Robust learning algorithms for spiking and rate-based neural networks

Inspired by the remarkable properties of the human brain, the fields of machine learning, computational neuroscience and neuromorphic engineering have achieved significant synergistic progress in the last decade. Powerful neural network models rooted in machine learning have been proposed as models for neuroscience and for applications in neuromorphic engineering. However, the aspect of robustness is often neglected in these models. Both biological and engineered substrates show diverse imperfections that deteriorate the performance of computation models or even prohibit their implementation. This thesis describes three projects aiming at implementing robust learning with local plasticity rules in neural networks. First, we demonstrate the advantages of neuromorphic computations in a pilot study on a prototype chip. Thereby, we quantify the speed and energy consumption of the system compared to a software simulation and show how on-chip learning contributes to the robustness of learning. Second, we present an implementation of spike-based Bayesian inference on accelerated neuromorphic hardware. The model copes, via learning, with the disruptive effects of the imperfect substrate and benefits from the acceleration. Finally, we present a robust model of deep reinforcement learning using local learning rules. It shows how backpropagation combined with neuromodulation could be implemented in a biologically plausible framework. The results contribute to the pursuit of robust and powerful learning networks for biological and neuromorphic substrates

Learning spatio-temporal spike train encodings with ReSuMe, DelReSuMe, and Reward-modulated Spike-timing Dependent Plasticity in Spiking Neural Networks

SNNs are referred to as the third generation of ANNs. Inspired from biological observations and recent advances in neuroscience, proposed methods increase the power of SNNs. Today, the main challenge is to discover efficient plasticity rules for SNNs. Our research aims are to explore/extend computational models of plasticity. We make various achievements using ReSuMe, DelReSuMe, and R-STDP based on the fundamental plasticity of STDP. The information in SNNs is encoded in the patterns of firing activities. For biological plausibility, it is necessary to use multi-spike learning instead of single-spike. Therefore, we focus on encoding inputs/outputs using multiple spikes. ReSuMe is capable of generating desired patterns with multiple spikes. The trained neuron in ReSuMe can fire at desired times in response to spatio-temporal inputs. We propose alternative architecture for ReSuMe dealing with heterogeneous synapses. It is demonstrated that the proposed topology exactly mimic the ReSuMe. A novel extension of ReSuMe, called DelReSuMe, has better accuracy using less iteration by using multi-delay plasticity in addition to weight learning under noiseless and noisy conditions. The proposed heterogeneous topology is also used for DelReSuMe. Another plasticity extension based on STDP takes into account reward to modulate synaptic strength named R-STDP. We use dopamine-inspired STDP in SNNs to demonstrate improvements in mapping spatio-temporal patterns of spike trains with the multi-delay mechanism versus single connection. From the viewpoint of Machine Learning, Reinforcement Learning is outlined through a maze task in order to investigate the mechanisms of reward and eligibility trace which are the fundamental in R-STDP. To develop the approach we implement Temporal-Difference learning and novel knowledge-based RL techniques on the maze task. We develop rule extractions which are combined with RL and wall follower algorithms. We demonstrate the improvements on the exploration efficiency of TD learning for maze navigation tasks

Über die Selbstorganisation einer hierarchischen Gedächtnisstruktur für kompositionelle Objektrepräsentation im visuellen Kortex

