. Semi-chronic motorized microdrive and control algorithm for autonomously isolating and maintaining optimal extracellular action potentials. J Neurophysiol 93: 570 -579, 2005. First published June 30, 2004 doi: 10.1152/jn.00369.2004. A system was developed that can autonomously position recording electrodes to isolate and maintain optimal quality extracellular signals. The system consists of a novel motorized miniature recording microdrive and a control algorithm. The microdrive was designed for chronic operation and can independently position four glass-coated Pt-Ir electrodes with micrometer precision over a 5-mm range using small (3 mm diam) piezoelectric linear actuators. The autonomous positioning algorithm is designed to detect, align, and cluster action potentials and then command the microdrive to optimize and maintain the neural signal. This system is shown to be capable of autonomous operation in monkey cortical tissue.

We demonstrate an adaptation strategy for adjusting the stride period in a hexapedal running robot. The robot is inspired by discoveries about the self-stabilizing properties of insects and uses a sprawled posture, a bouncing alternating-tripod gait, and passive compliance and damping in the limbs to achieve fast (over four body-lengths per second), stable locomotion. The robot is controlled by an open-loop motor pattern that activates the legs at fixed intervals. For maximum speed and efficiency, the stride period of the pattern should be adjusted to match changes in terrain (e.g., slopes) or loading conditions (e.g., carrying an object). An ideal adaptation strategy will complement the design philosophy behind the robot and take advantage of the self-stabilizing role of the mechanical system. In this paper we describe an adaptation scheme based on measurements of ground contact timing obtained from binary sensors on the robot’s feet. Wediscuss the motivation for the approach, putting it in the context of previous research on the dynamic properties of running machines and bouncing multi-legged animals, and we show the results of the experiments.

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The cognitive neural prosthetic (CNP) is a very versatile method for assisting paralyzed patients and patients with amputations. The CNP records the cognitive state of the subject, rather than signals strictly related to motor execution or sensation. We review a number of high-level cortical signals and their application for CNPs, including intention, motor imagery, decision making, forward estimation, executive function, attention, learning, and multi-effector movement planning. CNPs are defined by the cognitive function they extract, not the cortical region from which the signals are recorded. However, some cortical areas may be better than others for particular applications. Signals can also be extracted in parallel from multiple cortical areas using multiple implants, which in many circumstances can increase the range of applications of CNPs. The CNP approach relies on scientific understanding of the neural processes involved in cognition, and many of the decoding algorithms it uses also have parallels to underlying neural circuit functions.