The mammalian neocortex undergoes dramatic transformation during development, from a seemingly homogenous sheet of neuroepithelial cells into a complex structure that is tangentially divided into discrete areas. This process is thought to be controlled by a combination of intrinsic patterning mechanisms within the cortex and afferent axonal projections from the thalamus. However, roles of thalamic afferents in the formation of areas are still poorly understood. In this study, we show that genetically increasing or decreasing the size of the lateral geniculate nucleus of the mouse thalamus resulted in a corresponding change in the size of the primary visual area. Furthermore, elimination of most thalamocortical projections from the outset of their development resulted in altered areal gene expression patterns, particularly in the primary visual and somatosensory areas, where they lost sharp boundaries with adjacent areas. Together, these results demonstrate the critical roles of thalamic afferents in the establishment of neocortical areas.

The ability of neurons to identify correct synaptic partners is fundamental to the proper assembly and function of neural circuits. Relative to other steps in circuit formation such as axon guidance, our knowledge of how synaptic partner selection is regulated is severely limited. Drosophila Dpr and DIP immunoglobulin superfamily (IgSF) cell-surface proteins bind heterophilically and are expressed in a complementary manner between synaptic partners in the visual system. Here, we show that in the lamina, DIP misexpression is sufficient to promote synapse formation with Dpr-expressing neurons and that disrupting DIP function results in ectopic synapse formation. These findings indicate that DIP proteins promote synapses to form between specific cell types and that in their absence, neurons synapse with alternative partners. We propose that neurons have the capacity to synapse with a broad range of cell types and that synaptic specificity is achieved by establishing a preference for specific partners.

Individual animals vary in their behaviors. This is true even when they share the same genotype and were reared in the same environment. Clusters of covarying behaviors constitute behavioral syndromes, and an individual’s position along such axes of covariation is a representation of their personality. Despite these conceptual frameworks, the structure of behavioral covariation within a genotype is essentially uncharacterized and its mechanistic origins unknown. Passing hundreds of inbred Drosophila individuals through an experimental pipeline that captured hundreds of behavioral measures, we found sparse but significant correlations among small sets of behaviors. Thus, the space of behavioral variation has many independent dimensions. Manipulating the physiology of the brain, and specific neural populations, altered specific correlations. We also observed that variation in gene expression can predict an individual’s position on some behavioral axes. This work represents the first steps in understanding the biological mechanisms determining the structure of behavioral variation within a genotype.

No abstract

Fast object tracking in real time allows convenient tracking of very large numbers of animals and closed-loop experiments that control stimuli for many animals in parallel. We developed MARGO, a MATLAB-based, real-time animal tracking suite for custom behavioral experiments. We demonstrated that MARGO can rapidly and accurately track large numbers of animals in parallel over very long timescales, typically when spatially separated such as in multiwell plates. We incorporated control of peripheral hardware, and implemented a flexible software architecture for defining new experimental routines. These features enable closed-loop delivery of stimuli to many individuals simultaneously. We highlight MARGO’s ability to coordinate tracking and hardware control with two custom behavioral assays (measuring phototaxis and optomotor response) and one optogenetic operant conditioning assay. There are currently several open source animal trackers. MARGO’s strengths are 1) fast and accurate tracking, 2) high throughput, 3) an accessible interface and data output and 4) real-time closed-loop hardware control for for sensory and optogenetic stimuli, all of which are optimized for large-scale experiments.