A cerebellar granule cell-climbing fiber computation to learn to track long time intervals.

In classical cerebellar learning, Purkinje cells (PkCs) associate climbing fiber (CF) error signals with predictive granule cells (GrCs) that were active just prior (∼150 ms). The cerebellum also contributes to behaviors characterized by longer timescales. To investigate how GrC-CF-PkC circuits might learn seconds-long predictions, we imaged simultaneous GrC-CF activity over days of forelimb operant conditioning for delayed water reward. As mice learned reward timing, numerous GrCs developed anticipatory activity ramping at different rates until reward delivery, followed by widespread time-locked CF spiking. Relearning longer delays further lengthened GrC activations. We computed CF-dependent GrC→PkC plasticity rules, demonstrating that reward-evoked CF spikes sufficed to grade many GrC synapses by anticipatory timing. We predicted and confirmed that PkCs could thereby continuously ramp across seconds-long intervals from movement to reward. Learning thus leads to new GrC temporal bases linking predictors to remote CF reward signals-a strategy well suited for learning to track the long intervals common in cognitive domains.

Comparing preference related to comfort in torque-matched muscle contractions between two different types of functional electrical stimulation pulses in able-bodied participants.

CONTEXT/OBJECTIVE: Functional electrical stimulation (FES) is commonly used in rehabilitation to generate electrically-induced muscle contractions. FES has been shown to assist in the recovery of voluntary motor functions after stroke or spinal cord injury. However, discomfort associated with FES can motivate patients to withdraw their participation from FES therapy despite its benefits. To address this issue, a functional electrical stimulator, called MyndMove™ (MyndTec Inc., Canada), has been developed to generate more comfortable contractions than conventional stimulators. DESIGN: Cross-sectional, interventional, with two treatment arms. SETTING: A laboratory within a rehabilitation center. PARTICIPANTS: Twelve able-bodied participants. INTERVENTION: FES delivered with two different stimulators, MyndMove™ and Compex Motion (Compex, Switzerland), during muscle contractions of high, moderate and low stimulation intensity. OUTCOME MEASURES: Comfort-related preference to a given stimulator and the discomfort score rated through a Numeric Rating Scale (NRS-101) for both stimulators. RESULTS: Participants perceived a reduction in discomfort during high-intensity stimulation generated using MyndMove™. In addition, MyndMove™ stimulations were preferred in 60% of all contractions. The reduction in discomfort associated with MyndMove™ might be due the fact that MyndMove™ delivers less charge to generate contractions of equivalent intensity, compared to Compex Motion. CONCLUSION: Reducing discomfort during FES may help in generating stronger and more clinically useful contractions, increasing accessibility of FES therapy to include individuals with low tolerance to FES.

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.

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

CONTEXT: Firing rates of single cortical neurons can be volitionally modulated through biofeedback (i.e. operant conditioning), and this information can be transformed to control external devices (i.e. brain-machine interfaces; BMIs). However, not all neurons respond to operant conditioning in BMI implementation. Establishing criteria that predict neuron utility will assist translation of BMI research to clinical applications. FINDINGS: Single cortical neurons (n=7) were recorded extracellularly from primary motor cortex of a Long-Evans rat. Recordings were incorporated into a BMI involving up-regulation of firing rate to control the brightness of a light-emitting-diode and subsequent reward. Neurons were classified as 'fast-spiking', 'bursting' or 'regular-spiking' according to waveform-width and intrinsic firing patterns. Fast-spiking and bursting neurons were found to up-regulate firing rate by a factor of 2.43±1.16, demonstrating high utility, while regular-spiking neurons decreased firing rates on average by a factor of 0.73±0.23, demonstrating low utility. CONCLUSION/CLINICAL RELEVANCE: The ability to select neurons with high utility will be important to minimize training times and maximize information yield in future clinical BMI applications. The highly contrasting utility observed between fast-spiking and bursting neurons versus regular-spiking neurons allows for the hypothesis to be advanced that intrinsic electrophysiological properties may be useful criteria that predict neuron utility in BMI implementation.