These findings document further, nonmultiplicative effects of locomotion, reaching earlier processing stages than cortex.

In multicellular organisms, cells pack together to form tissues of intricate and well defined morphology. How such cell-packing geometries arise is an important open question in biology, because the functionality of many differentiated tissues depends on their reliable formation. We show that combining adhesive forces due to E-and N-cadherin with a quantitative description of cell membrane elasticity in an interfacial energy model explains not only the qualitative neighbor relations, but also the detailed geometry of a tissue. The characteristic cellular geometries in the eyes of both wild-type Drosophila and genetic mutants are accurately reproduced by using a fixed set of few, physically motivated parameters. The model predicts adhesion strengths in the eye epithelium, quantifies their role relative to membrane elasticity, and reveals how simple minimization of interfacial energy can give rise to complex geometric patterns of important biological functionality.cadherin ͉ Drosophila ͉ interfacial energy ͉ morphogenesis ͉ Surface Evolver

Responses of many neurons in primary visual cortex (V1) are suppressed by stimuli exceeding the classical receptive field (RF), an important property that might underlie the computation of visual saliency. Traditionally, it has proven difficult to disentangle the underlying neural circuits, including feedforward, horizontal intracortical, and feedback connectivity. Since circuit-level analysis is particularly feasible in the mouse, we asked whether neural signatures of spatial integration in mouse V1 are similar to those of higher-order mammals and investigated the role of parvalbumin-expressing (PVϩ) inhibitory interneurons. Analogous to what is known from primates and carnivores, we demonstrate that, in awake mice, surround suppression is present in the majority of V1 neurons and is strongest in superficial cortical layers. Anesthesia with isoflurane-urethane, however, profoundly affects spatial integration: it reduces the laminar dependency, decreases overall suppression strength, and alters the temporal dynamics of responses. We show that these effects of brain state can be parsimoniously explained by assuming that anesthesia affects contrast normalization. Hence, the full impact of suppressive influences in mouse V1 cannot be studied under anesthesia with isoflurane-urethane. To assess the neural circuits of spatial integration, we targeted PVϩ interneurons using optogenetics. Optogenetic depolarization of PVϩ interneurons was associated with increased RF size and decreased suppression in the recorded population, similar to effects of lowering stimulus contrast, suggesting that PVϩ interneurons contribute to spatial integration by affecting overall stimulus drive. We conclude that the mouse is a promising model for circuit-level mechanisms of spatial integration, which relies on the combined activity of different types of inhibitory interneurons.

En route from retina to cortex, visual information passes through the dorsolateral geniculate nucleus of the thalamus (dLGN), where extensive corticothalamic (CT) feedback has been suggested to modulate spatial processing. How this modulation arises from direct excitatory and indirect inhibitory CT feedback pathways remains enigmatic. Here we show that in awake mice, retinotopically organized cortical feedback sharpens receptive fields (RFs) and increases surround suppression in the dLGN. Guided by a network model indicating that widespread inhibitory CT feedback is necessary to reproduce these effects, we targeted the visual sector of the thalamic reticular nucleus (visTRN) for recordings. We found that visTRN neurons have large receptive fields, show little surround suppression, and exhibit strong feedback-dependent responses to large stimuli. These features make them an ideal candidate for mediating feedback-enhanced surround suppression in the dLGN. We conclude that cortical feedback sculpts spatial integration in dLGN, likely via recruitment of neurons in visTRN..