Event management for large scale event-driven digital hardware spiking neural networks

Abstract : The interest in brain-like computation has led to the design of a plethora of innovative neuromorphic systems. Individually, spiking neural networks (SNNs), event-driven simulation and digital hardware neuromorphic systems get a lot of attention. Despite the popularity of event-driven SNNs in soft- ware, very few digital hardware architectures are found. This is because existing hardware solutions for event management scale badly with the number of events. This paper introduces the structured heap queue, a pipelined digital hardware data structure, and demonstrates its suitability for event management. The structured heap queue scales gracefully with the number of events, allowing the efficient implementation of large scale digital hardware event-driven SNNs. The scaling is linear for memory, logarithmic for logic resources and constant for processing time. The use of the structured heap queue is demonstrated on eld-programmable gate array (FPGA) with an image segmentation experiment and a SNN of 65 536 neurons and 513 184 synapses. Events can be processed at the rate of 1 every 7 clock cycles and a 406 x 158 pixel image is segmented in 200 ms

Development of a Large-Scale Integrated Neurocognitive Architecture - Part 2: Design and Architecture

In Part 1 of this report, we outlined a framework for creating an intelligent agent based upon modeling the large-scale functionality of the human brain. Building on those results, we begin Part 2 by specifying the behavioral requirements of a large-scale neurocognitive architecture. The core of our long-term approach remains focused on creating a network of neuromorphic regions that provide the mechanisms needed to meet these requirements. However, for the short term of the next few years, it is likely that optimal results will be obtained by using a hybrid design that also includes symbolic methods from AI/cognitive science and control processes from the field of artificial life. We accordingly propose a three-tiered architecture that integrates these different methods, and describe an ongoing computational study of a prototype 'mini-Roboscout' based on this architecture. We also examine the implications of some non-standard computational methods for developing a neurocognitive agent. This examination included computational experiments assessing the effectiveness of genetic programming as a design tool for recurrent neural networks for sequence processing, and experiments measuring the speed-up obtained for adaptive neural networks when they are executed on a graphical processing unit (GPU) rather than a conventional CPU. We conclude that the implementation of a large-scale neurocognitive architecture is feasible, and outline a roadmap for achieving this goal

Bio-inspired multisensory integration of social signals

Emotions understanding represents a core aspect of human communication. Our social behaviours are closely linked to expressing our emotions and understanding others’ emotional and mental states through social signals. Emotions are expressed in a multisensory manner, where humans use social signals from different sensory modalities such as facial expression, vocal changes, or body language. The human brain integrates all relevant information to create a new multisensory percept and derives emotional meaning. There exists a great interest for emotions recognition in various fields such as HCI, gaming, marketing, and assistive technologies. This demand is driving an increase in research on multisensory emotion recognition. The majority of existing work proceeds by extracting meaningful features from each modality and applying fusion techniques either at a feature level or decision level. However, these techniques are ineffective in translating the constant talk and feedback between different modalities. Such constant talk is particularly crucial in continuous emotion recognition, where one modality can predict, enhance and complete the other. This thesis proposes novel architectures for multisensory emotions recognition inspired by multisensory integration in the brain. First, we explore the use of bio-inspired unsupervised learning for unisensory emotion recognition for audio and visual modalities. Then we propose three multisensory integration models, based on different pathways for multisensory integration in the brain; that is, integration by convergence, early cross-modal enhancement, and integration through neural synchrony. The proposed models are designed and implemented using third generation neural networks, Spiking Neural Networks (SNN) with unsupervised learning. The models are evaluated using widely adopted, third-party datasets and compared to state-of-the-art multimodal fusion techniques, such as early, late and deep learning fusion. Evaluation results show that the three proposed models achieve comparable results to state-of-the-art supervised learning techniques. More importantly, this thesis shows models that can translate a constant talk between modalities during the training phase. Each modality can predict, complement and enhance the other using constant feedback. The cross-talk between modalities adds an insight into emotions compared to traditional fusion techniques

Digital control networks for virtual creatures

Robot control systems evolved with genetic algorithms traditionally take the form of floating-point neural network models. This thesis proposes that digital control systems, such as quantised neural networks and logical networks, may also be used for the task of robot control. The inspiration for this is the observation that the dynamics of discrete networks may contain cyclic attractors which generate rhythmic behaviour, and that rhythmic behaviour underlies the central pattern generators which drive lowlevel motor activity in the biological world. To investigate this a series of experiments were carried out in a simulated physically realistic 3D world. The performance of evolved controllers was evaluated on two well known control tasks—pole balancing, and locomotion of evolved morphologies. The performance of evolved digital controllers was compared to evolved floating-point neural networks. The results show that the digital implementations are competitive with floating-point designs on both of the benchmark problems. In addition, the first reported evolution from scratch of a biped walker is presented, demonstrating that when all parameters are left open to evolutionary optimisation complex behaviour can result from simple components

Apprentissage et Contrôle dans les Architectures Neuronales

The brain, beyond its primary sensori-motor and regulation functions, is an outstanding adaptive system, capable of developping novel responses in novel situations. The principles of machine learning, a fast-developping domain, are at stake for a better understanding of the learning processes in the brain. Computational models of learning have provided several success stories, from which the "layered neural networks" are the most famous ones. This HDR dissertation presents different kinds neural networks models, displaying a more strict obedience to the biological constraints, in particular regarding the recurrent aspect of the neuronal interaction graph, the discreteness of the signals emitted by the neurons and the local aspect of the plasticity rules that govern the synaptic changes. We show in particular how recurrent neural networks organize their sensory input in different regions, how the the synaptic plasticity drives the network toward a more "simple" collective activity, allowing a better separation and prediction of the sensory stimuli, and how motor learning can rely on matching motor primitives with sensory data to organize the physical environment. Several projects are proposed, aiming at expanding some of those ideas into large-scale brain activity models, or also for the design of brain-computer interfaces.Au delà de ses fonctions primaires régulatrices et sensori-motrices, le cerveau est un formidable système adaptatif capable de développer des réponses nouvelles dans des contextes nouveaux. Les principes de l'apprentissage automatique ("machine learning"), en plein développement à l'heure actuelle, peuvent être utiles à la compréhension des processus d'apprentissage dans le cerveau. On parle de modèles computationnels de l'apprentissage, dont les "réseaux de neurones artificiels à couches" sont la réalisation la plus connue. Ce mémoire d'HDR présente des modèles de réseaux de neurones obéissant plus strictement aux contraintes biologiques, en particulier concernant le caractère récurrent du graphe d'interaction neuronale, le caractère discret des signaux émis par les neurones et le caractère local des règles de plasticité qui régissent les changements synaptiques. Nous montrons en particulier comment les réseaux de neurones récurrents organisent leurs données d'entrée en régions distinctes, comment la plasticité synaptique conduit les réseau de neurones vers des activités d'ensemble plus simples, permettant de mieux différencier et prédire les stimuli sensoriels, et comment l'apprentissage moteur peut se fonder sur l'appariement entre primitives motrices et données sensorielles pour organiser l'environnement physique. Différents projets sont proposés, visant à développer ces idées sur des modèles de l'activité du cerveau à large échelle, ou encore dans le cadre des interfaces cerveau-machine

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma