---
title: "Larval Zebrafish"
document_id: sobe-2025-main-ai
document_type: report_section
parent_document: ../report.md
section_id: main-section-07-larval-zebrafish
section_order: 7
section_count: 17
language: en
license: CC-BY-4.0
---
# Larval Zebrafish
> Selective-retrieval section 7 of 17 from the [complete AI-readable report](../report.md). Content is copied without editorial summarization; relative asset, data, and code paths are rebased for this directory.
- **Parent document:** [`report.md`](../report.md)
- **Section ID:** `main-section-07-larval-zebrafish`
- **Order:** 7 of 17
- **Words:** 5441
- **Previous:** [Part 2: State of Brain Emulation across Organisms — C. elegans](06-part-2-state-of-brain-emulation-across-organisms-c-elegans.md)
- **Next:** [Drosophila](08-drosophila.md)
### Anatomy & Behavior
The larval zebrafish (Danio rerio), defined as the developmental stage preceding 30 days post-fertilization (dpf), possesses a transparent body ranging from 3.5 to 7.8 mm in length at day 5 ([Kimmel et al., 1995](https://zfin.org/ZDB-PUB-961014-576); [Svara et al., 2022](https://www.nature.com/articles/s41592-022-01621-0)), roughly 500x the size of C. elegans. The larval brain, excluding its spine, measures roughly 0.08mm³ (400 × 800 × 250 μm, W × L × H) at this stage, contains approximately 100,000 neurons, a number that increases by two orders of magnitude to 10 million in adults (that is, 90-day old or older individuals), whose brains measure 0.4–2 mm in thickness and 4.5 mm in length ([Wullimann et al., 1996](https://doi.org/10.1007/978-3-0348-8979-7); [Bruzzone et al., 2021](https://www.nature.com/articles/s41598-021-90335-y#:~:text=Understanding%20how%20patterns%20of%20neuronal%20activity%20across,of%20neuronal%20units%E2%80%94estimated%20between%2080%2C000%20and%20100%2C000.); [Hill et al., 2003](https://doi.org/10.1093/toxsci/kfg241); [Hinsch and Zupanc, 2007](https://doi.org/10.1016/j.neuroscience.2007.01.071)). These neurons utilize action potential-based signaling, similar to mammals, enabling the study of vertebrate-like neural dynamics. However, comprehensive data on natural in vivo firing rates across the entire larval brain remain limited. Characterized examples reveal significant diversity: embryonic primary motoneurons exhibit rhythmic bursts containing high-frequency (40-50 Hz) action potentials ([Saint-Amant and Drapeau, 2000](https://doi.org/10.1523/JNEUROSCI.20-11-03964.2000)), while sensory afferents show low spontaneous rates of ~9 Hz ([Levi et al., 2015](https://doi.org/10.1152/jn.00414.2014)). Central neurons also vary, with cerebellar Purkinje cells displaying distinct tonic simple (~9 Hz) and phasic complex (~0.3 Hz) spiking ([Hsieh et al., 2014](https://doi.org/10.3389/fncir.2014.00147)), cerebellar output neurons showing a baseline spontaneous rate around 4 Hz ([Najac et al., 2023](https://doi.org/10.1016/j.cub.2023.06.045)), and other central neurons like vestibulospinal cells being largely silent at rest ([Hamling et al., 2023](https://doi.org/10.1101/2023.03.15.532859)).
The transparency of the larval brain, particularly in pigmentless mutants, facilitates high-resolution optical imaging of neural activity across the entire brain ([White et al., 2008](https://doi.org/10.1016/j.stem.2007.11.002); [Antinucci and Hindges, 2016](https://doi.org/10.1038/srep29490)). However, increasing pigmentation in wild-type larvae typically limits the practical window for such optical approaches to the first 1-2 weeks post-fertilization ([Volkov et al., 2022](https://www.nature.com/articles/s41598-022-25386-w)). Additionally, larval zebrafish respire through their skin until approximately day 15, allowing for unparalyzed and unanesthetized imaging in agarose-embedded preparations. This feature, combined with the ability to survive for extended periods without external food sources, enables long-duration in vivo neural recordings with minimal maintenance ([Hasani et al., 2023](https://doi.org/10.3389/fnins.2023.1127574)). These anatomical and developmental traits make the larval zebrafish a powerful model for studying neural dynamics, structural connectomics, and structure-function relationships in vertebrate neural circuits.
