Spectrum sensing in wireless communications serves as a crucial binary classification tool in cognitive radios, facilitating the detection of available radio spectrums for secondary users, especially in scenarios with high Signal-to-Noise Ratio (SNR). Leveraging Liquid State Machines (LSMs), which emulate spiking neural networks like the ones in the human brain, prove to be highly effective for real-time data monitoring for such temporal tasks. The inherent advantages of LSM-based recurrent neural networks, such as low complexity, high power efficiency, and accuracy, surpass those of traditional deep learning and conventional spectrum sensing methods. The architecture of the liquid state machine processor and its training methods are crucial for the performance of an LSM accelerator. This thesis presents one such LSM-based accelerator that explores novel architectural improvements for LSM hardware. Through the adoption of triplet-based Spike-Timing-Dependent Plasticity (STDP) and various spike encoding schemes on the spectrum dataset within the LSM, we investigate the advantages offered by these proposed techniques compared to traditional LSM models on the FPGA. FPGA boards, known for their power efficiency and low latency, are well-suited for time-critical machine learning applications. The thesis explores these novel onboard learning methods, shares the results of the suggested architectural changes, explains the trade-offs involved, and explores how the improved LSM model's accuracy can benefit different classification tasks. Additionally, we outline the future research directions aimed at further enhancing the accuracy of these models. / Master of Science / Machine Learning (ML) and Artificial Intelligence (AI) have significantly shaped various applications in recent years. One notable domain experiencing substantial positive impact is spectrum sensing within wireless communications, particularly in cognitive radios. In light of spectrum scarcity and the underutilization of RF spectrums, accurately classifying spectrums as occupied or unoccupied becomes crucial for enabling secondary users to efficiently utilize available resources. Liquid State Machines (LSMs), made of spiking neural networks resembling human brain, prove effective in real-time data monitoring for this classification task. Exploiting the temporal operations, LSM accelerators and processors, facilitate high performance and accurate spectrum monitoring than conventional spectrum sensing methods. The architecture of the liquid state machine processor's training and optimal learning methods plays a pivotal role in the performance of a LSM accelerator. This thesis delves into various architectural enhancements aimed at spectrum classification using a liquid state machine accelerator, particularly implemented on an FPGA board. FPGA boards, known for their power efficiency and low latency, are well-suited for time-critical machine learning applications. The thesis explores onboard learning methods, such as employing a targeted encoder and incorporating Triplet Spike Timing-Dependent Plasticity (Triplet STDP) in the learning reservoir. These enhancements propose improvements in accuracy for conventional LSM models. The discussion concludes by presenting results of the architectural implementations, highlighting trade-offs, and shedding light on avenues for enhancing the accuracy of conventional liquid state machine-based models further.

Aujourd'hui, la plupart des ordinateurs sont encore basés sur des concepts développés il y a plus de 60 ans par Alan Turing et John von Neumann. Cependant, ces ordinateurs numériques ont déjà commencé à atteindre certaines limites physiques via la technologie de la microélectronique au silicium (dissipation, vitesse, limites d'intégration, consommation d'énergie). Des approches alternatives, plus puissantes, plus efficaces et moins consommatrices d'énergie, constituent depuis plusieurs années un enjeu scientifique majeur. Beaucoup de ces approches s'inspirent naturellement du cerveau humain, dont les principes opérationnels sont encore loin d'être compris. Au début des années 2000, la communauté scientifique s'est aperçue qu'une modification du réseau neuronal récurrent (RNN), plus simple et maintenant appelée Reservoir Computing (RC), est parfois plus efficace pour certaines fonctionnalités, et est un nouveau paradigme de calcul qui s'inspire du cerveau. Sa structure est assez semblable aux concepts classiques de RNN, présentant généralement trois parties: une couche d'entrée pour injecter l'information dans un système dynamique non-linéaire (Write-In), une seconde couche où l'information d'entrée est projetée dans un espace de grande dimension (appelé réservoir dynamique) et une couche de sortie à partir de laquelle les informations traitées sont extraites par une fonction dite de lecture-sortie. Dans