At present, there is a huge lag between the artificial and the biological information processing systems in terms of their capability to learn. This lag could be certainly reduced by gaining more insight into the higher functions of the brain like learning and memory. For instance, primate visual cortex is thought to provide the long-term memory for the visual objects acquired by experience. The visual cortex handles effortlessly arbitrary complex objects by decomposing them rapidly into constituent components of much lower complexity along hierarchically organized visual pathways. How this processing architecture self-organizes into a memory domain that employs such compositional object representation by learning from experience remains to a large extent a riddle. The study presented here approaches this question by proposing a functional model of a self-organizing hierarchical memory network. The model is based on hypothetical neuronal mechanisms involved in cortical processing and adaptation. The network architecture comprises two consecutive layers of distributed, recurrently interconnected modules. Each module is identified with a localized cortical cluster of fine-scale excitatory subnetworks. A single module performs competitive unsupervised learning on the incoming afferent signals to form a suitable representation of the locally accessible input space. The network employs an operating scheme where ongoing processing is made of discrete successive fragments termed decision cycles, presumably identifiable with the fast gamma rhythms observed in the cortex. The cycles are synchronized across the distributed modules that produce highly sparse activity within each cycle by instantiating a local winner-take-all-like operation. Equipped with adaptive mechanisms of bidirectional synaptic plasticity and homeostatic activity regulation, the network is exposed to natural face images of different persons. The images are presented incrementally one per cycle to the lower network layer as a set of Gabor filter responses extracted from local facial landmarks. The images are presented without any person identity labels. In the course of unsupervised learning, the network creates simultaneously vocabularies of reusable local face appearance elements, captures relations between the elements by linking associatively those parts that encode the same face identity, develops the higher-order identity symbols for the memorized compositions and projects this information back onto the vocabularies in generative manner. This learning corresponds to the simultaneous formation of bottom-up, lateral and top-down synaptic connectivity within and between the network layers. In the mature connectivity state, the network holds thus full compositional description of the experienced faces in form of sparse memory traces that reside in the feed-forward and recurrent connectivity. Due to the generative nature of the established representation, the network is able to recreate the full compositional description of a memorized face in terms of all its constituent parts given only its higher-order identity symbol or a subset of its parts. In the test phase, the network successfully proves its ability to recognize identity and gender of the persons from alternative face views not shown before. An intriguing feature of the emerging memory network is its ability to self-generate activity spontaneously in absence of the external stimuli. In this sleep-like off-line mode, the network shows a self-sustaining replay of the memory content formed during the previous learning. Remarkably, the recognition performance is tremendously boosted after this off-line memory reprocessing. The performance boost is articulated stronger on those face views that deviate more from the original view shown during the learning. This indicates that the off-line memory reprocessing during the sleep-like state specifically improves the generalization capability of the memory network. The positive effect turns out to be surprisingly independent of synapse-specific plasticity, relying completely on the synapse-unspecific, homeostatic activity regulation across the memory network. The developed network demonstrates thus functionality not shown by any previous neuronal modeling approach. It forms and maintains a memory domain for compositional, generative object representation in unsupervised manner through experience with natural visual images, using both on- ("wake") and off-line ("sleep") learning regimes. This functionality offers a promising departure point for further studies, aiming for deeper insight into the learning mechanisms employed by the brain and their consequent implementation in the artificial adaptive systems for solving complex tasks not tractable so far.Gegenwärtig besteht immer noch ein enormer Abstand zwischen der Lernfähigkeit von künstlichen und biologischen Informationsverarbeitungssystemen. Dieser Abstand ließe sich durch eine bessere Einsicht in die höheren Funktionen des Gehirns wie Lernen und Gedächtnis verringern. Im visuellen Kortex etwa werden die Objekte innerhalb kürzester Zeit entlang der hierarchischen Verarbeitungspfade in ihre Bestandteile zerlegt und so durch eine Komposition von Elementen niedrigerer Komplexität dargestellt. Bereits bekannte Objekte werden so aus dem Langzeitgedächtnis abgerufen und wiedererkannt. Wie eine derartige kompositionell-hierarchische Gedächtnisstruktur durch die visuelle Erfahrung zustande kommen kann, ist noch weitgehend ungeklärt. Um dieser Frage nachzugehen, wird hier ein funktionelles Modell eines lernfähigen rekurrenten neuronalen Netzwerkes vorgestellt. Im Netzwerk werden neuronale Mechanismen implementiert, die der kortikalen Verarbeitung und Plastizität zugrunde liegen. Die hierarchische Architektur des Netzwerkes besteht aus zwei nacheinander geschalteten Schichten, die jede eine Anzahl von verteilten, rekurrent vernetzten Modulen beherbergen. Ein Modul umfasst dabei mehrere funktionell separate Subnetzwerke. Jedes solches Modul ist imstande, aus den eintreffenden Signalen eine geeignete Repräsentation für den lokalen Eingaberaum unüberwacht zu lernen. Die fortlaufende Verarbeitung im Netzwerk setzt sich zusammen aus diskreten Fragmenten, genannt Entscheidungszyklen, die man mit den schnellen kortikalen Rhythmen im gamma-Frequenzbereich in Verbindung setzen kann. Die Zyklen sind synchronisiert zwischen den verteilten Modulen. Innerhalb eines Zyklus wird eine lokal umgrenzte winner-take-all-ähnliche Operation in Modulen durchgeführt. Die Kompetitionsstärke wächst im Laufe des Zyklus an. Diese Operation aktiviert in Abhängigkeit von den Eingabesignalen eine sehr kleine Anzahl von Einheiten und verstärkt sie auf Kosten der anderen, um den dargebotenen Reiz in der Netzwerkaktivität abzubilden. Ausgestattet mit adaptiven Mechanismen der bidirektionalen synaptischen Plastizität und der homöostatischen Aktivitätsregulierung, erhält das Netzwerk natürliche Gesichtsbilder von verschiedenen Personen dargeboten. Die Bilder werden der unteren Netzwerkschicht, je ein Bild pro Zyklus, als Ansammlung von Gaborfilterantworten aus lokalen Gesichtslandmarken zugeführt, ohne Information über die Personenidentität zur Verfügung zu stellen. Im Laufe der unüberwachten Lernprozedur formt das Netzwerk die Verbindungsstruktur derart, dass die Gesichter aller dargebotenen Personen im Netzwerk in Form von dünn besiedelten Gedächtnisspuren abgelegt werden. Hierzu werden gleichzeitig vorwärtsgerichtete (bottom-up) und rekurrente (lateral, top-down) synaptische Verbindungen innerhalb und zwischen den Schichten gelernt. Im reifen Verbindungszustand werden infolge dieses Lernens die einzelnen Gesichter als Komposition ihrer Bestandteile auf generative Art gespeichert. Dank der generativen Art der gelernten Struktur reichen schon allein das höhere Identitätssymbol oder eine kleine Teilmenge von zugehörigen Gesichtselementen, um alle Bestandteile der gespeicherten Gesichter aus dem Gedächtnis abzurufen. In der Testphase kann das Netzwerk erfolgreich sowohl die Identität als auch das Geschlecht von Personen aus vorher nicht gezeigten Gesichtsansichten erkennen. Eine bemerkenswerte Eigenschaft der entstandenen Gedächtnisarchitektur ist ihre Fähigkeit, ohne Darbietung von externen Stimuli spontan Aktivitätsmuster zu generieren und die im Gedächtnis abgelegten Inhalte in diesem schlafähnlichen "off-line" Regime wiederzugeben. Interessanterweise ergibt sich aus der Schlafphase ein direkter Vorteil für die Gedächtnisfunktion. Dieser Vorteil macht sich durch eine drastisch verbesserte Erkennungsrate nach der Schlafphase bemerkbar, wenn das Netwerk mit den zuvor nicht dargebotenen Ansichten von den bereits bekannten Personen konfrontiert wird. Die Leistungsverbesserung nach der Schlafphase ist umso deutlicher, je stärker die Alternativansichten vom Original abweichen. Dieser positive Effekt ist zudem komplett unabhängig von der synapsenspezifischen Plastizität und kann allein durch die synapsenunspezifische, homöostatische Regulation der Aktivität im Netzwerk erklärt werden. Das entwickelte Netzwerk demonstriert so eine im Bereich der neuronalen Modellierung bisher nicht gezeigte Funktionalität. Es kann unüberwacht eine Gedächtnisdomäne für kompositionelle, generative Objektrepräsentation durch die Erfahrung mit natürlichen Bildern sowohl im reizgetriebenen, wachähnlichen Zustand als auch im reizabgekoppelten, schlafähnlichen Zustand formen und verwalten. Diese Funktionalität bietet einen vielversprechenden Ausgangspunkt für weitere Studien, die die neuronalen Lernmechanismen des Gehirns ins Visier nehmen und letztendlich deren konsequente Umsetzung in technischen, adaptiven Systemen anstreben

