Reservoir computing using a delayed feedback system: towards photonic implementations

Delayed feedback systems are known to exhibit a rich dynamical behavior, showing a wide variety of dynamical regimes. We use this richness to implement reservoir computing, a processing concept in machine learning. In this paper we demonstrate the proof of principle on an electronic system, however the approach is readily transferable to photonics, promising fast and computationally efficient all-optical processing. Using only one single node with delayed feedback instead of an entire network of nodes, we succeed in obtaining state-of-the-art results on benchmarks such as spoken digit recognition and system identification

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

Online Training of an Opto-Electronic Reservoir Computer

Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals. Its analog implementations equal and sometimes outperform other digital algorithms on a series of benchmark tasks. Their performance can be increased by switching from offline to online training method. Here we present the first online trained opto-electronic reservoir computer. The system is tested on a channel equalisation task and the algorithm is executed by an FPGA chip. We report performances close to previous implementations and demonstrate the benefits of online training on a non-stationary task that could not be easily solved using offline methods.info:eu-repo/semantics/publishe

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

Improved preservation of ovarian tissue morphology that is compatible with antigen detection using a fixative mixture of formalin and acetic acid

STUDY QUESTION Can ovarian tissue morphology be better preserved whilst enabling histological molecular analyses following fixation with a novel fixative, neutral buffered formalin (NBF) with 5% acetic acid (referred to hereafter as Form-Acetic)? SUMMARY ANSWER Fixation with Form-Acetic improved ovarian tissue histology compared to NBF in multiple species while still enabling histological molecular analyses. WHAT IS KNOWN ALREADY NBF fixation results in tissue shrinkage in various tissue types including the ovary. Components of ovarian tissue, notably follicles, are particularly susceptible to NBF-induced morphological alterations and can lead to data misrepresentation. Bouin’s solution (which contains 5% acetic acid) better preserves tissue architecture compared to NBF but is limited for immunohistochemical analyses. STUDY DESIGN, SIZE, DURATION A comparison of routinely used fixatives, NBF and Bouin’s, and a new fixative, Form-Acetic was carried out. Ovarian tissue was used from three different species: human (n = 5 patients), sheep (n = 3; 6 ovaries; 3 animals per condition) and mouse (n = 14 mice; 3 ovaries from 3 different animals per condition). PARTICIPANTS/MATERIALS, SETTING, METHODS Ovarian tissue from humans (aged 13 weeks to 32 years), sheep (reproductively young i.e. 3–6 months) and mice (10 weeks old) were obtained and fixed in 2 ml NBF, Bouin’s or Form-Acetic for 4, 8, and 24 h at room temperature. Tissues were embedded and sectioned. Five-micron sections were stained with haemotoxylin and eosin (H&E) and the percentage of artefact (clear space as a result of shrinkage) between ovarian structures was calculated. Additional histological staining using Periodic acid-Schiff and Masson’s trichrome were performed on 8 and 24 h NBF, Bouin’s and Form-Acetic fixed samples to assess the compatibility of the new fixative with stains. On ovarian tissue fixed for both 8 and 24 h in NBF and Form-Acetic, immunohistochemistry (IHC) studies to detect FOXO3a, FoxL2, collagen IV, laminin and anti-Müllerian hormone (AMH) proteins were performed in addition to the terminal deoxynucleotidyl transferase nick end labelling (TUNEL) assay to determine the compatibility of Form-Acetic fixation with types of histological molecular analyses. MAIN RESULTS AND THE ROLE OF CHANCE Fixation in Form-Acetic improved ovarian tissue morphology compared to NBF from all three species and either slightly improved or was comparable to Bouin’s for human, mouse and sheep tissues. Form-Acetic was compatible with H&E, Periodic acid-Schiff and Masson’s trichrome staining and all proteins (FOXO3a, FoxL2, collagen IV and laminin and AMH) could be detected via IHC. Furthermore, Form-Acetic, unlike NBF, enabled antigen recognition for most of the proteins tested without the need for antigen retrieval. Form-Acetic also enabled the detection of damaged DNA via the TUNEL assay using fluorescence. LARGE SCALE DATA N/A LIMITATIONS, REASONS FOR CAUTION In this study, IHC analysis was performed on a select number of protein types in ovarian tissue thus encouraging further studies to confirm the use of Form-Acetic in enabling the detection of a wider range of protein forms in addition to other tissue types. WIDER IMPLICATIONS OF THE FINDINGS The simplicity in preparation of Form-Acetic and its superior preservative properties whilst enabling forms of histological molecular analyses make it a highly valuable tool for studying ovarian tissue. We, therefore, recommend that Form-Acetic replaces currently used fixatives and encourage others to introduce it into their research workflow. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the Oxford Medical Research Council Doctoral Training Programme (Oxford MRC-DTP) grant awarded to B.D.B. (Grant no. MR/N013468/1), the Fondation Hoffmann supporting R.A. and the Petroleum Technology Development Fund (PTDF) awarded to B.V.A

