Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented

Artificial Neurogenesis: An Introduction and Selective Review

International audienceIn this introduction and review—like in the book which follows—we explore the hypothesis that adaptive growth is a means of producing brain-like machines. The emulation of neural development can incorporate desirable characteristics of natural neural systems into engineered designs. The introduction begins with a review of neural development and neural models. Next, artificial development— the use of a developmentally-inspired stage in engineering design—is introduced. Several strategies for performing this " meta-design " for artificial neural systems are reviewed. This work is divided into three main categories: bio-inspired representations ; developmental systems; and epigenetic simulations. Several specific network biases and their benefits to neural network design are identified in these contexts. In particular, several recent studies show a strong synergy, sometimes interchange-ability, between developmental and epigenetic processes—a topic that has remained largely under-explored in the literature

Interoceptive robustness through environment-mediated morphological development

Typically, AI researchers and roboticists try to realize intelligent behavior in machines by tuning parameters of a predefined structure (body plan and/or neural network architecture) using evolutionary or learning algorithms. Another but not unrelated longstanding property of these systems is their brittleness to slight aberrations, as highlighted by the growing deep learning literature on adversarial examples. Here we show robustness can be achieved by evolving the geometry of soft robots, their control systems, and how their material properties develop in response to one particular interoceptive stimulus (engineering stress) during their lifetimes. By doing so we realized robots that were equally fit but more robust to extreme material defects (such as might occur during fabrication or by damage thereafter) than robots that did not develop during their lifetimes, or developed in response to a different interoceptive stimulus (pressure). This suggests that the interplay between changes in the containing systems of agents (body plan and/or neural architecture) at different temporal scales (evolutionary and developmental) along different modalities (geometry, material properties, synaptic weights) and in response to different signals (interoceptive and external perception) all dictate those agents' abilities to evolve or learn capable and robust strategies

Evolutionary Robotics: a new scientific tool for studying cognition

We survey developments in Artificial Neural Networks, in Behaviour-based Robotics and Evolutionary Algorithms that set the stage for Evolutionary Robotics in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments which is an essential aspect of real cognition that is often either bypassed or modelled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion; the origins of learning; and the ontogenetic acquisition of entrainment

L’INTELLECT INCARNÉ: Sur les interprétations computationnelles, évolutives et philosophiques de la connaissance

Modern cognitive science cannot be understood without recent developments in computer science, artificial intelligence (AI), robotics, neuroscience, biology, linguistics, and psychology. Classic analytic philosophy as well as traditional AI assumed that all kinds of knowledge must eplicitly be represented by formal or programming languages. This assumption is in contradiction to recent insights into the biology of evolution and developmental psychology of the human organism. Most of our knowledge is implicit and unconscious. It is not formally represented, but embodied knowledge which is learnt by doing and understood by bodily interacting with ecological niches and social environments. That is true not only for low-level skills, but even for high-level domains of categorization, language, and abstract thinking. Embodied cognitive science, AI, and robotics try to build the embodied mind in an artificial evolution. From a philosophical point of view, it is amazing that the new ideas of embodied mind and robotics have deep roots in 20th-century philosophy.Die moderne Kognitionswissenschaft kann nicht verstanden werden ohne Einbeziehung der neuesten Errungenschaften aus der Computerwissenschaft, künstlichen Intelligenz (AI), Robotik, Neurowissenschaft, Biologie, Linguistik und Psychologie. Die klassische analytische Philosophie, wie auch die traditionelle AI, setzten voraus, dass alle Arten des Wissens explizit durch formale oder Programmsprachen dargestellt werden müssen. Diese Annahme steht im Widerspruch zu den rezenten Einsichten in die Evolutionsbiologie und Entwicklungspsychologie des menschlichen Organismus. Der größte Teil unseres Wissens ist implizit und unbewusst. Es ist kein formal repräsentiertes, sondern ein verkörpertes Wissen, das durch Handeln gelernt und durch körperliche Interaktion mit ökologischen Nischen und gesellschaftlichen Umgebungen verstanden wird. Dies gilt nicht nur für niedere Fertigkeiten, sondern auch für höher gestellte Domänen: Kategorisierung, Sprache und abstraktes Denken. Die verkörperte Erkenntniswissenschaft, AI und Robotik versuchen, den verkörperten Geist in einer artifiziellen Evolution zu bilden. Vom philosophischen Standpunkt gesehen ist es erstaunlich, wie tief die neuen Ideen des verkörperten Geistes und der Robotik in der Philosophie des 20. Jahrhunderts verankert sind.La science cognitive moderne ne peut être comprise sans les progrès récents en informatique, intelligence artificielle, robotique, neuroscience, biologie, linguistique et psychologie. La philosophie analytique classique et l’intelligence artificielle traditionnelle présumaient que toutes les sortes de savoir devaient être représentées explicitement par des langages formels ou programmatiques. Cette thèse est en contradiction avec les découvertes récentes en biologie de l’évolution et en psychologie évolutive de l’organisme humain. La majeure partie de notre savoir est implicite et inconsciente. Elle n’est pas représentée formellement, mais constitue un savoir incarné, qui s’acquiert par l’action et se comprend en interaction corporelle avec nos niches écologiques et nos environnements sociaux. Cela n’est pas seulement vrai pour nos aptitudes élémentaires, mais aussi pour nos facultés supérieures de catégorisation, de langage et de pensée abstraite. Science cognitive incarnée, l’intelligence artificielle, ainsi que la robotique, tentent de construire un intellect incarné en évolution artificielle. Du point de vue philosophique, il est admirable de voir à quel point les nouvelles idées d’intellect incarné et de robotique sont ancrées dans la philosophie du XXe siècle

Bio-mimetic Spiking Neural Networks for unsupervised clustering of spatio-temporal data

Spiking neural networks aspire to mimic the brain more closely than traditional artificial neural networks. They are characterised by a spike-like activation function inspired by the shape of an action potential in biological neurons. Spiking networks remain a niche area of research, perform worse than the traditional artificial networks, and their real-world applications are limited. We hypothesised that neuroscience-inspired spiking neural networks with spike-timing-dependent plasticity demonstrate useful learning capabilities. Our objective was to identify features which play a vital role in information processing in the brain but are not commonly used in artificial networks, implement them in spiking networks without copying constraints that apply to living organisms, and to characterise their effect on data processing. The networks we created are not brain models; our approach can be labelled as artificial life. We performed a literature review and selected features such as local weight updates, neuronal sub-types, modularity, homeostasis and structural plasticity. We used the review as a guide for developing the consecutive iterations of the network, and eventually a whole evolutionary developmental system. We analysed the model’s performance on clustering of spatio-temporal data. Our results show that combining evolution and unsupervised learning leads to a faster convergence on the optimal solutions, better stability of fit solutions than each approach separately. The choice of fitness definition affects the network’s performance on fitness-related and unrelated tasks. We found that neuron type-specific weight homeostasis can be used to stabilise the networks, thus enabling longer training. We also demonstrated that networks with a rudimentary architecture can evolve developmental rules which improve their fitness. This interdisciplinary work provides contributions to three fields: it proposes novel artificial intelligence approaches, tests the possible role of the selected biological phenomena in information processing in the brain, and explores the evolution of learning in an artificial life system