Our ability to evoke intelligent processing on artificial neural systems goes hand in hand with a coherent union of concepts in neuroscience, machine learning and engineering. Neftci will describe recent advances in neuromimetic inference and learning algorithms that address this challenge from a neuromorphic systems perspective. These algorithms range from finite state machines synthesized with neural models of working memory, attention and action selection for solving cognitive tasks; to the learning of probabilistic generative models with models of stochastic sampling and plasticity in spiking neural networks. These advances form the groundwork for a domain-specific language for probabilistic models that can be compiled against neural substrates. Combined with state-of-the-art neuromorphic electronic hardware, this framework will provide a unique technology for studying the processes of the mind at multiple levels of investigation.