Cardiac rhythm management devices provide therapies for both arrhythmias and resynchronisation but not heart failure, which affects millions of patients worldwide. This paper reviews recent advances in biophysics and mathematical engineering that provide a novel technological platform for addressing heart disease and enabling beat-to-beat adaptation of cardiac pacing in response to physiological feedback. The technology consists of silicon hardware central pattern generators (hCPGs) that may be trained to emulate accurately the dynamical response of biological central pattern generators (bCPGs). We discuss the limitations of present CPGs and appraise the advantages of analog over digital circuits for application in bioelectronic medicine. To test the system, we have focused on the cardio-respiratory oscillators in the medulla oblongata that modulate heart rate in phase with respiration to induce respiratory sinus arrhythmia (RSA). We describe here a novel, scalable hCPG comprising physiologically realistic (Hodgkin-Huxley type) neurones and synapses. Our hCPG comprises two neurones that antagonise each other to provide rhythmic motor drive to the vagus nerve to slow the heart. We show how recent advances in modelling allow the motor output to adapt to physiological feedback such as respiration. In rats, we report on the restoration of RSA using an hCPG that receives diaphragmatic electromyography input and use it to stimulate the vagus nerve at specific time points of the respiratory cycle to slow the heart rate. We have validated the adaptation of stimulation to alterations in respiratory rate. We demonstrate that the hCPG is tuneable in terms of the depth and timing of the RSA relative to respiratory phase. These pioneering studies will now permit an analysis of the physiological role of RSA as well as its any potential therapeutic use in cardiac disease. Abbreviations ADC, analog-to-digital converter; bCPG, biological central pattern generator; BötC, Bötzinger complex; CPG, central pattern generator; CPU, central processing unit; CRT, cardiac resynchronisation therapy; cVN, central vagus nerve; DAC, digital-to-analog converter; hCPG, hardware central pattern generator; HR, heart rate; GPN, glossopharyngeal nerve; LBBB, left bundle branch block; NHS, National Health Service (UK); PN, phrenic nerve; r.m.s, root mean square; RSA, respiratory sinus arrhythmia; RTN, retro-trapezoid nucleus; RVLM, rostral ventro lateral medulla; sCPG, software central pattern generator; SN, sympathetic nerve; τ AV , atrioventricular delay; τ VV , interventricular delay; VN, vagus nerve; VRG, ventral respiratory group.Alain Nogaret is a physicist with interest in building and studying artificial neurons and networks that make constructive use of the principles of nonlinear science to develop novel therapies for chronic diseases. He has demonstrated enhanced transmission of electric pulses by stochastic resonance in semiconductor neurons that have dendrites, soma and axon compartments. He has also demonstrated the coexistenc...