Communication in the brain evolves concurrently across various length- and time-scales: In vertebrate nervous systems, activity is processed through closed loops on short physical scales, such as the familiar monosynaptic and disynaptic reflex arcs, and at the same time over loops that traverse through high-order cortical and the limbic areas. In this sense, memory and decision making cannot be viewed as isolated to a given area or pathway, but are truly distributed.

Neuronal networks that concurrently operate over multiple scales of space and time can now be probed by the advent of optical tools and multisite arrays of electrodes to image large swaths of brain tissue, including almost every neuron in such preparations as the juvenile zebra fish and a significant fraction of neurons within different areas of mouse cortex. In this spirit, we adopt a comparative viewpoint and address lessons from detailed measurements in small brains up to indirect EEG/fMRI measurements in large, human brains.

From a theoretical perspective, we will focus on hierarchical models that begin to bridge single-neuron processing to network dynamics to interactions in networks of networks, and to relate these hierarchies to a principled computational or functional objectives. From an experimental perspective, we will consider sensorimotor coding and control. One goal is to parse the contributions of circuit-level contributions versus cellular contributions to representational dynamics. We will explore the empirically observed relationships between population activity on different scales and cognitive processes that are mediated by this activity, such as working memory and information recall.