Staff Profiles

Teaching Computer Science, Artificial Intelligence, and Robotics

Supervision

Currently supervising two PhD projects:

• Robats: exploring and mapping the world through swarm robots and sonar.
• Mathematical modelling of biological systems: a novel alternative for detailed modelling of insulin secretion.

My research is in Dynamic Systems. I am particularly interested in the manner of which Biological Systems resolve problems in dynamics, and learn to apply those concepts to Engineering, Medical and Biosystems problems.

Research group membership

Intelligent Systems Research Centre

Research grants and awards

• Higher Education Innovation Funding (HEIF) 5, Business Proof of Concept fund, Control of Wind Turbines, £50k, Sept 2011.

• Higher Education Innovation Funding (HEIF) 5, Business Proof of Concept fund, Rate Control of Combustion Engines, £50k, Sept 2012.

Research projects

• Rate Control of Chaos (patented)

This project uses a method of control to limit the nonlinear dynamics of complex control problems into stable control domains. Applications in the areas of lasers, combustion engines, HVAC, wind turbines, and bioreactors.A physical proof of concept of a RCC diesel engine is available.

• Biodynamical Research Project

This project explores the use of computational criticality of nonlinear dynamic systems to control the dynamics of biological systems, with the aim of addressing problems in medicine, engineering and software development.

Journal articles

• olde Scheper TV, Meredith RM, Mansvelder HD, van Pelt J, van Ooyen A, 'Dynamic Hebbian Cross-Correlation Learning Resolves the Spike Timing Dependent Plasticity Conundrum'
  Frontiers in Computational Neuroscience 11 (2018)
  ISSN: 1662-5188 eISSN: 1662-5188
  Abstract Spike Timing-Dependent Plasticity has been found to assume many different forms. The classic STDP curve, with one potentiating and one depressing window, is only one of many possible curves that describe synaptic learning using the STDP mechanism. It has been shown experimentally that STDP curves may contain multiple LTP and LTD windows of variable width, and even inverted windows. The underlying STDP mechanism that is capable of producing such an extensive, and apparently incompatible, range of learning curves is still under investigation. In this paper, it is shown that STDP originates from a combination of two dynamic Hebbian cross-correlations of local activity at the synapse. The correlation of the presynaptic activity with the local postsynaptic activity is a robust and reliable indicator of the discrepancy between the presynaptic neuron and the postsynaptic neuron's activity. The second correlation is between the local postsynaptic activity with dendritic activity which is a good indicator of matching local synaptic and dendritic activity. We show that this simple time-independent learning rule can give rise to many forms of the STDP learning curve. The rule regulates synaptic strength without the need for spike matching or other supervisory learning mechanisms. Local differences in dendritic activity at the synapse greatly affect the cross-correlation difference which determines the relative contributions of different neural activity sources. Dendritic activity due to nearby synapses, action potentials, both forward and back-propagating, as well as inhibitory synapses will dynamically modify the local activity at the synapse, and the resulting STDP learning rule. The dynamic Hebbian learning rule ensures furthermore, that the resulting synaptic strength is dynamically stable, and that interactions between synapses do not result in local instabilities. The rule clearly demonstrates that synapses function as independent localized computational entities, each contributing to the global activity, not in a simply linear fashion, but in a manner that is appropriate to achieve local and global stability of the neuron and the entire dendritic structure.Website 
• olde Scheper TV, 'Biologically inspired rate control of chaos'
  Chaos 27 (2017)
  ISSN: 1054-1500 eISSN: 1089-7682
  Abstract The overall intention of chaotic control is to eliminate chaos, and to force the system to become
  stable in the classical sense. In this paper, I demonstrate a more subtle method that does not
  eliminate all traces of chaotic behaviour; yet it consistently, and reliably, can provide control as
  intended. The Rate Control of Chaos (RCC) method is derived from metabolic control processes,
  and has several remarkable properties. RCC can control complex systems continuously, and unsupervised, it can also maintain control across bifurcations, and in the presence of significant systemic noise. Specifically, I show that RCC can control a typical set of chaotic models, including the 3 and 4 dimensional chaotic Lorenz systems, in all modes. Furthermore, it is capable of controlling spatiotemporal chaos without supervision, and maintains control of the system across bifurcations.
  This property of RCC allows a dynamic system to operate in parameter spaces that are difficult to
  control otherwise. This may be particularly interesting for the control of forced systems, or dynamic systems that are chaotically perturbed. These control properties of RCC are applicable to a range of dynamic systems, thereby appearing to have far-reaching effects beyond just controlling chaos. RCC may also point to the existence of a biochemical control function of an enzyme, to stabilise the dynamics of the reaction cascade.
  Website 
• olde Scheper T, Mansvelder H, van Ooyen A, 'Short Term Depression Unmasks the Ghost Frequency'
  PLoS ONE 7 (12) (2012) pp.e50189-
  ISSN: 1932-6203
  Abstract

