Human muscle spindles act as forward sensory models.

Modern theories of motor control incorporate forward models that combine sensory information and motor commands to predict future sensory states. Such models circumvent unavoidable neural delays associated with on-line feedback control. Here we show that signals in human muscle spindle afferents during unconstrained wrist and finger movements predict future kinematic states of their parent muscle. Specifically, we show that the discharges of type Ia afferents are best correlated with the velocity of length changes in their parent muscles approximately 100-160 ms in the future and that their discharges vary depending on motor sequences in a way that cannot be explained by the state of their parent muscle alone. We therefore conclude that muscle spindles can act as "forward sensory models": they are affected both by the current state of their parent muscle and by efferent (fusimotor) control, and their discharges represent future kinematic states. If this conjecture is correct, then sensorimotor learning implies learning how to control not only the skeletal muscles but also the fusimotor system.

Tailoring the local interaction between graphene layers in graphite at the atomic scale and above using scanning tunneling microscopy.

With recent developments in carbon-based electronics, it is imperative to understand the interplay between the morphology and electronic structure in graphene and graphite. We demonstrate controlled and repeatable vertical displacement of the top graphene layer from the substrate mediated by the scanning tunneling microscopy (STM) tip-sample interaction, manifested at the atomic level as well as over superlattices spanning several tens of nanometers. Besides the full-displacement, we observed the first half-displacement of the surface graphene layer, confirming that a reduced coupling rather than a change in lateral layer stacking is responsible for the triangular/honeycomb atomic lattice transition phenomenon, clearing the controversy surrounding it. Furthermore, an atomic scale mechanical stress at a grain boundary in graphite, resulting in the localization of states near the Fermi energy, is revealed through voltage-dependent imaging. A method of producing graphene nanoribbons based on the manipulation capabilities of the STM is also implemented.

Nonautonomous elementary net systems and their application to programmable logic control

In this paper, a novel approach to Petri net modeling of programmable logic controller (PLC) programs is presented. The modeling approach is a simple extension of elementary net systems, and a graphical design tool that supports the use of this modeling approach is provided. A key characteristic of the model is that the binary sensory inputs and binary actuation outputs of the PLC are explicitly represented. This leads to the following two improvements: outputs are unambiguous, and interaction patterns are more clearly represented in the graphical form. The use of this modeling approach produces programs that are simple, lightweight, and portable. The approach is demonstrated by applying it to the development of a control module for a MonTech Positioning Station. © 2008 IEEE.