Throwing a glance at the neural code: rapid information transmission in the visual system

Our visual system can operate at fascinating speeds. Psychophysical experiments teach us that the processing of complex natural images and visual object recognition require a mere split second. Even in everyday life, our gaze seldom rests for long on any particular spot of the visual scene before a sudden movement of the eyes or the head shifts it to a new location. These observations challenge our understanding of how neurons in the visual system of the brain represent, process, and transmit the relevant visual information quickly enough. This article argues that the speed of visual processing provides an adjuvant framework for studying the neural code in the visual system. In the retina, which constitutes the first stage of visual processing, recent experiments have highlighted response features that allow for particularly rapid information transmission. This sets the stage for discussing some of the fundamental questions in the research of neural coding. How do downstream brain regions read out signals from the retina and combine them with intrinsic signals that accompany eye movements? And, how do the neural response features ultimately affect perception and behavior

Structural neurobiology: missing link to a mechanistic understanding of neural computation

High-resolution, comprehensive structural information is often the final arbiter between competing mechanistic models of biological processes, and can serve as inspiration for new hypotheses. In molecular biology, definitive structural data at atomic resolution are available for many macromolecules; however, information about the structure of the brain is much less complete, both in scope and resolution. Several technical developments over the past decade, such as serial block-face electron microscopy and trans-synaptic viral tracing, have made the structural biology of neural circuits conceivable: we may be able to obtain the structural information needed to reconstruct the network of cellular connections for large parts of, or even an entire, mouse brain within a decade or so. Given that the brain's algorithms are ultimately encoded by this network, knowing where all of these connections are should, at the very least, provide the data needed to distinguish between models of neural computation