Recording from the same neurons chronically in motor cortex.

Two rhesus monkeys were implanted with silicon arrays of 96 microelectrodes. Neural activity was recorded periodically over a period of weeks to months. We have developed a method to determine whether single units in two separate recording sessions represent the same neuron.

Control of a brain-computer interface without spike sorting.

Two rhesus monkeys were trained to move a cursor using neural activity recorded with silicon arrays of 96 microelectrodes implanted in the primary motor cortex. We have developed a method to extract movement information from the recorded single and multi-unit activity in the absence of spike sorting. By setting a single threshold across all channels and fitting the resultant events with a spline tuning function, a control signal was extracted from this population using a Bayesian particle-filter extraction algorithm.

Functional network reorganization during learning in a brain-computer interface paradigm.

Efforts to study the neural correlates of learning are hampered by the size of the network in which learning occurs. To understand the importance of learning-related changes in a network of neurons, it is necessary to understand how the network acts as a whole to generate behavior. Here we introduce a paradigm in which the output of a cortical network can be perturbed directly and the neural basis of the compensatory changes studied in detail.