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Macroscopic oscillations in the brain are involved in various cognitive andphysiological processes, yet their precise function is not not completelyunderstood. Communication Through Coherence (CTC) theory proposes that theserhythmic electrical patterns might serve to regulate the information flowbetween neural populations. Thus, to communicate effectively, neuralpopulations must synchronize their oscillatory activity, ensuring that inputvolleys from the presynaptic population reach the postsynaptic one at itsmaximum phase of excitability. We consider an Excitatory-Inhibitory (E-I)network whose macroscopic activity is described by an exact mean-field model.The E-I network receives periodic inputs from either one or two externalsources, for which effective communication will not be achieved in the absenceof control. We explore strategies based on optimal control theory forphase-amplitude dynamics to design a control that sets the target population inthe optimal phase to synchronize its activity with a specific presynaptic inputsignal and establish communication. The control mechanism resembles the role ofa higher cortical area in the context of selective attention. To design thecontrol, we use the phase-amplitude reduction of a limit cycle and leveragerecent developments in this field in order to find the most effective controlstrategy regarding a defined cost function. Furthermore, we present resultsthat guarantee the local controllability of the system close to the limitcycle.

Optimal control of oscillatory neuronal models with applications to communication through coherence