Abstract-Objective: Measures of Transfer Entropy (TE) quantify the direction and strength of coupling between two complex systems. Standard approaches assume stationarity of the observations, and therefore are unable to track time-varying changes in nonlinear information transfer with high temporal resolution. In this study, we aim to define and validate novel instantaneous measures of transfer entropy to provide an improved assessment of complex non-stationary cardio-respiratory interactions. Methods: We here propose a novel Instantaneous point-process Transfer Entropy (ipT E) and validate its assessment as applied to cardiovascular and cardio-respiratory dynamics. In particular, heartbeat and respiratory dynamics are characterized through discrete time series, and modeled with probability density functions predicting the time of the next physiological event as a function of the past history. Likewise, non-stationary interactions between heartbeat and blood pressure dynamics are characterized as well. Furthermore, we propose a new measure of information transfer, the instantaneous point-process Information Transfer (ipInf T r), which is directly derived from point-processbased definitions of the Kolmogorov-Smirnov distance. Results and Conclusion: Analysis on synthetic data, as well as on experimental data gathered from healthy subjects undergoing postural changes confirms that ipT E, as well as ipInf T r measures are able to dynamically track changes in physiological systems coupling. Significance: This novel approach opens new avenues in the study of hidden, transient, non-stationary physiological states involving multivariate autonomic dynamics in cardiovascular health and disease. The proposed method can also be tailored for the study of complex multi-system physiology (e.g., brain-heart or, more in general, brain-body interactions).