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20 Janelia Publications
Showing 1-10 of 20 resultsWhat can we learn from a connectome? We constructed a simplified model of the first two stages of the fly visual system, the lamina and medulla. The resulting hexagonal lattice convolutional network was trained using backpropagation through time to perform object tracking in natural scene videos. Networks initialized with weights from connectome reconstructions automatically discovered well-known orientation and direction selectivity properties in T4 neurons and their inputs, while networks initialized at random did not. Our work is the first demonstration, that knowledge of the connectome can enable in silico predictions of the functional properties of individual neurons in a circuit, leading to an understanding of circuit function from structure alone.
To support cognitive function, the CA3 region of the hippocampus performs computations involving attractor dynamics. Understanding how cellular and ensemble activities of CA3 neurons enable computation is critical for elucidating the neural correlates of cognition. Here we show that CA3 comprises not only classically described pyramid cells with thorny excrescences, but also includes previously unidentified 'athorny' pyramid cells that lack mossy-fiber input. Moreover, the two neuron types have distinct morphological and physiological phenotypes and are differentially modulated by acetylcholine. To understand the contribution of these athorny pyramid neurons to circuit function, we measured cell-type-specific firing patterns during sharp-wave synchronization events in vivo and recapitulated these dynamics with an attractor network model comprising two principal cell types. Our data and simulations reveal a key role for athorny cell bursting in the initiation of sharp waves: transient network attractor states that signify the execution of pattern completion computations vital to cognitive function.
Localized translation plays a crucial role in synaptic plasticity and memory consolidation. However, it has not been possible to follow the dynamics of memory-associated mRNAs in living neurons in response to neuronal activity in real time. We have generated a novel mouse model where the endogenous Arc/Arg3.1 gene is tagged in its 3' untranslated region with stem-loops that bind a bacteriophage PP7 coat protein (PCP), allowing visualization of individual mRNAs in real time. The physiological response of the tagged gene to neuronal activity is identical to endogenous Arc and reports the true dynamics of Arc mRNA from transcription to degradation. The transcription dynamics of Arc in cultured hippocampal neurons revealed two novel results: (i) A robust transcriptional burst with prolonged ON state occurs after stimulation, and (ii) transcription cycles continue even after initial stimulation is removed. The correlation of stimulation with Arc transcription and mRNA transport in individual neurons revealed that stimulus-induced Ca activity was necessary but not sufficient for triggering Arc transcription and that blocking neuronal activity did not affect the dendritic transport of newly synthesized Arc mRNAs. This mouse will provide an important reagent to investigate how individual neurons transduce activity into spatiotemporal regulation of gene expression at the synapse.
Highlights: With the ability to correct for the aberrations introduced by biological specimens, adaptive optics—a method originally developed for astronomical telescopes—has been applied to optical microscopy to recover diffraction-limited imaging performance deep within living tissue. In particular, this technology has been used to improve image quality and provide a more accurate characterization of both structure and function of neurons in a variety of living organisms. Among its many highlights, adaptive optical microscopy has made it possible to image large volumes with diffraction-limited resolution in zebrafish larval brains, to resolve dendritic spines over 600μm deep in the mouse brain, and to more accurately characterize the orientation tuning properties of thalamic boutons in the primary visual cortex of awake mice.
Recent advances in understanding intracellular amino acid transport and mechanistic target of rapamycin complex 1 (mTORC1) signaling shed light on solute carrier 38, family A member 9 (SLC38A9), a lysosomal transporter responsible for the binding and translocation of several essential amino acids. Here we present the first crystal structure of SLC38A9 from Danio rerio in complex with arginine. As captured in the cytosol-open state, the bound arginine was locked in a transitional state stabilized by transmembrane helix 1 (TM1) of drSLC38A9, which was anchored at the groove between TM5 and TM7. These anchoring interactions were mediated by the highly conserved WNTMM motif in TM1, and mutations in this motif abolished arginine transport by drSLC38A9. The underlying mechanism of substrate binding is critical for sensitizing the mTORC1 signaling pathway to amino acids and for maintenance of lysosomal amino acid homeostasis. This study offers a first glimpse into a prototypical model for SLC38 transporters.
