Towards a neural hierarchy of time scales for motor control

Animals show remarkable rich motion skills which are still far from realizable with robots. Inspired by the neural circuits which generate rhythmic motion patterns in the spinal cord of all vertebrates, one main research direction points towards the use of central pattern generators in robots. On of the key advantages of this, is that the dimensionality of the control problem is reduced. In this work we investigate this further by introducing a multi-timescale control hierarchy with at its core a hierarchy of recurrent neural networks. By means of some robot experiments, we demonstrate that this hierarchy can embed any rhythmic motor signal by imitation learning. Furthermore, the proposed hierarchy allows the tracking of several high level motion properties (e.g.: amplitude and offset), which are usually observed at a slower rate than the generated motion. Although these experiments are preliminary, the results are promising and have the potential to open the door for rich motor skills and advanced control

Hierarchical Temporal Representation in Linear Reservoir Computing

Recently, studies on deep Reservoir Computing (RC) highlighted the role of layering in deep recurrent neural networks (RNNs). In this paper, the use of linear recurrent units allows us to bring more evidence on the intrinsic hierarchical temporal representation in deep RNNs through frequency analysis applied to the state signals. The potentiality of our approach is assessed on the class of Multiple Superimposed Oscillator tasks. Furthermore, our investigation provides useful insights to open a discussion on the main aspects that characterize the deep learning framework in the temporal domain.Comment: This is a pre-print of the paper submitted to the 27th Italian Workshop on Neural Networks, WIRN 201

Dynamic clustering of time series with Echo State Networks

In this paper we introduce a novel methodology for unsupervised analysis of time series, based upon the iterative implementation of a clustering algorithm embedded into the evolution of a recurrent Echo State Network. The main features of the temporal data are captured by the dynamical evolution of the network states, which are then subject to a clustering procedure. We apply the proposed algorithm to time series coming from records of eye movements, called saccades, which are recorded for diagnosis of a neurodegenerative form of ataxia. This is a hard classification problem, since saccades from patients at an early stage of the disease are practically indistinguishable from those coming from healthy subjects. The unsupervised clustering algorithm implanted within the recurrent network produces more compact clusters, compared to conventional clustering of static data, and provides a source of information that could aid diagnosis and assessment of the disease.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Climate, people, fire and vegetation: new insights into vegetation dynamics in the Eastern Mediterranean since the 1st century AD

Anatolia forms a bridge between Europe, Africa and Asia and is influenced by all three continents in terms of climate, vegetation and human civilisation. Unfortunately, well-dated palynological records focussing on the period from the end of the classical Roman period until subrecent times are rare for Anatolia and completely absent for southwest Turkey, resulting in a lacuna in knowledge concerning the interactions of climatic change, human impact, and environmental change in this important region. Two well-dated palaeoecological records from the Western Taurus Mountains, Turkey, provide a first relatively detailed record of vegetation dynamics from late Roman times until the present in SW Turkey. Combining pollen, non-pollen palynomorphs, charcoal, sedimentological, archaeological data, and newly developed multivariate numerical analyses allows for the disentangling of climatic and anthropogenic influences on vegetation change. Results show changes in both the regional pollen signal as well as local soil sediment characteristics match shifts in regional climatic conditions. Both climatic as well as anthropogenic change had a strong influence on vegetation dynamics and land use. A moist environmental trend during the late-3rd century caused an increase in marshes and wetlands in the moister valley floors, limiting possibilities for intensive crop cultivation at such locations. A mid-7th century shift to pastoralism coincided with a climatic deterioration as well as the start of Arab incursions into the region, the former driving the way in which the vegetation developed afterwards. Resurgence in agriculture was observed in the study during the mid-10th century AD, coinciding with the Medieval Climate Anomaly. An abrupt mid-12th century decrease in agriculture is linked to socio-political change, rather than the onset of the Little Ice Age. Similarly, gradual deforestation occurring from the 16th century onwards has been linked to changes in land use during Ottoman times. The pollen data reveal that a fast rise in <i>Pinus</i> pollen after the end of the Beyşehir Occupation Phase need not always occur. The notion of high <i>Pinus</i> pollen percentages indicating an open landscape incapable of countering the influx of pine pollen is also deemed unrealistic. While multiple fires occurred in the region through time, extended fire periods, as had occurred during the Bronze Age and Beyşehir Occupation Phase, did not occur, and no signs of local fire activity were observed. Fires were never a major influence on vegetation dynamics. While no complete overview of post-BO Phase fire events can be presented, the available data indicates that fires in the vicinity of Gravgaz may have been linked to anthropogenic activity in the wider surroundings of the marsh. Fires in the vicinity of Bereket appeared to be linked to increased abundance of pine forests. There was no link with specifically wet or dry environmental conditions at either site. While this study reveals much new information concerning the impact of climate change and human occupation on the environment, more studies from SW Turkey are required in order to properly quantify the range of the observed phenomena and the magnitude of their impacts

