As physicists, we are all familiar with such powerful principles as maximum entropy and least action, which play a central role in our understanding of the physical universe.  This success coupled with the Darwinian notion of survival of the fittest suggests that optimization principles could be helpful for making sense of biological phenomena.  I will describe some of the progress we have made towards understanding sensory processing in the cerebral cortex using efficient coding principles, including accurate predictions of neural responses in stages of the auditory pathway that are poorly understood.  Complimenting these efforts, we have used both theoretical and experimental methods to study the mechanisms underlying neural activity in the cortex.  For example, we have developed the first neural network model capable of learning a sparse representation of natural scenes --- a global objective --- using only local learning rules; and we have experimentally measured the relative contributions of inhibitory and excitatory synaptic input to individual neurons in the auditory cortex, explaining their greater responsiveness for sounds coming from some spatial locations over others.  In addition to basic questions about sensory coding and computation, my research group is also interested in understanding the mechanistic basis for our ability to focus on important sounds in our environment while ignoring distracters.  I will conclude with a description of the auditory selective attention behavioral paradigm we have developed, the neural correlates of attention we have observed, and our ongoing efforts to apply a unique set of experimental tools to the mechanistic study of attention.