Evolutionary morphogenesis for multi-cellular systems

With a gene required for each phenotypic trait, direct genetic encodings may show poor scalability to increasing phenotype length. Developmental systems may alleviate this problem by providing more efficient indirect genotype to phenotype mappings. A novel classification of multi-cellular developmental systems in evolvable hardware is introduced. It shows a category of developmental systems that up to now has rarely been explored. We argue that this category is where most of the benefits of developmental systems lie (e.g. speed, scalability, robustness, inter-cellular and environmental interactions that allow fault-tolerance or adaptivity). This article describes a very simple genetic encoding and developmental system designed for multi-cellular circuits that belongs to this category. We refer to it as the morphogenetic system. The morphogenetic system is inspired by gene expression and cellular differentiation. It focuses on low computational requirements which allows fast execution and a compact hardware implementation. The morphogenetic system shows better scalability compared to a direct genetic encoding in the evolution of structures of differentiated cells, and its dynamics provides fault-tolerance up to high fault rates. It outperforms a direct genetic encoding when evolving spiking neural networks for pattern recognition and robot navigation. The results obtained with the morphogenetic system indicate that this "minimalist” approach to developmental systems merits further stud

On microelectronic self-learning cognitive chip systems

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche

Dynamically reconfigurable bio-inspired hardware

During the last several years, reconfigurable computing devices have experienced an impressive development in their resource availability, speed, and configurability. Currently, commercial FPGAs offer the possibility of self-reconfiguring by partially modifying their configuration bitstream, providing high architectural flexibility, while guaranteeing high performance. These configurability features have received special interest from computer architects: one can find several reconfigurable coprocessor architectures for cryptographic algorithms, image processing, automotive applications, and different general purpose functions. On the other hand we have bio-inspired hardware, a large research field taking inspiration from living beings in order to design hardware systems, which includes diverse topics: evolvable hardware, neural hardware, cellular automata, and fuzzy hardware, among others. Living beings are well known for their high adaptability to environmental changes, featuring very flexible adaptations at several levels. Bio-inspired hardware systems require such flexibility to be provided by the hardware platform on which the system is implemented. In general, bio-inspired hardware has been implemented on both custom and commercial hardware platforms. These custom platforms are specifically designed for supporting bio-inspired hardware systems, typically featuring special cellular architectures and enhanced reconfigurability capabilities; an example is their partial and dynamic reconfigurability. These aspects are very well appreciated for providing the performance and the high architectural flexibility required by bio-inspired systems. However, the availability and the very high costs of such custom devices make them only accessible to a very few research groups. Even though some commercial FPGAs provide enhanced reconfigurability features such as partial and dynamic reconfiguration, their utilization is still in its early stages and they are not well supported by FPGA vendors, thus making their use difficult to include in existing bio-inspired systems. In this thesis, I present a set of architectures, techniques, and methodologies for benefiting from the configurability advantages of current commercial FPGAs in the design of bio-inspired hardware systems. Among the presented architectures there are neural networks, spiking neuron models, fuzzy systems, cellular automata and random boolean networks. For these architectures, I propose several adaptation techniques for parametric and topological adaptation, such as hebbian learning, evolutionary and co-evolutionary algorithms, and particle swarm optimization. Finally, as case study I consider the implementation of bio-inspired hardware systems in two platforms: YaMoR (Yet another Modular Robot) and ROPES (Reconfigurable Object for Pervasive Systems); the development of both platforms having been co-supervised in the framework of this thesis

A Practical Investigation into Achieving Bio-Plausibility in Evo-Devo Neural Microcircuits Feasible in an FPGA

Many researchers has conjectured, argued, or in some cases demonstrated, that bio-plausibility can bring about emergent properties such as adaptability, scalability, fault-tolerance, self-repair, reliability, and autonomy to bio-inspired intelligent systems. Evolutionary-developmental (evo-devo) spiking neural networks are a very bio-plausible mixture of such bio-inspired intelligent systems that have been proposed and studied by a few researchers. However, the general trend is that the complexity and thus the computational cost grow with the bio-plausibility of the system. FPGAs (Field- Programmable Gate Arrays) have been used and proved to be one of the flexible and cost efficient hardware platforms for research' and development of such evo-devo systems. However, mapping a bio-plausible evo-devo spiking neural network to an FPGA is a daunting task full of different constraints and trade-offs that makes it, if not infeasible, very challenging. This thesis explores the challenges, trade-offs, constraints, practical issues, and some possible approaches in achieving bio-plausibility in creating evolutionary developmental spiking neural microcircuits in an FPGA through a practical investigation along with a series of case studies. In this study, the system performance, cost, reliability, scalability, availability, and design and testing time and complexity are defined as measures for feasibility of a system and structural accuracy and consistency with the current knowledge in biology as measures for bio-plausibility. Investigation of the challenges starts with the hardware platform selection and then neuron, cortex, and evo-devo models and integration of these models into a whole bio-inspired intelligent system are examined one by one. For further practical investigation, a new PLAQIF Digital Neuron model, a novel Cortex model, and a new multicellular LGRN evo-devo model are designed, implemented and tested as case studies. Results and their implications for the researchers, designers of such systems, and FPGA manufacturers are discussed and concluded in form of general trends, trade-offs, suggestions, and recommendations

