The Trouble with Weight-Dependent STDP

We fit a weight-dependent STDP rule to the classic data of Bi and Poo (1998), showing that this rule leads to slow learning in a simulation with an integrate-and-fire neuron. The slowness of learning is explained by an inequality between the range of initial weights in the data and the largest relative potentiation. We show that slow learning can be overcome with an increased learning rate, but that this approach leads to rapid forgetting in the presence of realistic levels of background spiking. Our study demonstrates that weight-dependent STDP rules, commonly used in neural simulations, have biologically unrealistic consequences. We discuss the implications of this finding for several interpretations of weight-dependent plasticity and STDP more generally, and recommend directions for further research.