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. Pairwise cross-correlograms, the autocorrelogram, waveform shape, and mean firing rate were used together as identifying features of a neuron. When two units recorded on separate days were compared using these features, their similarity scores tended to be either high, indicating two recordings from the same neuron, or low, indicating different neurons. Although these metrics are individually weak, together they produce a strong classifier. Some neurons were recorded for >100 days. These monkeys performed a center-out reaching task, and we found that the firing properties of chronically recorded neurons were stable over time.

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. The animals achieved high-quality control comparable to the performance of decoding schemes based on sorted spikes. Our results suggest that even the simplest signal processing is sufficient for high-quality neuroprosthetic control.

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. Using a brain-computer interface, dozens of simultaneously recorded neurons in the motor cortex of awake, behaving monkeys are used to control the movement of a cursor in a three-dimensional virtual-reality environment. This device creates a precise, well-defined mapping between the firing of the recorded neurons and an expressed behavior (cursor movement). In a series of experiments, we force the animal to relearn the association between neural firing and cursor movement in a subset of neurons and assess how the network changes to compensate. We find that changes in neural activity reflect not only an alteration of behavioral strategy but also the relative contributions of individual neurons to the population error signal.