Larval zebrafish display a diverse and adaptive behavioral repertoire, enabling them to navigate and thrive in dynamic environments ([Zocchi et al., 2025](https://www.cell.com/current-biology/fulltext/S0960-9822(24)01624-5)). After day 3-4, they exhibit a range of stereotyped movements, including slow swims (scoots), rapid escape responses (C-starts), and specialized maneuvers like J-turns for predation ([Privat et al, 2020](https://www.cell.com/current-biology/fulltext/S0960-9822%2819%2931453-8)).
Video 2 - Larval Zebrafish Behavior
[Spontaneous movement](https://www.youtube.com/shorts/MnGWPK7-Odg)
[Phototaxis, Hunting, Swimming](https://youtu.be/ykIj-9a_ss4?si=n5MFHXailrU76lDm&t=718)
These behaviors are finely tuned to sensory inputs: larvae respond to visual stimuli with optomotor and optokinetic reflexes, acoustic/vibrational cues with escape movements, and tactile stimuli with highly directional turns. They also display adaptive behaviors such as phototaxis, visual background adaptation, and alarm responses to chemical cues, which help them navigate, camouflage, and avoid predators. Social behaviors, such as shoaling and aggregation, emerge by day 9–10 and are influenced by learned preferences for conspecifics. Additionally, larvae exhibit circadian rhythms, with periods of activity during the day and immobility at night, resembling sleep-like states ([Fero et al., 2012](https://doi.org/10.1007/978-1-60761-922-2_12)).
Zebrafish larvae are also capable of multiple forms of learning and memory. Starting at five days post fertilization, they begin to habituate to repeated stimuli, showing distinct forms of rapid, short-term, and long-term habituation that depend on specific neural mechanisms ([Roberts et al., 2013](https://doi.org/10.3389/fncir.2013.00126)). For example, long-term habituation of the C-start escape response requires protein synthesis and shares mechanistic similarities with learning in other species. Zebrafish larvae can also undergo associative learning - for instance, they learn to move their tails in response to light signals and to avoid areas associated with electric shock. They also display social learning, as shown by their specific preferences for shoaling partners based on their early-life exposure to the appearance of other fish ([Roberts et al., 2013](https://doi.org/10.3389/fncir.2013.00126)).
### Neural Dynamics
#### Neural activity recording
Neural activity in larval zebrafish can be recorded using head-fixed or freely-moving preparations ([Hasani et al., 2023](https://doi.org/10.3389/fnins.2023.1127574)). In head-fixed preparations, the fish's head is immobilized in agarose, which limits natural behavior but enables stable imaging. Several approaches have been developed to increase behavioral output: the tail can be freed to monitor movement intentions, and fish can perform fictive navigation in virtual environments by using their tail movements to control the change in the virtual environment ([Ahrens et al., 2012](https://doi.org/10.1038/nature11057); [Trivedi and Bollmann, 2013](https://doi.org/10.3389/fncir.2013.00086); [Vladimirov et al., 2014](https://doi.org/10.1038/nmeth.3040); [Torigoe et al., 2021](https://doi.org/10.1038/s41467-021-26010-7)).
------------------------------------------------------------------------
Figure 12 - Overview of the optical neural recording landscape in Larval Zebrafish: Radar plots based on the optical recording literature cited in the report. We plot the following dimensions of fixated brain recordings: spatial resolution, brain volume, temporal resolution, and (estimated) individual and cumulative recording duration. Only recordings from fixated (A) and no freely moving experiments are available. The outer ring is normalized to the maximum known values. Each ring represents one order of magnitude. The data for this figure is available in the [linked data repository](https://docs.google.com/spreadsheets/d/14rglcvdX8Bl6oSWgNnnlOVM_Oot0dHjfFIRHU2Y-mC0/edit?gid=0#gid=0).
