The inner ear is a fluid-filled closed-epithelial structure whose function requires maintenance of an internal hydrostatic pressure and fluid composition. The endolymphatic sac (ES) is a dead-end epithelial tube connected to the inner ear whose function is unclear. ES defects can cause distended ear tissue, a pathology often seen in hearing and balance disorders. Using live imaging of zebrafish larvae, we reveal that the ES undergoes cycles of slow pressure-driven inflation followed by rapid deflation. Absence of these cycles in mutants leads to distended ear tissue. Using serial-section electron microscopy and adaptive optics lattice light-sheet microscopy, we find a pressure relief valve in the ES comprised of partially separated apical junctions and dynamic overlapping basal lamellae that separate under pressure to release fluid. We propose that this lmx1-dependent pressure relief valve is required to maintain fluid homeostasis in the inner ear and other fluid-filled cavities.

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.

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.

What 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.

In 2015, a novel way to convert photoconvertible fluorescent proteins was reported that uses the intercept of blue and far-red light instead of traditional violet or near-UV light illumination. This Minireview describes and contrasts this distinct two-step mechanism termed primed conversion with traditional photoconversion. We provide a comprehensive overview of what is known to date about primed conversion and focus on the molecular requirements for it to take place. We provide examples of its application to axially confined photoconversion in complex tissues as well as super-resolution microscopy. Further, we describe why and when it is useful, including its advantages and disadvantages, and give an insight into potential future development in the field.

Among the proteins required for lipid metabolism in Mycobacterium tuberculosis are a significant number of uncharacterized serine hydrolases, especially lipases and esterases. Using a streamlined synthetic method, a library of immolative fluorogenic ester substrates was expanded to better represent the natural lipidomic diversity of Mycobacterium. This expanded fluorogenic library was then used to rapidly characterize the global structure activity relationship (SAR) of mycobacterial serine hydrolases in M. smegmatis under different growth conditions. Confirmation of fluorogenic substrate activation by mycobacterial serine hydrolases was performed using nonspecific serine hydrolase inhibitors and reinforced the biological significance of the SAR. The hydrolases responsible for the global SAR were then assigned using gel-resolved activity measurements, and these assignments were used to rapidly identify the relative substrate specificity of previously uncharacterized mycobacterial hydrolases. These measurements provide a global SAR of mycobacterial hydrolase activity, a picture of cycling hydrolase activity, and a detailed substrate specificity profile for previously uncharacterized hydrolases.

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 mammalian hippocampus forms a cognitive map using neurons that fire according to an animal's position ("place cells") and many other behavioral and cognitive variables. The responses of these neurons are shaped by their presynaptic inputs and the nature of their postsynaptic integration. In CA1 pyramidal neurons, spatial responses in vivo exhibit a strikingly supralinear dependence on baseline membrane potential. The biophysical mechanisms underlying this nonlinear cellular computation are unknown. Here, through a combination of in vitro, in vivo, and in silico approaches, we show that persistent sodium current mediates the strong membrane potential dependence of place cell activity. This current operates at membrane potentials below the action potential threshold and over seconds-long timescales, mediating a powerful and rapidly reversible amplification of synaptic responses, which drives place cell firing. Thus, we identify a biophysical mechanism that shapes the coding properties of neurons composing the hippocampal cognitive map.