---
title: "Mouse"
document_id: sobe-2025-main-ai
document_type: report_section
parent_document: ../report.md
section_id: main-section-09-mouse
section_order: 9
section_count: 17
language: en
license: CC-BY-4.0
---
# Mouse
> Selective-retrieval section 9 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-09-mouse`
- **Order:** 9 of 17
- **Words:** 7336
- **Previous:** [Drosophila](08-drosophila.md)
- **Next:** [Humans](10-humans.md)
### Anatomy & Behavior
The house mouse (Mus musculus) is one of the most widely used model organisms in neuroscience. At birth, the typical mouse weighs approximately 1 gram, and males reach around 36 grams while females reach around 27 grams ([JAX](https://www.jax.org/jax-mice-and-services/strain-data-sheet-pages/body-weight-chart-000664)). In laboratory conditions, mice typically live 2-3 years, with some individuals reaching up to 5 years under optimal conditions, though wild specimens rarely survive beyond 1-2 years due to predation and environmental pressures.
The mouse brain reaches approximately 90% of its adult size by two weeks of age ([Orr et al., 2016](https://doi.org/10.3389/fnins.2016.00504)). Neuron counts increase from approximately 57 million at 4 weeks to a peak of 69 million by 15 weeks before stabilizing at 63-70 million in adulthood ([Fu et al., 2012](https://doi.org/10.1007/s00429-012-0462-x); [Herculano-Houzel et al., 2006](https://doi.org/10.1073/pnas.0604911103)). These neurons are packed into a volume of 420-460 mm³ ([Vincent et al., 2010](https://journals.sagepub.com/doi/10.4137/MRI.S5885)) – ~5,000x larger than the brains of Drosophila and larval zebrafish (~0.04-0.08 mm³), and roughly 3,000x smaller than the human brain (1,200 cm³). The neurons are distributed across major regions, including the cerebellum (~52 million neurons), neocortex (~8.6 million neurons), and olfactory bulb (~7.2 million neurons). Outside the brain, the spinal cord contains an estimated 8 million neurons ([Fu et al., 2012](https://doi.org/10.1007/s00429-012-0462-x)). Typical firing rates in vivo are estimated to fall broadly within a range from below 0.001 Hz to 50 Hz or more, varying significantly with cell type and behavioral state. Energy budget models for the rodent cortex use an estimated average firing rate of 4 Hz ([Attwell & Laughlin, 2001](https://doi.org/10.1097/00004647-200110000-00001); [Howarth et al., 2012](https://doi.org/10.1038/jcbfm.2012.35)), a value derived from earlier in vivo studies in rats where population averages ranged from 1.5-4 Hz (and individual neurons from 0.15-16 Hz). The electrophysiological properties of neurons and synapses in the mouse brain, particularly within the cortex, are relatively well characterized. Combined with the extensive knowledge of cell types derived from transcriptomics, the mouse emerges as an attractive model system for whole-brain emulation efforts.
Video 4 - Rodent behavior (rat, as no similar mouse videos were available)
[Various complex motor and learning behaviors ](https://www.youtube.com/watch?v=t8XZ-_EgWB8&ab_channel=MouseAgility)
Even before birth, the maternal environment and hormonal factors mold behavioral tendencies. Individual differences in traits like anxiety and exploration emerge and gradually stabilize throughout development. However, major life events such as social stress or environmental challenges can still significantly influence the adult behavioral phenotype ([Brust et al., 2015](https://doi.org/10.1186/1742-9994-12-S1-S17)).
Adult mice exhibit a sophisticated behavioral repertoire encompassing complex social, cognitive, and reproductive domains. Their social organization features intricate dominance hierarchies maintained through scent marking, aggressive displays, and vocal and chemical communication. Cognitively, they demonstrate remarkable capabilities in spatial navigation, associative learning, and behavioral flexibility, readily adapting to environmental changes and remembering both positive and aversive experiences. Even among genetically identical individuals, mice show stable personality differences in traits like anxiety, exploration, and sociability - variations that persist despite standardized laboratory conditions. Their reproductive behavior involves elaborate courtship rituals, and females display comprehensive maternal care, including nest building, pup retrieval, and nursing. These core behavioral patterns remain relatively stable throughout adulthood, though aging gradually diminishes exploratory drive, learning speed, and overall activity levels ([Brust et al., 2015](https://doi.org/10.1186/1742-9994-12-S1-S17)).
### Neural Dynamics
#### Neural activity recording
In recent years, we have seen remarkable progress in our ability to record neural activity in behaving mice. Key approaches include optical techniques, such as calcium and voltage imaging, and electrophysiological recordings, notably with high-density probes like Neuropixels. Nevertheless, fundamental tradeoffs remain between the number of neurons that can be recorded simultaneously, temporal resolution, and the animal’s freedom of movement. While electrophysiology offers unparalleled temporal resolution for individual spikes of up to a few thousand neurons (as discussed later), current approaches to calcium imaging in mice have also seen tremendous advances and can be broadly divided into head-fixed and freely moving preparations, each with distinct advantages and limitations.
In awake, head-fixed preparations, mice are typically positioned on treadmills that allow some degree of movement while maintaining the stability needed for high-quality imaging. This approach has enabled increasingly comprehensive recordings of neural activity. The MICrONS consortium, for example, recently demonstrated simultaneous recording of approximately 75,000 excitatory neurons across layers 2-5 of visual cortex at 6-10 Hz, spanning multiple visual areas during 14 80-minute sessions over 6 days, close to 20h in total ([MICrONS Consortium, 2024](https://doi.org/10.1101/2021.07.28.454025)). Additionally, the Allen Institute for Brain Science has produced extensive open-access calcium imaging datasets under standardized conditions, often targeting specific cell types. Notable examples include their 'Visual Coding 2P' dataset, featuring recordings from nearly 60,000 neurons during passive sensing ([de Vries et al., 2019](https://www.nature.com/articles/s41593-019-0550-9)), and their 'Visual Behavior 2P' dataset, with data from over 50,000 neurons collected during active behavioral tasks ([Piet et al., 2024](https://doi.org/10.1016/j.neuron.2024.02.008)), provide deep insights into cortical function.
