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The recent explosion of interest in brain-machine interfaces (BMIs) has spurred research into choosing the optimal input signal source for a desired application. The signals with highest bandwidth--single neuron action potentials or spikes--typically are difficult to record for more than a few years after implantation of intracortical electrodes.(More)
The loss of a limb or paralysis resulting from spinal cord injury has devastating consequences on quality of life. One approach to restoring lost sensory and motor abilities in amputees and patients with tetraplegia is to supply them with implants that provide a direct interface with the CNS. Such brain-machine interfaces might enable a patient to exert(More)
Plasticity is a crucial component of normal brain function and a critical mechanism for recovery from injury. In vitro, associative pairing of presynaptic spiking and stimulus-induced postsynaptic depolarization causes changes in the synaptic efficacy of the presynaptic neuron, when activated by extrinsic stimulation. In vivo, such paradigms can alter the(More)
Brain-machine interfaces (BMIs) use signals recorded directly from the brain to control an external device, such as a computer cursor or a prosthetic limb. These control signals have been recorded from different levels of the brain, from field potentials at the scalp or cortical surface to single neuron action potentials. At present, the more invasive(More)
Loss of hand use is considered by many spinal cord injury survivors to be the most devastating consequence of their injury. Functional electrical stimulation (FES) of forearm and hand muscles has been used to provide basic, voluntary hand grasp to hundreds of human patients. Current approaches typically grade pre-programmed patterns of muscle activation(More)
Rapid reaching movements of human and non-human primates are often characterized by irregular multi-peaked velocity profiles. How to interpret these irregularities is still under debate. While some reports assert that these irregularities are the result of a continuous controller interacting with the environment, we and others hold that the velocity(More)
Control of reaching movements requires an accurate estimate of the state of the limb, yet sensory signals are inherently noisy, because of both noise at the receptors themselves and the stochastic nature of the information representation by neural discharge. One way to derive an accurate representation from noisy sensor data is to combine it with the output(More)
OBJECTIVE Brain machine interfaces (BMIs) that decode control signals from motor cortex have developed tremendously in the past decade, but virtually all rely exclusively on vision to provide feedback. There is now increasing interest in developing an afferent interface to replace natural somatosensation, much as the cochlear implant has done for the sense(More)
As long as 150 years ago, when Fritz and Hitzig demonstrated the electrical excitability of the motor cortex, scientists and fiction writers were considering the possibility of interfacing a machine with the human brain. Modern attempts have been driven by concrete technological and clinical goals. The most advanced of these has brought the perception of(More)
Current multielectrode techniques enable the simultaneous recording of spikes from hundreds of neurons. To study neural plasticity and network structure it is desirable to infer the underlying functional connectivity between the recorded neurons. Functional connectivity is defined by a large number of parameters, which characterize how each neuron(More)