--- title: State of Brain Emulation 2025 At a Glance — AI Edition document_id: sobe-2025-glance-ai language: en license: CC-BY-4.0 document_type: summary edition_version: "1.0" artifact_url: https://brainemulation.mxschons.com/ai/at-a-glance.md canonical_pdf: ../assets/State-of-Brain-Emulation-2025-at-a-glance.pdf publication_authority: ../assets/State-of-Brain-Emulation-2025-at-a-glance.pdf figure_manifest: manifest.json --- # State of Brain Emulation 2025 At a Glance — AI Edition > This is a hand-authored, condensed AI adaptation of the final three-page PDF, not a verbatim transcription. It removes reference mockups and editor comments, normalizes stale marketing copy, and places publication figures beside available data. The PDF remains authoritative for wording and layout. ## Document metadata - **Type:** Summary, AI-readable adaptation - **Edition:** 1.0 - **Publication authority:** [Published three-page At a Glance PDF](../assets/State-of-Brain-Emulation-2025-at-a-glance.pdf) - **License:** CC BY 4.0 - **Relationship to source:** Condensed adaptation; not a verbatim transcription Brain emulation models are computer programs that digitally replicate brains in physical detail: their wiring, activity, how connections change over time, and how behavior emerges. Such models could offer a digital way to study neurological disease, cognition, and biological solutions to hard computational problems. Modern AI is enormously useful but architecturally unlike a biological brain; brain emulation aims for biological resemblance as well as useful behavior. After more than a year of expert interviews, literature review, and dataset collection, the report finds substantial progress in proof-of-concept brain models. Its central finding is that the main barrier is not hardware or algorithms, but more and higher-quality experimental data. Data acquisition has improved roughly fivefold per decade since the 1980s, yet no organism's entire brain has been recorded at single-cell resolution. ## 1. Simulation capabilities ![Simulation capabilities](assets/glance/glance-fig-01-publication.png) ### Machine-readable figure record: `glance-fig-01` ```yaml id: glance-fig-01 document_id: sobe-2025-glance-ai document_path: at-a-glance.md anchor: glance-fig-01 canonical_url: https://brainemulation.mxschons.com/ai/at-a-glance.md#glance-fig-01 figure: "Simulation capabilities" assets: - "assets/glance/glance-fig-01-publication.png" data_status: publication_raster_with_contextual_current_sources data_sources: - "data/current-repository/simulations/computational-models.tsv" generator: code/run_all_figures.py generator_function: generate_sim_heatmap_compact publication_export_members: - "published PDF page 1" canonical_pdf_pages: - 1 publication_panel_labels: - "Izhikevich (2008)" - "Sarma (2016)" - "Huang (2019)" - "Kim (2019)" - "Billeh (2020)" - "Yamazaki (2021)" - "Higuchi (2022)" - "Wang (2023)" - "Cowley (2024)" - "Lappalainen (2024)" - "Liu (2024)" - "Lu (2024)" - "Shiu (2024)" - "Simeon (2024)" - "Vishwanathan (2024)" - "Zhao (2025)" - "Immer (2025)" note: "The exact final-PDF panel is preserved. Its caption says 12 landmark simulations, but the panel visibly contains 17 labeled columns. The current repository snapshot has 24 rows and a changed roster, including