### Machine-readable figure record: `main-fig-12`
```yaml
id: main-fig-12
document_id: sobe-2025-main-ai
document_path: report.md
anchor: main-fig-12
canonical_url: https://brainemulation.mxschons.com/ai/report.md#main-fig-12
figure: "Zebrafish optical recording landscape"
assets:
- "../assets/report/main-fig-12-publication.png"
data_status: final_circulation_export_raster_with_contextual_current_sources
data_sources:
- "../data/current-repository/recordings/neurodynamics-papers.tsv"
- "../data/current-repository/recordings/neurodynamics-organisms.tsv"
generator: ../code/run_all_figures.py
generator_function: generate_neuro_rec_radar
publication_export_members:
- "images/image6.png"
canonical_pdf_pages:
- 53
```
#### Inline contextual data (not an exact publication input): zebrafish studies
```tsv
First author Year Organism Fixated / moving Method Number of neurons Temporal resolution: Hz Duration single session min Duration total repeated sessions min Resolution in µm isotropic Individuals studied Brain volume dimensions SNR Perturbation DOI/Link
Favre-Bulle 2018 Zebrafish Larvae fixated Calcium 80000 4.0 25.0 150.0 1 none https://doi.org/10.1016/j.cub.2018.09.060
Yang 2022 Zebrafish Larvae fixated Calcium 40000 3.3 25.0 150.0 1 none https://doi.org/10.1038/s41592-022-01417-2
Voleti 2019 Zebrafish Larvae fixated Calcium 6000 25.75 25.0 150.0 1 none https://doi.org/10.1038/s41592-019-0579-4
Bruzzone 2021 Zebrafish Larvae fixated Calcium 52000 1.0 25.0 150.0 1 none https://doi.org/10.1038/s41598-021-90335-y
Wang 2023 Zebrafish Larvae fixated Voltage 25000 200.0 25.0 150.0 1 none https://www.biorxiv.org/content/10.1101/2023.12.15.571964v1
```
#### Inline contextual data (not an exact publication input): organism normalization maxima
```tsv
Organism Total Neuron Count Maximum Neuron Firing Rate (Hz) Average Liftime in Minutes Isotropic resolution Single Neuron recording in µm ref_id supporting_refs ref_note confidence validated_by
C. Elegans 302 10 21600 1 none none
Zebrafish Larvae 100000 80 14400 1 none none
Drosophila 140000 200 57600 1 none none
Mouse 70000000 200 1576800 1 none none
Human 87000000000 415 39420000 1 none none
```
#### Figure-generation source map (valid Python; not standalone plotting code)
```python
GENERATOR_PATH = "../code/run_all_figures.py"
SOURCE_FUNCTION = 'generate_neuro_rec_radar'
SELECTED_SOURCE_LINES = (
'normalized_alpha = 1 - (1 - base_alpha)**(1/num_recs)',
'normalized_alpha = base_alpha',
'alpha=normalized_alpha,',
)
```
------------------------------------------------------------------------
Initial progress towards larger scale brain imaging came from one-photon techniques like light-sheet microscopy, which achieved simultaneous recording of ~80,000 neurons (about 80-90% of all neurons) at 1 Hz ([Ahrens et al., 2013](https://doi.org/10.1038/nmeth.2434)), later improved to 4 Hz using electrically tunable lenses ([Favre-Bulle et al., 2018](https://doi.org/10.1016/j.cub.2018.09.060)). Recent advances include DaXi microscopy, achieving 3.3 Hz over 0.03 mm³ (37.7% of total brain volume, approx. 40,000 neurons), and SCAPE microscopy reaching 25.75 Hz over smaller volumes of 0.00481 mm³ (6% total brain volume, approx. 6,000 neurons) ([Yang et al., 2022](https://doi.org/10.1038/s41592-022-01417-2); [Voleti et al., 2019](https://doi.org/10.1038/s41592-019-0579-4)). More recently, the ZAPBench dataset ([Lueckmann et al., 2025](https://arxiv.org/abs/2503.02618)) showcased light-sheet microscopy recording of over 70,000 neurons throughout nearly the entire larval zebrafish brain at approximately 1 Hz. However, scattered light from the excitation laser is visible to the fish in all one-photon approaches and can interfere with presented visual stimuli. Two-photon microscopy uses infrared light that is invisible to the fish, though care must be taken as fish eyes absorb infrared light, and exposure can be lethal. While two-photon imaging eliminates visual interference, its main limitation is speed: current two-photon technology can record 52,000 neurons distributed throughout 80% of the brain's volume at 1 Hz ([Bruzzone et al., 2021](https://doi.org/10.1038/s41598-021-90335-y)). However, even with these advances, head-fixed preparations fundamentally constrain the fish's natural behavior, particularly during complex tasks like hunting.
