Introduction: The Third International Conference on Epigenetic Robotics

This paper summarizes the paper and poster contributions to the Third International Workshop on Epigenetic Robotics. The focus of this workshop is on the cross-disciplinary interaction of developmental psychology and robotics. Namely, the general goal in this area is to create robotic models of the psychological development of various behaviors. The term "epigenetic" is used in much the same sense as the term "developmental" and while we could call our topic "developmental robotics", developmental robotics can be seen as having a broader interdisciplinary emphasis. Our focus in this workshop is on the interaction of developmental psychology and robotics and we use the phrase "epigenetic robotics" to capture this focus

Synaptic Learning for Neuromorphic Vision - Processing Address Events with Spiking Neural Networks

Das Gehirn übertrifft herkömmliche Computerarchitekturen in Bezug auf Energieeffizienz, Robustheit und Anpassungsfähigkeit. Diese Aspekte sind auch für neue Technologien wichtig. Es lohnt sich daher, zu untersuchen, welche biologischen Prozesse das Gehirn zu Berechnungen befähigen und wie sie in Silizium umgesetzt werden können. Um sich davon inspirieren zu lassen, wie das Gehirn Berechnungen durchführt, ist ein Paradigmenwechsel im Vergleich zu herkömmlichen Computerarchitekturen erforderlich. Tatsächlich besteht das Gehirn aus Nervenzellen, Neuronen genannt, die über Synapsen miteinander verbunden sind und selbstorganisierte Netzwerke bilden. Neuronen und Synapsen sind komplexe dynamische Systeme, die durch biochemische und elektrische Reaktionen gesteuert werden. Infolgedessen können sie ihre Berechnungen nur auf lokale Informationen stützen. Zusätzlich kommunizieren Neuronen untereinander mit kurzen elektrischen Impulsen, den so genannten Spikes, die sich über Synapsen bewegen. Computational Neuroscientists versuchen, diese Berechnungen mit spikenden neuronalen Netzen zu modellieren. Wenn sie auf dedizierter neuromorpher Hardware implementiert werden, können spikende neuronale Netze wie das Gehirn schnelle, energieeffiziente Berechnungen durchführen. Bis vor kurzem waren die Vorteile dieser Technologie aufgrund des Mangels an funktionellen Methoden zur Programmierung von spikenden neuronalen Netzen begrenzt. Lernen ist ein Paradigma für die Programmierung von spikenden neuronalen Netzen, bei dem sich Neuronen selbst zu funktionalen Netzen organisieren. Wie im Gehirn basiert das Lernen in neuromorpher Hardware auf synaptischer Plastizität. Synaptische Plastizitätsregeln charakterisieren Gewichtsaktualisierungen im Hinblick auf Informationen, die lokal an der Synapse anliegen. Das Lernen geschieht also kontinuierlich und online, während sensorischer Input in das Netzwerk gestreamt wird. Herkömmliche tiefe neuronale Netze werden üblicherweise durch Gradientenabstieg trainiert. Die durch die biologische Lerndynamik auferlegten Einschränkungen verhindern jedoch die Verwendung der konventionellen Backpropagation zur Berechnung der Gradienten. Beispielsweise behindern kontinuierliche Aktualisierungen den synchronen Wechsel zwischen Vorwärts- und Rückwärtsphasen. Darüber hinaus verhindern Gedächtnisbeschränkungen, dass die Geschichte der neuronalen Aktivität im Neuron gespeichert