Figure 14 - Overview of the optical neural recording landscape in Mouse: Radar plots based on the optical recording literature cited in the report. We plot the following dimensions of brain recordings: spatial resolution, brain volume, temporal resolution, and (estimated) individual and cumulative recording duration. The plots split recordings from fixated (A) and freely moving experiments (B). 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).
A\) fixated
B\) moving
### Machine-readable figure record: `main-fig-14`
```yaml
id: main-fig-14
document_id: sobe-2025-main-ai
document_path: report.md
anchor: main-fig-14
canonical_url: https://brainemulation.mxschons.com/ai/report.md#main-fig-14
figure: "Mouse optical recording landscape"
assets:
- "../assets/report/main-fig-14a-publication.png"
- "../assets/report/main-fig-14b-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/image52.png"
- "images/image19.png"
canonical_pdf_pages:
- 81
```
#### Inline contextual data (not an exact publication input): mouse 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
Microns Consortium 2024 Mouse fixated Calcium 75000 1.0 80.0 480.0 1 none https://doi.org/10.1101/2021.07.28.454025
Manley 2024 Mouse fixated Calcium 1000000 1.0 60.0 120.0 1 none https://doi.org/10.1016/j.neuron.2024.02.011
Kim 2016 Mouse fixated Calcium 40000 1.0 5.0 30.0 1 none https://doi.org/10.1016/j.celrep.2016.12.004
Zong 2022 Mouse moving Calcium 1000 30.0 10.0 40.0 1 none https://doi.org/10.1016/j.cell.2022.02.017
Bai 2024 Mouse fixated Voltage 300 400.0 20.0 20.0 1 none https://www.nature.com/articles/s41592-024-02458-5
```
#### 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,',
)
```
Even more expansive capabilities have been shown using another 2P-imaging approach (light beads microscopy), which has enabled simultaneous imaging of up to 1 million neurons across the dorsal cortex while maintaining cellular resolution at 2 Hz ([Manley et al., 2024](https://doi.org/10.1016/j.neuron.2024.02.011)). In parallel, developments in surgical approaches have enhanced optical access – curved glass windows ("crystal skull") that replace the dorsal cranium – remain viable for imaging at least 11 weeks post-surgery, providing access to an estimated 800,000-1,100,000 neurons spanning over 30 neocortical areas, though the maximum sustainable recording durations possible with this approach have not been systematically characterized ([Kim et al., 2016](https://doi.org/10.1016/j.celrep.2016.12.004)). However, despite these technological advances, head-fixed preparations fundamentally limit the range of natural behaviors that can be studied. To mitigate this limitation and expand the repertoire of investigable behaviors, sophisticated virtual reality (VR) environments have been integrated with head-fixed preparations. These setups allow mice to perform complex tasks, such as spatial navigation or decision-making, while neural activity is monitored with cellular resolution using 2P microscopy ([Dombeck et al., 2010](https://www.nature.com/articles/nn.2648); [Harvey et al., 2012](https://www.nature.com/articles/nature10918)).
Advances in freely moving imaging have also been substantial, driven particularly by the development of lightweight microscopes. The MINI2P microscope weighs under 3g and uses a flexible 0.7mm fiber bundle cable, enabling two-photon imaging at 15-40 Hz from 300-600 neurons per plane across up to four imaging planes (approximately 1000 neurons total) within a 420 × 420 μm² field of view. When stitching across multiple fields of view to cover a 2.2 × 2.2 mm² area, over 10,000 neurons can be recorded, though at reduced temporal resolution ([Zong et al., 2022](https://doi.org/10.1016/j.cell.2022.02.017)). Even lighter microscopes have since been developed, such as the 0.43g TINIscope ([Xue et al., 2023](https://doi.org/10.1093/nsr/nwad294)). However, imaging systems compatible with freely moving mice face particularly harsh tradeoffs between the number of neurons recorded, temporal resolution, and signal quality. Even with optimal recording conditions, calcium indicators remain too slow to capture individual action potentials for many neurons in mice. Recent advances in voltage imaging offer promise for tracking individual spikes, with new approaches enabling simultaneous recording from over 300 spiking neurons in the mouse cortex at 400 Hz for over 20 minutes ([Bai et al., 2024](https://www.nature.com/articles/s41592-024-02458-5)).
An alternative to either calcium or voltage imaging is electrophysiology. A large-scale example of such an effort is the International Brain Laboratory ([The International Brain Laboratory, 2017](https://doi.org/10.1016/j.neuron.2017.12.013)). This initiative was able to collect a “comprehensive set of recordings from 115 mice in 11 labs performing a decision-making task with sensory, motor, and cognitive components, obtained with 547 Neuropixels probe insertions covering 267 brain areas in the left forebrain and midbrain and the right hindbrain and cerebellum”. Mice were trained to turn a wheel in response to the position of a visual grating. This generated roughly 60 GB of electrophysiology data and 1 TB of video data, of roughly 150 hours in total. The Allen Institute has also generated extensive Neuropixels datasets. For example, their 'Visual Coding Neuropixels' dataset, derived from mice passively viewing visual stimuli, comprises recordings from over 40,000 quality-controlled units cumulatively across many experiments, typically using up to six probes per mouse to target visual cortex, hippocampus, and thalamus ([de Vries et al., 2023](https://doi.org/10.7554/eLife.85550)). An even larger dataset, the 'Visual Behavior Neuropixels' project, was acquired while mice performed a change detection task, yielding over 200,000 units recorded from 81 mice using a similar multi-probe strategy that included visual cortical, thalamic, hippocampal, and midbrain structures ([Allen Institute for Brain Science, 2022](https://portal.brain-map.org/circuits-behavior/visual-behavior-neuropixels)). It is important to note that these large unit counts are aggregates from numerous experiments; individual multi-probe recordings simultaneously capture activity from hundreds to potentially a few thousand units, depending on the number of probes and the density of recorded regions.
