Robust Design of Inhibitory Neuronal Networks Displaying Rhythmic Activity

Central pattern generators (CPGs) are neuronal networks that autonomously produce patterns of phase-locked activity. The need for bioelectronic implants that adapt to physiological feedback calls for novel methods for designing synthetic CPGs that respond identically to their biological counterparts. Here, we consider optimization-based parameter estimation for identifying network parameters that give rise to activity with specific temporal properties. We demonstrate that reducing a network to the phase resetting curves (PRCs) of its component neurons allows for the sequential parameter estimation of each single neuron separately. In this way, the challenges associated with estimating all network parameters simultaneously may be avoided. We highlight a possible application of our approach by estimating parameters of a CPG emulating the phase-locked activity associated with ECG data. This work paves the way for the design of synthetic networks which may be interfaced with nervous systems.