Improved preservation of ovarian tissue morphology that is compatible with antigen detection using a fixative mixture of formalin and acetic acid

STUDY QUESTION Can ovarian tissue morphology be better preserved whilst enabling histological molecular analyses following fixation with a novel fixative, neutral buffered formalin (NBF) with 5% acetic acid (referred to hereafter as Form-Acetic)? SUMMARY ANSWER Fixation with Form-Acetic improved ovarian tissue histology compared to NBF in multiple species while still enabling histological molecular analyses. WHAT IS KNOWN ALREADY NBF fixation results in tissue shrinkage in various tissue types including the ovary. Components of ovarian tissue, notably follicles, are particularly susceptible to NBF-induced morphological alterations and can lead to data misrepresentation. Bouin’s solution (which contains 5% acetic acid) better preserves tissue architecture compared to NBF but is limited for immunohistochemical analyses. STUDY DESIGN, SIZE, DURATION A comparison of routinely used fixatives, NBF and Bouin’s, and a new fixative, Form-Acetic was carried out. Ovarian tissue was used from three different species: human (n = 5 patients), sheep (n = 3; 6 ovaries; 3 animals per condition) and mouse (n = 14 mice; 3 ovaries from 3 different animals per condition). PARTICIPANTS/MATERIALS, SETTING, METHODS Ovarian tissue from humans (aged 13 weeks to 32 years), sheep (reproductively young i.e. 3–6 months) and mice (10 weeks old) were obtained and fixed in 2 ml NBF, Bouin’s or Form-Acetic for 4, 8, and 24 h at room temperature. Tissues were embedded and sectioned. Five-micron sections were stained with haemotoxylin and eosin (H&E) and the percentage of artefact (clear space as a result of shrinkage) between ovarian structures was calculated. Additional histological staining using Periodic acid-Schiff and Masson’s trichrome were performed on 8 and 24 h NBF, Bouin’s and Form-Acetic fixed samples to assess the compatibility of the new fixative with stains. On ovarian tissue fixed for both 8 and 24 h in NBF and Form-Acetic, immunohistochemistry (IHC) studies to detect FOXO3a, FoxL2, collagen IV, laminin and anti-Müllerian hormone (AMH) proteins were performed in addition to the terminal deoxynucleotidyl transferase nick end labelling (TUNEL) assay to determine the compatibility of Form-Acetic fixation with types of histological molecular analyses. MAIN RESULTS AND THE ROLE OF CHANCE Fixation in Form-Acetic improved ovarian tissue morphology compared to NBF from all three species and either slightly improved or was comparable to Bouin’s for human, mouse and sheep tissues. Form-Acetic was compatible with H&E, Periodic acid-Schiff and Masson’s trichrome staining and all proteins (FOXO3a, FoxL2, collagen IV and laminin and AMH) could be detected via IHC. Furthermore, Form-Acetic, unlike NBF, enabled antigen recognition for most of the proteins tested without the need for antigen retrieval. Form-Acetic also enabled the detection of damaged DNA via the TUNEL assay using fluorescence. LARGE SCALE DATA N/A LIMITATIONS, REASONS FOR CAUTION In this study, IHC analysis was performed on a select number of protein types in ovarian tissue thus encouraging further studies to confirm the use of Form-Acetic in enabling the detection of a wider range of protein forms in addition to other tissue types. WIDER IMPLICATIONS OF THE FINDINGS The simplicity in preparation of Form-Acetic and its superior preservative properties whilst enabling forms of histological molecular analyses make it a highly valuable tool for studying ovarian tissue. We, therefore, recommend that Form-Acetic replaces currently used fixatives and encourage others to introduce it into their research workflow. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the Oxford Medical Research Council Doctoral Training Programme (Oxford MRC-DTP) grant awarded to B.D.B. (Grant no. MR/N013468/1), the Fondation Hoffmann supporting R.A. and the Petroleum Technology Development Fund (PTDF) awarded to B.V.A

Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing

Many information processing challenges are difficult to solve with traditional Turing or von Neumann approaches. Implementing unconventional computational methods is therefore essential and optics provides promising opportunities. Here we experimentally demonstrate optical information processing using a nonlinear optoelectronic oscillator subject to delayed feedback.We implement a neuro-inspired concept, called Reservoir Computing, proven to possess universal computational capabilities. We particularly exploit the transient response of a complex dynamical system to an input data stream. We employ spoken digit recognition and time series prediction tasks as benchmarks, achieving competitive processing figures of merit.The project PHOCUS acknowledges the financial support of the Future and Emerging Technologies (FET) programme within the Seventh Frame- work Programme for Research of the European Commission, under FET-Open grant number: 240763. Moreover, this work was supported by MICINN (Spain), and FEDER, under Projects TEC2009-14101 (DeCoDicA), FIS2007-60327 (FISICOS), and 0200950I190 (Proyecto Intra- murales Especiales).Peer reviewe