The papers in this special section examine the use of fuzzy analytics and stochastic methods in the field of neuroscience. Recent theoretical and technological advancements provide new and deeper insights into the fundamental mechanisms of information processing in the neural system. This important process is accompanied by the tremendous rise of experimental data, which are waiting for further exploration. Modern methodologies and tools from neuroimaging, brain imaging, optogenetic devices, and in vitro and in vivo multielectrode recordings today generate high-quality neurophysiological data with a resolution quality that has never been reached before. These accelerating developments offer promising pathways to enhance our comprehension of the nervous system. Most innovative approaches of computational neuroscience lead to more realistic biophysical models that provide amazing chances for refined analyses of intracellular signaling and dynamics in heterogeneous neural networks, intrinsic connections of space-time processes, multisensory integration, and conditional behavior or links between brain regions in economic and daily-life decision making. Significant computational challenges arise from the high complexity of neural systems and the large number of constituents with yet unknownfunctional interconnections.