Optoelectronic Reservoir Computing

Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input layer and a single output layer. Within these constraints many implementations are possible. Here we report an opto-electronic implementation of reservoir computing based on a recently proposed architecture consisting of a single non linear node and a delay line. Our implementation is sufficiently fast for real time information processing. We illustrate its performance on tasks of practical importance such as nonlinear channel equalization and speech recognition, and obtain results comparable to state of the art digital implementations.Comment: Contains main paper and two Supplementary Material

Minimal approach to neuro-inspired information processing

© 2015 Soriano, Brunner, Escalona-Morán, Mirasso and Fischer. To learn and mimic how the brain processes information has been a major research challenge for decades. Despite the efforts, little is known on how we encode, maintain and retrieve information. One of the hypothesis assumes that transient states are generated in our intricate network of neurons when the brain is stimulated by a sensory input. Based on this idea, powerful computational schemes have been developed. These schemes, known as machine-learning techniques, include artificial neural networks, support vector machine and reservoir computing, among others. In this paper, we concentrate on the reservoir computing (RC) technique using delay-coupled systems. Unlike traditional RC, where the information is processed in large recurrent networks of interconnected artificial neurons, we choose a minimal design, implemented via a simple nonlinear dynamical system subject to a self-feedback loop with delay. This design is not intended to represent an actual brain circuit, but aims at finding the minimum ingredients that allow developing an efficient information processor. This simple scheme not only allows us to address fundamental questions but also permits simple hardware implementations. By reducing the neuro-inspired reservoir computing approach to its bare essentials, we find that nonlinear transient responses of the simple dynamical system enable the processing of information with excellent performance and at unprecedented speed. We specifically explore different hardware implementations and, by that, we learn about the role of nonlinearity, noise, system responses, connectivity structure, and the quality of projection onto the required high-dimensional state space. Besides the relevance for the understanding of basic mechanisms, this scheme opens direct technological opportunities that could not be addressed with previous approaches.The authors acknowledge support by MINECO (Spain) under Projects TEC2012-36335 (TRIPHOP) and FIS2012-30634 (Intense@cosyp), FEDER and Govern de les Illes Balears via the program Grups Competitius. The work of MS was supported by the Conselleria d'Educació, Cultura i Universitats del Govern de les Illes Balears and the European Social Fund.Peer Reviewe

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.Comment: 51 pages, 19 figures, IEEE Acces

Reservoir Computing: computation with dynamical systems

In het onderzoeksgebied Machine Learning worden systemen onderzocht die kunnen leren op basis van voorbeelden. Binnen dit onderzoeksgebied zijn de recurrente neurale netwerken een belangrijke deelgroep. Deze netwerken zijn abstracte modellen van de werking van delen van de hersenen. Zij zijn in staat om zeer complexe temporele problemen op te lossen maar zijn over het algemeen zeer moeilijk om te trainen. Recentelijk zijn een aantal gelijkaardige methodes voorgesteld die dit trainingsprobleem elimineren. Deze methodes worden aangeduid met de naam Reservoir Computing. Reservoir Computing combineert de indrukwekkende rekenkracht van recurrente neurale netwerken met een eenvoudige trainingsmethode. Bovendien blijkt dat deze trainingsmethoden niet beperkt zijn tot neurale netwerken, maar kunnen toegepast worden op generieke dynamische systemen. Waarom deze systemen goed werken en welke eigenschappen bepalend zijn voor de prestatie is evenwel nog niet duidelijk. Voor dit proefschrift is onderzoek gedaan naar de dynamische eigenschappen van generieke Reservoir Computing systemen. Zo is experimenteel aangetoond dat de idee van Reservoir Computing ook toepasbaar is op niet-neurale netwerken van dynamische knopen. Verder is een maat voorgesteld die gebruikt kan worden om het dynamisch regime van een reservoir te meten. Tenslotte is een adaptatieregel geïntroduceerd die voor een breed scala reservoirtypes de dynamica van het reservoir kan afregelen tot het gewenste dynamisch regime. De technieken beschreven in dit proefschrift zijn gedemonstreerd op verschillende academische en ingenieurstoepassingen

All-optical Reservoir Computing

Reservoir Computing is a novel computing paradigm which uses a nonlinear recurrent dynamical system to carry out information processing. Recent electronic and optoelectronic Reservoir Computers based on an architecture with a single nonlinear node and a delay loop have shown performance on standardized tasks comparable to state-of-the-art digital implementations. Here we report an all-optical implementation of a Reservoir Computer, made of off-the-shelf components for optical telecommunications. It uses the saturation of a semiconductor optical amplifier as nonlinearity. The present work shows that, within the Reservoir Computing paradigm, all-optical computing with state-of-the-art performance is possible

