Focused-ion-beam scanning electron microscopy (FIB-SEM) has become an essential tool for studying neural tissue at resolutions below 10 nm × 10 nm × 10 nm, producing data sets optimized for automatic connectome tracing. We present a technical advance, ultrathick sectioning, which reliably subdivides embedded tissue samples into chunks (20 μm thick) optimally sized and mounted for efficient, parallel FIB-SEM imaging. These chunks are imaged separately and then 'volume stitched' back together, producing a final three-dimensional data set suitable for connectome tracing.

Brain networks that mediate motivated behavior in the context of aversive and rewarding experiences involve the prefrontal cortex (PFC) and ventral tegmental area (VTA). Neurons in both regions are activated by stress and reward, and by learned cues that predict aversive or appetitive outcomes. Recent studies have proposed that separate neuronal populations and circuits in these regions encode learned aversive versus appetitive contexts. But how about the actual experience? Do the same or different PFC and VTA neurons encode unanticipated aversive and appetitive experiences? To address this, we recorded unit activity and local field potentials (LFP) in the dorsomedial PFC (dmPFC) and VTA of male rats as they were exposed, in the same recording session, to reward (sucrose) or stress (tail pinch) spaced one hour apart. As expected, experience-specific neuronal responses were observed. About 15-25% of single units in each region responded by excitation or inhibition to either stress or reward, and only stress increased LFP theta oscillation power in both regions and coherence between regions. But the largest number of responses (29% dmPFC and 30% VTA units) involved dual-valence neurons that responded to both stress and reward exposure. Moreover, the temporal profile of neuronal population activity in dmPFC and VTA as assessed by principal component analysis were similar during both types of experiences. These results reveal that aversive and rewarding experiences engage overlapping neuronal populations in the dmPFC and the VTA. These populations may provide a locus of vulnerability for stress related disorders, which are often associated with anhedonia. Animals must recognize unexpected harmful and rewarding events in order to survive. How the brain represents these competing experiences is not fully understood. Two interconnected brain regions implicated in encoding both rewarding and stressful events are the dmPFC and the VTA. In either region, separate neurons and associated circuitry are assumed to respond to events with positive or negative valence. We find, however, that a significant subpopulation of neurons in dmPFC and VTA encode both rewarding and aversive experiences. These dual-valence neurons may provide a computational advantage for flexible planning of behavior when organisms face unexpected rewarding and harmful experiences.

No abstract available.

The RNA genome of retroviruses is encased within a protein capsid. To gather insight into the assembly and function of this capsid, we used electron cryotomography to image human immunodeficiency virus (HIV) and equine infectious anemia virus (EIAV) particles. While the majority of viral cores appeared closed, a variety of unclosed structures including rolled sheets, extra flaps, and cores with holes in the tip were also seen. Simulations of nonequilibrium growth of elastic sheets recapitulated each of these aberrations and further predicted the occasional presence of seams, for which tentative evidence was also found within the cryotomograms. To test the integrity of viral capsids in vivo, we observed that  25% of cytoplasmic HIV complexes captured by TRIM5α had holes large enough to allow internal green fluorescent protein (GFP) molecules to escape. Together, these findings suggest that HIV assembly at least sometimes involves the union in space of two edges of a curling sheet and results in a substantial number of unclosed forms.

Modern supervised learning algorithms can learn very accurate and complex discriminating functions. But when these classifiers fail, this complexity can also be a drawback because there is no easy, intuitive way to diagnose why they are failing and remedy the problem. This important question has received little attention. To address this problem, we propose a novel method to analyze and understand a classifier's errors. Our method centers around a measure of how much influence a training example has on the classifier's prediction for a test example. To understand why a classifier is mispredicting the label of a given test example, the user can find and review the most influential training examples that caused this misprediction, allowing them to focus their attention on relevant areas of the data space. This will aid the user in determining if and how the training data is inconsistently labeled or lacking in diversity, or if the feature representation is insufficient. As computing the influence of each training example is computationally impractical, we propose a novel distance metric to approximate influence for boosting classifiers that is fast enough to be used interactively. We also show several novel use paradigms of our distance metric. Through experiments, we show that it can be used to find incorrectly or inconsistently labeled training examples, to find specific areas of the data space that need more training data, and to gain insight into which features are missing from the current representation. 

Code is available at https://github.com/kristinbranson/InfluentialNeighbors.

The eukaryotic core promoter recognition complex was generally thought to play an essential but passive role in the regulation of gene expression. However, recent evidence now indicates that core promoter recognition complexes together with ’non-prototypical’ subunits may have a vital regulatory function in driving cell-specific programmes of transcription during development. Furthermore, new roles for components of these complexes have been identified beyond development; for example, in mediating interactions with chromatin and in maintaining active gene expression across cell divisions.

In practice, understanding the spatial relationships between the surfaces of an object, can significantly improve the performance of object recognition systems. In this paper we propose a novel framework to recognize objects in pictures taken from arbitrary viewpoints. The idea is to maintain the frontal views of the major faces of objects in a global flat map. Then an unfolding warping technique is used to change the pose of the query object in the test view so that all visible surfaces of the object can be observed from a frontal viewpoint, improving the handling of serious occlusions and large viewpoint changes. We demonstrate the effectiveness of our approach through analysis of recognition trials of complex objects with comparison to popular methods.

It is known that sensory deprivation, including postnatal whisker trimming, can lead to severe deficits in the firing rate properties of cortical neurons. Recent results indicate that development of synchronous discharge among cortical neurons is also activity influenced, and that correlated discharge is significantly impaired following loss of bilateral sensory input in rats. Here we investigate whether unilateral whisker trimming (unilateral deprivation or UD) after birth interferes in the same way with the development of synchronous discharge in cortex. We measured the coincidence of spikes among pairs of neurons recorded under urethane anesthesia in one whisker barrel field deprived by trimming all contralateral whiskers for 60 days after birth (UD), and in untrimmed controls (CON). In the septal columns around barrels, UD significantly increased the coincident discharge among cortical neurons compared with CON, most notably in layers II/III. In contrast, synchronous discharge was normal between layer IV UD barrel neurons: i.e., not different from CON. Thus, while bilateral whisker deprivation (BD) produced a global deficit in the development of synchrony in layer IV, UD did not block the development of synchrony between neurons in layer IV barrels and increased synchrony within septal circuits. We conclude that changes in synchronous discharge after UD are unexpectedly different from those recorded after BD, and we speculate that this effect may be due to the driven activity from active commissural inputs arising from the contralateral hemisphere that received normal activity levels during postnatal development.

Compositional generation underlies the systematic and essentially unlimited construction of complex concepts from simpler parts, as is foundational to intelligent behavior, but its underlying neural mechanisms remain unclear. Here we reveal a neural implementation of hierarchical compositional construction of abstract sequences. We demonstrate that in an open-ended setting with very sparse feedback, rats innately utilize hierarchical composition to construct adaptive action sequences that would have been difficult to discover from scratch. Prefrontal neural population representations of these abstract sequences adhere to a low-dimensional format that encodes the orderly progression of elemental units comprising the sequence while converging to a sequence-general endpoint. Higher-level compositions in the hierarchy are systematically related to their lower-level constituent parts, reusing much of the representation, while providing context separation and satisfying format constraints. These neural representations are geometrically identical across animals, pointing to a convergent solution for how knowledge is hierarchically assembled via a compositional mechanism.