l'approche RC, la procédure d'apprentissage est effectuée uniquement dans la couche de sortie, tandis que la couche d'entrée et la couche réservoir sont fixées de manière aléatoire, ce qui constitue l'originalité principale du RC par rapport aux méthodes RNN. Cette fonctionnalité permet d'obtenir plus d'efficacité, de rapidité, de convergence d'apprentissage, et permet une mise en œuvre expérimentale. Cette thèse de doctorat a pour objectifs d'implémenter pour la première fois le RC photoniques en utilisant des dispositifs de télécommunication. Notre mise en œuvre expérimentale est basée sur un système dynamique non linéaire à retard, qui repose sur un oscillateur électro-optique (EO) avec une modulation de phase différentielle. Cet oscillateur EO a été largement étudié dans le contexte de la cryptographie optique du chaos. La dynamique présentée par de tels systèmes est en effet exploitée pour développer des comportements complexes dans un espace de phase à dimension infinie, et des analogies avec la dynamique spatio-temporelle (tels que les réseaux neuronaux) sont également trouvés dans la littérature. De telles particularités des systèmes à retard ont conforté l'idée de remplacer le RNN traditionnel (généralement difficile à concevoir technologiquement) par une architecture à retard d'EO non linéaire. Afin d'évaluer la puissance de calcul de notre approche RC, nous avons mis en œuvre deux tests de reconnaissance de chiffres parlés (tests de classification) à partir d'une base de données standard en intelligence artificielle (TI-46 et AURORA-2), et nous avons obtenu des performances très proches de l'état de l'art tout en établissant un nouvel état de l'art en ce qui concerne la vitesse de classification. Notre approche RC photonique nous a en effet permis de traiter environ 1 million de mots par seconde, améliorant la vitesse de traitement de l'information d'un facteur supérieur à ~3. / Nowadays most of computers are still based on concepts developed more than 60 years ago by Alan Turing and John von Neumann. However, these digital computers have already begun to reach certain physical limits of their implementation via silicon microelectronics technology (dissipation, speed, integration limits, energy consumption). Alternative approaches, more powerful, more efficient and with less consume of energy, have constituted a major scientific issue for several years. Many of these approaches naturally attempt to get inspiration for the human brain, whose operating principles are still far from being understood. In this line of research, a surprising variation of recurrent neural network (RNN), simpler, and also even sometimes more efficient for features or processing cases, has appeared in the early 2000s, now known as Reservoir Computing (RC), which is currently emerging new brain-inspired computational paradigm. Its structure is quite similar to the classical RNN computing concepts, exhibiting generally three parts: an input layer to inject the information into a nonlinear dynamical system (Write-In), a second layer where the input information is projected in a space of high dimension called dynamical reservoir and an output layer from which the processed information is extracted through a so-called Read-Out function. In RC approach the learning procedure is performed in the output layer only, while the input and reservoir layer are randomly fixed, being the main originality of RC compared to the RNN methods. This feature allows to get more efficiency, rapidity and a learning convergence, as well as to provide an experimental implementation solution. This PhD thesis is dedicated to one of the first photonic RC implementation using telecommunication devices. Our experimental implementation is based on a nonlinear delayed dynamical system, which relies on an electro-optic (EO) oscillator with a differential phase modulation. This EO oscillator was extensively studied in the context of the optical chaos cryptography. Dynamics exhibited by such systems are indeed known to develop complex behaviors in an infinite dimensional phase space, and analogies with space-time dynamics (as neural network ones are a kind of) are also found in the literature. Such peculiarities of delay systems supported the idea of replacing the traditional RNN (usually difficult to design technologically) by a nonlinear EO delay architecture. In order to evaluate the computational power of our RC approach, we implement two spoken digit recognition tests (classification tests) taken from a standard databases in artificial intelligence TI-46 and AURORA-2, obtaining results very close to state-of-the-art performances and establishing state-of-the-art in classification speed. Our photonic RC approach allowed us to process around of 1 million of words per second, improving the information processing speed by a factor ~3.