  Short Term Plasticity (STP) has been shown to exist extensively in synapses throughout the brain. Its function is more or less clear in the sense that it alters the probability of synaptic transmission at short time scales. However, it is still unclear what effect STP has on the dynamics of neural networks. We show, using a novel dynamic STP model, that Short Term Depression (STD) can affect the phase of frequency coded input such that small networks can perform temporal signal summation and determination with high accuracy. We show that this property of STD can readily solve the problem of the ghost frequency, the perceived pitch of a harmonic complex in absence of the base frequency. Additionally, we demonstrate that this property can explain dynamics in larger networks. By means of two models, one of chopper neurons in the Ventral Cochlear Nucleus and one of a cortical microcircuit with inhibitory Martinotti neurons, it is shown that the dynamics in these microcircuits can reliably be reproduced using STP. Our model of STP gives important insights into the potential roles of STP in self-regulation of cortical activity and long-range afferent input in neuronal microcircuits.

  Website 
• Scheper T olde, 'Why metabolic systems are rarely chaotic'
  BioSystems 94 (1-2) (2008) pp.145-152
  ISSN: 0303-2647 eISSN: 1872-8324
  Abstract One of the mysteries surrounding the phenomenon of chaos is that it can rarely be found in biological systems. This has led to many discussions of the possible presence and interpretation of chaos in biological signals. It has caused empirical biologists to be very sceptical of models that have chaotic properties or even employ chaos for problem solving tasks. In this paper, it is demonstrated that there exists a possible mechanism that is part of the catalytical reaction mechanisms which may be responsible for controlling enzymatic reactions such that they do not become chaotic. It is proposed that where these mechanisms are not present or not effective, chaos may still occur in biological system.Website 
• Crook N, Scheper TO, Pathirana V, 'Self-organised Dynamic Recognition States for Chaotic Neural Networks'
  Information Sciences 150 (1-2) (2003) pp.59-75
  ISSN: 0020-0255
  Abstract Chaos offers several advantages to the Engineer over other non-chaotic dynamics. One is that chaotic systems are often significantly easier to control than other linear or non-linear systems, requiring only small, appropriately timed perturbations to constrain them within specific unstable periodic orbits (UPOs). Another is that chaotic attractors contain an infinite number of these UPOs. If individual UPOs can be made to represent specific internal states of a system, then a chaotic attractor can be turned into an infinite state machine. In this paper we investigate this possibility with respect to chaotic neural networks. We present a method by which a network can self-select UPOs in response to specific input values. These UPOs correspond to network recognition states for these input values.Website 
• Crook NT, olde Scheper T, Pathirana V, 'Self organised dynamic recognition states for chaotic neural networks'
  Information Sciences 150 (1-2) (2003) pp.59-75
  ISSN: 0020-0255
  Abstract Chaos offers several advantages to the Engineer over other non-chaotic dynamics. One is that chaotic systems are often significantly easier to control than other linear or non-linear systems, requiring only small, appropriately timed perturbations to constrain them within specific unstable periodic orbits (UPOs). Another is that chaotic attractors contain an infinite number of these UPOs. If individual UPOs can be made to represent specific internal states of a system, then a chaotic attractor can be turned into an infinite state machine. In this paper we investigate this possibility with respect to chaotic neural networks. We present a method by which a network can self-select UPOs in response to specific input values. These UPOs correspond to network recognition states for these input values.Website 
• Crook NT, olde Scheper T, 'Adaptation based on memory dynamics in a chaotic neural network'
  Cybernetics and Systems 33 (4) (2002) pp.341-378
  ISSN: 0196-9722
  Abstract The complex dynamics that emerge from systems governed by deterministic chaos offer significant advantages to the neuromorphic engineer. Included in these is the potential for a very large memory store and the ease with which chaotic systems can be controlled. By definition, a chaotic system is a periodic. However, during the course of its trajectory through state space, the chaotic system will come infinitely close to points that it has previously visited. These almost repeating trajectories are referred to as Unstable Periodic Orbits (UPOs). Normally, under the influence of chaos, the trajectory would move away exponentially fast from its previous path, thereby describing a new path on the surface of the attractor. It is possible to apply a simple delayed feedback control mechanism to a chaotic system that will constrain it within one of its UPOs. This article presents a neural implementation of this delayed feedback mechanism. The network presented here is able to stabilize different UPOs in response to different input signals, with each UPO corresponding to a dynamic recognition state for that input. We also present two learning rules for this network, which enables it to adapt to novel inputs in a self-organized manner.Website 
• olde Scheper T, Klinkenberg D, van Pelt J, Pennartz C, 'A model of molecular circadian clocks: multiple mechanisms for phase shifting and a requirement for strong non-linear interactions'
  Journal of Biological Rhythms 14 (3) (1999)
  ISSN: 0748-7304
  Website 
• olde Scheper T, Klinkenberg D, Pennartz C, van Pelt J, 'A mathematical model for the intracellular circadian rhythm generator'
  Journal of Neuroscience 19 (1) (1999)
  ISSN: 1529-2401 eISSN: 1529-2401
  
