In this correspondence information theoretical tools are used to investigate the statistical properties of modeled cochlear nucleus globular bushy cell spike trains. The firing patterns are obtained from a simulation software that generates sample spike trains from any auditory input. Here we analyze for the first time the responses of globular bushy cells to voiced and unvoiced speech sounds. Classical entropy estimates, such as the direct method, are improved upon by considering a time-varying and time-dependent entropy estimate. With this method we investigated the relationship between the predictability of the neuronal response and the frequency content in the auditory signals. The analysis quantifies the temporal precision of the neuronal coding and the memory in the neuronal response.

Many application tasks require the cooperation of two or more robots. Humans are good at cooperation in shared workspaces, because they anticipate and adapt to the intentions and actions of others. In contrast, multi-agent and multi-robot systems rely on communication to exchange their intentions. This causes problems in domains where perfect communication is not guaranteed, such as rescue robotics, autonomous vehicles participating in traffic, or robotic soccer. In this paper, we introduce a computational model for implicit coordination, and apply it to a typical coordination task from robotic soccer: regaining ball possession. The computational model specifies that performance prediction models are necessary for coordination, so we learn them off-line from observed experience. By taking the perspective of the team mates, these models are then used to predict utilities of others, and optimize a shared performance model for joint actions. In several experiments conducted with our robotic soccer team, we evaluate the performance of implicit coordination