Neuron-Type-Specific Utility in a Brain-Machine Interface: a Pilot Study

Operant conditioning reveals task-specific responses of single neurons in a brain-machine interface

OBJECTIVE

Volitional modulation of single cortical neurons holds great potential for the implementation of brain-machine interfaces (BMIs) because it can induce a rapid acquisition of arbitrary associations between machines and neural activity. It can also be used as a framework to study the limits of single-neuron control in BMIs.

APPROACH

We tested the control of a one-dimensional actuator in two BMI tasks which differed only in the neural contingency that determined when a reward was dispensed. A thresholded activity task, commonly implemented in single-neuron BMI control, consisted of reaching or exceeding a neuron activity level, while the second task consisted of reaching and maintaining a narrow neuron activity level (i.e. windowed activity task).

MAIN FINDINGS

Single neurons in layer V of the motor cortex of rats improved performance during both the thresholded activity and windowed activity BMI tasks. However, correct performance during the windowed activity task was accompanied by activation of neighboring neurons, not in direct control of the BMI. In contrast, only neurons in direct control of the BMI were active at the time of reward during the thresholded activity task.

SIGNIFICANCE

These results suggest that thresholded activity single-neuron BMI implementations are more appropriate compared to windowed activity BMI tasks to capitalize on the adaptability of cortical circuits to acquire novel arbitrary skills.

Operant conditioning of motor cortex neurons reveals neuron-subtype-specific responses in a brain-machine interface task

Operant conditioning is implemented in brain-machine interfaces (BMI) to induce rapid volitional modulation of single neuron activity to control arbitrary mappings with an external actuator. However, intrinsic factors of the volitional controller (i.e. the brain) or the output stage (i.e. individual neurons) might hinder performance of BMIs with more complex mappings between hundreds of neurons and actuators with multiple degrees of freedom. Improved performance might be achieved by studying these intrinsic factors in the context of BMI control. In this study, we investigated how neuron subtypes respond and adapt to a given BMI task. We conditioned single cortical neurons in a BMI task. Recorded neurons were classified into bursting and non-bursting subtypes based on their spike-train autocorrelation. Both neuron subtypes had similar improvement in performance and change in average firing rate. However, in bursting neurons, the activity leading up to a reward increased progressively throughout conditioning, while the response of non-bursting neurons did not change during conditioning. These results highlight the need to characterize neuron-subtype-specific responses in a variety of tasks, which might ultimately inform the design and implementation of BMIs.