Convergence in Finite Cournot Oligopoly with Social and Individual Learning.

Cahier du GREThA n°2007-0

A Neural Representation of Naturalistic Motion-Guided Behavior in the Zebrafish Brain

All animals must transform ambiguous sensory data into successful behavior. This requires sensory representations that accurately reflect the statistics of natural stimuli and behavior. Multiple studies show that visual motion processing is tuned for accuracy under naturalistic conditions, but the sensorimotor circuits extracting these cues and implementing motion-guided behavior remain unclear. Here we show that the larval zebrafish retina extracts a diversity of naturalistic motion cues, and the retinorecipient pretectum organizes these cues around the elements of behavior. We find that higher-order motion stimuli, gliders, induce optomotor behavior matching expectations from natural scene analyses. We then image activity of retinal ganglion cell terminals and pretectal neurons. The retina exhibits direction-selective responses across glider stimuli, and anatomically clustered pretectal neurons respond with magnitudes matching behavior. Peripheral computations thus reflect natural input statistics, whereas central brain activity precisely codes information needed for behavior. This general principle could organize sensorimotor transformations across animal species

Whole-Brain Imaging Using Genetically Encoded Activity Sensors in Vertebrates

In the mid-twentieth century, the development of electrophysiology revolutionized the way that the brain could be studied, allowing scientists to advance beyond anatomy and neuroethology and address questions involving brain function. These recordings offered a temporally and spatially high-resolution readout of the activity of single cells and enabled a detailed understanding of the input–output function of individual neurons. Nevertheless, understanding the brain one neuron at a time seems like a daunting task. Over the last two decades, a considerable amount of research has focused on understanding the brain at the mesoscale of brain circuits and networks, trying to bridge the gap from single neurons to the function of the whole brain in generating behavior. This is a large, open and exciting field that encompasses theory, computational models, behavioral studies, genetic manipulations and many more approaches. Importantly, the current interest in brain circuits is fueled by the development of new techniques that allow us to acquire data relevant to addressing network function and the activity of large populations of neurons. In this chapter, we present an introduction to whole-brain, single-cell resolution imaging in a behaving vertebrate model organism, the larval zebrafish. We describe the fundamental concepts developed during the last five years that are important for understanding large-scale imaging techniques in vertebrates from experimental design to data acquisition and analysis

Estimating Information Processing in a Memory System: The Utility of Meta-analytic Methods for Genetics

10.1371/journal.pgen.1005718PLoS Genetics1112e100571