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In order to understand how the neural system encodes and processes information, research has focused on the study of neural representations of simple stimuli, paying no particular attention to it's temporal structure, with the assumption that a deeper understanding of how the neural system processes simpli fied stimuli will lead to an understanding of how the brain functions as a whole [1]. However, time is intrinsically bound to neural processing as all sensory, motor, and cognitive processes are inherently dynamic. Despite the importance of neural and stimulus dynamics, little is known of how the neural system represents rich spatio-temporal stimulus, which ultimately link the neural system to a continuously changing environment. The purpose of this thesis is to understand whether and how temporally-structured neural activity modulates the processing of information within the brain, proposing in turn that, the precise interaction between the spatio-temporal structure of the stimulus and the neural system is particularly relevant, particularly when considering the ongoing plasticity mechanisms which allow the neural system to learn from experience. In order to answer these questions, three studies were conducted. First, we studied the impact of spiking temporal structure on a single neuron spiking response, and explored in which way the functional connections to pre-synaptic neurons are modulated through adaptation. Our results suggest that, in a generic spiking neuron, the temporal structure of pre-synaptic excitatory and inhibitory neurons modulate both the spiking response of that same neuron and, most importantly, the speed and strength of learning. In the second, we present a generic model of a spiking neural network that processes rich spatio-temporal stimuli, and explored whether the processing of stimulus within the network is modulated due to the interaction with an external dynamical system (i.e. extracellular media), as well as several plasticity mechanisms. Our results indicate that the memory capacity, that re ects a dynamic short-term memory of incoming stimuli, can be extended on the presence of plasticity and through the interaction with an external dynamical system, while maintaining the network dynamics in a regime suitable for information processing. Finally, we characterized cortical signals of human subjects (electroencephalography, EEG) associated to a visual categorization task. Among other aspects, we studied whether changes in the dynamics of the stimulus leads to a changes in the neural processing at the cortical level, and introduced the relevance of large-scale integration for cognitive processing. Our results suggest that the dynamic synchronization across distributed cortical areas is stimulus specific and specifically linked to perceptual grouping. Taken together, the results presented here suggest that the temporal structure of the stimulus modulates how the neural system encodes and processes information within single neurons, network of neurons and cortical areas. In particular, the results indicate that timing modulates single neuron connectivity structures, the memory capability of networks of neurons, and the cortical representation of a visual stimuli. While the learning of invariant representations remains as the best framework to account for a number of neural processes (e.g. long-term memory [2]), the reported studies seem to provide support the idea that, at least to some extent, the neural system functions in a non-stationary fashion, where the processing of information is modulated by the stimulus dynamics itself. Altogether, this thesis highlights the relevance of understanding adaptive processes and their interaction with the temporal structure of the stimulus, arguing that a further understanding how the neural system processes dynamic stimuli is crucial for the further understanding of neural processing itself, and any theory that aims to understand neural processing should consider the processing of dynamic signals. 1. Frankish, K., and Ramsey, W. The Cambridge Handbook of Cognitive Science. Cambridge University Press, 2012. // 2. McGaugh, J. L. Memory{a Century of Consolidation. Science 287, 5451 (Jan. 2000), 248{251.

