Biology subjects

Zylbertal, A.

Publications and source records attributed to Zylbertal, A..

3 recordsLinked to original sources

bioRxiv2024

Mirror-assisted light-sheet microscopy: a simple upgrade to enable bi-directional sample excitation

SignificanceLight-sheet microscopy is a powerful imaging technique that achieves optical sectioning via selective illumination of individual sample planes. However, when the sample contains opaque or scattering tissue, the incident light-sheet may not be able to uniformly excite the entire sample. For example, in the context of larval zebrafish whole-brain imaging, occlusion by the eyes prevents access to a significant portion of the brain during common implementations using unidirectional illumination. AimWe introduce mirror-assisted light-sheet microscopy (mLSM), a simple inexpensive method that can be implemented on existing digitally scanned light-sheet setups by adding a single optical element - a mirrored micro-prism. The prism is placed near the sample to generate a second excitation path for accessing previously obstructed regions. ApproachScanning the laser beam onto the mirror generates a second, reflected illumination path that circumvents the occlusion. Online tuning of the scanning and laser power waveforms enables near uniform sample excitation with dual illumination. ResultsmLSM produces cellular-resolution images of zebrafish brain regions inaccessible to unidirectional illumination. Imaging quality in regions illuminated by the direct or reflected sheet is adjustable by translating the excitation objective. The prism does not interfere with visually guided behaviour. ConclusionsmLSM presents an easy to implement, cost-effective way to upgrade an existing light-sheet system to obtain more imaging data from a biological sample.

neuroscience↗

bioRxiv2022

A recurrent network architecture explains tectal activity dynamics and experience-dependent behaviour

The ongoing activity of neuronal populations represents an internal brain state that influences how sensory information is processed to control behaviour. Conversely, external sensory inputs perturb network dynamics, resulting in lasting effects that persist beyond the duration of the stimulus. However, the relationship between these dynamics and circuit architecture and their impact on sensory processing, cognition and behaviour are poorly understood. By combining cellular-resolution calcium imaging with mechanistic network modelling, we aimed to infer the spatial and temporal network interactions in the zebrafish optic tectum that shape its ongoing activity and state-dependent responses to visual input. We showed that a simple recurrent network architecture, wherein tectal dynamics are dominated by fast, short range, excitation countered by long-lasting, activity-dependent suppression, was sufficient to explain multiple facets of population activity including intermittent bursting, trial-to-trial sensory response variability and spatially-selective response adaptation. Moreover, these dynamics also predicted behavioural trends such as selective habituation of visually evoked prey-catching responses. Overall, we demonstrate that a mechanistic circuit model, built upon a uniform recurrent connectivity motif, can estimate the incidental state of a dynamic neural network and account for experience-dependent effects on sensory encoding and visually guided behaviour.

neuroscience↗

bioRxiv2018

A computational model of the escape response latency in the Giant Fiber System of Drosophila melanogaster

The Giant Fiber System (GFS) is a multi-component neuronal pathway mediating rapid escape response in the adult fruit-fly Drosophila melanogaster, usually in the face of a threatening visual stimulus. Two branches of the circuit promote the response by stimulating an escape jump followed by flight initiation. Our recent work demonstrated an age-associated decline in the speed of signal propagation through the circuit, measured as the stimulus-to-muscle depolarization response latency. The decline is likely due to the diminishing number of inter-neuronal gap junctions in the GFS of ageing flies. In this work, we presented a realistic conductance-based, computational model of the GFS that recapitulates our experimental results and identifies some of the critical anatomical and physiological components governing the circuit's response latency. According to our model, anatomical properties of the GFS neurons have a stronger impact on the transmission than neuronal membrane conductance densities. The model provides testable predictions for the effect of experimental interventions on the circuit's performance in young and ageing flies.

neuroscience↗