Electrical coupling in circuits can produce non-intuitive circuit dynamics, as seen in both experimental work from the crustacean stomatogastric ganglion and in computational models inspired by the connectivity in this preparation. Ambiguities in interpreting the results of electrophysiological recordings can arise if sets of pre- or postsynaptic neurons are electrically coupled, or if the electrical coupling exhibits some specificity (e.g. rectifying, or voltage-dependent). Even in small circuits, electrical coupling can produce parallel pathways that can allow information to travel by monosynaptic and/or polysynaptic pathways. Consequently, similar changes in circuit dynamics can arise from entirely different underlying mechanisms. When neurons are coupled both chemically and electrically, modifying the relative strengths of the two interactions provides a mechanism for flexibility in circuit outputs. This, together with neuromodulation of gap junctions and coupled neurons is important both in developing and adult circuits. This article is protected by copyright. All rights reserved.

Animals efficiently learn to navigate their environment. In the laboratory, naive mice can localize new spatial targets within a handful of trials. It is unclear how such efficiency is possible, as existing models require far more experience to achieve comparable performance. Taking inspiration from behavior as mice learned to intercept hidden spatial targets, we designed agents that generate structured behavioral trajectories by controlling their speed and angular velocity between anchor points. To rapidly learn good anchors, agents use Bayesian inference on past trajectories to infer the probability that an anchor will be successful and active sampling to refine hypothesized anchors. Agents learn within tens of trials to intercept a hidden target, capturing the evolution of behavioral structure and the upper limits of learning efficiency observed in mice. This algorithm further explains how mice avoid obstacles and rapidly adapt to target switches, and it naturally encompasses both egocentric and allocentric strategies for navigation.

Developmental genes can have complex cis-regulatory regions with multiple enhancers. Early work revealed remarkable modularity of enhancers, whereby distinct DNA regions drive gene expression in defined spatiotemporal domains. Nevertheless, a few reports have shown that enhancers function in multiple developmental stages, implying that enhancers can be pleiotropic. Here, we have studied the activity of the enhancers of the shavenbaby gene throughout D. melanogaster development. We found that all seven shavenbaby enhancers drive expression in multiple tissues and developmental stages. We explored how enhancer pleiotropy is encoded in two of these enhancers. In one enhancer, the same transcription factor binding sites contribute to embryonic and pupal expression, revealing site pleiotropy, whereas for a second enhancer, these roles are encoded by distinct sites. Enhancer pleiotropy may be a common feature of cis-regulatory regions of developmental genes, and site pleiotropy may constrain enhancer evolution in some cases.

Neural computations are implemented by activity in spatially distributed neural circuits. Cellular imaging fills a unique niche in linking activity of specific types of neurons to behavior, over spatial scales spanning single neurons to entire brain regions, and temporal scales from milliseconds to months. Imaging may soon make it possible to track activity of all neurons in a brain region, such as a cortical column. We review recent methodological advances that facilitate optical imaging of neuronal populations in vivo, with an emphasis on calcium imaging using protein indicators in mice. We point out areas that are particularly ripe for future developments.

Determining how neurons transform synaptic input and encode information in action potential (AP) firing output is required for understanding dendritic integration, neural transforms and encoding. Limitations in the speed of imaging 3D volumes of brain encompassing complex dendritic arbors using conventional galvanometer mirror-based laser-scanning microscopy has hampered fully capturing fluorescent sensors of activity throughout an individual neuron's entire complement of synaptic inputs and somatic APs. To address this problem, we have developed a two-photon microscope that achieves high-speed scanning by employing inertia-free acousto-optic deflectors (AODs) for laser beam positioning, enabling random-access sampling of hundreds to thousands of points-of-interest restricted to a predetermined neuronal structure, avoiding wasted scanning of surrounding extracellular tissue. This system is capable of comprehensive imaging of the activity of single neurons within the intact and awake vertebrate brain. Here, we demonstrate imaging of tectal neurons within the brains of albino tadpoles labeled using single-cell electroporation for expression of a red space-filling fluorophore to determine dendritic arbor morphology, and either the calcium sensor jGCaMP7s or the glutamate sensor iGluSnFR as indicators of neural activity. Using discrete, point-of-interest scanning we achieve sampling rates of 3 Hz for saturation sampling of entire arbors at 2 μm resolution, 6 Hz for sequentially sampling 3 volumes encompassing the dendritic arbor and soma, and 200-250 Hz for scanning individual planes through the dendritic arbor. This system allows investigations of sensory-evoked information input-output relationships of neurons within the intact and awake brain.

