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Welcome to the CIRG, part of the School of Systems Engineering at the University of Reading which cooperates closely with the School of Pharmacy on projects concerned with Cybernetic Intelligence and interfaces between nervous system and machines. Cybernetic intelligence is the study of intelligence and its application. It is an approach characterised by its emphasis on sub-symbolic know-ledge representation and bottom-up (ie. data driven) problem solving. Cybernetic intelligence describes theoretical, mathematical and philosophical aspects of consciousness and intelligence and their application to the design of intelligent machines and the control of complex systems.
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Man Machine Interfaces
Project Cyborg
The US Professor and visionary, Norbert Wiener, founded the field of Cybernetics in the 1940's. He envisaged that one day electronic systems he called "Nervous Prostheses" would be developed that would allow those with spinal injuries to control their paralysed limbs using signals detected in their brain In the UK two internationally renowned professors, in the School of Systems Engineering at the University of Reading, Brian Andrews and Kevin Warwick together with the eminent neurosurgeon Peter Teddy have just taken a step closer to this dream. The team have come together from different branches of Cybernetics and Neurosurgery. Professor Warwick specializes in the field of Artificial Intelligence and Robotics and Brian Andrews in the field of Biomedical Engineering, Neural Prostheses and Spinal Injuries. Peter Teddy has a long involvement with neural implants and is the head of Neurosurgery at Oxford. Although seemingly worlds apart, these fields have many common threads.

The principal investigators Andrews, Warwick and Teddy, lead a large team of surgeons and researchers including, Brian Gardner, Ali Jamous, Amjad Shad and Mark Gasson of the world famous National Spinal Injuries Centre (NSIC)-Stoke Mandeville Hospital, the Radcliffe Infirmary in Oxford and the University of Reading, UK. The team are supported by the David Tolkien Trust, Computer Associates, Tumbleweed and Fujitsu.

A sophisticated new microelectronic implant has been developed that allows two-way connection to the nervous system. In one direction, the natural activity of nerves are detected and in the other, nerves can be activated by applied electrical pulses. It is envisaged that such neural connections may, in the future, help people with spinal cord injury or limb amputation.
Department of Cybernetics Robots
Cybernetic Implant 1 (1998)
Cybernetic Implant 1 (1998)
Cybernetic Implant 2 (2002)
Cybernetic Implant 2 (2002)
Professor Kevin Warwick
The next step towards true Cyborgs?
Photos not for reproduction without permission of the CIRG
The Neurally Controlled Robot  Project -  Animat
Our research group is interested in the study of dissociated cultured neurone interactions, and how their network-level interactions may play a role in low-level formation of memory and learning mechanisms. There is strong evidence in research showing that neurones exhibit an intrinsic networking capability which allows basic organisational behaviours to emerge in random networks to some extent, even without the structured spatial organisation naturally existing in-vivo.
Recent multi-Electrode-Array (MEA) technologies allow the recording and stimulation of such neuronal cultures at multiple sites.
Our goal is to harness this computational power and map the input/output sites of culture recordings to mobile robot embodiments, with an aim to introduce learning by attempting to solve basic tasks such as object avoidance and maze navigation.
The Animat Project

In a ground-breaking project at the School of Systems Engineering, members of CIRG are interfacing computers with growing cultures of neurons via electrode arrays, with the aim of having the cultures learn to control mobile robots. This could result in an enormous step forward in understanding the function and developmental process of neurons and neuronal networks, and contribute to our understanding of biological mechanisms underpinning such fundamental properties as memory or learning. Animat could also constitute a viable and ethically more acceptable platform for investigation of neural diseases, such as Alzheimer’s Disease or Parkinson’s Disease, and ultimately could be used for testing new pharmacological treatments. This exciting project opens up as well almost endless possibilities for intelligent robotics platforms and may lead to creation of truly autonomous robots that could be deployed in conditions that precludes frequent human intervention, e.g. for deep space exploration.

