  Humans and animals exhibit a range of interesting behaviors in dynamic
environments, and it is unclear how our brains actively reformat this dense
sensory information to enable these behaviors. Experimental neuroscience is
undergoing a revolution in its ability to record and manipulate hundreds to
thousands of neurons while an animal is performing a complex behavior. As these
paradigms enable unprecedented access to the brain, a natural question that
arises is how to distill these data into interpretable insights about how
neural circuits give rise to intelligent behaviors. The classical approach in
systems neuroscience has been to ascribe well-defined operations to individual
neurons and provide a description of how these operations combine to produce a
circuit-level theory of neural computations. While this approach has had some
success for small-scale recordings with simple stimuli, designed to probe a
particular circuit computation, often times these ultimately lead to disparate
descriptions of the same system across stimuli. Perhaps more strikingly, many
response profiles of neurons are difficult to succinctly describe in words,
suggesting that new approaches are needed in light of these experimental
observations. In this thesis, we offer a different definition of
interpretability that we show has promise in yielding unified structural and
functional models of neural circuits, and describes the evolutionary
constraints that give rise to the response properties of the neural population,
including those that have previously been difficult to describe individually.
We demonstrate the utility of this framework across multiple brain areas and
species to study the roles of recurrent processing in the primate ventral
visual pathway; mouse visual processing; heterogeneity in rodent medial
entorhinal cortex; and facilitating biological learning.
