The basal ganglia play a critical role in the regulation of voluntary action in vertebrates. Our understanding of the function of the basal ganglia relies heavily upon anatomical information, but continued progress will require an understanding of the specific functional roles played by diverse cell types and their connectivity. An increasing number of mouse lines allow extensive identification, characterization, and manipulation of specified cell types in the basal ganglia. Despite the promise of genetically modified mice for elucidating the functional roles of diverse cell types, there is relatively little anatomical data obtained directly in the mouse. Here we have characterized the retrograde labeling obtained from a series of tracer injections throughout the dorsal striatum of adult mice. We found systematic variations in input along both the medial-lateral and anterior-posterior neuraxes in close agreement with canonical features of basal ganglia anatomy in the rat. In addition to the canonical features we have provided experimental support for the importance of non-canonical inputs to the striatum from the raphe nuclei and the amygdala. To look for organization at a finer scale we have analyzed the correlation structure of labeling intensity across our entire dataset. Using this analysis we found substantial local heterogeneity within the large-scale order. From this analysis we conclude that individual striatal sites receive varied combinations of cortical and thalamic input from multiple functional areas, consistent with some earlier studies in the rat that have suggested the presence of a combinatorial map.

For each environment a rodent has explored, its hippocampus contains a map consisting of a unique subset of neurons, called place cells, that have spatially tuned spiking there, with the remaining neurons being essentially silent. Using whole-cell recording in freely moving rats exploring a novel maze, we observed differences in intrinsic cellular properties and input-based subthreshold membrane potential levels underlying this division into place and silent cells. Compared to silent cells, place cells had lower spike thresholds and peaked versus flat subthreshold membrane potentials as a function of animal location. Both differences were evident from the beginning of exploration. Additionally, future place cells exhibited higher burst propensity before exploration. Thus, internal settings appear to predetermine which cells will represent the next novel environment encountered. Furthermore, place cells fired spatially tuned bursts with large, putatively calcium-mediated depolarizations that could trigger plasticity and stabilize the new map for long-term storage. Our results provide new insight into hippocampal memory formation.

Rodents move their whiskers to locate and identify objects. Cortical areas involved in vibrissal somatosensation and sensorimotor integration include the vibrissal area of the primary motor cortex (vM1), primary somatosensory cortex (vS1; barrel cortex), and secondary somatosensory cortex (S2). We mapped local excitatory pathways in each area across all cortical layers using glutamate uncaging and laser scanning photostimulation. We analyzed these maps to derive laminar connectivity matrices describing the average strengths of pathways between individual neurons in different layers and between entire cortical layers. In vM1, the strongest projection was L2/3→L5. In vS1, strong projections were L2/3→L5 and L4→L3. L6 input and output were weak in both areas. In S2, L2/3→L5 exceeded the strength of the ascending L4→L3 projection, and local input to L6 was prominent. The most conserved pathways were L2/3→L5, and the most variable were L4→L2/3 and pathways involving L6. Local excitatory circuits in different cortical areas are organized around a prominent descending pathway from L2/3→L5, suggesting that sensory cortices are elaborations on a basic motor cortex-like plan.

How does the brain compute? Answering this question necessitates neuronal connectomes, annotated graphs of all synaptic connections within defined brain areas. Further, understanding the energetics of the brain’s computations requires vascular graphs. The assembly of a connectome requires sensitive hardware tools to measure neuronal and neurovascular features in all three dimensions, as well as software and machine learning for data analysis and visualization. We present the state of the art on the reconstruction of circuits and vasculature that link brain anatomy and function. Analysis at the scale of tens of nanometers yields connections between identified neurons, while analysis at the micrometer scale yields probabilistic rules of connection between neurons and exact vascular connectivity.

An agglomerative clustering algorithm merges the most similar pair of clusters at every iteration. The function that evaluates similarity is traditionally handdesigned, but there has been recent interest in supervised or semisupervised settings in which ground-truth clustered data is available for training. Here we show how to train a similarity function by regarding it as the action-value function of a reinforcement learning problem. We apply this general method to segment images by clustering superpixels, an application that we call Learning to Agglomerate Superpixel Hierarchies (LASH). When applied to a challenging dataset of brain images from serial electron microscopy, LASH dramatically improved segmentation accuracy when clustering supervoxels generated by state of the boundary detection algorithms. The naive strategy of directly training only supervoxel similarities and applying single linkage clustering produced less improvement.

The ways in which cells set the size of intracellular structures is an important but largely unsolved problem [1]. Early embryonic divisions pose special problems in this regard. Many checkpoints common in somatic cells are missing from these divisions, which are characterized by rapid reductions in cell size and short cell cycles [2]. Embryonic cells must therefore possess simple and robust mechanisms that allow the size of many of their intracellular structures to rapidly scale with cell size.

Most neurons of the central complex belong to 10 secondary (larvally produced) lineages. In the late larva, undifferentiated axon tracts of these lineages form a primordium in which all of the compartments of the central complex can be recognized as discrete entities. Four posterior lineages (DPMm1, DPMpm1, DPMpm2, and CM4) generate the classes of small-field neurons that interconnect the protocerebral bridge, fan-shaped body, noduli, and ellipsoid body. Three lineages located in the anterior brain, DALv2, BAmv1, and DALcl2, form the large-field neurons of the ellipsoid body and fan-shaped body, respectively. These lineages provide an input channel from the optic tubercle and connect the central complex with adjacent anterior brain compartments. Three lineages in the posterior cortex, CM3, CP2, and DPMpl2, connect the posterior brain neuropil with specific layers of the fan-shaped body. Even though all of the compartments of the central complex are prefigured in the late larval brain by the axon tracts of the above-mentioned lineages, the neuropil differentiates during the first 2 days of the pupal period when terminal branches and synapses of secondary neurons are formed. During this phase the initially straight horizontal layers of the central complex bend in the frontal plane, which produces the characteristic shape of the fan-shaped and ellipsoid body. Our analysis provides a comprehensive picture of the lineages that form the central complex, and will facilitate future studies that address the structure or function of the central complex at the single cell level.

In the rodent vibrissal system, active sensation and sensorimotor integration are mediated in part by connections between barrel cortex and vibrissal motor cortex. Little is known about how these structures interact at the level of neurons. We used Channelrhodopsin-2 (ChR2) expression, combined with anterograde and retrograde labeling, to map connections between barrel cortex and pyramidal neurons in mouse motor cortex. Barrel cortex axons preferentially targeted upper layer (L2/3, L5A) neurons in motor cortex; input to neurons projecting back to barrel cortex was particularly strong. Barrel cortex input to deeper layers (L5B, L6) of motor cortex, including neurons projecting to the brainstem, was weak, despite pronounced geometric overlap of dendrites with axons from barrel cortex. Neurons in different layers received barrel cortex input within stereotyped dendritic domains. The cortico-cortical neurons in superficial layers of motor cortex thus couple motor and sensory signals and might mediate sensorimotor integration and motor learning.

A specialist neuron uses an intriguing process to help control the body's response to hunger. A lipid pathway involving the breakdown of cellular components regulates the expression of a neuropeptide that affects feeding and body weight.

An important open question in biophysics is to understand how mechanical forces shape membrane-bounded cells and their organelles. A general solution to this problem is to calculate the bending energy of an arbitrarily shaped membrane surface, which can include both lipids and cytoskeletal proteins, and minimize the energy subject to all mechanical constraints. However, the calculations are difficult to perform, especially for shapes that do not possess axial symmetry. We show that the spherical harmonics parameterization (SHP) provides an analytic description of shape that can be used to quickly and reliably calculate minimum energy shapes of both symmetric and asymmetric surfaces. Using this method, we probe the entire set of shapes predicted by the bilayer couple model, unifying work based on different computational approaches, and providing additional details of the transitions between different shape classes. In addition, we present new minimum-energy morphologies based on non-linear models of membrane skeletal elasticity that closely mimic extreme shapes of red blood cells. The SHP thus provides a versatile shape description that can be used to investigate forces that shape cells.