#### Neurotransmitters and Neuromodulators
Several genetically encoded indicators now enable monitoring of key neuromodulatory systems in the mouse brain, including sensors for acetylcholine, dopamine, histamine, norepinephrine, and serotonin ([Muir et al., 2024](https://doi.org/10.1126/science.adn6671)). Additionally, sensors have been developed for select neuropeptides, including CRF, dynorphins, enkephalins, GRP, orexins, oxytocin, and somatostatin. However, these tools cover only a small portion of known neuromodulatory systems. Mass spectrometry studies have identified between 500-850 peptides in the mouse brain, with roughly half being classical neuropeptides derived from about 40-45 neuropeptide precursor genes, and half being peptides derived from intracellular proteins ([Fricker et al., 2010](https://doi.org/10.1039/C003317K); [Zhang et al., 2012](http://dx.doi.org/10.1021/pr3001699)). Developing tools to monitor this broader range of signaling molecules remains an important challenge for understanding neuromodulation in vivo.
#### Perturbation
Optogenetic techniques enable precise spatiotemporal control of neural activity. Advanced approaches like two-photon optogenetics aim for cellular-scale targeting, while three-photon excitation can offer access to deeper brain regions, typically 1-1.3 mm into brain tissue, allowing targeting of structures like layer 5 of the cortex ([Xu et al., 2024](https://doi.org/10.1016/j.cell.2024.07.036); [Lee et al., 2020](https://www.frontiersin.org/journals/neural-circuits/articles/10.3389/fncir.2020.00018/full); [Adesnik and Abdeladim, 2021](https://www.nature.com/articles/s41593-021-00902-9)). Nonetheless, the capabilities offered by optogenetics, including both one-photon and multi-photon methods, have enabled several discoveries, including neural populations and activity patterns responsible for diverse behaviors ([Piatkevich and Boyden, 2023](https://doi.org/10.1017/S0033583523000033)). These behaviors include parental care ([Kohl et al., 2018](https://doi.org/10.1038/s41586-018-0027-0)), spatial object recognition ([Kempadoo et al., 2016](https://doi.org/10.1073/pnas.1616515114)), aggression against intruders ([Lin et al., 2011](https://doi.org/10.1038/nature09736)), breathing rhythm ([Sherman et al., 2015](https://doi.org/10.1038/nn.3938)), social memory ([Oliva et al., 2020](https://doi.org/10.1038/s41586-020-2758-y)) and social-spatial association formation ([Murugan et al., 2017](https://doi.org/10.1016/j.cell.2017.11.002)), visual perception ([Lee et al., 2012](https://doi.org/10.1038/nature11312)), wakefulness ([Cho et al., 2017](https://doi.org/10.1016/j.neuron.2017.05.020)), locomotion ([Hagglund et al., 2013](https://doi.org/10.1073/pnas.1304365110)), sleep ([Kitamura et al., 2017](https://doi.org/10.1126/science.aam6808)), face gender discrimination ([Afraz et al., 2015](https://doi.org/10.1073/pnas.1423328112)), water ([Zimmerman et al., 2016](https://doi.org/10.1038/nature18950)) and food consumption ([Nectow et al., 2017](https://doi.org/10.1016/j.cell.2017.06.045)), responses to odors ([Root et al., 2014](https://doi.org/10.1038/nature13897)), movement ([Gritton et al., 2019](https://doi.org/10.1038/s41593-019-0341-3)) and aversion or preference of a place ([Kim et al., 2019](https://doi.org/10.1038/s41593-019-0342-2)), reward-seeking ([Otis et al., 2017](https://doi.org/10.1038/nature21376)), parental behavior ([Stagkourakis et al., 2020](https://doi.org/10.1016/j.cell.2020.07.007)) and encoding of places ([Zhang et al., 2013](https://doi.org/10.1126/science.1232627)). Suthard et al. demonstrated that optogenetic stimulation of hippocampal engram cells can recapitulate the cellular activity patterns seen during natural fear memory recall, revealing coordinated neuron-astrocyte dynamics that underlie both naturally and artificially induced fear states ([Suthard et al., 2024](https://doi.org/10.1016/j.celrep.2024.113850)). Importantly, one- and two-photon optogenetic perturbations in the mouse have helped uncover circuit mechanisms underlying a variety of neural computations involved in perception and action (e.g., [Reinhold et al., 2015](https://doi.org/10.1038/nn.4153); [Li et al., 2015](https://doi.org/10.1038/nature14178); [Lien and Scanziani, 2018](https://doi.org/10.1038/s41586-018-0148-5); [Carrillo-Reid et al., 2019](https://doi.org/10.1016/j.cell.2019.05.045); [Marshel et al., 2019](https://doi.org/10.1126/science.aaw5202); [Chettih and Harvey, 2019](https://doi.org/10.1038/s41586-019-0997-6); [Keller et al., 2020](https://doi.org/10.1038/s41586-020-2319-4); [Daie et al., 2021](https://doi.org/10.1038/s41593-020-00776-3); [Green et al., 2023](https://doi.org/10.1038/s41586-023-06357-1); [Vinograd et al., 2024](https://doi.org/10.1038/s41586-024-07915-x)) and even enabled comparative studies with human subjects, such as shedding light on the mechanisms of dissociative first-person experience ([Vesuna et al., 2020](https://doi.org/10.1038/s41586-020-2731-9)).
