Neurons perform computations by integrating inputs from thousands of synapses-mostly in the dendritic tree-to drive action potential firing in the axon. One fruitful approach to studying this process is to record from neurons using patch-clamp electrodes, fill the recorded neurons with a substance that allows subsequent staining, reconstruct the three-dimensional architectures of the dendrites, and use the resulting functional and structural data to develop computer models of dendritic integration. Accurately producing quantitative reconstructions of dendrites is typically a tedious process taking many hours of manual inspection and measurement. Here we present ShuTu, a new software package that facilitates accurate and efficient reconstruction of dendrites imaged using bright-field microscopy. The program operates in two steps: (1) automated identification of dendritic processes, and (2) manual correction of errors in the automated reconstruction. This approach allows neurons with complex dendritic morphologies to be reconstructed rapidly and efficiently, thus facilitating the use of computer models to study dendritic structure-function relationships and the computations performed by single neurons.

Information about sensory stimuli is represented by spatiotemporal patterns of neural activity. The complexity of the central nervous system, however, frequently obscures the origin and properties of signals and noise that underlie these activity patterns. We minimized this constraint by examining mechanisms governing correlated activity in mouse retinal ganglion cells (RGCs) under conditions in which light-evoked responses traverse a specific circuit, the rod bipolar pathway. Signals and noise in this circuit produced correlated synaptic input to neighboring On and Off RGCs. Temporal modulation of light intensity did not alter the degree to which noise in the input to nearby RGCs was correlated, and action potential generation in individual RGCs was largely insensitive to differences in network noise generated by dynamic and static light stimuli. Together, these features enable noise in shared circuitry to diminish simultaneous action potential generation in neighboring On and Off RGCs under a variety of conditions.

The pea aphid, Acyrthosiphon pisum, is an emerging genomic model system for studies of polyphenisms, bacterial symbioses, host-plant specialization, and the vectoring of plant viruses. Here we provide estimates of nucleotide diversity and linkage disequilibrium (LD) in native (European) and introduced (United States) populations of the pea aphid. Because introductions can cause population bottlenecks, we hypothesized that U.S. populations harbor lower levels of nucleotide diversity and higher levels of LD than native populations.

Human mitochondrial transcription factor 1 (mtTF1) has been sequenced and is a nucleus-encoded DNA binding protein of 204 amino acids (24,400 daltons). Expression of human mtTF1 in bacteria yields a protein with correct physical properties and the ability to activate mitochondrial DNA promoters. Analysis of the protein’s sequence reveals no similarities to any other DNA binding proteins except for the existence of two domains that are characteristic of high mobility group (HMG) proteins. Human mtTF1 is most closely related to a DNA binding HMG-box region in hUBF, a human protein known to be important for transcription by RNA polymerase I.

Fiber-optic epifluorescence imaging with one-photon excitation benefits from its ease of use, cheap light sources, and full-frame acquisition, which enables it for favorable temporal resolution of image acquisition. However, it suffers from a lack of robustness against autofluorescence and light scattering. Moreover, it cannot easily eliminate the out-of-focus background, which generally results in low-contrast images. In order to overcome these limitations, we have implemented fast out-of-phase imaging after optical modulation (Speed OPIOM) for dynamic contrast in fluorescence endomicroscopy. Using a simple and cheap optical-fiber bundle-based endomicroscope integrating modulatable light sources, we first showed that Speed OPIOM provides intrinsic optical sectioning, which restricts the observation of fluorescent labels at targeted positions within a sample. We also demonstrated that this imaging protocol efficiently eliminates the interference of autofluorescence arising from both the fiber bundle and the specimen in several biological samples. Finally, we could perform multiplexed observations of two spectrally similar fluorophores differing by their photoswitching dynamics. Such attractive features of Speed OPIOM in fluorescence endomicroscopy should find applications in bioprocessing, clinical diagnostics, plant observation, and surface imaging.

A neuron that extracts directionally selective motion information from upstream signals lacking this selectivity must compare visual responses from spatially offset inputs. Distinguishing among prevailing algorithmic models for this computation requires measuring fast neuronal activity and inhibition. In the Drosophila melanogaster visual system, a fourth-order neuron-T4-is the first cell type in the ON pathway to exhibit directionally selective signals. Here we use in vivo whole-cell recordings of T4 to show that directional selectivity originates from simple integration of spatially offset fast excitatory and slow inhibitory inputs, resulting in a suppression of responses to the nonpreferred motion direction. We constructed a passive, conductance-based model of a T4 cell that accurately predicts the neuron's response to moving stimuli. These results connect the known circuit anatomy of the motion pathway to the algorithmic mechanism by which the direction of motion is computed.

We present a novel microscopy technique to measure the scattered wavefront emitted from an optically transparent microscopic object. The complex amplitude is decoded via phase stepping in a common-path interferometer, enabling high mechanical stability. We demonstrate theoretically and practically that the incoherent summation of multiple illumination directions into a single image increases the resolving power and facilitates image reconstruction in diffraction tomography. We propose a slice-by-slice object-scatter extraction algorithm entirely based in real space in combination with ordinary z-stepping. Thereby the computational complexity affiliated with tomographic methods is significantly reduced. Using the first order Born approximation for weakly scattering objects it is possible to obtain estimates of the scattering density from the exitwaves.

Connectomes derived from volume EM imaging of the brain can generate detailed physical models of every neuron, and simulators such as NEURON or GENESIS are designed to work with such models. In principal, combining these technologies, plus transmitter and channel models, should allow detailed and accurate simulation of real neural circuits. Here we experiment with this combination, using a well-studied system (motion detection in Drosophila. Since simulation requires both the physical geometry (which we have) and the models of the synapses (which are not currently available), we built approximate synapses corresponding to their known and estimated function. Once we did so, we reproduced direction selectivity in T4 cells, one of the main functions of this neural circuit. This verified the basic functionality of both extraction and simulations, and provided a biologically relevant computation we could use in further experiments. We then compared models with different degrees of physical realism, from full detailed models down to models consisting of a single node, to examine the tradeoff of simulation resources required versus accuracy achieved. Our results show that much simpler models may be adequate, at least in the case of medulla neurons in Drosophila. Such models can be easily derived from fully detailed models, and result in simulations that are much smaller, much faster, and accurate enough for many purposes. Biologically, we show that a lumped neuron model reproduces the main motion detector operation, confirming the result of Gruntman, that dendritic compution is not required for this function.

A central model that describes how behavioral sequences are produced features a neural architecture that readies different movements simultaneously, and a mechanism where prioritized suppression between the movements determines their sequential performance. We previously described a model whereby suppression drives a Drosophila grooming sequence that is induced by simultaneous activation of different sensory pathways that each elicit a distinct movement (Seeds et al. 2014). Here, we confirm this model using transgenic expression to identify and optogenetically activate sensory neurons that elicit specific grooming movements. Simultaneous activation of different sensory pathways elicits a grooming sequence that resembles the naturally induced sequence. Moreover, the sequence proceeds after the sensory excitation is terminated, indicating that a persistent trace of this excitation induces the next grooming movement once the previous one is performed. This reveals a mechanism whereby parallel sensory inputs can be integrated and stored to elicit a delayed and sequential grooming response.