no Immer (2025) row; it is context rather than the panel's exact input. The 17 visible labels are transcribed separately." ``` #### Inline contextual data (not an exact publication input): current model-capability matrix ```tsv First Author Year Organism Connectivity accuracy Percentage of neurons Neurontypes Plasticity Functional Accuracy Neuromodulation Temporal resolution Behavior Personality-defining Characteristics Learning Link Gertler et al. 2008 Mouse 0 0 2 0 3 1 2 0 0 0 https://doi.org/10.1523/JNEUROSCI.2660-08.2008 Anonymous 2024 Zebrafish 0 2 0 0 1 0 1 0 0 0 https://openreview.net/forum?id=oCHsDpyawq Wang 2023 Mouse 1 1 0 0 1 0 1 0 0 0 https://www.biorxiv.org/content/10.1101/2023.03.21.533548v1 Huang 2019 Drosophila 2 1 2 1 2 0 2 0 0 0 https://doi.org/10.3389/fninf.2018.00099 Higuchi 2022 Drosophila 2 1 2 2 3 1 2 1 0 1 https://doi.org/10.1101/2022.11.01.512969 Verhulst et al. 2018 Human 1 1 1 1 1 0 2 0 0 1 https://www.sciencedirect.com/science/article/pii/S0378595517303477?via%3Dihub Yamazaki et al. 2021 Human 1 2 2 0 2 0 2 0 0 0 https://doi.org/10.1016/j.neuroscience.2021.01.014 Rodarie 2022 Mouse 2 1 2 1 2 0 2 1 0 0 https://infoscience.epfl.ch/record/297317 Billeh 2020 Mouse 2 1 2 0 3 0 2 1 0 0 https://www.cell.com/neuron/fulltext/S0896-6273(20)30067-2?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0896627320300672%3Fshowall%3Dtrue Liu 2024 Zebrafish 2 1 2 0 1 0 2 2 0 0 https://doi.org/10.1101/2024.12.19.629427 Lu 2024 Human 1 2 1 0 2 0 2 1 0 0 https://doi.org/10.1038/s43588-024-00731-3 Cowley 2024 Drosophila 2 0 2 0 1 0 1 2 0 0 https://www.nature.com/articles/s41586-024-07451-8 Gambosi et al. 2024 Mouse 2 1 2 2 2 1 2 2 0 1 https://www.worldscientific.com/doi/10.1142/S012906572450045X Kim et al. 2019 C. elegans 3 2 2 0 1 0 2 1 0 0 https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2019.00008/full Simeon et al. 2024 C. elegans 3 3 0 0 1 0 1 0 0 0 https://www.biorxiv.org/content/biorxiv/early/2024/03/06/2024.02.13.580186.full.pdf Sarma 2018 C. elegans 3 3 3 0 3 1 2 2 0 0 https://pubmed.ncbi.nlm.nih.gov/30201845/ Lappalainen et al. 2024 Drosophila 3 1 2 0 1 0 1 1 0 0 https://www.nature.com/articles/s41586-024-07939-3 Shiu et al. 2024 Drosophila 3 2 1 0 2 0 2 1 0 0 https://www.nature.com/articles/s41586-024-07763-9 Zhao 2024 C. elegans 3 1 3 0 3 0 2 2 0 0 https://www.nature.com/articles/s43588-024-00738-w Pronold 2024 Human 1 1 1 0 2 0 2 0 0 0 https://academic.oup.com/cercor/article/34/10/bhae409/7826014 Schepper et al. 2022 Mouse 2 0 2 2 3 0 2 2 0 0 https://www.nature.com/articles/s42003-022-04213-y Jiang 2024 Mouse 2 0 2 1 2 0 2 1 0 0 https://academic.oup.com/cercor/article/34/9/bhae378/7779257 Vishwanathan 2024 Zebrafish 3 1 2 0 1 0 1 2 0 0 https://www.nature.com/articles/s41593-024-01784-3 Izhikevich and Edelman 2008 Human 1 1 1 0 0 0 0 https://doi.org/10.1073/pnas.0712231105 ``` #### Figure-generation source map (valid Python; not standalone plotting code) ```python GENERATOR_PATH = "code/run_all_figures.py" SOURCE_FUNCTION = 'generate_sim_heatmap_compact' SELECTED_SOURCE_LINES = ( 'def generate_sim_heatmap_compact():', 'Compact version of simulation capabilities heatmap.', '# Build heatmap data: rows=capabilities, columns=simulations (TRANSPOSED)', 'heatmap_data = neuro_sim_df[data_columns].T # Transpose!', 'heatmap_data.columns = col_labels', 'heatmap_filled = heatmap_data.fillna(-1)', 'heatmap = sns.heatmap(', 'heatmap_filled,', '# Create horizontal legend between title and heatmap', "save_figure(fig, 'neural-simulation-capabilities-heatmap-compact')", ) ``` A comparison of simulation fidelity across ten biological dimensions for landmark neural simulations. Connectivity modeling is stronger than behavioral validation and several other biological dimensions. ## 2. Connectomics cost per neuron ![Connectomics cost per neuron](assets/glance/glance-fig-02.png) ### Machine-readable figure record: `glance-fig-02` ```yaml id: glance-fig-02 document_id: sobe-2025-glance-ai document_path: at-a-glance.md anchor: glance-fig-02 canonical_url: https://brainemulation.mxschons.com/ai/at-a-glance.md#glance-fig-02 figure: "Connectomics cost per neuron" assets: - "assets/glance/glance-fig-02.png" data_status: publication_raster_with_contextual_current_sources data_sources: - "data/current-repository/costs/neuron-reconstruction-estimates.tsv" generator: code/run_all_figures.py generator_function: generate_cost_per_neuron_compact publication_export_members: - "images/image5.png" ``` #### Inline contextual data (not an exact publication input): cost estimates ```tsv Type Organism Year Neurons Approximate inflation adjusted order of magnitude total costs for scanning and tracing ($) Reconstructed Neurons / 1 Million $ Cost / Neuron Reference Estimate C. elegans (White et al 1986) 1986 302 $5,000,000 60 $16,556 Estimate: 4 FTEs at $20k / year for 10 years with 50% institutional overhead and $250k in EM costs = $1.45M Budget Fruitfly Zheng et al, 2018 none none (Murthy, Seung, et al., 2024) 2018 140000 $30,000,000 4,667 $214 Estimate based on conversations with experts and authors. Budget Zebrafish (Svara et al., 2022) 2021 100000 $10,000,000 10,000 $100 Conversation with authors Estimate Mouse, BRAIN CONNECTS 10 mm^3 none none (NIH, 2024) 2024 1500000 $43,000,000 45,455 $29 BRAIN Connects Initiative funding Estimate Mouse, Wellcome Trust Report Estimate (10nm isotropic) 2023 70000000 2,121,212 $306.95 from here Estimate Mouse, 15nm isotropic with current proofreading (EM) 2025 70000000 2,121,212 $92 from here Illustration Mouse, 1000x less proofreading: EM 10nm isotropic 2030 70000000 2,121,212 $7.66 from here Illustration Mouse, 1000x less proofreading: EM 15nm isotropic 2030 70000000 2,121,212 $3.08 from here Illustration Mouse, 1000x less proofreading: ExM 15nm isotropic 2030 70000000 2,121,212 $2.16 from here ``` #### Figure-generation source map (valid Python; not standalone plotting code) ```python GENERATOR_PATH = "code/run_all_figures.py" SOURCE_FUNCTION = 'generate_cost_per_neuron_compact' SELECTED_SOURCE_LINES = ( "df['CostPerNeuron'] = df['Cost / Neuron'].replace('[\\\\$,]', '', regex=True).astype(float)", "row['Year'], row['CostPerNeuron'],", ) ``` Connectomics costs declined from roughly $16,000 per reconstructed neuron in 1986 toward projections near $1 per neuron by 2030. Whole mouse and human connectomes nevertheless imply budget thresholds on the order of $1 billion and $100 billion. ## 3. Neural recording information rate ![Neural recording information rate](assets/glance/glance-fig-03.png) ### Machine-readable figure record: `glance-fig-03` ```yaml id: glance-fig-03 document_id: sobe-2025-glance-ai document_path: at-a-glance.md anchor: glance-fig-03 canonical_url: https://brainemulation.mxschons.com/ai/at-a-glance.md#glance-fig-03 figure: "Neural recording information rate" assets: - "assets/glance/glance-fig-03.png" data_status: publication_raster_with_contextual_current_sources data_sources: - "data/current-repository/recordings/neural-information-rate.tsv" generator: code/run_all_figures.py generator_function: generate_neuro_recordings_compact publication_export_members: - "images/image1.png" ``` #### Inline contextual data (not an exact publication input): plotted recording rows ```tsv Year Month Authors Method normalized_species effective_temporal_resolution_hz calculated_information_rate doi 2021.0 2.0 Demas et al. Imaging 2.0 2000000.0 https://doi.org/10.1101/2021.02.21.432164 2014.0 1.0 Maccione et al. Ephys 200.0 640000.0 https://doi.org/10.1113/jphysiol.2013.262840 2019.0 4.0 Stringer et al. Ephys 200.0 533600.0 https://doi.org/10.1101/306019 2017.0 3.0 Hilgen et al. Ephys 200.0 446800.0 https://doi.org/10.1016/j.celrep.2017.02.038 2019.0 11.0 Steinmetz et al. Ephys 200.0 353800.0 https://doi.org/10.1038/s41586-019-1787-x 2017.0 7.0 Pachitariu et al. Imaging 30.0 300000.0 https://doi.org/10.1101/061507 2019.0 10.0 Bartolo et al. Ephys 200.0 205200.0 https://doi.org/10.1371/journal.pcbi.1007514 2020.0 2.0 Bartolo et al. Ephys 200.0 165600.0 https://doi.org/10.1523/JNEUROSCI.2072-19.2019 2017.0 9.0 Mitz et al. Ephys 200.0 161400.0 https://doi.org/10.1016/j.jneumeth.2017.01.016 2018.0 11.0 Chen et al. Imaging 2.0 160000.0 https://doi.org/10.1016/j.neuron.2018.09.042 2020.0 7.0 Sahasrabuddhe et al. Ephys 200.0 158200.0 https://doi.org/10.1101/2020.07.17.209403 2009.0 10.0 Stafford et al. Ephys 200.0 148800.0 https://doi.org/10.1016/j.neuron.2009.09.021 2017.0 11.0 Jun et al. Ephys 200.0 148200.0 https://doi.org/10.1038/nature24636 2017.0 9.0 Cong et al. Imaging 1.6 128000.0 https://doi.org/10.7554/eLife.28158 2014.0 4.0 Schwarz et al. Ephys 200.0 113000.0 https://doi.org/10.1038/nmeth.2936 2013.0 11.0 Ifft et al. Ephys 200.0 99400.0 https://doi.org/10.1126/scitranslmed.3006159 2015.0 7.0 Shobe et al. Ephys 200.0 83600.0 https://doi.org/10.1152/jn.00464.2015 2018.0 4.0 Chung et al. Ephys 200.0 75000.0 https://doi.org/10.1101/242693 2009.0 3.0 Fitzsimmons et al. Ephys 200.0 66800.0 https://doi.org/10.3389/neuro.07.003.2009 2013.0 3.0 Ahrens et al. Imaging 0.8 64000.0 https://doi.org/10.1038/nmeth.2434 2014.0 8.0 Ito et al. Ephys 200.0 63000.0 https://doi.org/10.1371/journal.pone.0105324 2006.0 7.0 Lin et al. Ephys 200.0 52000.0 https://doi.org/10.1016/j.jneumeth.2005.12.032 2012.0 3.0 Bansal et al. Ephys 200.0 51400.0 https://doi.org/10.1152/jn.00781.2011 2003.0 7.0 Nicolelis et al. Ephys 200.0 49400.0 https://doi.org/10.1073/pnas.1934665100 2012.0 10.0 Marre et al. Ephys 200.0 45600.0 https://doi.org/10.1523/JNEUROSCI.0723-12.2012 2012.0 6.0 Churchland et al. Ephys 200.0 43600.0 https://doi.org/10.1038/nature11129 2011.0 11.0 O'Doherty et al. Ephys 200.0 40000.0 https://doi.org/10.1038/nature10489 2006.0 5.0 Kim et al. Ephys 200.0 38400.0 https://doi.org/10.1088/1741-2560/3/2/009 2004.0 6.0 Sanchez et al. Ephys 200.0 37000.0 