The ability to select between competing options and adapt to new situations underlies our impressive capabilities of playing soccer, flying aircrafts and skiing on the Olympics. To select between actions, the brain needs an accurate representation of the state of the body and the environment it is in. Despite the sophistication of our sensory system, it is unlikely to extract a complete and accurate representation of the state due to noise and long sensory delays. To avoid instabilities due to these factors, the brain builds internal models that predict sensory outcome of motor actions. These predictions are integrated with the incoming sensory feedback to update the estimate of the current state. By recording neural activity from the posterior parietal cortex (PPC) in both humans and non-human primates, I present recent evidence from our lab showing that PPC contains an adaptive internal forward model that learns to compensate for delayed visual feedback. I will also discuss clinical brain-machine interface (BMI) studies in human with tetraplegia that have taken steps to elucidate the mechanisms behind acquisition of new skills. By training a participant with tetraplegia to control a computer cursor by modulating the neural activity of PPC neurons, we found that some patterns of activity were generated more easily than others. The easier-to-learn patterns of activity were combinations of pre-existing neuronal patterns, whereas the difficult-to-learn activity patterns were different from the neuronal patterns that the participant had experienced in the past. Importantly, there were neuronal patterns that PPC could not generate indicating that neuroplasticity in learning is constrained by the pre-existing structure of the brain. This fundamental constraint may explain why learning novel tasks can be challenging.

Prof. Vasileios Christopoulos