Architecture for Neuronal Cell Control of a Mobile Robot

It is usually expected that the intelligent controlling mechanism of a robot is a computer system. Research is however now ongoing in which biological neural networks are being cultured and trained to act as the brain of an interactive real world robot – thereby either completely replacing or operating in a cooperative fashion with a computer system. Studying such neural systems can give a distinct insight into biological neural structures and therefore such research has immediate medical implications. In particular, the use of rodent primary dissociated cultured neuronal networks for the control of mobile ‘animats’
(artificial animals, a contraction of animal and materials) is a novel approach to discovering the computational capabilities of networks of biological neurones. A dissociated culture of this nature requires appropriate embodiment in some form, to enable appropriate development in a controlled environment within which appropriate stimuli may be received via sensory data but ultimate influence over motor actions retained. The principal aims of the present research are to assess the computational and learning capacity of dissociated cultured neuronal networks with a view to advancing network level processing of artificial neural networks. This has been approached by the creation of an artificial hybrid system (animat) involving closed loop control of a mobile robot by a dissociated culture of rat neurons. This 'closed loop' interaction with the environment through both sensing and effecting enables investigation of its learning capacity.

D. Xydas, D. Norcott, K. Warwick, B. Whalley, S. Nasuto, V. Becerra, M. Hammond, J. Downes, and S. Marshall, “Architecture for Neuronal Cell Control of a Mobile Robot”, Springer Tracts in Advanced Robotics - Proceedings of European Robotics Symposium 2008, vol. 44, pp. 23-31, 2008.

Typical glass MEA, showing large contact pads which lead to the electrode
column – row arrangement
Electrode arrays in the centre of the MEA, as typically seen
under an optical microscope,
Single electrode close-up, showing a number of neuronal cells
in close proximity along with a vast number of neural connections between them.
Miabot Pro with 8-way sonar array pack
Virtual animat designed using
mainly basic geometrical primitive shapes and exported as VRML file
Embodied Machine Intelligence
The group has witnessed considerable success in the field of autonomous intelligent robotics. The group's interactive robots are on permanent exhibition in the Science Museums in London, Birmingham and Linz.

This research has been documented in major texts surveying the field (it is the only UK entry in MIT's RoboSapiens), and has also received high profile international media exposure on numerous television documentaries shown by BBC and the Discovery Channel, amongst many others.

In partnership with Eaglemoss Publishing Ltd, the group developed the Cybot, a robot kit as part of a magazine 'Real Robots', resulting in royalties from national and international sales in excess of 1.2m for the University of Reading. Over 50% of these funds are channelled back into post doctoral research assistantships and postgraduate studentships.
Humanoid Robot Tournament
The first ‘Androids Advance!’ humanoid robot tournament was launched as a pilot study to assess the format and infrastructure as such a public engagement tool, and to raise awareness of a proposed subsequent nationwide tournament. The pilot involved sixteen teams from schools in London and the Southeast of England. Each team had to 'program and upgrade' a biped robot in order to successfully compete in a series of disciplines. This culminated in a live tournament day held at the Science Museum in London. The Androids Advance Challenge proved to be a great success with schools, the public and all those involved in its organisation, by delivering a high impact event to a very wide audience.
Intelligent Control
The group's developments in intelligent control have been applied to optimize spacecraft trajectories and to find invariant relative satellite motion with funding from the European Space Agency.

There is considerable work within the group on data-based modelling for a priori unknown nonlinear systems. Novel algorithms have been developed for various types of applications, e.g. control, signal processing, pattern recognition and communications. Examples of our research include system identification of nonlinear time series/dynamical systems with heterogeneous noise, new kernel classifier construction algorithms for imbalanced data sets, sparse probability density estimators for pattern recognition, and data detection and phase noise cancellation for OFDM wireless communication systems.

Recently completed work includes: the development of ef ficient fuzzy controllers which map linear control laws for small signals, but which exhibit much greater robustness than their linear counterparts; the development of methods for feedback linearisation using dynamic neural networks; and an investigation on the use of optic flow and CMAC networks for robot balancing.

The group has extensive laboratory equipment where real time control experiments are performed. The equipment includes various robot manipulators, mobile robots, a 3D crane system, mobile robots, single and double inverted pendulums, a magnetic bearings device, as well as state-of-the-art data acquisition equipment, and real-time control software.
Gravity Assist Space Pruning (GASP)

This work, which has received funding from the European Space Agency, focuses on the problem of optimisation of spacecraft trajectories with multiple gravity assists. A gravity assist occurs when a space probe performs a swing-by of a planet, effectively stealing some of the planet's momentum to gain velocity (of course, the amount that the planet is slowed down by is negligible!). Performing one or more gravity assists allows the probe to require less fuel and to reach further distances

A multiple gravity assist (MGA) mission is where swing-bys are performed of several planets in a row. For example, the recent Cassini-Huygens mission reached Saturn by the swingby sequence Earth-Venus-Venus-Earth-Jupiter-Saturn.

