This paper describes an environment for the design, simulation, and control of Internet-based force-reflecting telerobotic systems. We define these systems as using a segment of the computer network to connect the master to the slave. Computer networks introduce a time delay that is best described by a time-varying random process. Thus, known techniques for controlling time-delay telerobots are not directly applicable, and an environment for iterative designing and testing is necessary. The underlying software architecture sup ports tools for modeling the delay of the computer network, design ing a stable controller, simulating the performance of a telerobotic system, and testing the control algorithms using a force-reflecting input device. Furthermore, this setup provides data about including the Internet into more general telerobotic control architectures. To demonstrate the features of this environment, the complete proce dure for the design of a telerobotic controller is discussed. First, the delay parameters of an Internet segment are identified by prob ing the network. Then, these parameters are used in the design of a controller that includes a quasi-optimal estimator to compensate small data losses. Finally, simulations of the complete telerobotic system and emulations using a planar force-reflecting master and a virtual slave exemplify a typical design-and-test sequence.
Since its first experimental signatures, the so called “critical brain hypothesis” has been extensively studied. Yet, its actual foundations remain elusive. According to a widely accepted teleological reasoning, the brain would be poised to a critical state to optimize the mapping of the noisy and ever changing real-world inputs, thus suggesting that primary sensory cortical areas should be critical. We investigated whether a single barrel column of the somatosensory cortex of the anesthetized rat displays a critical behavior. Neuronal avalanches were recorded across all cortical layers in terms of both multi-unit activities and population local field potentials, and their behavior during spontaneous activity compared to the one evoked by a controlled single whisker deflection. By applying a maximum likelihood statistical method based on timeseries undersampling to fit the avalanches distributions, we show that neuronal avalanches are power law distributed for both multi-unit activities and local field potentials during spontaneous activity, with exponents that are spread along a scaling line. Instead, after the tactile stimulus, activity switches to a transient across-layers synchronization mode that appears to dominate the cortical representation of the single sensory input.
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