Integer Echo State Networks: Hyperdimensional Reservoir Computing

We propose an approximation of Echo State Networks (ESN) that can be efficiently implemented on digital hardware based on the mathematics of hyperdimensional computing. The reservoir of the proposed Integer Echo State Network (intESN) is a vector containing only n-bits integers (where n<8 is normally sufficient for a satisfactory performance). The recurrent matrix multiplication is replaced with an efficient cyclic shift operation. The intESN architecture is verified with typical tasks in reservoir computing: memorizing of a sequence of inputs; classifying time-series; learning dynamic processes. Such an architecture results in dramatic improvements in memory footprint and computational efficiency, with minimal performance loss.Comment: 10 pages, 10 figures, 1 tabl

A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines

Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural network weights. Conventional processing architectures are not well suited for simulating neural networks, often requiring large amounts of energy and time. Additionally, synapses in biological neural networks are not binary connections, but exhibit a nonlinear response function as neurotransmitters are emitted and diffuse between neurons. Inspired by neuroscience principles, we present a digital neuromorphic architecture, the Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex synaptic response functions without requiring additional hardware components. We consider the paradigm of spiking neurons with temporally coded information as opposed to non-spiking rate coded neurons used in most neural networks. In this paradigm we examine liquid state machines applied to speech recognition and show how a liquid state machine with temporal dynamics maps onto the STPU-demonstrating the flexibility and efficiency of the STPU for instantiating neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN

Temporal Data Analysis Using Reservoir Computing and Dynamic Memristors

Temporal data analysis including classification and forecasting is essential in a range of fields from finance to engineering. While static data are largely independent of each other, temporal data have a considerable correlation between the samples, which is important for temporal data analysis. Neural networks thus offer a more general and flexible approach since they do not depend on parameters of specific tasks but are driven only by the data. In particular, recurrent neural networks have gathered much attention since the temporal information captured by the recurrent connections improves the prediction performance. Recently, reservoir computing (RC), which evolves from recurrent neural networks, has been extensively studied for temporal data analysis as it can offer efficient temporal processing of recurrent neural networks with a low training cost. This dissertation presents a hardware implementation of the RC system using an emerging device - memristor, followed by a theoretical study on hierarchical architectures of the RC system. A RC hardware system based on dynamic tungsten oxide (WOx) memristors is first demonstrated. The internal short-term memory effects of the WOx memristors allow the memristor-based reservoir to nonlinearly map temporal inputs into reservoir states, where the projected features can be readily processed by a simple linear readout function. We use the system to experimentally demonstrate two standard benchmarking tasks: isolated spoken digit recognition with partial inputs and chaotic system forecasting. High classification accuracy of 99.2% is obtained for spoken digit recognition and autonomous chaotic time series forecasting has been demonstrated over the long term. We then investigate the influence of the hierarchical reservoir structure on the properties of the reservoir and the performance of the RC system. Analogous to deep neural networks, stacking sub-reservoirs in series is an efficient way to enhance the nonlinearity of data transformation to high-dimensional space and expand the diversity of temporal information captured by the reservoir. These deep reservoir systems offer better performance when compared to simply increasing the size of the reservoir or the number of sub-reservoirs. Low-frequency components are mainly captured by the sub-reservoirs in the later stages of the deep reservoir structure, similar to observations that more abstract information can be extracted by layers in the late stage of deep neural networks. When the total size of the reservoir is fixed, the tradeoff between the number of sub-reservoirs and the size of each sub-reservoir needs to be carefully considered, due to the degraded ability of the individual sub-reservoirs at small sizes. Improved performance of the deep reservoir structure alleviates the difficulty of implementing the RC system on hardware systems. Beyond temporal data classification and prediction, one of the interesting applications of temporal data analysis is inferring the neural connectivity patterns from the high-dimensional neural activity recording data. By computing the temporal correlation between the neural spikes, connections between the neurons can be inferred using statistics-based techniques, but it becomes increasingly computationally expensive for large scale neural systems. We propose a second-order memristor-based hardware system using the natively implemented spike-timing-dependent plasticity learning rule for neural connectivity inference. By incorporating biological features such as transmission delay to the neural networks, the proposed concept not only correctly infers the direct connections but also distinguishes direct connections from indirect connections. Effects of additional biophysical properties not considered in the simulation and challenges of experimental memristor implementation will be also discussed.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167995/1/moonjohn_1.pd