A bottom-up approach to emulating emotions using neuromodulation in agents

A bottom-up approach to emulating emotions is expounded in this thesis. This is intended to be useful in research where a phenomenon is to be emulated but the nature of it can not easily be defined. This approach not only advocates emulating the underlying mechanisms that are proposed to give rise to emotion in natural agents, but also advocates applying an open-mind as to what the phenomenon actually is. There is evidence to suggest that neuromodulation is inherently responsible for giving rise to emotions in natural agents and that emotions consequently modulate the behaviour of the agent. The functionality provided by neuromodulation, when applied to agents with self-organising biologically plausible neural networks, is isolated and studied. In research efforts such as this the definition should emerge from the evidence rather than postulate that the definition, derived from limited information, is correct and should be implemented. An implementation of a working definition only tells us that the definition can be implemented. It does not tell us whether that working definition is itself correct and matches the phenomenon in the real world. If this model of emotions was assumed to be true and implemented in an agent, there would be a danger of precluding implementations that could offer alternative theories as to the relevance of neuromodulation to emotions. By isolating and studying different mechanisms such as neuromodulation that are thought to give rise to emotions, theories can arise as to what emotions are and the functionality that they provide. The application of this approach concludes with a theory as to how some emotions can operate via the use of neuromodulators. The theory is explained using the concepts of dynamical systems, free-energy and entropy.EPSRC Stirling University, Computing Science departmental gran