Each training step for a variational autoencoder (VAE) requires us to sample from the approximate posterior, so we usually choose simple (e.g. factorised) approximate posteriors in which sampling is an efficient computation that fully exploits GPU parallelism. However, such simple approximate posteriors are often insufficient, as they eliminate statistical dependencies in the posterior. While it is possible to use normalizing flow approximate posteriors for continuous latents, some problems have discrete latents and strong statistical dependencies. The most natural approach to model these dependencies is an autoregressive distribution, but sampling from such distributions is inherently sequential and thus slow. We develop a fast, parallel sampling procedure for autoregressive distributions based on fixed-point iterations which enables efficient and accurate variational inference in discrete state-space latent variable dynamical systems. To optimize the variational bound, we considered two ways to evaluate probabilities: inserting the relaxed samples directly into the pmf for the discrete distribution, or converting to continuous logistic latent variables and interpreting the K-step fixed-point iterations as a normalizing flow. We found that converting to continuous latent variables gave considerable additional scope for mismatch between the true and approximate posteriors, which resulted in biased inferences, we thus used the former approach. Using our fast sampling procedure, we were able to realize the benefits of correlated posteriors, including accurate uncertainty estimates for one cell, and accurate connectivity estimates for multiple cells, in an order of magnitude less time.
The superficial layers of the superior colliculus (sSC) receive retinal input and project to thalamic regions - the dorsal lateral geniculate (dLGN) and lateral posterior (LP; or pulvinar) nuclei -that convey visual information to cortex. A critical step towards understanding the functional impact of sSC neurons on these parallel thalamo-cortical pathways is determining whether different classes of sSC neurons, which are known to respond to different features of visual stimuli, innervate overlapping or distinct thalamic targets. Here, we identified a transgenic mouse line that labels sSC neurons that project to dLGN but not LP. We utilized selective expression of fluorophores and channelrhodopsin in this and previously characterized mouse lines to demonstrate that distinct cell types give rise to sSC projections to dLGN and LP. We further show that the glutamatergic sSC cell type that projects to dLGN also provides input to the sSC cell type that projects to LP. These results clarify the cellular origin of parallel sSC-thalamo-cortical pathways and reveal an interaction between these pathways via local connections within the sSC.
The ability to automatize the analysis of video for monitoring animals and insects is of great interest for behavior science and ecology [1]. In particular, honeybees play a crucial role in agriculture as natural pollinators. However, recent studies has shown that phenomena such as colony collapse disorder are causing the loss of many colonies [2]. Due to the high number of interacting factors to explain these events, a multi-faceted analysis of the bees in their environment is required. We focus in our work in developing tools to help model and understand their behavior as individuals, in relation with the health and performance of the colony. In this paper, we report the development of a new system for the detection, locali- zation and tracking of honeybee body parts from video on the entrance ramp of the colony. The proposed system builds on the recent advances in Convolutional Neu- ral Networks (CNN) for Human pose estimation and evaluates the suitability for the detection of honeybee pose as shown in Figure 1. This opens the door for novel animal behavior analysis systems that take advantage of the precise detection and tracking of the insect pose.
Many eukaryotic transcription factors (TFs) contain intrinsically disordered low-complexity domains (LCDs), but how they drive transactivation remains unclear. Here, live-cell single-molecule imaging reveals that TF-LCDs form local high-concentration interaction hubs at synthetic and endogenous genomic loci. TF-LCD hubs stabilize DNA binding, recruit RNA polymerase II (Pol II), and activate transcription. LCD-LCD interactions within hubs are highly dynamic, display selectivity with binding partners, and are differentially sensitive to disruption by hexanediols. Under physiological conditions, rapid and reversible LCD-LCD interactions occur between TFs and the Pol II machinery without detectable phase separation. Our findings reveal fundamental mechanisms underpinning transcriptional control and suggest a framework for developing single-molecule imaging screens for novel drugs targeting gene regulatory interactions implicated in disease.