• olde Scheper T, Klinkenberg D, van Pelt J, Pennartz C, 'A Model of Molecular Circadian Clocks: Multiple Mechanisms for Phase Shifting and a Requirement for Strong Nonlinear Interactions'
  Journal of Biological Rhythms 14 (3) (1999) pp.213-220
  ISSN: 0748-7304
  Abstract A fundamental question in the field of circadian rhythms concerns the biochemical and molecular nature of the oscillator. There is strong evidence that circadian oscillators are cell autonomous and rely on periodic gene expression. In Drosophila, Neurospora, Aplysia, and vertebrates, circadian oscillators are thought to be based on molecular autoregulatory loops composed of transcription, translation, and negative feedback by proteins on nuclear transcription. By studying a mathematical model of molecular clocks based on this general concept, the authors sought to determine which features such clocks must have to generate robust and stable oscillations and to allow entrainment by external stimuli such as light. The model produced circadian oscillations as an emergent property even though a time delay in protein synthesis and rate constants of the feedback loop were much shorter than 24 h. Along with the delay in protein production, strong nonlinear interactions in macromolecular synthesis and nuclear feedback appeared to be required for the model to show well-behaved oscillatory behavior. Realistic phase-shifting patterns induced by external stimuli could be achieved by multiple mechanisms—namely, up-and downward perturbations of protein or mRNA synthesis or degradation rates. The model makes testable predictions about interactions between clock elements and mechanisms of entrainment and may help to understand the functions of the intricate molecular interactions governing circadian rhythmogenesis.Website 

Book chapters

• Fuente LA, Lones MA, Crook NT, Olde Scheper TV, 'Harmonic versus chaos controlled oscillators in hexapedal locomotion' in Information Processing in Cells and Tissues, (2015)
  Abstract

  The behavioural diversity of chaotic oscillator can be controlled into periodic dynamics and used to model locomotion using central pattern generators. This paper shows how controlled chaotic oscillators may improve the adaptation of the robot locomotion behaviour to terrain uncertainties when compared to nonlinear harmonic oscillators. This is quantitatively assesses by the stability, changes of direction and steadiness of the robotic movements. Our results show that the controlled Wu oscillator promotes the emergence of adaptive locomotion when deterministic sensory feedback is used. They also suggest that the chaotic nature of chaos controlled oscillators increases the expressiveness of  pattern generators to explore new locomotion gaits.

