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

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda

Resurrecting biodiversity: advanced assisted reproductive technologies and biobanking

Biodiversity is defined as the presence of a variety of living organisms on the Earth that is essential for human survival. However, anthropogenic activities are causing the sixth mass extinction, threatening even our own species. For many animals, dwindling numbers are becoming fragmented populations with low genetic diversity, threatening long-term species viability. With extinction rates 1000–10,000 times greater than natural, ex situ and in situ conservation programmes need additional support to save species. The indefinite storage of cryopreserved (−196°C) viable cells and tissues (cryobanking), followed by assisted or advanced assisted reproductive technology (ART: utilisation of oocytes and spermatozoa to generate offspring; aART: utilisation of somatic cell genetic material to generate offspring), may be the only hope for species’ long-term survival. As such, cryobanking should be considered a necessity for all future conservation strategies. Following cryopreservation, ART/aART can be used to reinstate lost genetics back into a population, resurrecting biodiversity. However, for this to be successful, species-specific protocol optimisation and increased knowledge of basic biology for many taxa are required. Current ART/aART is primarily focused on mammalian taxa; however, this needs to be extended to all, including to some of the most endangered species: amphibians. Gamete, reproductive tissue and somatic cell cryobanking can fill the gap between losing genetic diversity today and future technological developments. This review explores species prioritisation for cryobanking and the successes and challenges of cryopreservation and multiple ARTs/aARTs. We here discuss the value of cryobanking before more species are lost and the potential of advanced reproductive technologies not only to halt but also to reverse biodiversity loss

The role of structure and complexity on Reservoir Computing quality

We explore the effect of structure and connection complexity on the dynamical behaviour of Reservoir Computers (RC). At present, considerable effort is taken to design and hand-craft physical reservoir computers. Both structure and physical complexity are often pivotal to task performance, however, assessing their overall importance is challenging. Using a recently proposed framework, we evaluate and compare the dynamical freedom (referring to quality) of neural network structures, as an analogy for physical systems. The results quantify how structure affects the range of behaviours exhibited by these networks. It highlights that high quality reached by more complex structures is often also achievable in simpler structures with greater network size. Alternatively, quality is often improved in smaller networks by adding greater connection complexity. This work demonstrates the benefits of using abstract behaviour representation, rather than evaluation through benchmark tasks, to assess the quality of computing substrates, as the latter typically has biases, and often provides little insight into the complete computing quality of physical systems

Reservoir computing quality : connectivity and topology

We explore the effect of connectivity and topology on the dynamical behaviour of Reservoir Computers. At present, considerable effort is taken to design and hand-craft physical reservoir computers. Both structure and physical complexity are often pivotal to task performance, however, assessing their overall importance is challenging. Using a recently developed framework, we evaluate and compare the dynamical freedom (referring to quality) of neural network structures, as an analogy for physical systems. The results quantify how structure affects the behavioural range of networks. It demonstrates how high quality reached by more complex structures is often also achievable in simpler structures with greater network size. Alternatively, quality is often improved in smaller networks by adding greater connection complexity. This work demonstrates the benefits of using dynamical behaviour to assess the quality of computing substrates, rather than evaluation through benchmark tasks that often provide a narrow and biased insight into the computing quality of physical systems

Multiple delay nonlinear wavelength dynamics for photonic Reservoir Computing

International audienceReservoir computing is an emerging field of research intended to develop novel computational machines based on operating principles issued from the neural network community and the brain research sciences. Practical implementations of the corresponding principles is a strongly open question, which is precisely addressed from the photonic viewpoint in the frame of a recently funded EC project PHOCUS. The main idea is here to benefit from the intrinsic dynamic complexity of delay systems, which experimental realization are well known in photonics, in order to perform the central role of a reservoir computer, the reservoir itself. This paper reports on the photonic implementation, the numerical simulations of the corresponding reservoir subjected to the NARMA sequences, and the computational power results that were obtained both numerically and experimentally

Computational performance of a single bandpass electro-optic delay oscillator

International audienc