One of the fundamental assumptions in neuroscience is that brains store information in the synaptic connectivity between neurons in a network. Paradigmatic theories propose that experience drives coordinated changes in synaptic connections that record information about relevant experiences in the “wiring diagram” of a network and optimize network responses to future inputs. This process is thought to endow brains with the ability to create models of the world, which are core components of intelligent behavior. Although a large amount of knowledge has been accumulated about the function and plasticity of individual synapses it remains unclear whether and how modifications of multiple synapses are coordinated, and whether experience-driven plasticity of network structure and function is consistent with existing theories of memory. Direct experimental tests of these theories will ultimately require dense reconstructions of wiring diagrams with synaptic resolution, which remains a major technical challenge in neuroscience. We address this issue using serial block face scanning electron microscopy (SBEM), a technique for imaging the ultrastructure of biological samples with nanometer resolution throughout large volumes. Datasets obtained with this method allow for the dense annotation of neurons and their synaptic connections to reconstruct wiring diagrams of neuronal circuits. This approach is combined with large-scale optical measurements of neuronal activity patterns in the intact brain, with behavioral discrimination learning paradigms, and with computer simulations of structured neuronal networks. We use the olfactory system of adult zebrafish as an experimental model, taking advantage of the small size and genetic accessibility of the zebrafish brain. Our approach will allow us to directly examine how learning shapes the connectivity of neuronal circuits, and how coordinated modifications of connectivity change the dynamics of neuronal population activity. Additional approaches will manipulate neuronal activity and analyze behavior to further explore causal relationships between experience, changes in neuronal activity patterns and behavior. These approaches can test and possibly refine highly influential theories of information processing and learning in neuronal networks. The results are expected to provide mechanistic insights into elementary neuronal computations that are of key importance for higher brain functions and cognition. This knowledge is likely to be highly valuable as a basis to understand how aberrant neuronal connectivity can cause brain dysfunctions in neuropsychiatric conditions. The results will also be of philosophical interest because they will advance our understanding of how brains interpret the world and interact with it. Moreover, detailed insights into the connectivity of biological memory networks is highly desired to push progress in machine learning and artificial intelligence.