### Connectomics
Establishing the IARPA MICrONS consortium in 2016 marked an important development in mouse connectomics. The consortium's work progressed in two major phases – first imaging a focused volume in cortical layer 2/3 (250 x 140 x 90 μm), then expanding to image, segment and partially reconstruct an entire cubic millimeter spanning six cortical layers and three higher visual areas. MICrONS combined in vivo calcium imaging with electron microscopy, generating over a petabyte of imaging data ([MICrONS Consortium et al., 2025](https://www.nature.com/articles/s41586-025-08790-w)). During this period, a parallel effort by Motta et al. mapped a 90 x 90 x 60 μm volume of somatosensory cortex within just 4,000 person-hours ([Motta et al., 2019](https://www.science.org/doi/10.1126/science.aay3134)), largely comprising targeted manual work to correct the automated reconstruction (focusing on resolving algorithm-identified axon splits and mergers, and completing dendritic spine attachments).
In 2023, the NIH launched the \$150 million BRAIN Initiative Connectivity Across Scales (BRAIN CONNECTS) program, funding 11 projects over 5 years to develop tools for brain-wide connectivity mapping. One prominent project within this initiative, a \$33 million Harvard-led effort (in collaboration with Google Research, Allen Institute, MIT, Cambridge University, Princeton University, Johns Hopkins University) aiming to reconstruct and proofread 10 mm³ of the mouse hippocampus using high-throughput electron microscopy ([Januszewski, 2023](https://research.google/blog/google-research-embarks-on-effort-to-map-a-mouse-brain/)), was seemingly impacted by changes in NIH funding priorities in early 2025 ([Markowitz, 2025](https://x.com/DavidAMarkowitz/status/1925281382676209919)), though the technical work appears to be continuing independently of NIH support. However, the BRAIN CONNECTS program includes other significant synaptic connectomics efforts. For instance, a project led by the Allen Institute received approximately \$6.1 million in its first year to image up to 10 mm³ of the mouse cortico-basal ganglia-thalamo-cortical loop at synaptic resolution ([NIH, 2023](https://reporter.nih.gov/project-details/10665386)). This project uses serial section tilt TEM tomography and aims to develop a pipeline for high-throughput integrated volumetric electron microscopy for whole mouse brain connectomics, including linking to cell types via gene expression data. While focusing on just a fraction (roughly 2-3%) of the mouse brain, this volume represents a notable scaling challenge, approximately 10 times larger than the previous MICrONS dataset. The five-year project will use two 91-beam electron microscopes operating in parallel. Success would demonstrate whether current electron microscopy and automated reconstruction technologies can scale sufficiently to tackle the complete mouse connectome eventually. Notably, maintaining a similar 5-year timeline for imaging an entire mouse brain would require scaling to approximately 40-50 such microscopes working in parallel, highlighting both the technical challenges and infrastructure requirements for mapping complete mammalian brains at synaptic resolution.
Regarding expansion microscopy in the mouse brain, the availability of sophisticated genetic tools combined with the prohibitive scale of EM-based reconstruction has accelerated the development of alternative approaches. E11 Bio, one of the first FROs launched by Convergent Research, recently detailed its PRISM (Protein-barcode Reconstruction via Iterative Staining with Molecular annotations) platform, which addresses key bottlenecks in light-microscopy connectomics by providing neurons with unique molecular signatures for self-correcting reconstruction ([Park et al., 2025](https://doi.org/10.1101/2025.09.26.678648)). The platform achieves this by combinatorially expressing 18 antigenically distinct, cell-filling proteins via AAVs, which are then visualized in 5x expanded tissue through iterative immunostaining. This approach enables automated proofreading across spatial gaps of hundreds of microns and was shown to increase automatic tracing accuracy by 8-fold over conventional single-color methods. In a demonstration on a ~10 million µm³ volume of the mouse hippocampus, the technique also enabled detailed molecular mapping of synapses, revealing that large, complex synaptic structures known as 'thorny excrescences' tend to have similar sizes when they are clustered closely together on the same dendrite.
Among other ExM developments, ExA-SPIM ([Glaser et al., 2024](https://doi.org/10.7554/eLife.91979.2)) demonstrated unprecedented imaging throughput, achieving 946 megavoxels per second with an effective resolution of 250x250x750 nm³ after 4x expansion. This effective resolution is calculated by dividing the microscope's native optical resolution by the tissue's linear expansion factor, indicating the resolving power relative to the sample's original, unexpanded dimensions. However, this resolution is far from sufficient for dense connectomic reconstruction. The team successfully imaged entire 3x expanded mouse brains in just 24 hours per channel, tracking sparsely labeled subcortical projection neurons and their axonal projections across the brain. This combination of high throughput, multi-color capability, and relatively low system cost – approximately \$175,000 to \$250,000, depending on the laser configuration (A. Glaser, 2024, personal communication) – makes it particularly promising for whole-brain mapping efforts. This potential has motivated the development of new optical systems, including a custom lens that, when combined with higher expansion factors of 3-12×, will enable effective lateral resolutions of 50-150nm, potentially bringing the system not far from the resolution required for dense reconstruction. Meanwhile, Tavakoli et al with their LICONN (light-microscopy based connectomics) approach demonstrated dense reconstruction in mouse cortex, imaging a ~1 million cubic micrometer volume spanning cortical layers II/III-IV at effective resolutions of ~20nm laterally and ~50 nm axially through ~16-fold expansion, with the 0.47 teravoxel dataset acquired in just 6.5 hours at an effective voxel rate of 17 MHz ([Tavakoli et al., 2025](https://www.nature.com/articles/s41586-025-08985-1)). In parallel, Kang et al. developed multiplexed expansion revealing (multiExR), achieving visualization of more than 20 distinct proteins within the same mouse brain specimen through sequential rounds of antibody staining and imaging while achieving median registration precision of 25-39 nm ([Kang et al., 2024](https://www.nature.com/articles/s41467-024-53729-w)).
