Both vertebrates and invertebrates perceive illusory motion, known as "reverse-phi," in visual stimuli that contain sequential luminance increments and decrements. However, increment (ON) and decrement (OFF) signals are initially processed by separate visual neurons, and parallel elementary motion detectors downstream respond selectively to the motion of light or dark edges, often termed ON- and OFF-edges. It remains unknown how and where ON and OFF signals combine to generate reverse-phi motion signals. Here, we show that each of Drosophila's elementary motion detectors encodes motion by combining both ON and OFF signals. Their pattern of responses reflects combinations of increments and decrements that co-occur in natural motion, serving to decorrelate their outputs. These results suggest that the general principle of signal decorrelation drives the functional specialization of parallel motion detection channels, including their selectivity for moving light or dark edges.

In the hippocampus, the classical pyramidal cell type of the subiculum acts as a primary output, conveying hippocampal signals to a diverse suite of downstream regions. Accumulating evidence suggests that the subiculum pyramidal cell population may actually be comprised of discrete subclasses. Here, we investigated the extent and organizational principles governing pyramidal cell heterogeneity throughout the mouse subiculum. Using single-cell RNA-seq, we find that the subiculum pyramidal cell population can be deconstructed into eight separable subclasses. These subclasses were mapped onto abutting spatial domains, ultimately producing a complex laminar and columnar organization with heterogeneity across classical dorsal-ventral, proximal-distal, and superficial-deep axes. We further show that these transcriptomically defined subclasses correspond to differential protein products and can be associated with specific projection targets. This work deconstructs the complex landscape of subiculum pyramidal cells into spatially segregated subclasses that may be observed, controlled, and interpreted in future experiments.

Extracting a connectome from an electron microscopy (EM) data set requires identification of neurons and determination of synapses between neurons. As manual extraction of this information is very time-consuming, there has been extensive research effort to automatically segment the neurons to help guide and eventually replace manual tracing. Until recently, there has been comparatively less research on automatically detecting the actual synapses between neurons. This discrepancy can, in part, be attributed to several factors: obtaining neuronal shapes is a prerequisite first step in extracting a connectome, manual tracing is much more time-consuming than annotating synapses, and neuronal contact area can be used as a proxy for synapses in determining connections.
However, recent research has demonstrated that contact area alone is not a sufficient predictor of synaptic connection. Moreover, as segmentation has improved, we have observed that synapse annotation is consuming a more significant fraction of overall reconstruction time. This ratio will only get worse as segmentation improves, gating overall possible speed-up. Therefore, we address this problem by developing algorithms that automatically detect pre-synaptic neurons and their post-synaptic partners. In particular, pre-synaptic structures are detected using a Deep and Wide Multiscale Recursive Network, and post-synaptic partners are detected using a MLP with features conditioned on the local segmentation.
This work is novel because it requires minimal amount of training, leverages advances in image segmentation directly, and provides a complete solution for polyadic synapse detection. We further introduce novel metrics to evaluate our algorithm on connectomes of meaningful size. These metrics demonstrate that complete automatic prediction can be used to effectively characterize most connectivity correctly.

We describe the implementation and use of an adaptive imaging framework for optimizing spatial resolution and signal strength in a light-sheet microscope. The framework, termed AutoPilot, comprises hardware and software modules for automatically measuring and compensating for mismatches between light-sheet and detection focal planes in living specimens. Our protocol enables researchers to introduce adaptive imaging capabilities in an existing light-sheet microscope or use our SiMView microscope blueprint to set up a new adaptive multiview light-sheet microscope. The protocol describes (i) the mechano-optical implementation of the adaptive imaging hardware, including technical drawings for all custom microscope components; (ii) the algorithms and software library for automated adaptive imaging, including the pseudocode and annotated source code for all software modules; and (iii) the execution of adaptive imaging experiments, as well as the configuration and practical use of the AutoPilot framework. Setup of the adaptive imaging hardware and software takes 1-2 weeks each. Previous experience with light-sheet microscopy and some familiarity with software engineering and building of optical instruments are recommended. Successful implementation of the protocol recovers near diffraction-limited performance in many parts of typical multicellular organisms studied with light-sheet microscopy, such as fruit fly and zebrafish embryos, for which resolution and signal strength are improved two- to fivefold.

Marking functionally distinct neuronal ensembles with high spatiotemporal resolution is a key challenge in systems neuroscience. We recently introduced CaMPARI, an engineered fluorescent protein whose green-to-red photoconversion depends on simultaneous light exposure and elevated calcium, which enabled marking active neuronal populations with single-cell and subsecond resolution. However, CaMPARI (CaMPARI1) has several drawbacks, including background photoconversion in low calcium, slow kinetics and reduced fluorescence after chemical fixation. In this work, we develop CaMPARI2, an improved sensor with brighter green and red fluorescence, faster calcium unbinding kinetics and decreased photoconversion in low calcium conditions. We demonstrate the improved performance of CaMPARI2 in mammalian neurons and in vivo in larval zebrafish brain and mouse visual cortex. Additionally, we herein develop an immunohistochemical detection method for specific labeling of the photoconverted red form of CaMPARI. The anti-CaMPARI-red antibody provides strong labeling that is selective for photoconverted CaMPARI in activated neurons in rodent brain tissue.