Inner ear cochlear spiral ganglion neurons (SGNs) transmit auditory information to the brainstem. Recent single cell RNA-Seq studies have revealed heterogeneities within SGNs. Nonetheless, much remains unknown about the transcriptome of SGNs, especially which genes are specifically expressed in SGNs. To address these questions we needed a deeper and broader gene coverage than that in previous studies. We performed bulk RNA-Seq on mouse SGNs at five ages, and on two reference cell types (hair cells and glia). Their transcriptome comparison identified genes previously unknown to be specifically expressed in SGNs. To validate our dataset and provide useful genetic tools for this research field, we generated two knockin mouse strains: and . Our comprehensive analysis confirmed the SGN-selective expression of the candidate genes, testifying to the quality of our transcriptome data. These two mouse strains can be used to temporally label SGNs or to sort them.

SIGNIFICANCE: one-photon fluorescence imaging of calcium and voltage indicators expressed in neurons enables noninvasive recordings of neural activity with submillisecond precision. However, data acquisition speed is limited by the frame rate of cameras.

AIM: We developed a compressive streak fluorescence microscope to record fluorescence in individual neurons at high speeds ( frames per second) exceeding the nominal frame rate of the camera by trading off spatial pixels for temporal resolution.

APPROACH: Our microscope leverages a digital micromirror device for targeted illumination, a galvo mirror for temporal scanning, and a ridge regression algorithm for fast computational reconstruction of fluorescence traces with high temporal resolution.

RESULTS: In simulations, the ridge regression algorithm reconstructs traces of high temporal resolution with limited signal loss. Validation experiments with fluorescent beads and experiments in larval zebrafish demonstrate accurate reconstruction with a data compression ratio of 10 and accurate recordings of neural activity with 200- to 400-Hz sampling speeds.

CONCLUSIONS: Our compressive microscopy enables new experimental capabilities to monitor activity at a sampling speed that outpaces the nominal frame rate of the camera.

In this review, we discuss the emerging field of computational behavioral analysis-the use of modern methods from computer science and engineering to quantitatively measure animal behavior. We discuss aspects of experiment design important to both obtaining biologically relevant behavioral data and enabling the use of machine vision and learning techniques for automation. These two goals are often in conflict. Restraining or restricting the environment of the animal can simplify automatic behavior quantification, but it can also degrade the quality or alter important aspects of behavior. To enable biologists to design experiments to obtain better behavioral measurements, and computer scientists to pinpoint fruitful directions for algorithm improvement, we review known effects of artificial manipulation of the animal on behavior. We also review machine vision and learning techniques for tracking, feature extraction, automated behavior classification, and automated behavior discovery, the assumptions they make, and the types of data they work best with. Expected final online publication date for the Annual Review of Neuroscience Volume 39 is July 08, 2016. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.

Transcriptomics experiments and computational predictions both enable systematic discovery of new functional RNAs. However, many putative noncoding transcripts arise instead from artifacts and biological noise, and current computational prediction methods have high false positive rates. I discuss prospects for improving computational methods for analyzing and identifying functional RNAs, with a focus on detecting signatures of conserved RNA secondary structure. An interesting new front is the application of chemical and enzymatic experiments that probe RNA structure on a transcriptome-wide scale. I review several proposed approaches for incorporating structure probing data into the computational prediction of RNA secondary structure. Using probabilistic inference formalisms, I show how all these approaches can be unified in a well-principled framework, which in turn allows RNA probing data to be easily integrated into a wide range of analyses that depend on RNA secondary structure inference. Such analyses include homology search and genome-wide detection of new structural RNAs.

We describe a general computational method for designing proteins that self-assemble to a desired symmetric architecture. Protein building blocks are docked together symmetrically to identify complementary packing arrangements, and low-energy protein-protein interfaces are then designed between the building blocks in order to drive self-assembly. We used trimeric protein building blocks to design a 24-subunit, 13-nm diameter complex with octahedral symmetry and a 12-subunit, 11-nm diameter complex with tetrahedral symmetry. The designed proteins assembled to the desired oligomeric states in solution, and the crystal structures of the complexes revealed that the resulting materials closely match the design models. The method can be used to design a wide variety of self-assembling protein nanomaterials.