DOI: 10.5061/dryad.q573n5tm5
title: Data for: Ongoing and visually evoked activity in the zebrafish optic tectum and adjacent brain structures

description: 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.
description: Imaging data was acquired using a
 custom-built digitally scanned light-sheet microscope. The excitation path
 included a 488 nm laser source (OBIS, Coherent, Santa Clara, California),
 a pair of galvanometer scan mirrors (Cambridge Technology, Bedford,
 Massachusetts) and objective (Plan 4X, 4x/0.1 NA, Olympus, Tokyo, Japan).
 A water-immersion detection objective (XLUMPlLFLN, 20x/1.0 NA, Olympus), a
 tube lens (f=200 mm), two relay lenses (f=100 mm) in a 4f configuration,
 and sCMOS camera (Orca Flash 4.0, Hamamatsu, Hamamatsu, Japan) were used
 in the orthogonal detection path. For remote focusing (Fahrbach et al.,
 2013), an electrically tunable lens (ETL, EL-16-40-TC-VIS-20D, Optotune,
 Dietikon, Switzerland) was installed between the relay lenses, conjugate
 to the back focal plane of the objective. Volumes (375 x 410 x 75 μm)
 comprising 19 imaging planes spaced 4 μm apart, were acquired at 5
 volumes/s. Each plane received laser excitation for 1 ms (duty cycle 9%)
 resulting in average laser power at sample of 12.4 μW. To keep the
 observed population of neurons in each plane in focus throughout long
 imaging sessions, we implemented an automatic correction for slow drift in
 the Z direction. At the beginning of the experiment we acquired two
 reference stacks, centred on two of the imaging planes, by incrementally
 biasing the Z scanning mirror and ETL in steps of 1.5% of their scan
 amplitude. During the course of the experiment, Z drift was estimated
 every 30 seconds by comparing recent images to these reference stacks,
 finding the reference images with the maximal XY cross-correlation, and
 averaging the two drift estimates. Every five minutes the Z scan mirror
 and ETL signals were biased to offset any detected drift, according to the
 average of the ten most recent Z drift estimates. For functional
 imaging, larval zebrafish were mounted in a custom 3D printed chamber (SLS
 Nylon 12, 3DPRINTUK, London, United Kingdom) in 3% low-melting point
 agarose (Sigma-Aldrich, St. Louis, Missouri) at 5 dpf and allowed to
 recover overnight before functional imaging at 6 dpf. Visual
 stimuli were back-projected (ML750ST, Optoma, New
 Taipei City, Taiwan) onto a curved screen forming the wall of
 the imaging chamber in front of the animal, at a viewing distance of ~10
 mm. A coloured filter (Follies Pink No. 344, Roscolux, Stamford,
 Connecticut) was placed in front of the projector to block green light
 from the collection optics. Visual stimuli were designed in Matlab
 (MathWorks, Natik, Massachusetts) using Psychophysics toolbox (Brainard,
 1997). Stimuli comprised 10° dark spots on a bright magenta background,
 moving at 20°/s either left→right or right→left across ~110° of frontal
 visual space. Two or three elevation angles were used, calibrated for each
 fish during preliminary imaging by finding elevations separated by at
 least 15° that produce robust observable tectal activation (typically a
 very low elevation stimulus ~25° below the horizon, a low elevation
 stimulus ~10° below the horizon, and a high elevation stimulus ~5° above
 the horizon). Eye movements were tracked during imaging
 experiments at 50 Hz under 850 nm illumination using a sub-stage
 GS3-U3-41C6NIR-C camera (Point Grey, Richmond, Canada). The angle of each
 eye was inferred online using a convolutional neural network (three 5x5
 convolutional layers with 1, 1 and 4 channels, each followed by stride-2
 max pooling layer, and a single fully connected layer), pre-trained by
 annotating images from multiple fish covering a wide range of eye
 positions. Eye movements were categorized as a convergent saccade if both
 eyes made nasally directed saccades within 150 ms of one another.
 Microscope control, stimulus presentation and behaviour tracking were
 implemented using LabVIEW (National Instruments, Austin, Texas) and
 Matlab. All calcium imaging
 data analysis was performed using Matlab scripts. Volume motion correction
 was performed by 3D translation-based registration using the Matlab
 function ‘imregtform’, with gradient descent optimizer and mutual
 information as the image similarity metric. A registration template was
 generated as the time-average of the first 10 volumes and then iteratively
 updated following each block of 10 newly registered volumes from the first
 500 frames. This template was then used to register all remaining volumes.
 For elavl3:H2B-GCaMP6s experiments, 2D regions of interest (ROIs)
 corresponding to cell nuclei were computed from each template imaging
 plane using the cell detection code provided by (Kawashima et al.,
 2016). For RGC axonal
 arbor imaging, two ROIs encompassing the tectal neuropil were manually
 defined for each imaging plane. The time-varying raw fluorescence
 signal <em>F<sub>raw</sub>(t)</em> for each ROI
 was extracted by computing the mean value of all pixels within the ROI
 mask at each time-point. A slowly varying baseline fluorescence
 <em>F<sub>0</sub>(t)</em> was estimated by taking
 the 10<sup>th</sup> percentile of a sliding 20-volume window,
 and was used to calculate the proportional change in
 fluorescence. These values were subsequently zero-centred by subtracting the mean for each ROI, and ROIs with a slow drift in their baseline fluorescence (for which the standard deviation of mean-normalised <em>F<sub>0</sub>(t) </em>was greater than 0.45)<em> </em>were discarded. To estimate spike trains, the zero-centred  time series for each cell was deconvolved using a thresholded OASIS (Friedrich et al., 2017) with a first-order autoregressive model (AR(1)) and an automatically-estimated transient decay time constant for each ROI. To standardize the 3D coordinates of detected cell nuclei, template volumes were registered onto the <em>Tg(elavl3:H2B-RFP) </em>reference brain in the ZBB brain atlas (Marquart et al., 2017) using the ANTs toolbox version 2.1.0 (Avants et al., 2011) with affine and warp transformations. As an example, to register the 3D image volume in ‘fish1_01.nrrd’ to the reference brain ‘ref.nrrd’, the following parameters were used: antsRegistration -d 3 -float 1 -o [fish1_, fish1_Warped.nii.gz] -n BSpline -r [ref.nrrd, fish1_01.nrrd, 1] -t Rigid[0.1] -m GC[ref.nrrd, fish1_01.nrrd, 1, 32, Regular, 0.25] -c [200×200×200×0,1e-8, 10] -f 12×8×4×2 -s 4×3×2×1 -t Affine[0.1] -m GC[ref.nrrd, fish1_01.nrrd, 1, 32, Regular, 0.25] -c [200×200×200×0,1e-8,10] -f 12×8×4×2 -s 4×3×2×1 -t SyN[0.1,6,0] -m CC[ref.nrrd, fish1_01.nrrd, 1, 2] -c [200×200×200x200×10,1e-7,10] -f 12×8×4x2×1 -s 4×3×2x1×0 Following registration, tectal ROIs were labelled using a manually created 3D mask.