Due to the fact that all the planets are moving simultaneously in relation to each other, the search space of a MGA mission has a huge number of local minima, meaning that traditional optimisation techniques have trouble finding good solutions. The key observation behind GASP was that the vast majority of this space can be shown to be of very poor quality, requiring unfeasibly large thrusts during the mission. Instead, by studying each phase of the mission separately, such infeasible regions could be identified and pruned from the problem, leaving a much reduced space that could then be optimised much more effectively. The original GASP technique performs the pruning based on an efficient sequential two-dimensional grid sampling. The technique is also highly computationally efficient as its complexity is polynomial in both time and space.
Invariant Relative Satellite Motion

This recent project, which received funding from the European Space Agency, employed a Hamiltonian formulation of relative satellite motion and a variant of Newton's method to locate periodic or quasi-periodic relative satellite motion. The perturbations considered in the model included nonlinear gravitational effects, the oblateness of the Earth (J2 effect) and eccentricity of the reference orbit. Advantages of using Newton's method includes simplicity of implementation, repeatability of solutions due to its non-random nature, and fast convergence. In order to evaluate the effect of the quality of the model used to generate the periodic reference trajectory, a study involving closed loop control of a simulated chief/deputy satellite formation was performed. See ESA final report for more details.
Neural Networks
Neural networks, or artificial neural networks, are mathematical structures consisting of interconnected processing units which can be trained to reproduce complex input-output patterns.

Reseach on the development and applications neural networks carried out at CIRG is closely interleaved with the other sub-topics, particulary with intelligent control, embodied machine intelligence, and computational neuroscience.

In particular, recent work has been carried out on the development and analysis of dynamic neural networks, which are neural networks which can be described as nonlinear dynamical systems, on evolving neural network structures, and on the development of the plastic self-organising map (see figure).
Intelligent Search
Stochastic Diffusion Search (SDS) is a population-based, pattern-matching algorithms. It belongs to the family of swarm Intelligence and naturally inspired search and optimisation algorithms which includes Ant Colony Optimization, Particle Swarm Optimization and Genetic Algorithms. Unlike stigmergetic communication employed in Ant Colony Optimization, which is based on modification of the physical properties of a simulated environment, SDS uses a form of direct (one-to-one) communication between the agents similar to the tandem calling mechanism employed by one species of ants, Leptothorax acervorum.

In SDS agents perform cheap, partial evaluations of a hypothesis (a candidate solution to the search problem). They then share information about hypotheses (diffusion of information) through direct one-to-one communication. As a result of the diffusion mechanism, high-quality solutions can be identified from clusters of agents with the same hypothesis.

Research carried out by CIRG members on Stochastic Diffusion Search (SDS) has led to a comprehensive theoretical characterisation of SDS, which is so far one of few SI algorithms with proven global convergence and characterisation of its resource allocation capacity. Further research is ongoing in collaboration with the Goldsmiths College, London, on extensions of SDS and applications as a powerful self organising computing resources management framework.
Applied Cognitive Systems
"Cognitive systems are natural or artificial information processing systems, including those responsible for perception, learning, reasoning and decision-making and for communication and action" (DTI Foresight initiative). This definition facilitated the conclusion that current artificial systems/robots are poor cognitive systems. A need was identified to improve devices that we use every day, including assistive technologies and to generate medical benefits. Our research aims to create flexible, robust and adaptive applied cognitive systems. There is much overlap and mutual benefit in the themes of CIRG, with a strong link to embodied machine intelligence here. ACS interacts with an environment, including virtual domains, seeking performance improvement through analogies with human/animal behaviours. EMI interacts with a physical environment and seeks performance improvements through any appropriate method. A common thread is the utilisation of Cybernetic feedback where interaction with the environment improves performance. Cognitive Systems research has been a pillar of Cybernetics since the coalescence of the research area in the 50s.
Genetics-Based Machine Learning is a family of optimisation techniques inspired by evolution that improve on a population of initially random solutions by selecting the most promising solutions and repeatedly "breeding" new solutions from them. As the generations progress, the population moves towards the best solution to a problem.

