Which features trigger action potentials in cortical neurons in vivo?

We study the initiation of action potentials (APs) in in vivo recordings of cortical neurons from cat visual cortex. It was shown that cortical neurons are not simple threshold devices, emitting an AP each time a fixed voltage threshold is reached, but that the emission of an AP partly depends on the rate of change of the membrane potential preceding an AP. In this paper we investigate systematically which features of the membrane potential lead to an AP by means of machine learning methods. We use support vector machines (SVMs) to discriminate between trajectories of the membrane potential which lead to an AP within the next ms and trajectories which do not lead to the initiation of an AP. For every point in a trajectory of the membrane potential (MP) we compute a set of 11 features and use a forward selection algorithm to find out the relevant features for the occurrence of an AP. Based on the results we construct a reduced prediction model. This model suggests that AP occurrences can be predicted best by a combination of the 1st temporal derivative of the MP at distance to the AP maximum, the MP itself and the mean MP over a longer range.