For many challenging problems where the mathematical description is not explicitly defined, artificial intelligence methods appear to be much more robust compared to traditional algorithms. Such methods share the common property of learning from examples in order to “explore” the problem to solve. Then, they generalize these examples to new and unseen input signals. The reservoir computing paradigm is a bio-inspired approach drawn from the theory of artificial Recurrent Neural Networks (RNNs) to process time-dependent data. This machine learning method was proposed independently by several research groups in the early 2000s. It has enabled a breakthrough in analog information processing, with several experiments demonstrating state-of-the-art performance for a wide range of hard nonlinear tasks. These tasks include for instance dynamic pattern classification, grammar modeling, speechrecognition, nonlinear channel equalization, detection of epileptic seizures, robot control, timeseries prediction, brain-machine interfacing, power system monitoring, financial forecasting, or handwriting recognition. A Reservoir Computer (RC) is composed of three different layers. There is first the neural network itself, called “reservoir”, which consists of a large number of internal variables (i.e. reservoir states) all interconnected together to exchange information. The internal dynamics of such a system, driven by a function of the inputs and the former reservoir states, is thus extremely rich. Through an input layer, a time-dependent input signal is applied to all the internal variables to disturb the neural network dynamics. Then, in the output layer, all these reservoir states are processed, often by taking a linear combination thereof at each time-step, to compute the output signal. Let us note that the presence of a non-linearity somewhere in the system is essential to reach high performance computing on nonlinear tasks. The principal novelty of the reservoir computing paradigm was to propose an RNN where most of the connection weights are generated randomly, except for the weights adjusted to compute the output signal from a linear combination of the reservoir states. In addition, some global parameters can be tuned to get the best performance, depending on the reservoir architecture and on the task. This simple and easy process considerably decreases the training complexity compared to traditional RNNs, for which all the weights needed to be optimized. RC algorithms can be programmed using modern traditional processors. But these electronic processors are better suited to digital processing for which a lot of transistors continuously need to be switched on and off, leading to higher power consumption. As we can intuitively understand, processors with hardware directly dedicated to RC operations – in otherwords analog bio-inspired processors – could be much more efficient regarding both speed and power consumption. Based on the same idea of high speed and low power consumption, the last few decades have seen an increasing use of coherent optics in the transport of information thanks to its high bandwidth and high power efficiency advantages. In order to address the future challenge of high performance, high speed, and power efficient nontrivial computing, it is thus natural to turn towards optical implementations of RCs using coherent light. Over the last few years, several physical implementations of RCs using optics and (opto)electronics have been successfully demonstrated. In the present PhD thesis, the reservoirs are based on a large coherently driven linear passive fiber cavity. The internal states are encoded by time-multiplexing in the cavity. Each reservoir state is therefore processed sequentially. This reservoir architecture exhibits many qualities that were either absent or not simultaneously present in previous works: we can perform analog optical signal processing; the easy tunability of each key parameter achieves the best operating point for each task; the system is able to reach a strikingly weak noise floor thanks to the absence of active elements in the reservoir itself; a richer dynamics is provided by operating in coherent light, as the reservoir states are encoded in both the amplitude and the phase of the electromagnetic field; high power efficiency is obtained as a result of the passive nature and simplicity of the setup. However, it is important to note that at this stage we have only obtained low optical power consumption for the reservoir itself. We have not tried to minimize the overall power consumption, including all control electronics. The first experiment reported in chapter 4 uses a quadratic non-linearity on each reservoir state in the output layer. This non-linearity is provided by a readout photodiode since it produces a current proportional to the intensity of the light. On a number of benchmark tasks widely used in the reservoir computing community, the error rates demonstrated with this RC architecture – both in simulation and experimentally – are, to our knowledge, the lowest obtained so far. Furthermore, the analytic model describing our experiment is also of interest, asit constitutes a very simple high performance RC algorithm. The setup reported in chapter 4 requires offline digital post-processing to compute its output signal by summing the weighted reservoir states at each time-step. In chapter 5, we numerically study a realistic model of an optoelectronic “analog readout layer” adapted on the setup presented in chapter 4. This readout layer is based on an RLC low-pass filter acting as an integrator over the weighted reservoir states to autonomously generate the RC output signal. On three benchmark tasks, we obtained very good simulation results that need to be confirmed experimentally in the future. These promising simulation results pave the way for standalone high performance physical reservoir computers.The RC architecture presented in chapter 5 is an autonomous optoelectronic implementation able to electrically generate its output signal. In order to contribute to the challenge of all-optical computing, chapter 6 highlights the possibility of processing information autonomously and optically using an RC based on two coherently driven passive linear cavities. The first one constitutes the reservoir itself and pumps the second one, which acts as an optical integrator onthe weighted reservoir states to optically generate the RC output signal after sampling. A sine non-linearity is implemented on the input signal, whereas both the reservoir and the readout layer are kept linear. Let us note that, because the non-linearity in this system is provided by a Mach-Zehnder modulator on the input signal, the input signal of this RC configuration needs to be an electrical signal. On the contrary, the RC implementation presented in chapter 5 processes optical input signals, but its output is electrical. We obtained very good simulation results on a single task and promising experimental results on two tasks. At the end of this chapter, interesting perspectives are pointed out to improve the performance of this challenging experiment. This system constitutes the first autonomous photonic RC able to optically generate its output signal. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