wird, so dass Verfahren wie Backpropagation-Through-Time nicht möglich sind. Neuartige Lösungen für diese Probleme wurden von Computational Neuroscientists innerhalb des Zeitrahmens dieser Arbeit vorgeschlagen. In dieser Arbeit werden spikende neuronaler Netzwerke entwickelt, um Aufgaben der visuomotorischen Neurorobotik zu lösen. In der Tat entwickelten sich biologische neuronale Netze ursprünglich zur Steuerung des Körpers. Die Robotik stellt also den künstlichen Körper für das künstliche Gehirn zur Verfügung. Auf der einen Seite trägt diese Arbeit zu den gegenwärtigen Bemühungen um das Verständnis des Gehirns bei, indem sie schwierige Closed-Loop-Benchmarks liefert, ähnlich dem, was dem biologischen Gehirn widerfährt. Auf der anderen Seite werden neue Wege zur Lösung traditioneller Robotik Probleme vorgestellt, die auf vom Gehirn inspirierten Paradigmen basieren. Die Forschung wird in zwei Schritten durchgeführt. Zunächst werden vielversprechende synaptische Plastizitätsregeln identifiziert und mit ereignisbasierten Vision-Benchmarks aus der realen Welt verglichen. Zweitens werden neuartige Methoden zur Abbildung visueller Repräsentationen auf motorische Befehle vorgestellt. Neuromorphe visuelle Sensoren stellen einen wichtigen Schritt auf dem Weg zu hirninspirierten Paradigmen dar. Im Gegensatz zu herkömmlichen Kameras senden diese Sensoren Adressereignisse aus, die lokalen Änderungen der Lichtintensität entsprechen. Das ereignisbasierte Paradigma ermöglicht eine energieeffiziente und schnelle Bildverarbeitung, erfordert aber die Ableitung neuer asynchroner Algorithmen. Spikende neuronale Netze stellen eine Untergruppe von asynchronen Algorithmen dar, die vom Gehirn inspiriert und für neuromorphe Hardwaretechnologie geeignet sind. In enger Zusammenarbeit mit Computational Neuroscientists werden erfolgreiche Methoden zum Erlernen räumlich-zeitlicher Abstraktionen aus der Adressereignisdarstellung berichtet. Es wird gezeigt, dass Top-Down-Regeln der synaptischen Plastizität, die zur Optimierung einer objektiven Funktion abgeleitet wurden, die Bottom-Up-Regeln übertreffen, die allein auf Beobachtungen im Gehirn basieren. Mit dieser Einsicht wird eine neue synaptische Plastizitätsregel namens "Deep Continuous Local Learning" eingeführt, die derzeit den neuesten Stand der Technik bei ereignisbasierten Vision-Benchmarks erreicht. Diese Regel wurde während eines Aufenthalts an der Universität von Kalifornien, Irvine, gemeinsam abgeleitet, implementiert und evaluiert. Im zweiten Teil dieser Arbeit wird der visuomotorische Kreis geschlossen, indem die gelernten visuellen Repräsentationen auf motorische Befehle abgebildet werden. Drei Ansätze werden diskutiert, um ein visuomotorisches Mapping zu erhalten: manuelle Kopplung, Belohnungs-Kopplung und Minimierung des Vorhersagefehlers. Es wird gezeigt, wie diese Ansätze, welche als synaptische Plastizitätsregeln implementiert sind, verwendet werden können, um einfache Strategien und Bewegungen zu lernen. Diese Arbeit ebnet den Weg zur Integration von hirninspirierten Berechnungsparadigmen in das Gebiet der Robotik. Es wird sogar prognostiziert, dass Fortschritte in den neuromorphen Technologien und bei den Plastizitätsregeln die Entwicklung von Hochleistungs-Lernrobotern mit geringem Energieverbrauch ermöglicht