X-ray microscopy efforts also show promise. Early demonstrations using X-ray holographic nano-tomography (XNH) achieved sub-100 nm resolution across 300x200x1000 μm tissue volumes ([Kuan et al., 2020](https://doi.org/10.1038/s41593-020-0704-9)). More recently, Bosch et al. developed a correlative workflow combining in vivo calcium imaging, synchrotron X-ray tomography, and volume electron microscopy to investigate both function and structure within the same tissue ([Bosch et al., 2022](https://doi.org/10.1038/s41467-022-30199-6)). In follow-up work, Bosch et al. also demonstrated another key advance: using X-ray ptychographic tomography under cryogenic conditions with specialized radiation-resistant resins to achieve sub-40nm resolution capable of resolving individual synapses ([Bosch et al., 2023](https://doi.org/10.1101/2023.11.16.567403)), an important achievement for x-ray connectomics. While whole-brain X-ray imaging has been demonstrated at cellular resolution ([Humbel et al., 2024](https://doi.org/10.48550/arXiv.2405.13971)), achieving synaptic resolution across large volumes remains an active area of development.
The mouse hippocampus connectome is expected to generate an estimated 25 petabytes of data ([Google Research, 2023](https://research.google/blog/google-research-embarks-on-effort-to-map-a-mouse-brain/)). Imaging a whole mouse brain (approximately 500 mm³) at 10 nm isotropic resolution would theoretically generate approximately 5 x 10¹⁷ voxels (or 3.2 x 10¹⁶ voxels at 25 nm isotropic resolution). At 1 byte per voxel for a single channel, this would thus require approximately 0.5 exabytes (or 32 petabytes at 25nm isotropic resolution). The significant storage requirements of connectomes at the scale of the whole mouse brain (and beyond) have motivated the development of compression techniques capable of alleviating data management constraints: EM-compressor, for example, can decrease the storage needed to store raw EM data by as much as 128x without compromising subsequent neuron reconstruction ([Li et al., 2024](https://doi.org/10.1007/978-3-031-77786-8_16)). Meanwhile, neuron reconstruction efforts are ongoing for the MICrONS cubic millimeter, containing an estimated 120,000 neurons. Automatic synapse detection has identified over 523 million synapses within the volume, and, as of January 2025 (v1300), over 1,700 axons have been manually proofread and cleaned, establishing over 500,000 verified connections to somas within the dataset ([MICrONS Consortium, 2024](https://www.microns-explorer.org/cortical-mm3#proofreading-status)).
### Computational Modeling
#### The Blue Brain Project
The Blue Brain Project (BBP) was a pioneering effort to construct large-scale, biophysically detailed in silico models of rat cortical microcircuitry. Although BBP focused on rat rather than mouse cortex, we include it for historical context, as its biophysically detailed reconstruction methods and data-integration pipelines have substantially informed subsequent mouse modeling. Having been started before large-scale connectomic datasets were available, the BBP developed various tools to integrate disparate datasets describing cell types and connectivity in the rodent cortex. Cortical anatomy of their most significant recent model release ([Reimann et al., 2024](https://elifesciences.org/reviewed-preprints/99688)) was based on a three-dimensional cell atlas, in which cell bodies of 60 morphologically distinct cell types were placed based on experimentally reported densities. From there, detailed multi-compartment models of cells were expanded based on known shape constraints and populated with ion channels based on parameter-tuning to recreate in vitro recordings. Connectivity is inferred based on the principle of axonal-dendritic overlap: computationally generated neuronal morphologies, cloned from available reconstructions, are placed in the model volume, and connections are formed where their processes are sufficiently close, with subsequent extensive pruning to match experimentally observed synaptic densities. While the resulting synaptic density statistics are compared against experimental data for validation, this approach assumes that geometric proximity is the primary determinant of local connectivity and does not directly incorporate more recently available, detailed maps of specific circuit wiring, potentially missing more complex organizational rules. Synapses are modelled on a detailed level, including a pool of available neurotransmitter vesicles and short-term plasticity. In total, they developed a detailed model of 36 mm³ of rat somatosensory cortex (a process they term 'reconstruction', which, it is important to note, involves extensive model building with numerous assumptions, distinct from data-driven connectomic reconstructions), comprising 4.2 million neurons with 14.2 billion synapses between them. They had access to the [Blue Brain 5](https://www.cscs.ch/computers/blue-brain-5/) supercomputer providing 0.8 TFlops of computational power.