A Practical Hardware Implementation of Systemic Computation

It is widely accepted that natural computation, such as brain computation, is far superior to typical computational approaches addressing tasks such as learning and parallel processing. As conventional silicon-based technologies are about to reach their physical limits, researchers have drawn inspiration from nature to found new computational paradigms. Such a newly-conceived paradigm is Systemic Computation (SC). SC is a bio-inspired model of computation. It incorporates natural characteristics and defines a massively parallel non-von Neumann computer architecture that can model natural systems efficiently. This thesis investigates the viability and utility of a Systemic Computation hardware implementation, since prior software-based approaches have proved inadequate in terms of performance and flexibility. This is achieved by addressing three main research challenges regarding the level of support for the natural properties of SC, the design of its implied architecture and methods to make the implementation practical and efficient. Various hardware-based approaches to Natural Computation are reviewed and their compatibility and suitability, with respect to the SC paradigm, is investigated. FPGAs are identified as the most appropriate implementation platform through critical evaluation and the first prototype Hardware Architecture of Systemic computation (HAoS) is presented. HAoS is a novel custom digital design, which takes advantage of the inbuilt parallelism of an FPGA and the highly efficient matching capability of a Ternary Content Addressable Memory. It provides basic processing capabilities in order to minimize time-demanding data transfers, while the optional use of a CPU provides high-level processing support. It is optimized and extended to a practical hardware platform accompanied by a software framework to provide an efficient SC programming solution. The suggested platform is evaluated using three bio-inspired models and analysis shows that it satisfies the research challenges and provides an effective solution in terms of efficiency versus flexibility trade-off

Evolutionary and Computational Advantages of Neuromodulated Plasticity

The integration of modulatory neurons into evolutionary artificial neural networks is proposed here. A model of modulatory neurons was devised to describe a plasticity mechanism at the low level of synapses and neurons. No initial assumptions were made on the network structures or on the system level dynamics. The work of this thesis studied the outset of high level system dynamics that emerged employing the low level mechanism of neuromodulated plasticity. Fully-fledged control networks were designed by simulated evolution: an evolutionary algorithm could evolve networks with arbitrary size and topology using standard and modulatory neurons as building blocks. A set of dynamic, reward-based environments was implemented with the purpose of eliciting the outset of learning and memory in networks. The evolutionary time and the performance of solutions were compared for networks that could or could not use modulatory neurons. The experimental results demonstrated that modulatory neurons provide an evolutionary advantage that increases with the complexity of the control problem. Networks with modulatory neurons were also observed to evolve alternative neural control structures with respect to networks without neuromodulation. Different network topologies were observed to lead to a computational advantage such as faster input-output signal processing. The evolutionary and computational advantages induced by modulatory neurons strongly suggest the important role of neuromodulated plasticity for the evolution of networks that require temporal neural dynamics, adaptivity and memory functions

On mapping epilepsy : magneto- and electroencephalographic characterizations of epileptic activities

