Stickle, M. P. | Search bioRxiv

bioRxiv2022

Multi-areal neural dynamics encode human decision making

Value-based decision-making involves multiple cortical and subcortical brain areas, but the distributed nature of neurophysiological activity underlying economic choices in the human brain remains largely unexplored. Here, we use intracranial recordings from neurosurgical patients to show that risky choices are reflected in high frequency activity distributed across multiple prefrontal and subcortical brain regions, whereas reward-related computations are less widely represented and more modular. State space modeling reveals sub-second neural dynamics underlying choices during deliberation and allows high-accuracy trial-by-trial decoding of subjects choices robustly across patients despite differences in anatomical coverage. These results shed light into the neural basis of choice across brain areas and open the door to new intracranial approaches for brain state decoding.

neuroscience↗

bioRxiv2020

Transitioning from global to local computational strategies during brain-machine interface learning

When learning to use a brain-machine interface (BMI), the brain modulates neuronal activity patterns, exploring and exploiting the state space defined by their neural manifold. Neurons directly involved in BMI control can display marked changes in their firing patterns during BMI learning. However, whether these changes extend to neurons not directly involved in BMI control remains unclear. To clarify this issue, we studied BMI learning in animals that were required to control the position of a platform with their neural signals. Animals that learned to control the platform and improved their performance in the task shifted from a global strategy, where both direct and indirect neurons modified their firing patterns, to a local strategy, where only direct neurons modified their firing rate, as animals became expert in the task. These results provide important insights into what differentiates successful and unsuccessful BMI learning and the computational mechanisms adopted by the neurons.

neuroscience↗