Animals strategically scan the environment to form an accurate perception of their surroundings. Here we investigated the neuronal representations that mediate this behavior. Ca imaging and selective optogenetic manipulation during an active sensing task reveals that layer 5 pyramidal neurons in the vibrissae cortex produce a diverse and distributed representation that is required for mice to adapt their whisking motor strategy to changing sensory cues. The optogenetic perturbation degraded single-neuron selectivity and network population encoding through a selective inhibition of active dendritic integration. Together the data indicate that active dendritic integration in pyramidal neurons produces a nonlinearly mixed network representation of joint sensorimotor parameters that is used to transform sensory information into motor commands during adaptive behavior. The prevalence of the layer 5 cortical circuit motif suggests that this is a general circuit computation.

Widefield imaging of calcium dynamics is an emerging method for mapping regional neural activity but is currently limited to restrained animals. Here we describe cScope, a head-mounted widefield macroscope developed to image large-scale cortical dynamics in rats during natural behavior. cScope provides a 7.8 × 4 mm field of view and dual illumination paths for both fluorescence and hemodynamic correction and can be fabricated at low cost using readily attainable components. We also report the development of Thy-1 transgenic rat strains with widespread neuronal expression of the calcium indicator GCaMP6f. We combined these two technologies to image large-scale calcium dynamics in the dorsal neocortex during a visual evidence accumulation task. Quantitative analysis of task-related dynamics revealed multiple regions having neural signals that encode behavioral choice and sensory evidence. Our results provide a new transgenic resource for calcium imaging in rats and extend the domain of head-mounted microscopes to larger-scale cortical dynamics.

Many brain functions depend on the ability of neural networks to temporally integrate transient inputs to produce sustained discharges. This can occur through cell-autonomous mechanisms in individual neurons and through reverberating activity in recurrently connected neural networks. We report a third mechanism involving temporal integration of neural activity by a network of astrocytes. Previously, we showed that some types of interneurons can generate long-lasting trains of action potentials (barrage firing) following repeated depolarizing stimuli. Here we show that calcium signaling in an astrocytic network correlates with barrage firing; that active depolarization of astrocyte networks by chemical or optogenetic stimulation enhances; and that chelating internal calcium, inhibiting release from internal stores, or inhibiting GABA transporters or metabotropic glutamate receptors inhibits barrage firing. Thus, networks of astrocytes influence the spatiotemporal dynamics of neural networks by directly integrating neural activity and driving barrages of action potentials in some populations of inhibitory interneurons.

The mouse embryo has long been central to the study of mammalian development; however, elucidating the cell behaviors governing gastrulation and the formation of tissues and organs remains a fundamental challenge. A major obstacle is the lack of live imaging and image analysis technologies capable of systematically following cellular dynamics across the developing embryo. We developed a light-sheet microscope that adapts itself to the dramatic changes in size, shape, and optical properties of the post-implantation mouse embryo and captures its development from gastrulation to early organogenesis at the cellular level. We furthermore developed a computational framework for reconstructing long-term cell tracks, cell divisions, dynamic fate maps, and maps of tissue morphogenesis across the entire embryo. By jointly analyzing cellular dynamics in multiple embryos registered in space and time, we built a dynamic atlas of post-implantation mouse development that, together with our microscopy and computational methods, is provided as a resource.

Persistent and ramping neural activity in the frontal cortex anticipates specific movements. Preparatory activity is distributed across several brain regions, but it is unclear which brain areas are involved and how this activity is mediated by multi-regional interactions. The cerebellum is thought to be primarily involved in the short-timescale control of movement; however, roles for this structure in cognitive processes have also been proposed. In humans, cerebellar damage can cause defects in planning and working memory. Here we show that persistent representation of information in the frontal cortex during motor planning is dependent on the cerebellum. Mice performed a sensory discrimination task in which they used short-term memory to plan a future directional movement. A transient perturbation in the medial deep cerebellar nucleus (fastigial nucleus) disrupted subsequent correct responses without hampering movement execution. Preparatory activity was observed in both the frontal cortex and the cerebellar nuclei, seconds before the onset of movement. The silencing of frontal cortex activity abolished preparatory activity in the cerebellar nuclei, and fastigial activity was necessary to maintain cortical preparatory activity. Fastigial output selectively targeted the behaviourally relevant part of the frontal cortex through the thalamus, thus closing a cortico-cerebellar loop. Our results support the view that persistent neural dynamics during motor planning is maintained by neural circuits that span multiple brain regions, and that cerebellar computations extend beyond online motor control.