Epilepsy is one of the most common neurological disorder, affecting up to 10 individuals per 1000 persons. The disorder have been known for several thousand years, with the first clinical descriptions dating back to ancient times. Nonetheless, characterization of the dynamics underlying epilepsy remains largely unknown. Understanding these patophysiological processes requires unifying both a neurobiological perspective, as well as a technically advanced neuroimaging perspective. The incomplete insight into epilepsy dynamics is reflected by the insufficient treatment options. Approximately 30% of all patients do not respond to anti-epileptic drugs (AEDs) and thus suffers from recurrent seizures despite adequate pharmacological treatments. These pharmacoresistant patients often undergo epilepsy surgery evaluations. Epilepsy surgery aims to resect the part of the brain that generates the epileptic seizure activity (seizure onset zone, SOZ). Nonetheless, up to 50% of all patients relapse after surgery. This can be due to incomplete mapping of both the SOZ and of other structures that might be involved in seizure initiation and propagation. Such cortical and subcortical structures are collectively referred to as the epileptic network. Historically, epilepsy was considered to be either a generalized disorder involving the entire brain, or a highly localized, focal, disorder. The modern technological development of both structural and functional neuroimaging has drastically altered this view. This development has made significant contributions to the now prevailing view that both generalized and focal epilepsies arise from more or less widespread pathological network pathways. Visualization of these pathways play an important role in the presurgical planning. Thus, both improved characterization and understanding of such pathways are pivotal in improvement of epilepsy diagnostics and treatments. It is evident that epilepsy research needs to stand on two legs: Both improved understanding of pathological, neurobiological and neurophysiological process, and improved neuroimaging instrumentation. Epilepsy research do not only span from visualization to understanding of neurophysiological processes, but also from cellular, neuronal, microscopic processes, to dynamical, large-scale network processes. It is well known that neurons involved in epileptic activities exhibit specific, pathological firing patterns. Genetic mutations resulting in neuronal ion channel defects can cause severe, and even lethal, epileptic syndromes in children, clearly illustrating a role for neuron membrane properties in epilepsy. However, cellular processes themselves cannot explain how epileptic seizures can involve, and propagate across, large cortical areas and generate seizure-specific symptomatologies. A strict cellular perspective can neither explain epilepsy-associated pathological interactions between larger distant regions in between seizures. Instead, the dynamical effects of cellular synchronization across both mesoscopic and macroscopic scales also need to be considered. Today, the only means to study such effects in human subjects are by combinations of neuroimaging modalities. However, as all measurement techniques, these exhibit individual limitations that affect the kind of information that can be inferred from these. Thus, once more we reach the conclusion that epilepsy research needs to rest upon both a neurophysiological/neurobiological leg, and a technical/instrumentational leg. In accordance with this necessity of a dual approach to epilepsy, this thesis covers both neurophysiological aspects of epileptic activity development, as well as functional neuroimaging instrumentation development with focus on epileptic activity detection and localization. Part 1 (neurophysiological part) is concerned with the neurophysiological dynamical changes that underlie development of so called interictal epileptiform discharges (IEDs) with special focus on the role of low-frequency oscillations. To this aim, both conventional magnetoencephalography (MEG) and intracranial electroencephalography (iEEG) with neurostimulation is analyzed. Part 2 (instrumentation part) is concerned with development of cutting-edge, novel on-scalp magnetoencephalography (osMEG) within clinical epilepsy evaluations and research with special focus on IEDs. The theses cover both modeling of osMEG characteristics, as well as the first-ever osMEG recording of a temporal lobe epilepsy patient

Tissu numérique cellulaire à routage et configuration dynamiques

In the design of new machines or in the development of new concepts, mankind has often observed nature, looking for useful ideas and sources of inspiration. The design of electronic circuits is no exception, and a considerable number of realizations have drawn inspiration from three aspects of natural systems : the evolution of species (Phylogenesis), the development of an organism starting from a single cell (Ontogenesis), and learning, as performed by our brain (Epigenesis). These three axes, grouped under the acronym POE, have for the most part been exploited separately : evolutionary principles allow to solve problems for which it is hard to find a solution with a deterministic method, while some electronic circuits draw inspiration from healing process in living beings to achieve self-repair, and artificial neural networks have the capability to efficiently execute a wide range of tasks. At this time, no electronic tissue capable of bringing them together seems to exist. The introduction of reconfigurable circuits called Field Programmable Gate Arrays (FPGAs), whose behavior can be redefined as often as desired, made prototyping such systems easier. FPGAs, by allowing a relatively simple implementation in hardware, can considerably increase the systems' performance and are thus extensively used by researchers. However, they lack plasticity, not being able to easily modify themselves without an external intervention. This PhD thesis, developed in the framework of the European POEtic project, proposes to define a new reconfigurable electronic circuit, with the goal of supplying a new substrate for bio-inspired applications that bring all three axes into play. This circuit is mainly composed of a microprocessor and an array of reconfigurable elements, the latter having been designed during this thesis. Evolutionary processes are executed by the microprocessor, while epigenetic and ontogenetic mechanisms are applied in the reconfigurable array, to entities seen as multicellular artificial organisms. Relatively similar to current commercial FPGAs, this subsystem offers however some unique features. First, the basic elements of the array have the capability to partially reconfigure other elements. Auto-replication and differentiation mechanisms can exploit this capability to let an organism grow or to modify its behavior. Second, a distributed routing layer allows to dynamically create connections between parts of the circuit at runtime. With this feature, cells (artificial neurons, for example) implemented in the reconfigurable array can initiate new connections in order to modify the global system behavior. This distributed routing mechanism, one of the major contributions of this thesis, induced the realization of several algorithms. Based on a parallel implementation of Lee's algorithm, these algorithms are totally distributed, no global control being necessary to create new data paths. Four algorithms have been defined implemented in hardware in the form of routing units connected to 3, 4, 6, or 8 neighbors. These units are all identical and are responsible for the routing processes. An analysis of their properties allows us to define the best algorithm, coupled with the most efficient neighborhood, in terms of congestion and of the number of transistors needed for a hardware realization. We finish the routing chapter by proposing a fifth algorithm that, unlike the previous ones, is constructed only through local interactions between routing units. It offers a better scalability, at the price of increased hardware overhead. Finally, the POEtic chip, in which one of our algorithms has been implemented, has been physically realized. We present different POE mechanisms that take advantage of its new features. Among these mechanisms, we can notably cite auto-replication, evolvable hardware, developmental systems, and self-repair. All of these mechanisms have been developed with the help of a circuit simulator, also designed in the framework of this thesis