Use of Tracers and Isotopes to Evaluate Vulnerability of Water in Domestic Wells to Septic Waste

In Nebraska, a large number (\u3e200) of shallow sand-point and cased wells completed in coarse alluvial sediments along rivers and lakes still are used to obtain drinking water for human consumption, even though construction of sand-point wells for consumptive uses has been banned since 1987. The quality of water from shallow domestic wells potentially vulnerable to seepage from septic systems was evaluated by analyzing for the presence of tracers and multiple isotopes. Samples were collected from 26 sand-point and perforated, cased domestic wells and were analyzed for bacteria, coliphages, nitrogen species, nitrogen and boron isotopes, dissolved organic carbon (DOC), prescription and nonprescription drugs, or organic waste water contaminants. At least 13 of the 26 domestic well samples showed some evidence of septic system effects based on the results of several tracers including DOC, coliphages, NH4+, NO3–, N2, δ15N[NO3–] and boron isotopes, and antibiotics and other drugs. Sand-point wells within 30 m of a septic system and \u3c14 m deep in a shallow, thin aquifer had the most tracers detected and the highest values, indicating the greatest vulnerability to contamination from septic waste

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

Information processing using a single dynamical node as complex system

Novel methods for information processing are highly desired in our information-driven society. Inspired by the brain's ability to process information, the recently introduced paradigm known as 'reservoir computing' shows that complex networks can efficiently perform computation. Here we introduce a novel architecture that reduces the usually required large number of elements to a single nonlinear node with delayed feedback. Through an electronic implementation, we experimentally and numerically demonstrate excellent performance in a speech recognition benchmark. Complementary numerical studies also show excellent performance for a time series prediction benchmark. These results prove that delay-dynamical systems, even in their simplest manifestation, can perform efficient information processing. This finding paves the way to feasible and resource-efficient technological implementations of reservoir computing

Reservoir Computing Approach to Robust Computation using Unreliable Nanoscale Networks

As we approach the physical limits of CMOS technology, advances in materials science and nanotechnology are making available a variety of unconventional computing substrates that can potentially replace top-down-designed silicon-based computing devices. Inherent stochasticity in the fabrication process and nanometer scale of these substrates inevitably lead to design variations, defects, faults, and noise in the resulting devices. A key challenge is how to harness such devices to perform robust computation. We propose reservoir computing as a solution. In reservoir computing, computation takes place by translating the dynamics of an excited medium, called a reservoir, into a desired output. This approach eliminates the need for external control and redundancy, and the programming is done using a closed-form regression problem on the output, which also allows concurrent programming using a single device. Using a theoretical model, we show that both regular and irregular reservoirs are intrinsically robust to structural noise as they perform computation

Intrinsic Detectivity Limits of Organic Near-Infrared Photodetectors

Organic photodetectors (OPDs) with a performance comparable to that of conventional inorganic ones have recently been demonstrated for the visible regime. However, near-infrared photodetection has proven to be challenging and, to date, the true potential of organic semiconductors in this spectral range (800–2500&nbsp;nm) remains largely unexplored. In this work, it is shown that the main factor limiting the specific detectivity (D*) is non-radiative recombination, which is also known to be the main contributor to open-circuit voltage losses. The relation between open-circuit voltage, dark current, and noise current is demonstrated using four bulk-heterojunction devices based on narrow-gap donor polymers. Their maximum achievable D* is calculated alongside a large set of devices to demonstrate an intrinsic upper limit of D* as a function of the optical gap. It is concluded that OPDs have the potential to be a useful technology up to 2000&nbsp;nm, given that high external quantum efficiencies can be maintained at these low photon energies

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work