Recording neural activity in freely moving zebrafish presents unique technical challenges, as larvae move in rapid bursts reaching speeds of 10 cm/s and angular velocities of 600°/s, i.e., close to 2 revolutions around themselves per second ([Johnson et al., 2020](https://doi.org/10.1016/j.cub.2019.11.026); [Mearns et al., 2020](https://doi.org/10.1016/j.cub.2019.11.022)). These movements require sophisticated real-time tracking to keep the fish within the microscope's field of view. Higher imaging speeds typically come at the cost of either reduced spatial resolution or a smaller field of view. Current tracking approaches either move the stage to cancel the fish's motion ([Kim et al., 2017](https://doi.org/10.1038/nmeth.4429)) or use mirror systems to keep a stationary microscope focused on the fish ([Symvoulidis et al., 2017](https://doi.org/10.1038/nmeth.4459)). Several imaging techniques have been developed, including differential illumination focal filtering microscopy (DIFF), light field microscopy (LFM), and hybrid approaches combining LFM with confocal or light-sheet methods ([Cong et al., 2017](https://doi.org/10.7554/eLife.28158); [Zhang et al., 2021](https://doi.org/10.1038/s41587-020-0628-7)). Additional challenges include calcium indicators being too slow to track rapid neural changes during behaviors like hunting, and current setups restricting the fish's vertical movements, which are crucial for natural hunting where fish prefer to strike at prey from below ([Bolton et al., 2019](https://doi.org/10.7554/eLife.51975.sa2); [Mearns et al., 2020](https://doi.org/10.1016/j.cub.2019.11.022)). Additionally, counteracting the fish's movements by moving the imaging chamber can significantly alter their behavior, reducing how far, fast, and often they move ([Kim et al., 2017](https://doi.org/10.1038/nmeth.4429)).
Calcium imaging speeds still fall roughly two orders of magnitude behind the fastest neurons in zebrafish larvae ([Lueckmann et al., 2025](https://arxiv.org/abs/2503.02618)). Voltage imaging in larval zebrafish has also made remarkable strides in recent years, bringing the field closer to the goal of whole-brain voltage imaging at cellular resolution. Innovations in genetically encoded voltage indicators (GEVIs), such as ASAP3-Kv, Voltron2-Kv, and Positron2-Kv, have significantly improved brightness and signal-to-noise ratio, enabling the detection of single action potentials across large populations of neurons. Concurrently, advances in microscopy, particularly light-sheet techniques like remote refocusing and oblique plane microscopy, have pushed volumetric imaging rates to over 200 Hz, allowing researchers to capture cellular-resolution neural activity across approximately 25,000-33,000 neurons (25-33% of total brain) in head-fixed zebrafish brain even when at maximum firing rates ([Wang et al., 2023](https://www.biorxiv.org/content/10.1101/2023.12.15.571964v1)), although these techniques are still in active development, with data from only a single fish presented.
#### Neurotransmitters and Neuromodulators
Optical approaches for monitoring neurotransmitter dynamics are particularly well-suited for larval zebrafish given their transparency and accessibility for whole-brain imaging. Several GENIs have been successfully validated in zebrafish for glutamate ([Marvin et al., 2013](https://doi.org/10.1038/nmeth.2333)), GABA ([Marvin et al., 2019](http://doi.org/10.1038/s41592-019-0471-2)), acetylcholine ([Borden et al., 2020](https://doi.org/10.1101/2020.02.07.939504)), dopamine ([Sun et al., 2018](https://doi.org/10.1016/j.cell.2018.06.042)), noradrenaline ([Feng et al., 2019](https://doi.org/10.1016/j.neuron.2019.02.037)), and ATP ([Wu et al., 2021](https://doi.org/10.1016/j.neuron.2021.11.027)), but developing sensors for the more than 100 neuropeptides identified in the zebrafish brain ([Van Camp et al., 2016](https://doi.org/10.1016/j.jprot.2016.09.015)) remains a challenge.
#### Perturbation
The larval zebrafish represents an attractive model organism for optogenetic perturbation due to its transparency and amenability to genetic modifications. One-photon optogenetic approaches face limitations from light scattering, lack of z-axis resolution, and unwanted visual stimulation of the fish ([Chai et al., 2024](https://doi.org/10.1016/j.isci.2023.108385)). Two-photon optogenetics offers improved spatial precision and reduced scattering ([Turrini et al., 2024](https://www.nature.com/articles/s42003-024-06731-3)), though reliable single-neuron manipulation remains a technical challenge. Current approaches typically target anatomically defined regions rather than individual neurons, particularly when the fish is relatively stationary ([Chai et al., 2024](https://doi.org/10.1016/j.isci.2023.108385); [Turrini et al., 2024](https://www.nature.com/articles/s42003-024-06731-3)). Despite these technical limitations, optogenetics studies in the fish have led to several discoveries ([Piatkevich and Boyden, 2023](https://doi.org/10.1017/S0033583523000033)), including neural populations and activity patterns responsible for saccadic eye movements ([Schoonheim et al., 2010](https://doi.org/10.1523/JNEUROSCI.5193-09.2010)), for increasing sleep ([Oikonomou et al., 2019](https://doi.org/10.1016/j.neuron.2019.05.038)), for controlling swim turn direction ([Dunn et al., 2016](https://doi.org/10.7554/eLife.12741)), for providing sensory feedback to spinal circuits during fast locomotion ([Knafo et al., 2017](https://doi.org/10.7554/eLife.25260)), for producing coordinated swimming patterns ([Ljunggren et al., 2014](https://doi.org/10.1523/JNEUROSCI.4087-13.2014)), for stopping ongoing swimming ([Kimura et al., 2013](https://doi.org/10.1016/j.cub.2013.03.066)), and for contributing to movement in response to noxious stimuli ([Wee et al., 2019](https://doi.org/10.1038/s41593-019-0452-x)). Most recently, Chai et al. developed a system capable of enabling whole-brain calcium imaging in freely swimming larvae while simultaneously allowing targeted optogenetic stimulation of specific brain regions during stationary periods ([Chai et al., 2024](https://www.cell.com/iscience/fulltext/S2589-0042(23)02462-8)). Further development of tools that permit genetic access to limited populations of neurons would allow for more specific perturbation of individual populations of neurons.
