A common technique in neurocontrol is that of controlling a plant by static state feedback using the plant's inverse dynamics, which is approximated through a learning process. It is well known that in this control mode even small approximation errors or, which is the same, small perturbations of the plant may lead to instability. Here, a novel approach is proposed to overcome the problem of in...

The cerebellum has long been thought to play a crucial role in the forming of graceful movements, and it is viewed as a set of modules, each of which can be added to a control system to improve smooth coordinated movement, with improvements continuing and improving over time. The present paper proposes a new feedback error learning scheme for tracing in motor control system. In the scheme, the ...

The present functional magnetic resonance imaging (fMRI) experiment aims at clarifying the role of the rostral anterior cingulate cortex (rACC) in the evaluation of feedback in a deterministic environment. We tested, in particular, the response of the rACC to the detection of different types of feedback, and to varying levels of outcome expectancy. We used a problem-solving task in which subjec...

Performance feedback during learning is accompanied by a negative event-related potentials (ERP) component, the feedback-related negativity (FRN), which codes a reward prediction error. An open issue relates to the coding of feedback stimuli in observational learning. The present study aimed to determine differences in the neural processing of feedback in active and observational learners in a ...

BACKGROUND It has been suggested that a higher intra-individual variability benefits the motor learning of wheelchair propulsion. The present study evaluated whether feedback-induced variability on wheelchair propulsion technique variables would also enhance the motor learning process. Learning was operationalized as an improvement in mechanical efficiency and propulsion technique, which are th...

The proliferation of massive datasets has led to significant interests in distributed algorithms for solving large-scale machine learning problems. However, the communication overhead is a major bottleneck that hampers scalability systems. In this paper, we design two communication-efficient tasks. first one named EF-SIGNGD, which use 1-bit (sign-based) gradient quantization method save bits. M...

We present a theoretical analysis of online parameter tuning in statistical machine translation (SMT) from a coactive learning view. This perspective allows us to give regret and generalization bounds for latent perceptron algorithms that are common in SMT, but fall outside of the standard convex optimization scenario. Coactive learning also introduces the concept of weak feedback, which we app...

An important issue in motor learning/adaptation research is how the brain accepts the error information necessary for maintaining and improving task performance in a changing environment. The present study focuses on the effect of timing of error feedback. Previous research has demonstrated that adaptation to displacement of the visual field by prisms in a manual reaching task is significantly ...