Obtaining the elusive small-signal phase-resetting curve from individual neurons

For over 30 years, phase-resetting theory has been proposed to be a powerful tool in studying the dynamics of oscillatory systems, such as neurons. Much of the argument for this theory is bolstered by the powerful analytical tools that can be applied when phase-resetting curves (PRCs) in response to weak (type 0) inputs. However, most published experimental . data from excitable cells has been obtained with strong (type 1) inputs. In this paper we highlight the difficulties in obtaining small-signal PRCs and some of our efforts at improving these measurements. We also apply statistical methods to PRC experiments to provide a measure of statistical significance to the PRC data. Our results show that weak PRCs can be reliably obtained, and we introduce a new subclass of weak PRCs.