The degree by which a species can adapt to the demands of its changing environment defines how well it can exploit the resources of new ecological niches. Since the nervous system is the seat of an organism's behavior, studying adaptation starts from there. The nervous system adapts through neuronal plasticity, which may be considered as the brain's reaction to environmental perturbations. In a natural setting, these perturbations are always changing. As such, a full understanding of how the brain functions requires studying neuronal plasticity under temporally varying stimulation conditions, i.e., studying the role of plasticity in carrying out spatiotemporal computations. It is only then that we can fully benefit from the full potential of neural information processing to build powerful brain-inspired adaptive technologies. Here, we focus on homeostatic plasticity, where certain properties of the neural machinery are regulated so that they remain within a functionally and metabolically desirable range. Our main goal is to illustrate how homeostatic plasticity interacting with associative mechanisms is functionally relevant for spatiotemporal computations. The thesis consists of three studies that share two features: (1) homeostatic and synaptic plasticity act on a dynamical system such as a recurrent neural network. (2) The dynamical system is nonautonomous, that is, it is subject to temporally varying stimulation. In the first study, we develop a rigorous theory of spatiotemporal representations and computations, and the role of plasticity. Within the developed theory, we show that homeostatic plasticity increases the capacity of the network to encode spatiotemporal patterns, and that synaptic plasticity associates these patterns to network states. The second study applies the insights from the first study to the single node delay-coupled reservoir computing architecture, or DCR. The DCR's activity is sampled at several computational units. We derive a homeostatic plasticity rule acting on these units. We analytically show that the rule balances between the two necessary processes for spatiotemporal computations identified in the first study. As a result, we show that the computational power of the DCR significantly increases. The third study considers minimal neural control of robots. We show that recurrent neural control with homeostatic synaptic dynamics endows the robots with memory. We show through demonstrations that this memory is necessary for generating behaviors like obstacle-avoidance of a wheel-driven robot and stable hexapod locomotion.

Les fichiers sons qui accompagne mon document sont au format midi. Le programme que nous avons développés pour ce travail est en language Python. / Les réseaux de neurones à réservoir, dont le principe est de combiner un vaste réseau de neurones fixes avec un apprenant ne possédant aucune forme de mémoire, ont récemment connu un gain en popularité dans les communautés d’apprentissage machine, de traitement du signal et des neurosciences computationelles. Ces réseaux qui peuvent être classés en deux catégories : 1. les réseaux à états échoïques (ESN)[29] dont les activations des neurones sont des réels 2. les machines à états liquides (LSM)[43] dont les neurones possèdent des potentiels d’actions, ont été appliqués à différentes tâches [11][64][49][45][38] dont la génération de séquences mélodiques [30]. Dans le cadre de la présente recherche, nous proposons deux nouveaux modèles à base de réseaux de neurones à réservoir. Le premier est un modèle pour la reconnaissance de rythmes utilisant deux niveaux d’apprentissage, et avec lequel nous avons été en mesure d’obtenir des résultats satisfaisants tant au niveau de la reconnaissance que de la résistance au bruit. Le second modèle sert à l’apprentissage et à la génération de séquences périodiques. Ce modèle diffère du modèle génératif classique utilisé avec les ESN à la fois au niveau de ses entrées, puisqu’il possède une Horloge, ainsi qu’au niveau de l’algorithme d’apprentissage, puisqu’il utilise un algorithme que nous avons spécialement développé pour cette tache et qui se nomme "Orbite". La combinaison de ces deux éléments, nous a permis d’obtenir de bons résultats, pour la génération, le sur-apprentissage et l’extraction de données. Nous pensons également que ce modèle ouvre une fenêtre intéressante vers la réalisation d’un orchestre entièrement virtuel et nous proposons deux architectures possibles que pourrait avoir cet orchestre. Dans la dernière partie de ce travail nous présentons les outils que nous avons développés pour faciliter notre travail de recherche. / Reservoir computing, the combination of a recurrent neural network and one or more memoryless readout units, has seen recent growth in popularity in and machine learning, signal processing and computational neurosciences. Reservoir-based methods have been successfully applied to a wide range of time series problems [11][64][49][45][38] including music [30], and usually can be found in two flavours: Echo States Networks(ESN)[29], where the reservoir is composed of mean rates neurons, and Liquid Sates Machines (LSM),[43] where the reservoir is composed of spiking neurons. In this work, we propose two new models based upon the ESN architecture. The first one is a model for rhythm recognition that uses two levels of learning and with which we have been able to get satisfying results on both recognition and noise resistance. The second one is a model for learning and generating periodic sequences, with this model we introduced a new architecture for generative models based upon ESNs where the reservoir receives inputs from a clock, as well as a new learning algorithm that we called "Orbite". By combining these two elements within our model, we were able to get good results on generation, over-fitting and data extraction. We also believe that a combination of several instances of our model can serve as a basis for the elaboration of an entirely virtual orchestra, and we propose two architectures that this orchestra may have. In the last part of this work, we briefly present the tools that we have developed during our research.