A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

Rare neural correlations implement robotic conditioning with delayed rewards and disturbances

Neural conditioning associates cues and actions with following rewards. The environments in which robots operate, however, are pervaded by a variety of disturbing stimuli and uncertain timing. In particular, variable reward delays make it difficult to reconstruct which previous actions are responsible for following rewards. Such an uncertainty is handled by biological neural networks, but represents a challenge for computational models, suggesting the lack of a satisfactory theory for robotic neural conditioning. The present study demonstrates the use of rare neural correlations in making correct associations between rewards and previous cues or actions. Rare correlations are functional in selecting sparse synapses to be eligible for later weight updates if a reward occurs. The repetition of this process singles out the associating and reward-triggering pathways, and thereby copes with distal rewards. The neural network displays macro-level classical and operant conditioning, which is demonstrated in an interactive real-life human-robot interaction. The proposed mechanism models realistic conditioning in humans and animals and implements similar behaviors in neuro-robotic platforms

Induced pluripotent stem cell-based organ-on-a-chip as personalized drug screening tools: A focus on neurodegenerative disorders

The Organ-on-a-Chip (OoC) technology shows great potential to revolutionize the drugs development pipeline by mimicking the physiological environment and functions of human organs. The translational value of OoC is further enhanced when combined with patient-specific induced pluripotent stem cells (iPSCs) to develop more realistic disease models, paving the way for the development of a new generation of patient-on-a-chip devices. iPSCs differentiation capacity leads to invaluable improvements in personalized medicine. Moreover, the connection of single-OoC into multi-OoC or body-on-a-chip allows to investigate drug pharmacodynamic and pharmacokinetics through the study of multi-organs cross-talks. The need of a breakthrough thanks to this technology is particularly relevant within the field of neurodegenerative diseases, where the number of patients is increasing and the successful rate in drug discovery is worryingly low. In this review we discuss current iPSC-based OoC as drug screening models and their implication in development of new therapies for neurodegenerative disorders

Low Latency Event-Based Filtering and Feature Extraction for Dynamic Vision Sensors in Real-Time FPGA Applications

Dynamic Vision Sensor (DVS) pixels produce an asynchronous variable-rate address-event output that represents brightness changes at the pixel. Since these sensors produce frame-free output, they are ideal for real-time dynamic vision applications with real-time latency and power system constraints. Event-based ltering algorithms have been proposed to post-process the asynchronous event output to reduce sensor noise, extract low level features, and track objects, among others. These postprocessing algorithms help to increase the performance and accuracy of further processing for tasks such as classi cation using spike-based learning (ie. ConvNets), stereo vision, and visually-servoed robots, etc. This paper presents an FPGA-based library of these postprocessing event-based algorithms with implementation details; speci cally background activity (noise) ltering, pixel masking, object motion detection and object tracking. The latencies of these lters on the Field Programmable Gate Array (FPGA) platform are below 300ns with an average latency reduction of 188% (maximum of 570%) over the software versions running on a desktop PC CPU. This open-source event-based lter IP library for FPGA has been tested on two different platforms and scenarios using different synthesis and implementation tools for Lattice and Xilinx vendors

Estudio e implementación de algoritmos de fusión sensorial para sensores pulsantes y clásicos con protocolo AER de comunicación y aplicación en sistemas robóticos neuroinspirados

The objective of this thesis is to analyze, design, simulate and implement a model that follows the principles of the human nervous system when a reaching movement is made. The background of the thesis is the neuromorphic engineering field. This term was first coined in the late eighties by Caver Mead. Its main objective is to develop hardware devices, based on the neuron as the basic unit, to develop a range of tasks such as: decision making, image processing, learning, etc. During the last twenty years, this field of research has gathered a large number of researchers around the world. Spike-based sensors and devices that perform spike processing tasks have been developed. A neuro-inspired controller model based on the classic algorithms VITE and FLETE is proposed in this thesis (specifically, the two algorithms presented are: the VITE model which generates a non-planned trajectory and the FLETE model to generate the forces needed to hold a position reached). The hardware platforms used to implement them are a FPGA and a VLSI multi-chip setup. Then, considering how a reaching movement is performed by humans, these algorithms are translated under the constraints of each hardware device. The constraints are: spike-processing blocks described in VHDL for the FPGA and neurons LIF for the VLSI chips. To reach a successful translation of VITE algorithm under the constraints of the FPGA, a new spike-processing block is designed, simulated and implemented: GO Block. On the other hand, to perform an accurate translation of the VITE algorithm under VLSI requirements, the recent biological advances are studied. Then, a model which implements the co-activation of NMDA channels (this activity is related to the activity detected in the basal ganglia short time before a movement is made) is modeled, simulated and implemented. Once the model is defined for both platforms, it is simulated using the Matlab Simulink environment for FPGA and Brian simulator for VLSI chips. The hardware results of the algorithms translated are presented. The open-loop spike-based VITE (on both platforms) and closed-loop (FPGA) applied and connected to a robotic platform using the AER bus show an excellent behaviour in terms of power and resources consumption. They show also an accurate and precise functioning for reaching and tracking movements when the target is supplied by an AER retina or jAER. Thus, a full neuro-inspired architecture is implemented: from the sensor (retina) to the end effector (robot) going through the neuro-inspired controller designed. An alternative for the SVITE platform is also presented. A random element is added to the neuron model to include variability in the neural response. The results obtained for this variant, show a similar behaviour if a comparison with the deterministic algorithms is made. The possibility to include this pseudo-random controller in noise and / or random environment is demonstrated. Finally, this thesis claims that PFM is the most suitable modulation to drive motors in a neuromorphic hardware environment. It allows supplying the events directly to the motors. Furthermore, it is achieved that the system is not affected by spurious or noisy events. The novel results achieved with the VLSI multi-chip setup, this is the first attempt to control a robotic platform using sub-thresold low-power neurons, intended to set the basis for designing neuro-inspired controllers