### Connectomics
Early electron microscopy efforts in larval zebrafish demonstrated the feasibility of whole-brain imaging through multi-scale imaging approaches. Hildebrand et al. achieved imaging of an entire larval zebrafish brain and portions of the spinal cord, though at resolutions insufficient for dense reconstruction ([Hildebrand et al., 2017](https://www.nature.com/articles/nature22356)). A significant advance came with Svara et al., who achieved whole-brain electron microscopy imaging at synaptic resolution in a 5-day post-fertilization larval zebrafish, enabling tracing of neural connections across nearly the entire brain except for the retinae ([Svara et al., 2022](https://www.nature.com/articles/s41592-022-01621-0)). Parallel efforts are ongoing, including as part of integrated functional & structural studies that imaged another whole larval brain ([Lueckmann et al., 2025](https://arxiv.org/abs/2503.02618)) and as part of work at the Brain Science and Intelligent Technology Innovation Center of the Chinese Academy of Sciences, where a team led by Du Jiulin is combining both optical and electron microscopy approaches for cellular and synaptic level mapping respectively ([Du Jiulin, 2022](https://www.koushare.com/live/details/7420?vid=27639)). Small pieces of the brainstem ([Vishwanathan et al., 2024](https://www.nature.com/articles/s41593-024-01784-3)), hindbrain ([Boulanger-Weill et al., 2025](https://doi.org/10.1101/2025.03.14.643363)), and spinal cord have also been reconstructed ([Svara et al., 2018](https://www.cell.com/cell-reports/fulltext/S2211-1247(18)30756-3)).
Beyond electron microscopy, expansion microscopy (ExM) has emerged as a powerful complementary approach, first demonstrated in zebrafish by Freifeld et al., who resolved putative synaptic connections between fluorescently labeled cell populations ([Freifeld et al., 2017](https://doi.org/10.1073/pnas.1706281114)). Subsequent work has revealed detailed protein organization at specific synapses, such as those on Mauthner cells ([Cárdenas-García et al., 2024](https://doi.org/10.7554/elife.91931)). Recent ExM protocols enable the expansion of intact, several-millimeter-long animals up to 5 dpf while maintaining compatibility with genetically-encoded protein labeling, providing an essential link between structure and function by revealing both subsynaptic structures and intricate signaling pathways throughout the nervous system ([Steib et al., 2023](https://www.sciencedirect.com/science/article/pii/S2666166723002150?via%3Dihub); [Behzadi et al., 2024](https://arxiv.org/abs/2411.06676)). While x-ray-based approaches have been demonstrated using synchrotron radiation to achieve cellular resolution ([Osterwalder et al., 2021](https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11586/115860J/Three-dimensional-x-ray-microscopy-of-zebrafish-larvae/10.1117/12.2583639.short)), they have not yet been widely adopted for neural circuit mapping in larval zebrafish.
The Hildebrand et al. dataset requires approximately 2.7 terabytes of storage across all resolution levels ([Hildebrand et al., 2017](https://www.nature.com/articles/nature22356)). In comparison, the Svara et al. dataset likely requires approximately 15 terabytes of storage (assuming 2 byte per voxel at 7.33 teravoxels, the size of the current effort is ~11TB at 8 bits) ([mapZebrain, 2025](https://mapzebrain.org/home)). Neuron reconstruction efforts for the larval zebrafish are ongoing: while automated segmentation and synaptic detection of the Svara et al. dataset have been completed, comprehensive proofreading efforts continue to this day and are scheduled to be completed most likely in a few years.
### Computational Modeling
The landscape of larval zebrafish simulation efforts is distinctly shaped by three key factors: the current absence of a fully reconstructed connectome; its relative novelty as a brain simulation model organism in computational neuroscience compared to C. elegans; and the optical transparency of its brain, making whole-brain calcium imaging data readily accessible. These characteristics have led to simulation approaches that are generally not whole-brain, not connectome-constrained, and revolve around functional data as the key to model fitting and validation.
