Robust Nonlinear Control for Synchronising and Regulating Neural Activity

Abstract

Modulating neural activity in a systematic manner holds significant potential for advancing the understanding of brain functions and improving therapeutic strategies. To forecast the dynamics behind several brain activities, numerous neurobiological models have been developed, targeting both individual neurons and entire neural populations. In this context, control systems emerge as powerful tools for effectively linking inputs, such as neural stimuli, to measurable outputs. This study introduces a control framework aimed at regulating neural-mass activity, which has promising applications in pattern tracking, including rhythm generation and phase synchronisation. Given the strong connection of these mechanisms to real brain computations, the presented approach offers biologically relevant insights. To demonstrate this, the Wilson-Cowan model is used, in which stimuli are delivered via light signals to genetically engineered neurons expressing light-sensitive actuators. This proof of concept provides a foundation for future experimental applications in neurobiological systems control. Furthermore, building on previous results, this work integrates opsin dynamics, of the channelrhodopsin and halorhodopsin-type, to accurately model the optogenetic activation channels, enhancing the description of the actuation process.