Learning Classifier Systems (LCS) are a population-based evolutionary technique, but rather than the genome representing a vector of numbers, it instead codes for a set of rules. Using such biologically-inspired methodology, members of CIRG have applied novel variants of LCS to solve problems from steel mill quality control (see the figure below), to multiplexing to robotic vacuum cleaner path planning, showing improvements over other existing methods.
The need for abstraction arose from the data-mining of rules in the steel industry through application of the genetics-based machine learning technique of Learning Classifier Systems, which utilise a Q-learning type update for reinforcement learning. It was noted that many rules had similar patterns. For example, there were many rules of the type 'if side guide setting < width, then poor quality product' due to different product widths. This resulted in a rule-base that was unnecessarily hard to interpret and slow to learn. The initial development of the abstraction method was based on the known problem of Connect4 due to its vast search space, temporal nature and available patterns. The novel Abstraction algorithm developed successfully improved the domain performance as higher-order abstracted rules replaced generalised state-action rules in a complex multi-step problem. It is hoped that this algorithm will help to fulfil the intended use of the LCS technique as a test bed for artificial cognitive processes. The figure shows a graph of percentage base rules versus abstracted rules (solid line) as training progresses (circle line).
The importance of ‘emotions’ in control mechanisms for autonomous agents has been demonstrated using real and virtual robotic platforms. A novel agent architecture was developed to provide a foundation for ‘emotion’-based control. Instead of mapping states to actions, the novel system developed maps states to an analogue of emotions and then to states. This provided a non-linear, temporal control strategy that was non-deterministic and thus advantageous in tested exploratory domains. An appropriate test platform was created allowing real and virtual agents to coexist and allowed production of a number of emotional rules. The emotion-based architecture is shown to provide a number of benefits over conventional approaches, which include simpler behavioural programming and improved performance on complex exploration tasks. The two figures below show the results of conventional and emotional robot path planning.
Value system
Artificial cognitive systems have had success in single objective, single domains where the worth of each action may be evaluated/estimated. However, if the system needs in to choose between multiple goals or select an action when the worth estimate is poor, e.g. due to long chains between current state and eventual payoff, then a value system will be required. There is current research interest in the game of Othello as strategy learning benefits fro m its value system being updated at each given state. Thus learning becomes a two-stage process; 1. learn the values of moves at each state, 2. learn the optimum policy of moves through the states.
It is proposed that a biologically non-implausible model of working memory be created, incorporated into a general cognitive architecture, and embodied into an artificial agent (simulated and embodied in a real mobile robot), such that its interaction with a complex environment may be tested. Biological cognitive agents (e.g. humans, rats and other mammals) are located in the real world, so must act within it, whilst being constrained by it.
Computational Neuroscience
Computational neuroscience relates to the modelling and understanding of the brain using computers. As part of ongoing investigations at CIRG we are investigating the brain from a number of different perspectives, ranging from top down (EEG analysis) to bottom up (single neuronal reconstruction). These research directions are interlinked with efforts on the Human Computer Interfaces on one hand, and with research on Machine Learning on the other.

Electroencephalogram (EEG) Analysis
Measuring electrical potentials at various points on the scalp over time allows inferences to be made about the sources of electrical activity in the brain. Electroencephalogram (EEG) fluctuations due to synchronous patterns of activity of large pools of neurons seem to contain useful information about the state the brain in terms of the cognitive processing as well as its state of health. Research in CIRG concentrated on novel techniques for characterisation of synchrony patterns and their application towards earlier diagnosis of memory impairment. Such research is of great interest as it characterises fundamental cognitive process and also because of its practical potential for early diagnosis of dementia. This research is continued in collaboration with the School of Psychology and Applied Linguistics at the University of Reading and with the University of Magdeburg, Germany. New project in collaboration with the School of Psychology and Applied Linguistics, building on the successes of EEG analysis projects for BCI applications and in memory function, is concentrating on characterisation of EEG characteristics of linguistic processing without the need for averaging over multiple trials. This is extremely important as the standard averaging approach may mask important features of the information processing in the brain and is most certainly suboptimal for diagnosing subjects with brain damage which almost by definition is going to be subject specific. In collaboration with The University of Uberlandia, Brazil, research into characterising the EEG-like signals from the very early stages of the auditory tract may help the practitioners in early diagnosis of hearing impairments or in diagnosing tumours of the auditory tract.

Neuronal Reconstruction
Members of the group has been involved with researching the reconstruction of neurons from stacks of images obtained with a microscope. This is a complex and time-consuming task, and significant progress has been made in automating it through the development of the Neuromantic application, even though the image data can be strongly visually ambiguous.

The 3D reconstructions created via such techniques are useful for several reasons. Firstly, they can be used to help validate models of neuronal behaviour by allowing comparison between results obtained via electrophysiological testing and simulation. Secondly, comparing various statistical measures of shape between control and experimental groups in a biological trial can identify significant differences that could be associated with neurological disease and lend insight into how they may be treated/prevented.

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