One of the many challenges in long-term decoding from chronically implanted electrodes involves tracking changes in the firing properties of the neural ensemble while simultaneously reconstructing the desired signal. We provide an approach to this problem based on adaptive point process filtering. In particular, we construct a lock-step adaptive filter built upon stochastic models for: a) the receptive field parameters of individual neurons within the ensemble, b) the biological signal to be reconstructed, and c) the instantaneous likelihood of firing in each neuron given the current state of a) and b). We assessed the ability of this filter to maintain a good representation of movement information in a dynamic ensemble of primary motor neurons tuned to hand kinematics. We simulated a recording scenario for this ensemble, where neurons were continuously becoming lost to the recording device while recordings from other, previously unobserved neurons became available. We found that this adaptive decoding algorithm was able to maintain accurate estimates of hand direction, even after the entire neural population had been replaced multiple times, but that the hand velocity signal tended to degrade over long periods.