Plasticity of functional connectivity in the adult spinal cord.

This paper emphasizes several characteristics of the neural control of locomotion that provide opportunities for developing strategies to maximize the recovery of postural and locomotor functions after a spinal cord injury (SCI). The major points of this paper are: (i) the circuitry that controls standing and stepping is extremely malleable and reflects a continuously varying combination of neurons that are activated when executing stereotypical movements; (ii) the connectivity between neurons is more accurately perceived as a functional rather than as an anatomical phenomenon; (iii) the functional connectivity that controls standing and stepping reflects the physiological state of a given assembly of synapses, where the probability of these synaptic events is not deterministic; (iv) rather, this probability can be modulated by other factors such as pharmacological agents, epidural stimulation and/or motor training; (v) the variability observed in the kinematics of consecutive steps reflects a fundamental feature of the neural control system and (vi) machine-learning theories elucidate the need to accommodate variability in developing strategies designed to enhance motor performance by motor training using robotic devices after an SCI.

Electrolysis-based diaphragm actuators.

This work presents a new electrolysis-based microelectromechanical systems (MEMS) diaphragm actuator. Electrolysis is a technique for converting electrical energy to pneumatic energy. Theoretically electrolysis can achieve a strain of 136 000% and is capable of generating a pressure above 200 MPa. Electrolysis actuators require modest electrical power and produce minimal heat. Due to the large volume expansion obtained via electrolysis, small actuators can create a large force. Up to 100 µm of movement was achieved by a 3 mm diaphragm. The actuator operates at room temperature and has a latching and reversing capability.

Cognitive neural prosthetics.

Research on neural prosthetics has focused largely on using activity related to hand trajectories recorded from motor cortical areas. An interesting question revolves around what other signals might be read out from the brain and used for neural prosthetic applications. Recent studies indicate that goals and expected value are among the high-level cognitive signals that can be used and will potentially enhance the ability of paralyzed patients to communicate with the outside world. Other new findings show that local field potentials provide an excellent source of information about the cognitive state of the subject and are much easier to record and maintain than spike activity. Finally, new movable probe technologies will enable recording electrodes to seek out automatically the best signals for decoding cognitive variables.

Recording advances for neural prosthetics.

An important challenge for neural prosthetics research is to record from populations of neurons over long periods of time, ideally for the lifetime of the patient. Two new advances toward this goal are described, the use of local field potentials (LFPs) and autonomously positioned recording electrodes. LFPs are the composite extracellular potential field from several hundreds of neurons around the electrode tip. LFP recordings can be maintained for longer periods of time than single cell recordings. We find that similar information can be decoded from LFP and spike recordings, with better performance for state decodes with LFPs and, depending on the area, equivalent or slightly less than equivalent performance for signaling the direction of planned movements. Movable electrodes in microdrives can be adjusted in the tissue to optimize recordings, but their movements must be automated to be a practical benefit to patients. We have developed automation algorithms and a meso-scale autonomous electrode testbed, and demonstrated that this system can autonomously isolate and maintain the recorded signal quality of single cells in the cortex of awake, behaving monkeys. These two advances show promise for developing very long term recording for neural prosthetic applications.