  Website 
• olde Scheper, T, 'The spike generation processes: a case for low level computation' in Editors: Deutsch, A., Bravo de la Parra, R., de Boer, R.J., Diekmann, O., Jagers, P., Kisdi, E., Kretzschmar, M., Lansky, P., Metz, H. (ed.), The spike generation processes: a case for low level computation, Birkhauser Publishers (2008)
  ISBN: 9780817645557 eISBN: 9780817645564
  Abstract Over the last couple of years, it can be said that the focus of the computational aspects of neurons has moved from synaptic weight and firing rate encoding to temporal firing encoding. On the other hand, several elements of these models have been based on some conceptual assumptions that imply relative simple dynamic behaviour of neuronal membrane activity in an active-passive process. In line with recent advances that have produced a better understanding of the biochemical processes that occur within cells, it is proposed that the processes that are involved in a membrane depolarisation cascade are less static than have been assumed so far. In particular, the possibilities of low level computation at the membrane level need to be explored more extensively. In this chapter some computational properties of the spike generation processes are explored using phenomenological models.Website 
• Crook N, Dobbyn C, olde Scheper T, 'Chaos as a Desirable Stable State of Artificial Neural Networks' in Advances in Soft Computing: Soft Computing Techniques and Applications, Physica-Verlag (2000)
  

Conference papers

• olde Scheper TV, 'Exploiting chaos for fun and profit'
  (2017) pp.1-8
  Abstract Chaos in dynamical systems is still considered to be a somewhat curious, and generally undesirable property of nonlinear systems. Despite the plethora of chaotic control methods published over the last decades, only in a few instances has the control of chaos been used to address real world problems in engineering or medicine. This is partly due to the limits of the used control methods, which either require specific analytical knowledge of the system, or the system needs to have specific characteristics to be able to be controllable. The lack of solutions for engineering and biomedical problems may also be due to specific requirements that prevent the implementation of control methods and the, as yet unproven, benefits that controlled chaos may bring to these problems. The aim of a practical application of chaos control is to fully control chaos in theoretical problems first, and then show applicable solutions to physical problems of stability and control. This controlled chaotic state should then have clear and distinct dynamic advantages over uncontrolled chaos and steady state systems. The application of the Rate Control of Chaos (RCC) method, which is derived from metabolic control processes, has already been shown to be effective in controlling several engineering problems. RCC allows non-linear systems to be stabilised into controlled oscillations, even across bifurcations, and it also allows the system to operate in regions of the parameter space that are inaccessible without this method
  of control. For fun, I will show that RCC controls the N-Body problem; for profit, that it can control a bioreactor model to greatly improve yield. The RCC method promises to, finally, permit the control of complex dynamic systems.Website 
• Munoz-Balbontin M, olde Scheper TV, Aldea A, 'Developing a dynamic bio-inspired intelligent system to support diabetic patients'
  (2017) pp.41-47
  Abstract This paper proposes a new biologically-inspired phenomenological model of insulin release that can be used to continuously monitor a person with diabetes and therefore recommend insulin dose and predict events. Given all the parameters and conditions that have to be met in order to achieve stability, the proposed insulin-release system will be considered as highly non-linear and dynamical. The resulting model will study the mechanisms that keep the system from reaching a chaotic state (diabetes), based on the property of self-organised criticality. This property was selected since it has been previously identified and studied in other dynamical biological
  systems. The proposed model will then be used as part of a model-based reasoning system that aims to support patients with diabetes.Website 
• olde Scheper TV, 'Criticality in biocomputation'
  (2017) pp.323-328
  ISBN: 9782875870391
  Abstract Complexity in biological computation is one of the recognised means by which biological systems manage to function in a complex chaotic world. The ability to function and solve problems irrespective of scale and relative complexity, including higher-order interactions, is essential
  to the efficacy of biological systems. However, it has been unclear how the required complexity can be introduced to allow these functions to be realised. Nonlinear local interactions are required to combine into a global stable system. The property of criticality, that is exhibited by many nonlinear physical systems, can be exploited to allow local nonlinear oscillators to interact, resulting in a globally stable system. This concept introduces robustness, as well as, a means to control global stability.Website 
• olde Scheper TV, 'Homeostasis is a critical illusion of harmony'
  (2015) pp.1-2
  Abstract The concept of homeostasis has been always been a convenient principle to describe the experimentally observed stability of an organism or process. This stable state seems to underpin the balance of nature, reaching back to the ideas of a body in harmony and under control . Control mechanisms that enable this harmonic state are comparable to plant control processes, relying on feedback and error estimates to determine the control needed to maintain this stable state [2]. Unfortunately, for the poetic beauty of the concept, homeostatic systems are in fact not stable in the dynamic sense, nor has any relevant biological mechanism been identified that may function as the supervisory controller. Occasionally, alternative variants for homeostatic control are proposed. These encompass allostasis and similar mechanisms, which can be interpret as multi-state homeostatic control [6]. None of the existing feedback loop and multi-state proposals of homeostatic control are capable of explaining the multi-scale dynamic emerging semi-stable state associated with homeostasis [3]. To understand homeostasis as a dynamic process, one needs to have a dynamic system with the ability to change state without losing stability, respond flexibly to small and large perturbations, and have the ability to respond in a mostly scale-free manner. Assuming that a homeostatic system is an epiphenomenon, i.e. it is an apparent result of underlying stabilising mechanisms rather than the mechanism itself, it may be possible to explain the concept and its failings in a consistent dynamic systems manner. This will allow for a better understanding of the underlying mechanisms, and point towards possible ways to resolve the failings that currently cannot be addressed with the homeostatic paradigm. Rate Control of Chaos (RCC) is a non-linear method of control, derived from metabolic processes [4]. Its main features are the ability to control chaotic systems into unstable periodic orbits, maintaining stability across bifurcations, yet allowing the controlled chaotic system to remain slightly chaotic (in terms of small positive Lyapunov exponents). This property permits a set of RCC controlled chaotic systems to become connected into a stable periodic system, whose behaviour is not merely the result of the cumulative underlying dynamics. Nevertheless, such a combined system is still based on stable
  individual systems, and can be scale-free as demonstrated by a strong power law relation evident in its dynamic behaviour. The emergent dynamics and scale-free property of a connected rate controlled chaotic system demonstrates similar properties to a critical system. To demonstrate the strengths of criticality for homeostatic control, a set of RCC controlled Rössler oscillators [5] are connected to show the ability of the combined system to be scale invariant, and maintain a stable oscillation despite perturbations. Nevertheless, the system can still change state on by oscillating in different mulit-orbits depending on connectivity and underlying dynamics. Furthermore, a model of extracellular matrix formation [1] , allows more complex dynamics and the demonstration of the
  power law relation between the number of connected units and the total system oscillation  amplitude. These emerging critical systems demonstrate that seemingly stable systems can be scale-free. They can respond rapidly to varying scaled input, maintain stability, and show that interacting non-linear systems are controllable without the need for set points and linear control methods. The illiusion of harmony of a homeostatic system is maintained by an emergent critical system, but is not limited to the many failings of a homeostatic control method applied to complex, high order biological systems. Enticingly, it may also be that seemingly homeostatic biological systems are merely demonstrating their underlying emerging criticality.
• Crook N, Scheper TO, 'An Adaptive Chaotic Neural Network'
  (2002) pp.2580-2585
  
