Cognition and behavior unfold over time. The temporal aspects of thought and action reflect, at least in part, the temporal evolution of the activity of the populations of neurons that control them. It has long been hypothesized that the time course of neural activity arises from dynamical constraints imposed by the underlying neural circuitry. However, this has been difficult to show experimentally because it requires the ability to finely perturb neural activity in varied ways. Here, we propose to employ a brain-computer interface (BCI) paradigm to study neural dynamics. A BCI enables us to perturb neural activity by harnessing the animal's volitional control to drive the activity of a population of neurons into configurations that we specify. In this way, we can perform causal tests of dynamical constraints and their relation to behavior. We will study dynamical constraints imposed by motor preparation using multi-neuronal activity recorded in the motor cortex of macaque monkeys. We hypothesize that dynamical constraints exist in the motor cortex, and that these constraints are shaped during motor preparation to drive arm movements. To test these hypotheses, we will first challenge the animals to violate the putative dynamical constraints. Then, we will test the hypothesis that motor preparation sets up dynamical constraints appropriate for the upcoming arm movement. Finally, we will use the BCI paradigm to perturb neural activity during movement preparation to alter and evoke arm movements. Taken together, our proposed work will likely lead to a richer understanding of how networks of neurons give rise to population dynamics, and how those dynamics relate to neural computation and behavior.
Movement disorders, including Parkinson's disease and stroke, are prevalent. Our work will 1) provide deeper insight into how movements are prepared and executed, eventually leading to better treatments for movement disorders, and 2) improve the performance of brain-computer interfaces, which assist paralyzed patients and amputees by converting their neural activity into cursor or arm movements.
|Hennig, Jay A; Golub, Matthew D; Lund, Peter J et al. (2018) Constraints on neural redundancy. Elife 7:|
|Snyder, Adam C; Yu, Byron M; Smith, Matthew A (2018) Distinct population codes for attention in the absence and presence of visual stimulation. Nat Commun 9:4382|
|Golub, Matthew D; Sadtler, Patrick T; Oby, Emily R et al. (2018) Learning by neural reassociation. Nat Neurosci 21:607-616|