Les réseaux de neurones à réservoir, dont le principe est de combiner un vaste réseau de neurones fixes avec un apprenant ne possédant aucune forme de mémoire, ont récemment connu un gain en popularité dans les communautés d’apprentissage machine, de traitement du signal et des neurosciences computationelles. Ces réseaux qui peuvent être classés en deux catégories : 1. les réseaux à états échoïques (ESN)[29] dont les activations des neurones sont des réels 2. les machines à états liquides (LSM)[43] dont les neurones possèdent des potentiels d’actions, ont été appliqués à différentes tâches [11][64][49][45][38] dont la génération de séquences mélodiques [30]. Dans le cadre de la présente recherche, nous proposons deux nouveaux modèles à base de réseaux de neurones à réservoir. Le premier est un modèle pour la reconnaissance de rythmes utilisant deux niveaux d’apprentissage, et avec lequel nous avons été en mesure d’obtenir des résultats satisfaisants tant au niveau de la reconnaissance que de la résistance au bruit. Le second modèle sert à l’apprentissage et à la génération de séquences périodiques. Ce modèle diffère du modèle génératif classique utilisé avec les ESN à la fois au niveau de ses entrées, puisqu’il possède une Horloge, ainsi qu’au niveau de l’algorithme d’apprentissage, puisqu’il utilise un algorithme que nous avons spécialement développé pour cette tache et qui se nomme "Orbite". La combinaison de ces deux éléments, nous a permis d’obtenir de bons résultats, pour la génération, le sur-apprentissage et l’extraction de données. Nous pensons également que ce modèle ouvre une fenêtre intéressante vers la réalisation d’un orchestre entièrement virtuel et nous proposons deux architectures possibles que pourrait avoir cet orchestre. Dans la dernière partie de ce travail nous présentons les outils que nous avons développés pour faciliter notre travail de recherche. / Reservoir computing, the combination of a recurrent neural network and one or more memoryless readout units, has seen recent growth in popularity in and machine learning, signal processing and computational neurosciences. Reservoir-based methods have been successfully applied to a wide range of time series problems [11][64][49][45][38] including music [30], and usually can be found in two flavours: Echo States Networks(ESN)[29], where the reservoir is composed of mean rates neurons, and Liquid Sates Machines (LSM),[43] where the reservoir is composed of spiking neurons. In this work, we propose two new models based upon the ESN architecture. The first one is a model for rhythm recognition that uses two levels of learning and with which we have been able to get satisfying results on both recognition and noise resistance. The second one is a model for learning and generating periodic sequences, with this model we introduced a new architecture for generative models based upon ESNs where the reservoir receives inputs from a clock, as well as a new learning algorithm that we called "Orbite". By combining these two elements within our model, we were able to get good results on generation, over-fitting and data extraction. We also believe that a combination of several instances of our model can serve as a basis for the elaboration of an entirely virtual orchestra, and we propose two architectures that this orchestra may have. In the last part of this work, we briefly present the tools that we have developed during our research. / Les fichiers sons qui accompagne mon document sont au format midi. Le programme que nous avons développés pour ce travail est en language Python.

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