Electrical properties of single neurons were validated by optimising ion channel densities in different neuronal compartments to give rise to firing properties, action potential waveforms, and passive properties observed in vivo ([Reva et al., 2023](https://www.sciencedirect.com/science/article/pii/S2666389923002398)). Synaptic parameters were fitted similarly ([Ecker et al., 2020](https://onlinelibrary.wiley.com/doi/full/10.1002/hipo.23220)). The size of the model allowed modelling not only of local connections within a cortical column, but also mid-range connections between close brain regions ([Isbister et al., 2024](https://elifesciences.org/reviewed-preprints/99693v1)), and spontaneous activity was comparable to that observed in vivo. Long-range connections, such as sensory input, still had to be approximated. As an example of external sensory input, the movement of whiskers was modelled by directly injecting current into the soma of neurons projecting from the thalamus to the cortex, mimicking the flow of information in real brains. Notably, while the BBP model is explicitly positioned to represent a non-barrel somatosensory cortex, this validation approach relied on whisker stimulation, which primarily engages the barrel cortex. Similarly, other validation efforts utilized visual-like stimuli characteristic of the visual cortex, indicating that some key physiological validation data were drawn from cortical areas or sensory modalities different from the model's specified domain. This produced activity congruent with that observed in vivo on the millisecond scale in model parameterisations representing awake and anesthetized animals. Additionally, by artificially hyperpolarizing selected neurons in their simulation, they could reproduce results produced by optogenetically inactivating neurons in animals.
However, the project's reliance on algorithmic inference for connectivity, a necessity given the lack of comprehensive, experimentally-derived connectomes at the time ([Reimann et al., 2015](https://doi.org/10.3389/fncom.2015.00120)), remains a significant limitation. Critics such as Frégnac have critically observed that this method relies on a 'bootstrap' logic to generate 'realistic instantiations of possible connectomes,' aiming for a brain 'realistically connected in the statistical sense' rather than one based on direct, comprehensive empirical mapping ([Frégnac, 2021](https://doi.org/10.1523/ENEURO.0130-21.2021)). Furthermore, even when model outputs, such as activity patterns, resemble experimental data, the challenge of parameter degeneracy ([Marder, 2015](https://doi.org/10.1371/journal.pbio.1002147)) makes it difficult to ascertain whether the model truly captures the correct underlying biological mechanisms. Different configurations of neuronal and synaptic parameters could potentially produce similar macroscopic outputs, meaning that a match to some experimental data does not, by itself, confirm the model's biological accuracy or its generalization abilities as to predict novel neural phenomena.
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Figure 15 - Replication from Figure 1 in [Isbister et al., 2024:](https://elifesciences.org/reviewed-preprints/99693v1) Overview of the physiology and simulation workflow. 1. Anatomical model: Summary of the anatomical nbS1 model described in the companion paper. 2. Neuron physiology: Neurons were modeled as multi-compartment models with ion channel densities optimised using previously established methods and data from somatic and dendritic recordings of membrane potentials in vitro. 3. Synaptic physiology: Models of synapses were built using previously established methods and data from paired recordings in vitro. 4. Compensation for missing synapses: Excitatory synapses originating from outside nbS1 were compensated with noisy somatic conductance injection, parameterized by a novel algorithm. 5. In vivo-like activity: They calibrated an in silico activity regime compatible with in vivo spontaneous and stimulus-evoked activity. 6. In silico experimentation: Five laboratory experiments were recreated. Two were used for calibration, and three of them were extended beyond their original scope. 7. Open Source: Simulation software and a seven column subvolume of the model are available on Zenodo (see data availability statement). Data generalisations: Three data generalisation strategies were employed to obtain the required data. Left: Mouse to rat, middle: Adult to juvenile (P14) rat, right: Hindlimb (S1HL) and barrel field (S1BF) subregions to the whole nbS1. Throughout the figure, the corresponding purple icons show where these strategies were used.
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#### Billeh et al., 2020
While sharing the goal of large-scale, biophysically detailed cortical modeling with projects like the Blue Brain Project, the approach taken by the Allen Institute for Brain Science ([Billeh et al., 2020](https://www.cell.com/neuron/fulltext/S0896-6273(20)30067-2?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0896627320300672%3Fshowall%3Dtrue)) placed a distinct emphasis on the deep integration of concurrently acquired experimental data specific to the mouse visual cortex circuit they were modeling. By estimating cell densities and types from anatomical data, they modeled a 400µm-radius column of visual cortex containing ~52,000 neurons with high biological fidelity, surrounded by 179,000 point-neurons to avoid boundary artifacts. Connection parameters were inferred from various physiological datasets, and connections were optimised layer-wise to produce firing dynamics that matched in vivo Neuropixels recordings ([Siegle et al., 2021](https://doi.org/10.1038/s41586-020-03171-x)). Input was modelled via a module that mimics thalamic input to the visual cortex. This module comprised spatial-temporal filters following experimentally reported distributions, which were then connected to cortical neurons to give rise to established tunings. Connection strengths were set to match experimentally reported current strengths. This allows encoding arbitrary visual spatiotemporal stimuli (movies) via simulated thalamocortical spike trains.
The neural dynamics exhibited by the model were verified by comparing the activity of simulated neurons in response to drifting gratings with in vivo Neuropixels recordings, focusing on such response features as average firing rates, direction selectivity, and orientation selectivity for different neuron classes and cortical layers. Comparing these responses between the data and the model, including model versions with altered circuit architecture, resulted in several predictions regarding the organization of the visual cortical connectivity ([Billeh et al., 2020](https://www.cell.com/neuron/fulltext/S0896-6273(20)30067-2?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0896627320300672%3Fshowall%3Dtrue)), with some of the predictions confirmed by independent experimental studies ([Rossi et al., 2020](https://doi.org/10.1038/s41586-020-2894-4)). A later study ([Rimehaug et al., 2023](https://elifesciences.org/articles/87169)) found that model responses on the level of current source density were inconsistent with results from Neuropixels recordings, and improved it by adjusting connection weights and including feedback connections.
