Assistive algorithms influence neural representations in motor brain-computer interfaces

Abstract

Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using Brain-Computer Interfaces (BCIs), which map neural activity into movement via a decoder. We analyzed motor cortex activity as monkeys practiced BCI with a decoder that adapted to improve or maintain performance over days. Over time, task-relevant information became concentrated in fewer neurons, unlike with fixed decoders. At the population level, task information also became largely confined to a few neural modes that accounted for a small fraction of the population variance. A neural network model suggests the adaptive decoders directly contribute to forming these more compact neural representations. Our findings suggest that assistive decoders manipulate error information used for long-term learning computations like credit assignment, which may explain the altered neural representations and inform real-world BCI design. Assistive algorithms are widely used in brain-computer interfaces (BCIs), but their effects on neural representations remain unclear. Here, the authors show that adaptive BCIs lead the brain to learn compact representations, suggesting assistive algorithms shape long-term credit assignment.