Neural shaping with joint optimization of controller and plant under restricted dynamics

The prototypical brain-computer interface (BCI) algorithm translates brain activity into changes in the states of a computer program, for typing or cursor movement. Most approaches use neural decoding which learns how the user has encoded their intent in their noisy neural signals. Recent adaptive decoders for cursor movement improved BCI performance by modeling the user as a feedback controller; when this model accounts for adaptive control, the neural decoder is termed co-adaptive. This recent collection of control-inspired neural decoding strategies disregards a major antecedent conceptual realization, whereby the user could be induced to adopt an encoding strategy (control policy) such that the encoder-decoder pair (or equivalently, controller-plant pair) is optimal under a desired cost function. We call this alternate conceptual approach neural shaping, in contradistinction to neural decoding. Previous work illuminated the general form of optimal controller-plant pair under a cost representing information gain. For BCI applications requiring the user to issue discrete-valued commands, the information-gain-optimal pair, based on the posterior matching scheme, can be user-friendly. In this paper, we discuss the application of neural shaping to cursor control with continuous-valued states based on continuous-valued user commands. We examine the problem of jointly optimizing controller and plant under quadratic expected cost and restricted linear plant dynamics. This simplification reduces joint controller-plant selection to a static optimization problem, similar to approaches in structural engineering and other areas. This perspective suggests that recent BCI approaches that alternate between adaptive neural decoders and static neural decoders could be local Pareto-optimal, representing a suboptimal iterative-type solution to the optimal joint controller-plant problem.