The task of explaining brains is hampered by the dynamics and complexity of brains. Complexity results from the extraordinary processing power of brains that is reached by distributing the continuously incoming stream of sensory signals over myriads of nerve cells that all compute “bits and pieces” in parallel. Moreover, explanatory approaches to these computations are to be found on multiple levels of organizations, e.g. electric, molecular, synaptic, networks etc. Thus, the comprehension problem caused by complexity is to keep track where the relevant processes happen. Dynamics occur as the incoming sensory signals are not only processed straight “downstream” towards behavioral response, but are rather recurrently fed into the stream to be compared and computed with sensory signals that come in later. This mix forms an “ongoing brain activity” that cannot be understood as a simple stimulus-response behavior. Thus, the comprehension problem caused by dynamics is to keep track when the relevant processes happen. Explainers of brain phenomena (and their recipients) are permanently challenged to push the limits of their mental capacities in order to handle the dynamics and complexity they find in brains.
Simulation is a key to dynamics and complexity. Scientists use it as a standard method to explain even highly dynamic and complex brain phenomena. But the question is: How exactly does simulation help to unlock hardly explainable brain phenomena from dynamics and complexity? For answering this question I propose to focus on mental simulation as a mechanism that generates explanations. A detailed account of mental simulation can reveal the specific problems caused by dynamics and complexity. Solutions to these problems are simultaneously the specific roles of simulation in explaining brains.