#### Vishwanathan et al., 2024
Vishwanathan et al. implemented a highly simplified linear rate model as a proof of concept for connectome-constrained circuit simulation in larval zebrafish. It was based on a reconstructed 0.0024 mm³ brainstem volume (approximately 2.4% of the total brain volume) containing circuits involved in oculomotor and velocity-to-position integration ([Vishwanathan et al., 2024](https://www.nature.com/articles/s41593-024-01784-3)). Based on the identified ~3,000 neurons and ~75,000 synapses, they derived a directed graph weighted by synaptic counts between neurons, with weights normalized by the total synaptic input to each postsynaptic neuron and with prior physiological studies of neurotransmitter identities and anatomical mapping of circuit components used to constrain the model further.
Model validation leveraged previously collected two-photon calcium imaging data from 20 different larval zebrafish, combined with simultaneous eye position recordings. The validation focused on two key aspects of oculomotor integration: how neuron firing rates correlated with eye position during fixations, and how neural activity changed following rapid eye movements. For each neuron population, the authors compared how strongly neurons respond to eye position changes in the model versus experimental measurements from calcium imaging across 20 different zebrafish - the model, despite being based on a single specimen's connectivity, correctly predicted the characteristic distribution of position sensitivities for each population.
#### Liu et al., 2024
simZFish, developed by Liu et al., combines an embodied model that bridges sensory processing, neural control, and biomechanics to reproduce behavior. It is based on their previous behavioral and calcium imaging recordings of the optomotor response - a behavior where fish adjust their swimming to maintain position when their entire visual environment appears to move around them ([Liu et al., 2024](https://doi.org/10.1101/2024.12.19.629427); [Naumann et al., 2016](https://www.cell.com/cell/fulltext/S0092-8674(16)31402-7)). The model was fit using data from their 2016 work, where they identified distinct neural types in the pretectal circuit by analyzing calcium imaging recordings from 3,070 neurons across 12 fish, characterizing their firing patterns during visual stimulation, and establishing their functional connectivity through analysis of concurrent behavioral recordings from 38 fish.
This characterization was used to initialize a simplified, biologically inspired rate-coding network. In such networks, a common approach in computational neuroscience, the detailed spiking activity of individual neurons is not simulated. Instead, a large population of similar neurons is often abstracted as a single computational node (or unit). The activity of this node is then characterized by a single, continuous time-varying signal representing the average firing rate of that entire population. These rate-based units, whose outputs are these average firing rates, then interact with each other to form the network. This contrasts with the more granular, single-neuron spiking models that are a primary focus of other sections in this report. While this rate-coding neural simulation formed the control system, the model's key contribution lies in its detailed biomechanical implementation, comprising seven body segments with realistic hydrodynamics and a virtual visual system with two cameras mimicking the fish's eyes.
The model underwent comprehensive validation through multiple approaches: behavioral analysis comparing simulated and real fish responses to visual stimuli, comparison of artificial neural activity patterns to calcium imaging recordings, and finally, implementation in a physical robot tested in both controlled and natural environments. Both the simulation and the robot – see [some](https://www.biorxiv.org/content/biorxiv/early/2024/12/20/2024.12.19.629427/DC10/embed/media-10.mp4?download=true) of the videos from Liu et al. ([Liu et al., 2024](https://doi.org/10.1101/2024.12.19.629427)) – demonstrated the ability to maintain position in moving water through visual input alone.
#### Immer et al., 2025
Lueckmann et al. developed a black-box machine learning model trained on approximately two hours of 1 Hz calcium recordings from the ZAPBench dataset ([Lueckmann et al., 2025](https://arxiv.org/abs/2503.02618)), which captures the activity of approximately 70,000 neurons across the whole brain (2048 x 1152 x 72 voxels at 406 nm x 406 nm x 4 μm resolution, likely 70-80% of all neurons of the organism) from a single head-fixed but tail-free larval zebrafish during nine different behavioral tasks. The recordings were obtained using light-sheet microscopy while the fish was exposed to various visual stimuli, including forward-moving gratings to test gain adaptation, random dot patterns for decision-making, alternating light/dark flashes for startle responses, asymmetric illumination for phototaxis, and various other motion patterns to probe turning behavior and positional homeostasis. The fish's tail movements were monitored throughout the experiments via electrical recordings from motor nerves. This allowed real-time coupling between the fish's attempted swimming behavior and the visual stimuli presented ([Immer et al., 2025](https://www.arxiv.org/abs/2503.00073)).
