In the past decades, computational neuroscience has seen a burgeoning of normative approaches. These studies made significant advances in formulating formal theories of optimality, and optimal computations, identifying relevant physical and computational constraints under which those computations need to be implemented, developing analytical methods and numerical algorithms to solve the resulting constrained optimization problems, and relating these solutions to biological substrates. However, only a relatively small fraction of these studies attempted to make specific predictions about, and thus interpret in normative terms, the cellular-level electrophysiological properties of individual neurons or synapses. Small in numbers it may be, the potential impact of this particular line of research cannot be ignored as such theories may provide a way to bridge the gap between the cellular-molecular and the systems-level branches of neuroscience by connecting low-level properties of the nervous system to its high-level functions. Our workshop aims to highlight and discuss recent work in this field. Since much of the theoretical background in this field has been adopted from information theory, machine learning, and related fields, we expect that not only experimental and computational neuroscientists, but also machine learning researchers will be interested in the general topic and the specific talks.