.

Completed Projects:

Ram Singh (2012)

Ram's MEng project was to develop a real time simulator of cochlear filtering and auditory nerve activity.

Runchun's research project was to build neuromorphic VLSI circuits for spatio-temporal pattern recognition with spiking neurons and adaptive spike propagation delays, based on the concept of "polychronous" networks. He has designed an integrate and fire neuron, an axonal propagation delay circuit, a current synapse and associated circuits using analog VLSI.

One of the fundamental tasks underlying much of computer vision is the detection, tracking and recognition of visual features. It is an inherently difficult and challenging problem, and despite the advances in computational power, pixel resolution, and frame rates, even the state-of-the-art methods fall far short of the robustness, reliability and energy consumption of biological vision systems.

Silicon retinas, such as the Dynamic Vision Sensor (DVS) and Asynchronous Time-based Imaging Sensor (ATIS), attempt to replicate some of the benefits of biological retinas and provide a vastly different paradigm in which to sense and process the visual world. Tasks such as tracking and object recognition still require the identification and matching of local visual features, but the detection, extraction and recognition of features requires a fundamentally different approach, and the methods that are commonly applied to conventional imaging are not directly applicable.

This thesis explores methods to detect features in the spatio-temporal information from event-based vision sensors. The nature of features in such data is explored, and methods to determine and detect features are demonstrated. A framework for detecting, tracking, recognising and classifying features is developed and validated using real-world data and event-based variations of existing computer vision datasets and benchmarks.

The results presented in this thesis demonstrate the potential and efficacy of event-based systems. This work provides an in-depth analysis of different event-based methods for object recognition and classification and introduces two feature-based methods. Two learning systems, one event-based and the other iterative, were used to explore the nature and classification ability of these methods. The results demonstrate the viability of event-based classification and the importance and role of motion in event-based feature detection.

The motivation for project is idea that abstract, adaptive, hardware efficient, inter-neuronal transfer functions (or kernels) are the most important element in neuromorphic implementations of Spiking Neural Networks (SNN) which learn spatio-temporal patterns in hardware. In the absence of such abstract kernels, spiking neuromorphic system must realize very large numbers of synapses and their associated connectivity. The resultant hardware and bandwidth limitations create difficult tradeoffs which diminish the usefulness of such systems. In this thesis a novel model of spiking neurons is proposed. The proposed Synapto-dendritic Kernel Adapting Neuron (SKAN) uses the adaptation of their synapto-dendritic kernels in conjunction with an adaptive threshold to perform unsupervised learning and inference on spatio-temporal spike patterns. The hardware and connectivity requirements of the neuron model were minimized through the use of simple accumulator based kernels as well as through the use of timing information to perform a winner take all operation between the neurons. The learning and inference operations of SKAN are characterized and shown to be robust across a range of noise environments.

Advances in integrated circuit (IC) fabrication technology have reduced feature sizes to the order of nanometres, but have created several problems in IC design, such as lower noise immunity, increased process mismatch, and interconnect bottlenecks. Further, the failure of a few transistors may result in the failure of the entire chip, rendering it unusable. Similar to these problems of transistor failure and device mismatch in ICs, the brain is faced with the problems of heterogeneity of neuronal responses to stimuli and neuronal cell death. The biological nervous system functions well despite these problems, and this motivates us to apply its working principles in IC implementation. In this thesis, we draw inspiration from the brain and discuss how 'stochastic facilitation' can be used to perform useful and precise computation. We explore non-deterministic methodologies for computation in hardware and introduce the concept of stochastic electronics; a new way to design circuits and increase performance in noisy and mismatched fabrication environments. We illustrate this approach by presenting systems for both analogue and digital IC design.

For the analogue system, we propose a generic and trainable architecture, which uses device mismatch and nonlinearities explicitly. In this way, the reduced device matching in newer technologies becomes an advantage, rather than something that needs to be engineered out of the design. We have developed a novel neuromorphic system called a Trainable Analogue Block (TAB), which uses device mismatch as a means for random projections of the input to a higher dimensional space. The TAB framework is inspired by the principles of neural population coding operating in the biological nervous system. Three neuronal layers, namely input, hidden, and output, constitute the TAB framework, with the number of hidden layer neurons far exceeding the number of input layer neurons.

For the digital system, we use a stochastic computation (SC) framework to build massively parallel and low precision circuits to solve complex Bayesian inference problems. An advantage of the SC implementation is that it is robust to certain types of noise, which may become an issue in IC technology with feature sizes in the order of tens of nanometres due to their low noise margin, the effect of high-energy cosmicrays, and the low supply voltage. We present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits, and this implementation is referred to as the Bayesian INference in DAG (BIND). Our work describes canonical neural circuits, which are the basic building blocks of our models, and shows how these neural circuits can be easily implemented using digital logic gates. An advantage of our framework is that the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream. Our work presents a novel approach for implementing probabilistic networks using simple logic gates, with the ability to perform the computation in real time.

Patrick's research focuses on investigating the mechanisms that underlie tactile decoding. Specifically we seek to understand how information relating to area between the finger pad and object is interpreted and transformed by the brain into a format that is relevant for sensorimotor control during dexterous object manipulation. We are recording tactile afferent signals innervating the glabrous skin of the human finger pad. In addition, we are using a combination of mathematical and signal processing approaches to develop models for the analysis of the data. We hope that this will be a step towards designing robots with dexterous manipulation capabilities, inform the design of sensory feedback devices, and possibly guide the development of new methods to improve upon therapies for individuals with neurological disorders.