Although animal studies provided significant insights in understanding the neural basis of learning and adaptation, they often cannot dissociate between different learning mechanisms due to the lack of verbal communication. To overcome this limitation, we examined the mechanisms of learning and its limits in a human intracortical brain-machine interface (BMI) paradigm. A tetraplegic participant controlled a 2D computer cursor by modulating single-neuron activity in the anterior intraparietal area (AIP). By perturbing the neuron-to-movement mapping, the participant learned to modulate the activity of the recorded neurons to solve the perturbations by adopting a target re-aiming strategy. However, when no cognitive strategies were adequate to produce correct responses, AIP failed to adapt to perturbations. These findings suggest that learning is constrained by the pre-existing neuronal structure, although it is possible that AIP needs more training time to learn to generate novel activity patterns when cognitive re-adaptation fails to solve the perturbations.

Introduction

To dissociate between these two learning mechanisms, we need to explore whether brain areas are capable of generating activity patterns that cannot be associated with any possible natural movement. The lack of any introspective description of strategies in animal BMI models of learning make the dissociation between the two mechanisms at best correlative and inferred. For instance, given that animals cannot be verbally instructed on which effector to use to perform intended movements (e.g., thinking of using only the right wrist) in a BMI task, it would not be possible to rule out that animals learn to produce the correct responses by using cognitive strategies to explore portions of the intrinsic manifold that were not identified in the baseline portions of the task (e.g., generating unexplored patterns by thinking about wiggling the toes) rather than generating truly novel patterns of activity. These limitations reveal the importance of using human subjects, who can be instructed to follow specific rules during BMI tasks and can report whether cognitive strategies were adopted to produce the correct responses. In the current study, a human participant with tetraplegia (C3-C4 complete lesion; 6 years post-injury) was implanted with a microelectrode array in the anterior intraparietal area (AIP) and learned to modulate the activity of a single neuron to control a 2D cursor in a goal-decoding BMI center-out task with two peripheral stimuli. The participant was instructed to attempt intended movements using only the right wrist and to not switch effectors during the task. She was also instructed to verbally report the direction of the intended movement after each trial in addition to any other strategies she employed to solve the task. Only the start and the end position of the cursor were presented to the participant. In a BMI-pro task, the neuron’s intrinsic coding for desired direction of wrist movements was used to decode the desired goal. In a BMI-anti task, the neuron was trained to switch its activity so that it was active for desired movements opposite the direction of the goal stimulus. Besides the verbal feedback from the participant, we also looked at the behavior of neurons that were not used for decoding but were found to be selective for wrist movement and therefore were part of the pre-existing structure that plans wrist movements. These neurons are referred to as untrained neurons, as opposed to trained neurons that are used for decoding and, therefore, directly contribute to the BMI output. If the untrained neurons do not change their turning in the BMI-anti task, then the individual neuron mechanism predominates; if they flip their preferred directions, then intrinsic variable learning predominates. In favor of the intrinsic variable mechanism, we found that both trained and untrained neurons changed their tuning in the BMI-anti task. This change reflected an explicit strategic choice of the subject, as the participant reported that she solved the task by attempting intended wrist movements in the direction opposite to the stimulus (i.e., re-aiming strategy).

We further tested whether the learning mechanism in AIP varies with the complexity of the BMI task. In a second BMI perturbation experiment, the participant had to control the cursor by simultaneously modulating the activity of two trained neurons. Similar to the first experiment, the activity patterns of the trained and untrained neurons, as well as the verbal feedback from the participant, provide evidence in favor of the intrinsic variable learning mechanism. These findings suggest that the intrinsic variable learning mechanism predominates in AIP regardless of the task complexity. However, it could be argued that these tasks favor the intrinsic variable learning over the individual neuron learning, because there is always a cognitive solution that can produce the correct responses. We designed a BMI perturbation task in which no cognitive strategy could produce the neural activity patterns necessary to solve the task. In this case, an individual neuron mechanism was required. The participant could not produce the correct responses, indicating that AIP neurons were not capable of generating novel and arbitrary patterns of activity to solve the task. This suggests that either AIP neuron can only engage pre-existing structures of the neural network for short-term learning (about 1 h) or a much more extensive period of training may be required if AIP neurons can learn to generate entirely new neuronal activity patterns.