Efficient Learning Machines

Computer scienc

White Paper 11: Artificial intelligence, robotics & data science

198 p. : 17 cmSIC white paper on Artificial Intelligence, Robotics and Data Science sketches a preliminary roadmap for addressing current R&D challenges associated with automated and autonomous machines. More than 50 research challenges investigated all over Spain by more than 150 experts within CSIC are presented in eight chapters. Chapter One introduces key concepts and tackles the issue of the integration of knowledge (representation), reasoning and learning in the design of artificial entities. Chapter Two analyses challenges associated with the development of theories –and supporting technologies– for modelling the behaviour of autonomous agents. Specifically, it pays attention to the interplay between elements at micro level (individual autonomous agent interactions) with the macro world (the properties we seek in large and complex societies). While Chapter Three discusses the variety of data science applications currently used in all fields of science, paying particular attention to Machine Learning (ML) techniques, Chapter Four presents current development in various areas of robotics. Chapter Five explores the challenges associated with computational cognitive models. Chapter Six pays attention to the ethical, legal, economic and social challenges coming alongside the development of smart systems. Chapter Seven engages with the problem of the environmental sustainability of deploying intelligent systems at large scale. Finally, Chapter Eight deals with the complexity of ensuring the security, safety, resilience and privacy-protection of smart systems against cyber threats.18 EXECUTIVE SUMMARY ARTIFICIAL INTELLIGENCE, ROBOTICS AND DATA SCIENCE Topic Coordinators Sara Degli Esposti ( IPP-CCHS, CSIC ) and Carles Sierra ( IIIA, CSIC ) 18 CHALLENGE 1 INTEGRATING KNOWLEDGE, REASONING AND LEARNING Challenge Coordinators Felip Manyà ( IIIA, CSIC ) and Adrià Colomé ( IRI, CSIC – UPC ) 38 CHALLENGE 2 MULTIAGENT SYSTEMS Challenge Coordinators N. Osman ( IIIA, CSIC ) and D. López ( IFS, CSIC ) 54 CHALLENGE 3 MACHINE LEARNING AND DATA SCIENCE Challenge Coordinators J. J. Ramasco Sukia ( IFISC ) and L. Lloret Iglesias ( IFCA, CSIC ) 80 CHALLENGE 4 INTELLIGENT ROBOTICS Topic Coordinators G. Alenyà ( IRI, CSIC – UPC ) and J. Villagra ( CAR, CSIC ) 100 CHALLENGE 5 COMPUTATIONAL COGNITIVE MODELS Challenge Coordinators M. D. del Castillo ( CAR, CSIC) and M. Schorlemmer ( IIIA, CSIC ) 120 CHALLENGE 6 ETHICAL, LEGAL, ECONOMIC, AND SOCIAL IMPLICATIONS Challenge Coordinators P. Noriega ( IIIA, CSIC ) and T. Ausín ( IFS, CSIC ) 142 CHALLENGE 7 LOW-POWER SUSTAINABLE HARDWARE FOR AI Challenge Coordinators T. Serrano ( IMSE-CNM, CSIC – US ) and A. Oyanguren ( IFIC, CSIC - UV ) 160 CHALLENGE 8 SMART CYBERSECURITY Challenge Coordinators D. Arroyo Guardeño ( ITEFI, CSIC ) and P. Brox Jiménez ( IMSE-CNM, CSIC – US )Peer reviewe

Complex and Adaptive Dynamical Systems: A Primer

An thorough introduction is given at an introductory level to the field of quantitative complex system science, with special emphasis on emergence in dynamical systems based on network topologies. Subjects treated include graph theory and small-world networks, a generic introduction to the concepts of dynamical system theory, random Boolean networks, cellular automata and self-organized criticality, the statistical modeling of Darwinian evolution, synchronization phenomena and an introduction to the theory of cognitive systems. It inludes chapter on Graph Theory and Small-World Networks, Chaos, Bifurcations and Diffusion, Complexity and Information Theory, Random Boolean Networks, Cellular Automata and Self-Organized Criticality, Darwinian evolution, Hypercycles and Game Theory, Synchronization Phenomena and Elements of Cognitive System Theory.Comment: unformatted version of the textbook; published in Springer, Complexity Series (2008, second edition 2010