Distributed Central Pattern Generator Model for Robotics Application Based on Phase Sensitivity Analysis

A method is presented to predict phase relationships between coupled phase oscillators. As an illustration of how the method can be applied, a distributed Central Pattern Generator (CPG) model based on amplitude controlled phase oscillators is presented. Representative results of numerical integration of the CPG model are presented to illustrate its excellent properties in terms of transition speeds, robustness and independence on initial conditions. A particularly interesting feature of the CPG is the possibility to switch between different stable gaits by varying a single parameter. These characteristics make the CPG model an interesting solution for the decentralized control of multi-legged robots. The approach is discussed in the more general framework of coupled nonlinear systems, and design tools for nonlinear distributed control schemes applicable to Information technology and robotics

Learning of Closed-Loop Motion Control

Learning motion control as a unified process of designing the reference trajectory and the controller is one of the most challenging problems in robotics. The complexity of the problem prevents most of the existing optimization algorithms from giving satisfactory results. While model-based algorithms like iterative linear-quadratic-Gaussian (iLQG) can be used to design a suitable controller for the motion control, their performance is strongly limited by the model accuracy. An inaccurate model may lead to degraded performance of the controller on the physical system. Although using machine learning approaches to learn the motion control on real systems have been proven to be effective, their performance depends on good initialization. To address these issues, this paper introduces a two-step algorithm which combines the proven performance of a model-based controller with a model-free method for compensating for model inaccuracy. The first step optimizes the problem using iLQG. Then, in the second step this controller is used to initialize the policy for our PI$^2$-01 reinforcement learning algorithm. This algorithm is a derivation of the PI$^2$ algorithm enabling more stable and faster convergence. The performance of this method is demonstrated both in simulation and experimental results

Stochastic resonance in pattern recognition by a holographic neuron model

The recognition rate of holographic neural synapses, performing a pattern recognition task, is significantly higher when applied to natural, rather than artificial, images. This shortcoming of artificial images can be largely compensated for, if noise is added to the input pattern. The effect is the result of a trade-off between optimal representation of the stimulus (for which noise is favorable) and keeping as much as possible of the stimulus-specific information (for which noise is detrimental). The observed mechanism may play a prominent role for simple biological sensors

Direct Interrogation of Viral Peptides Presented by the Class I HLA of HIV-Infected T Cells

Identification of CD8+ cytotoxic T lymphocyte (CTL) epitopes has traditionally relied upon testing of overlapping peptide libraries for their reactivity with T cells in vitro. Here, we pursued deep ligand sequencing (DLS) as an alternative method of directly identifying those ligands that are epitopes presented to CTLs by the class I human leukocyte antigens (HLA) of infected cells. Soluble class I HLA-A*11:01 (sHLA) was gathered from HIV-1 NL4-3-infected human CD4+ SUP-T1 cells. HLA-A*11:01 harvested from infected cells was immunoaffinity purified and acid boiled to release heavy and light chains from peptide ligands that were then recovered by size-exclusion filtration. The ligands were first fractionated by high-pH high-pressure liquid chromatography and then subjected to separation by nano-liquid chromatography (nano-LC)–mass spectrometry (MS) at low pH. Approximately 10 million ions were selected for sequencing by tandem mass spectrometry (MS/MS). HLA-A*11:01 ligand sequences were determined with PEAKS software and confirmed by comparison to spectra generated from synthetic peptides. DLS identified 42 viral ligands presented by HLA-A*11:01, and 37 of these were previously undetected. These data demonstrate that (i) HIV-1 Gag and Nef are extensively sampled, (ii) ligand length variants are prevalent, particularly within Gag and Nef hot spots where ligand sequences overlap, (iii) noncanonical ligands are T cell reactive, and (iv) HIV-1 ligands are derived from de novo synthesis rather than endocytic sampling. Next-generation immunotherapies must factor these nascent HIV-1 ligand length variants and the finding that CTL-reactive epitopes may be absent during infection of CD4+ T cells into strategies designed to enhance T cell immunity

Resonant neurons and bushcricket behaviour

The resonant properties of the intrinsic dynamics of single neurons could play a direct role in behaviour. One plausible role is in the recognition of temporal patterns, such as that seen in the auditory communication systems of Orthoptera. Recent behavioural data from bushcrickets suggests that this behaviour has interesting resonance properties, but the underlying mechanism is unknown. Here we show that a very simple and general model for neural resonance could directly account for the different behavioural responses of bushcrickets to different song patterns