• Crook NT, olde Scheper T, 'Learning in a Chaotic Neural Network'
  (2002) pp.401-406
  ISBN: 2-930307-02-1
  Website 
• olde Scheper T, Crook NT, 'Delay Control Aspects of the Chaotic Rossler System'
  (2002) pp.132-
  ISBN: 3-906454-32-0
  Website 
• Crook NT, olde Scheper T, 'Adaptive Memory Dynamics in a Chaotic Neural Network'
  33 (4) (2002) pp.131-
  ISBN: 3-906454-32-0
  Website 
• Crook N, Scheper TO, 'Dynamic Recognition States for Chaotic Neural Networks'
  (2001) pp.62-72
  Website 
• Crook NT, olde Scheper T, 'A novel chaotic neural network architecture'
  (2001) pp.295-300
  ISBN: 2930307013
  

Other publications

• {OLDE SCHEPER} T, CARNELL AR, 'A method of controlling a dynamic physical system that exhibits a chaotic behaviour'
  (2013) Website 
• olde Scheper T, 'Chaos and Information in Dynamic Neural Networks'
  (2001)

Membership of professional bodies

• American Association for the Advancement of Science (AAAS)
• Institute of Electrical and Electronics Engineers (IEEE)
• The Organization for Computational Neuroscience (OCN)
• Society for Mathematical Biology (SMB)
• European Society of Mathematical and Theoretical Biology (ESMTB)
• Federation of European
 Neurosciences Societies (FENS)

Consultancy

This year:

• 2nd International Workshop on Artificial Intelligence for Diabetes, Vienna, 2017 (with Mireya Munoz-Balbontin)
• European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, 2017 
  
  

• Future Technologies Conference (FTC) 2017, Vancouver