#### Wang et al., 2025
Wang et al. based their work on cortical activity rather than on explicit connectivity information: Using a dataset containing 900 minutes of two-photon recordings of calcium traces in 67000 visual cortex neurons in response to natural movies, as well as recordings of behavioural variables such as pupil position and movement speed, they trained a recurrently connected deep neural network to predict the activity of individual neurons [(Wang et al., 2025)](https://www.nature.com/articles/s41586-025-08829-y). The whole network consists of a perspective network, which uses ray-tracing to infer the retinal activation of the mouse based on pupil position and stimulus, the modulation network, an LSTM network that encodes behavioural variables, a recurrent foundation core that captures abstract aspects of brain processing, and a readout network, which maps activity of the core network to individual neurons. By fixing the weights of the foundation core and only retraining encoding and decoding networks, the researchers were able to combine data from 8 mice and achieve higher accuracy than with networks trained end-to-end for each mouse.
The network quality was validated by predicting the activity of neurons in relation to novel visual stimuli and calculating the normalised cross-correlation with held-out in vivo recordings. Additionally, the properties of in silico neurons were compared to those of in vivo neurons, showing that they had developed the same parametric tuning properties concerning orientation and spatial position.
Figure 16 - Replication from Figure 3a from [Wang et al. (2023)](https://www.biorxiv.org/content/10.1101/2023.03.21.533548v1.full) Predictive accuracy of foundation models. Schematic of the training and testing paradigm. Natural movie data were used to train: 1) a combined model of the foundation cohort of mice with a single foundation core, and 2) foundation models vs. individual models of new mice.
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### Gap Analysis
The mouse is the most commonly used model organism in medicine. Accordingly, there is vast experience with it as a model organism, and it is highly relevant to vast parts of the life sciences.
But unlike smaller organisms discussed so far, the scale of data acquisition is herculean at minimum. The mouse brain represents a fundamental transition point, in particular for brain-wide functional recording. With ~70 million neurons spread across its sizable brain, no current or easily foreseeable technology – barring, speculatively, neural dust – will enable simultaneous recording from all neurons at physiologically relevant timescales. Light scattering restricts cellular-resolution imaging to superficial layers (~1-1.5 mm depth), making subcortical and deep cortical circuits inaccessible ([Marblestone et al., 2013](https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2013.00137/full)). While novel approaches like tissue transparency agents ([Ou et al., 2024](https://doi.org/10.1126/science.adm6869)) show promise, whole-brain imaging at cellular resolution remains physically infeasible due to unresolved scattering and absorption in deeper brain regions. This reality forces a pivot in approach: rather than pursuing exhaustive functional characterization, successful mouse brain emulation will require learning to predict neural dynamics from structural and molecular properties. The mouse thus serves as the critical test case for whether we can bridge the structure-to-function gap. This challenge will only become more pressing as we move toward larger mammalian brains.
### Table 10 — Model Organism Overview: Mouse
| Model Organism Overview: Mouse | Pros | Cons |
|---|---|---|
| Anticipated Scientific Insights | • Mammalian brain: Mammalian brains exhibit structural and functional similarities to the human brain, with thoroughly characterized homologues.
• Structure to function: For smaller volumes, like MICrONS, complete reconstruction of structure and function for every neuron is possible, which offers wide-ranging opportunities for understanding the structure-function relationships.
• Rich social and cognitive behavior: Verify whether simulations can achieve complex social and cognitive behavior patterns.
• Critical milestone: achieving brain emulation in mice is likely the milestone that will trigger massive investments in human-scale emulation efforts.
• Various applications in biomedical research : Given the mouse’s role in biomedical research, in-silico simulations might replace some in vivo experiments. | • Not miniature humans . Although mice and humans are both mammals, their brains are still reasonably different. For example, humans rely on vision much more than mice; mice would be classified as legally blind. |
| Experimental Tractability | • Strong validation with major research initiatives: BRAINS CONNECTS is evaluating the feasibility of reconstructing a whole mouse brain. MICrONS demonstrated the collection of aligned structure and function datasets. .
• Extensive electrophysiological and morphological data exist for [many cell types in the cortex](https://portal.brain-map.org/cell-types) and their [synaptic connections](https://portal.brain-map.org/connectivity/synaptic-physiology) . | • Very Large investments: Comprehensive mouse programs will require investments in the 100M to billion-dollar range
• Centralization : The scale of the mouse connectome likely necessitates moving away from a decentralized academic model towards a central, large-scale facility, which might reduce the number of actors who can contribute. |
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### Table 11 — Gaps and Opportunities: Mouse
| Gaps and opportunities: Mouse | Gaps (non-exhaustive selection) | Illustrative Project Opportunities |
|---|---|---|
| Neural dynamics | • Harsh Tradeoffs Between Coverage, Resolution, and Noise. Current methods force extreme compromises. Head-fixed systems achieve high coverage (~1M neurons at 6-10 Hz) but restrict natural behavior. Systems compatible with freely moving behavior enable studying naturalistic behavior, but they can only record about a thousand neurons. Voltage imaging resolves spikes at 400 Hz but is limited in coverage and recording duration ( [Bai et al., 2024](https://www.nature.com/articles/s41592-024-02458-5) ). In general, increasing coverage degrades signal-to-noise ratio (SNR) due to shot noise ( [Rupprecht, 2021](https://gcamp6f.com/2021/10/04/large-scale-calcium-imaging-noise-levels/) ), with setups compatible with free behavior suffering the steepest penalties.
• Limited Functional Data for Refinement and Validation. Achieving the scale of functional data needed for comprehensive model refinement and validation remains a significant challenge, as current recording approaches capture only a fraction of the brain's neurons and attempts to increase scale face inherent trade-offs with signal quality and noise levels ( [Rupprecht, 2021](https://gcamp6f.com/2021/10/04/large-scale-calcium-imaging-noise-levels/) ). | • Integrated Barcoding-Function Atlas . Combine E11 Bio’s PRISM with in vivo calcium/voltage imaging to generate a multi-modal dataset linking neural activity patterns to post-mortem structural connectivity.