Unlike traditional approaches that first segment individual neurons and extract their activity traces, this method worked directly with the raw volumetric video data to predict future frames of brain-wide activity. The authors implemented a 4D UNet architecture operating on three spatial dimensions plus time, trained end-to-end to minimize the mean absolute error between predicted and actual (voxel-based) calcium traces. The model was trained on approximately 1.4 hours of data (70% of the full 2-hour recording) spanning eight different behavioral conditions, with performance evaluated on held-out test segments from these conditions and a completely held-out ninth condition. Attempts to improve performance through pre-training on data from two other specimens proved unsuccessful. While these initial attempts at pre-training on data from two other specimens were unsuccessful, this single outcome on a new benchmark with few baselines offers limited insight into the ultimate viability of transfer learning across individuals, which remains a promising avenue for future exploration.
When validated against held-out recordings, the model achieved higher prediction accuracy for short temporal contexts, though it showed comparable performance to trace-based approaches when using longer temporal contexts. In completely held-out experimental conditions, the model demonstrated better generalization for one-step-ahead predictions than for longer forecast horizons. Successful prediction, at least when defined in terms of mean absolute error, thus remains a challenge for trace-based and video-based models, especially with longer temporal contexts. Importantly, this particular modeling approach focuses solely on predicting voxel-level neural activity from past activity and does not incorporate embodiment; it has no simulated body or environment to interact with, nor does it directly model how sensory inputs (like the visual stimuli presented to the fish) translate into neural activity or how neural activity translates into motor outputs (like tail movements).
While this work demonstrates the feasibility of purely ML-based approaches to neural activity prediction in Zebrafish larvae, the main challenge remains sample efficiency. These models lack biological priors constraining their predictions, and they require substantially more data to learn effectively. Importantly, the authors have made this valuable dataset publicly available in a convenient format. Future work will likely focus on improving sample efficiency through better architectures and finding ways to leverage data across individuals through cross-specimen pre-training. The connectome for this exact fish is expected to be released in a year or so, thus enabling a variety of approaches that incorporate serious biological priors.
### Gap Analysis
The larval zebrafish represents a unique convergence of experimental tractability and biological sophistication, making it a unique target for integrated brain emulation efforts. While larval zebrafish have minimal clinical / industrial utility, success in fully modeling a vertebrate brain could broadly validate whole-brain emulation efforts.
### Table 6 — Model Organism Overview: Larval zebrafish
| Model Organism Overview: Larval zebrafish | Pros | Cons |
|---|---|---|
| Anticipated Scientific Insights | • Similarity to Mammalian Brain Structure and Physiology: Spiking neurons, layered structures, more pronounced plasticity – offering richer testbeds than C. elegans .
• High-Resolution Structure – Function Mapping: Near-whole-brain calcium/voltage imaging can be paired with EM or ExM for synaptic-level detail, enabling correlational and causal studies.
• Diverse behaviors even in larvae: Prey capture, startle, and associative learning make the modeling of somewhat sophisticated behaviors theoretically possible. | • Developmental & Inter-individual Variability: The rapid development of larval zebrafish brains (typically studied at 5-7 days post-fertilization) creates significant registration challenges, as small age differences of days or even hours can lead to substantial physiological and structural changes This complicates efforts to create a "standard" reference connectome and hinders long-term studies. It also raises questions about circuit stereotypy across individuals.
• Ecological Validity Gaps: 3D naturalistic behaviors (e.g., full hunting, social interaction) are hard to record at high spatiotemporal resolution, raising questions about real-world relevance.
• Somewhat limited behavioral repertoire for advanced cognitive functions : At the age where typical whole brain recordings are performed (~5-6 days post fertilization), memory formation, individuality/“personality” and behaviors are limited ( [Roberts et al., 2013](https://doi.org/10.3389/fncir.2013.00126) ). |
| Experimental Tractability | • Strong & Growing Community: Active consortia, open resources (atlases, transgenic lines), imminent first vertebrate connectome release.
• Feasibility–Complexity Sweet Spot: ~100k neurons with vertebrate circuitry and small enough for near-complete connectomics and whole-brain imaging.
• Manageable Scale & Cost: Smaller facility/outlay than rodents, feasible HPC demands for ~100k neurons, and simpler housing.
• Whole-Brain Perturbation Feasibility: Structure-informed whole-brain optogenetics with full-brain imaging is realistic, potentially enabling systematic "perturbation atlases." | • No Standardized Pipeline: No universal protocol for connecting imaging, connectomics, and behavioral data from the same specimen.
