Explore how microfluidic chips can enhance your imaging experiments by increasing control, throughput, or flexibility. In this remote, personalized workshop, participants will receive expert guidance, support and chips to run tests on their own microscopes.

Advancements in imaging speed, depth and resolution have made structured illumination microscopy (SIM) an increasingly powerful optical sectioning (OS) and super-resolution (SR) technique, but these developments remain inaccessible to many life science researchers due to the cost, optical complexity and delicacy of these instruments. We address these limitations by redesigning the optical path using in-line fibre components that are compact, lightweight and easily assembled in a “Plug & Play” modality, without compromising imaging performance. They can be integrated into an existing widefield microscope with a minimum of optical components and alignment, making OS-SIM more accessible to researchers with less optics experience. We also demonstrate a complete SR-SIM imaging system with dimensions 300 mm × 300 mm × 450 mm. We propose to enable accessible SIM imaging by utilising its compact, lightweight and robust design to transport it where it is needed, and image in “unconventional” environments where factors such as temperature and biosafety considerations currently limit imaging experiments.

During development, the complex neuronal circuitry of the brain arises from limited information contained in the genome. After the genetic code instructs the birth of neurons, the emergence of brain regions, and the formation of axon tracts, it is believed that neuronal activity plays a critical role in shaping circuits for behavior. Current AI technologies are modeled after the same principle: connections in an initial weight matrix are pruned and strengthened by activity-dependent signals until the network can sufficiently generalize a set of inputs into outputs. Here, we challenge these learning-dominated assumptions by quantifying the contribution of neuronal activity to the development of visually guided swimming behavior in larval zebrafish. Intriguingly, dark-rearing zebrafish revealed that visual experience has no effect on the emergence of the optomotor response (OMR). We then raised animals under conditions where neuronal activity was pharmacologically silenced from organogenesis onward using the sodium-channel blocker tricaine. Strikingly, after washout of the anesthetic, animals performed swim bouts and responded to visual stimuli with 75% accuracy in the OMR paradigm. After shorter periods of silenced activity OMR performance stayed above 90% accuracy, calling into question the importance and impact of classical critical periods for visual development. Detailed quantification of the emergence of functional circuit properties by brain-wide imaging experiments confirmed that neuronal circuits came ‘online’ fully tuned and without the requirement for activity-dependent plasticity. Thus, contrary to what you learned on your mother's knee, complex sensory guided behaviors can be wired up innately by activity-independent developmental mechanisms.

Recent chronic imaging experiments in mice have revealed that the hippocampal code exhibits non-trivial turnover dynamics over long time scales. Specifically, the subset of cells which are active on any given session in a familiar environment changes over the course of days and weeks. While some cells transition into or out of the code after a few sessions, others are stable over the entire experiment. The mechanisms underlying this turnover are unknown. Here we show that the statistics of turnover are consistent with a model in which non-spatial inputs to CA1 pyramidal cells readily undergo plasticity, while spatially tuned inputs are largely stable over time. The heterogeneity in stability across the cell assembly, as well as the decrease in correlation of the population vector of activity over time, are both quantitatively fit by a simple model with Gaussian input statistics. In fact, such input statistics emerge naturally in a network of spiking neurons operating in the fluctuation-driven regime. This correspondence allows one to map the parameters of a large-scale spiking network model of CA1 onto the simple statistical model, and thereby fit the experimental data quantitatively. Importantly, we show that the observed drift is entirely consistent with random, ongoing synaptic turnover. This synaptic turnover is, in turn, consistent with Hebbian plasticity related to continuous learning in a fast memory system.