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

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

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

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