• Time-Limited Transparency: Beyond ~7-14 days, reduced optical clarity and morphological changes hinder long-term/adult study (though special lines can maintain transparency).
• Molecular Tool Gaps: Rapid larval development complicates viral barcoding or extended protein-expression protocols; the genetic toolkit for barcoding is relatively underdeveloped compared to other model organisms. Cell-type specific genetic access tools still need substantial development.
• Current Proofreading Bottleneck: While the first EM connectome completion is expected within 1-2 years, manual validation limits availability.
• Perturbation Coverage: Systematic perturbation of all ~100,000 neurons remains intractable within the brief larval stage. |
### Table 7 — Gaps and Opportunities: Larval Zebrafish
| Gaps and opportunities: Larval Zebrafish | Gaps (non-exhaustive selection) | Illustrative Project Opportunities |
|---|---|---|
| Neural dynamics | • Limited Functional Data: Current imaging techniques face fundamental trade-offs between spatial coverage and temporal resolution. One-photon light-sheet microscopy can image nearly all ~100,000 neurons but only at 1-4 Hz, while faster techniques like SCAPE microscopy (up to ~25 Hz) or voltage imaging (millisecond-scale) achieve higher temporal resolution at the cost of observing less than 10% of the brain. No current method achieves both whole-brain coverage and spike-timing resolution simultaneously.
• Behavioral Recording Constraints: Most high-resolution neural recordings rely on head-fixed preparations that restrict natural behavior. While recent advances enable tail movement in virtual environments or use mirror-based tracking systems for freely moving fish, these methods compromise either spatial or temporal resolution. Current setups particularly struggle with complex three-dimensional behaviors like hunting, limiting the ecological validity of collected neural data.
• Scarce data on neuromodulation: Very limited data exist on the effect of different neuromodulators in larval zebrafish. | • Extended ZAPBench 2.0: Expand current whole-brain c alcium imaging efforts to include recording s from more individuals and simultaneous high-resolution behavioral tracking (tail kinematics, eye movements) in head-fixed preparations.
• Whole-Brain Calcium / Voltage Imaging Scale-up: Increase the imaging rates and coverage of calcium and voltage imaging, ideally in freely behaving individuals.
• 3D Unrestricted Swimming Microscopy: Develop imaging systems specifically addressing the vertical movement limitation in current setups, enabling natural hunting behaviors where fish strike from below.
• Comprehensive Perturbation Atlas: Systematic optogenetic perturbations with whole-brain activity recording. Map circuit-wide effects of activating/inhibiting defined neuron populations during specific behaviors. |
| Connectomics | • Reconstruction Bottlenecks: Although automated segmentation and synaptic detection of the Svara et al. dataset are complete, comprehensive proofreading remains ongoing. The scale of manual intervention required for accurate proofreading continues to delay the availability of a fully reconstructed connectome.
• Molecular Information Limitations: EM datasets provide primarily morphological and connectivity information, in addition to limited data to distinguish excitatory vs. inhibitory neurons, lacking crucial details about neurotransmitter identities and receptor distributions. While expansion microscopy (ExM) could enable structural mapping and molecular annotation, no comprehensive whole-brain ExM effort has been completed in zebrafish.
• Lack of neuroplasticity datasets: No substantive neuroplasticity datasets exist. | • Transgenic Barcoding Feasibility Study: Develop rapid expression strategies for molecular barcoding compatible with larval stage timing.
• Whole-Brain ExM with Molecular Profiling: Create a comprehensive expansion microscopy dataset with molecular annotations at synaptic resolution.
• 10x the connectome data, including brain and spine, to better understand different stages and interindividual differences. |
| Computational Neuroscience | • Limited Experimental Electrophysiological Data: Similar to C. elegans .
• Missing Structural Foundation: The absence of a fully proofread connectome forces whole-brain models to rely primarily on functional data or partial circuit reconstructions. Even when structural data exists, it lacks crucial molecular information about neurotransmitter types and receptor distributions.
• Immature Model Development: Existing whole-brain models are few and currently lack detailed biophysical implementation. The lack of systematic perturbation data (e.g., comprehensive optogenetic studies) further complicates model validation. | • Port OpenWorm to the Larval Zebrafish: Collaborate with OpenWorm on shared infrastructure (3D environments, databases with receptors, etc.)
• Connectome-Constrained Biophysical Simulator: Build pilot-level compartmental models using the current version of the larval zebrafish whole-brain connectome and available ephys patch-clamp recordings.
• Embodied ML Models of Zebrafish Behavior: Develop physics-based differentiable simulation environments and train biophysical models supporting auto differentiation to replicate specific behaviors (hunting, escape responses) using zebrafish behavioral data. |