• Inverse Scattering Depth Benchmark . Test the limits of deep-brain imaging by integrating Kang et al. ’s inverse scattering algorithms ( [Kang et al., 2024](https://www.nature.com/articles/s41467-024-53729-w) ) with three-photon microscopy. Quantify maximum imaging depth in the mouse brain.
• Chronic Crystal Skull Endurance Study. Systematically quantify the maximum viable recording duration using "crystal skull" glass windows in mice. Track signal quality over 6–12 months post-implantation. |
| Connectomics | • EM-pipelines remain bottlenecked by proofreading requirements . Electron microscopy (EM), the most mature imaging approach, faces prohibitive costs due to manual proofreading, which accounts for >95% of total project costs ( [Jefferis et al., 2023](https://cms.wellcome.org/sites/default/files/2023-06/Connectomics-scaling-up-connectomics.pdf) ). Even with AI-assisted segmentation, proofreading a whole mouse connectome would require prohibitive amounts of human labor. Alternative approaches like E11 Bio’s PRISM (genetic barcoding + ExM) aim to bypass this bottleneck but remain unproven at scale.
• Molecularly Annotated Connectomes . EM provides only structural ("naked") connectomes, lacking synaptic-level molecular information. While ExM protocols now resolve ~20 proteins in expanded tissue ( [Tian et al., 2024](https://doi.org/10.1038/s41467-024-55305-8) ), scaling this to whole-brain volumes requires orders-of-magnitude improvements in staining throughput, antibody compatibility, and automated analysis.
• Inter-Individual Variability Unquantified. Synaptic wiring varies across mice due to experience-dependent plasticity. Current efforts focus on single specimens, but modeling learning/memory requires mapping multiple connectomes – a cost-prohibitive task even for small volumes ( [Abbott et al., 2020](https://doi.org/10.1016/j.cell.2020.08.010) ).
• Exascale Data Demands. A whole-brain EM dataset (~1 exabyte) strains storage and analysis pipelines. While tools like EM-compressor reduce raw data needs by 128× ( [Li et al., 2024](https://doi.org/10.1007/978-3-031-77786-8_16) ), they do not address the computational challenges of querying or analyzing petascale connectomes.
• Alternatives to EM remain underdeveloped . X-ray ptychography has demonstrated synaptic resolution ( [Bosch et al., 2023](https://doi.org/10.1101/2023.11.16.567403) ) but currently lacks throughput for whole-brain imaging. ExM has demonstrated molecular profiling but struggles with isotropic expansion and tissue distortion at scale. Correlative workflows (e.g., XRM-to-EM) remain experimental and labor-intensive ( [Jefferis et al., 2023](https://cms.wellcome.org/sites/default/files/2023-06/Connectomics-scaling-up-connectomics.pdf) ). | • Synaptic Receptor Necessity Study : Perform paired in vivo electrophysiology and post-mortem expansion microscopy (ExM) to map synaptic receptor distributions (AMPA, NMDA, GABA, etc.) in the same neurons. Determine the minimal set of molecular markers required to predict synaptic properties. Publish open datasets linking receptor density, synapse size, and functional measurements.
• AI Proofreading Scaling Laws : Quantify how automated proofreading accuracy (c.f, RoboEM) scales with training data volume and model size. Identify computationally optimal frontiers.
• Ultra-Expansion Protocol Development : Adapt re-PKA protocols to achieve 40–50× isotropic expansion in adult mouse brain tissue. Solve distortion challenges. |
| Computational Neuroscience | • Models remain circuit- or region-specific . Scaling simulations to the whole mouse brain poses substantial computational challenges. Further, the field is bottlenecked by the absence of a whole-brain connectome able to constrain model architecture and parameters ( [Igarashi, 2024](https://doi.org/10.1016/j.neures.2024.11.005) ).
• Structure-to-Function Translation Challenge . The mouse represents the first organism where inferring function from structure becomes essential for brain simulation. Even with a complete connectome, translating structural connectivity into functional circuit dynamics poses fundamental challenges that will require the development of novel approaches ( [Abbott et al., 2020](https://doi.org/10.1016/j.cell.2020.08.010) ).
• Lack of Standardized Benchmarks. While theoretical evaluation frameworks like the embodied Turing test have been proposed ( [Zador et al., 2023](https://www.nature.com/articles/s41467-023-37180-x) ), and initial practical benchmarks for specific sensory systems are emerging ( [Turishscheva et al., 2023](https://doi.org/10.48550/arXiv.2305.19654) ), comprehensive practical implementation remains challenging. Even if whole-brain mouse simulations were achieved, comparing results across different modeling approaches would remain difficult without clear quantitative benchmarks, creating a barrier for systematic progress. | • Virtual Reality Benchmarking Suite : Develop standardized VR environments to test mouse neuromechanical models (e.g., DeepMind’s biomechanical simulators) in naturalistic tasks like foraging, social interaction, and predator evasion.
• Whole-Brain Simulation Performance Comparison : Systematically analyze the computational costs of simulating mouse-scale networks (70M neurons) across frameworks (NEST, NEURON, ARBOR, Jaxley, BrainPy, NeuronGPU) at varying biophysical resolutions (LIF vs. HH models, different models of synaptic transmission, etc.).
• Neuromechanical Integration Standards : Develop open APIs and data formats to unify neural simulators with biomechanical engines (MuJoCo, PyBullet). Enable bidirectional sensory-motor integration for embodied tasks like locomotion. |