Digital Implementation of Bio-Inspired Spiking Neuronal Networks

Spiking Neural Network as the third generation of artificial neural networks offers a promising solution for future computing, prosthesis, robotic and image processing applications. This thesis introduces digital designs and implementations of building blocks of a Spiking Neural Networks (SNNs) including neurons, learning rule, and small networks of neurons in the form of a Central Pattern Generator (CPG) which can be used as a module in control part of a bio-inspired robot. The circuits have been developed using Verilog Hardware Description Language (VHDL) and simulated through Modelsim and compiled and synthesised by Altera Qurtus Prime software for FPGA devices. Astrocyte as one of the brain cells controls synaptic activity between neurons by providing feedback to neurons. A novel digital hardware is proposed for neuron-synapseastrocyte network based on the biological Adaptive Exponential (AdEx) neuron and Postnov astrocyte cell model. The network can be used for implementation of large scale spiking neural networks. Synthesis of the designed circuits shows that the designed astrocyte circuit is able to imitate its biological model and regulate the synapse transmission, successfully. In addition, synthesis results confirms that the proposed design uses less than 1% of available resources of a VIRTEX II FPGA which saves up to 4.4% of FPGA resources in comparison to other designs. Learning rule is an essential part of every neural network including SNN. In an SNN, a special type of learning called Spike Timing Dependent Plasticity (STDP) is used to modify the connection strength between the spiking neurons. A pair-based STDP (PSTDP) works on pairs of spikes while a Triplet-based STDP (TSTDP) works on triplets of spikes to modify the synaptic weights. A low cost, accurate, and configurable digital architectures are proposed for PSTDP and TSTDP learning models. The proposed circuits have been compared with the state of the art methods like Lookup Table (LUT), and Piecewise Linear approximation (PWL). The circuits can be employed in a large-scale SNN implementation due to their compactness and configurability. Most of the neuron models represented in the literature are introduced to model the behavior of a single neuron. Since there is a large number of neurons in the brain, a population-based model can be helpful in better understanding of the brain functionality, implementing cognitive tasks and studying the brain diseases. Gaussian Wilson-Cowan model as one of the population-based models represents neuronal activity in the neocortex region of the brain. A digital model is proposed for the GaussianWilson-Cowan and examined in terms of dynamical and timing behavior. The evaluation indicates that the proposed model is able to generate the dynamical behavior as the original model is capable of. Digital architectures are implemented on an Altera FPGA board. Experimental results show that the proposed circuits take maximum 2% of the resources of a Stratix Altera board. In addition, static timing analysis indicates that the circuits can work in a maximum frequency of 244 MHz

Computational modeling with spiking neural networks

This chapter reviews recent developments in the area of spiking neural networks (SNN) and summarizes the main contributions to this research field. We give background information about the functioning of biological neurons, discuss the most important mathematical neural models along with neural encoding techniques, learning algorithms, and applications of spiking neurons. As a specific application, the functioning of the evolving spiking neural network (eSNN) classification method is presented in detail and the principles of numerous eSNN based applications are highlighted and discussed