https://doi.org/10.1109/TBME.2004.827061 2007.0 7.0 Zacksenhouse et al. Ephys 200.0 36600.0 https://doi.org/10.1371/journal.pone.0000619 2008.0 11.0 Kim et al. Ephys 200.0 35800.0 https://doi.org/10.1088/1741-2560/5/4/010 2005.0 5.0 Lebedev et al. Ephys 200.0 35400.0 https://doi.org/10.1523/JNEUROSCI.4088-04.2005 2019.0 4.0 Stringer et al. Imaging 3.0 33786.0 https://doi.org/10.1126/science.aav7893 2009.0 8.0 Dickey et al. Ephys 200.0 32200.0 https://doi.org/10.1152/jn.90920.2008 2011.0 3.0 Truccolo et al. Ephys 200.0 29800.0 https://doi.org/10.1038/nn.2782 1993.0 8.0 Wilson and McNaughton Ephys 200.0 29600.0 https://doi.org/10.1126/science.8351520 2005.0 7.0 Santucci et al. Ephys 200.0 29400.0 https://doi.org/10.1111/j.1460-9568.2005.04320.x 2004.0 3.0 Hatsopoulos et al. Ephys 200.0 28600.0 https://doi.org/10.1152/jn.01245.2003 2007.0 7.0 Hamed et al. Ephys 200.0 28000.0 https://doi.org/10.1152/jn.00760.2006 1998.0 11.0 Nicolelis et al. Ephys 200.0 27000.0 https://doi.org/10.1038/2855 2020.0 7.0 Ota et al. Imaging 1.56 24960.0 https://doi.org/10.1101/2020.07.14.201699 1996.0 2.0 Skaggs et al. Ephys 200.0 23400.0 https://doi.org/10.1002/(SICI)1098-1063(1996)6:2<149::AID-HIPO6>3.0.CO;2-K 2005.0 5.0 Blanche et al. Ephys 200.0 20000.0 https://doi.org/10.1152/jn.01023.2004 2000.0 11.0 Wessberg et al. Ephys 200.0 20000.0 https://doi.org/10.1038/35042582 1997.0 4.0 Nicolelis et al. Ephys 200.0 20000.0 https://doi.org/10.1016/S0896-6273(00)80295-0 2003.0 8.0 Csicsvari et al. Ephys 200.0 19200.0 https://doi.org/10.1152/jn.00116.2003 2004.0 10.0 Stein et al. Ephys 200.0 18000.0 https://doi.org/10.1113/jphysiol.2004.068668 2003.0 10.0 Carmena et al. Ephys 200.0 18000.0 https://doi.org/10.1371/journal.pbio.0000042 1991.0 5.0 Meister et al. Ephys 200.0 16400.0 https://doi.org/10.1126/science.2035024 2017.0 2.0 Song et al. Imaging 30.0 15330.0 https://doi.org/10.1038/nmeth.4226 2016.0 6.0 Sofroniew et al. Imaging 5.0 15000.0 https://doi.org/10.7554/eLife.14472 1994.0 7.0 Wilson and McNaughton Ephys 200.0 14800.0 https://doi.org/10.1126/science.8036517 2003.0 7.0 Harris et al. Ephys 200.0 13600.0 https://doi.org/10.1038/nature01834 1999.0 7.0 Normann et al. Ephys 200.0 13600.0 https://doi.org/10.1016/S0042-6989(99)00040-1 2002.0 6.0 Taylor et al. Ephys 200.0 12800.0 https://doi.org/10.1126/science.1070291 1999.0 12.0 Williams et al. Ephys 200.0 12400.0 https://doi.org/10.1016/S1385-299X(99)00034-3 1996.0 3.0 Skaggs et al. Ephys 200.0 11400.0 https://doi.org/10.1126/science.271.5257.1870 2000.0 1.0 Porada et al. Ephys 200.0 10400.0 https://doi.org/10.1016/S0165-0270(99)00139-9 1996.0 7.0 Nordhausen et al. Ephys 200.0 10200.0 https://doi.org/10.1016/0006-8993(96)00321-6 1995.0 6.0 Nicolelis et al. Ephys 200.0 9600.0 https://doi.org/10.1126/science.7761855 1999.0 7.0 Chapin et al. Ephys 200.0 9200.0 https://doi.org/10.1038/10223 2016.0 10.0 Prevedel et al. Imaging 3.0 9000.0 https://doi.org/10.1038/nmeth.4040 1998.0 9.0 Brown et al. Ephys 200.0 6800.0 https://doi.org/10.1523/JNEUROSCI.18-18-07411.1998 1997.0 4.0 Chang et al. Ephys 200.0 6400.0 https://doi.org/10.1016/S0006-8993(97)00012-7 1993.0 2.0 Shaw et al. Ephys 200.0 6000.0 https://doi.org/10.1080/01616412.1993.11740106 1996.0 12.0 McHugh et al. Ephys 200.0 5800.0 https://doi.org/10.1016/S0092-8674(00)81828-0 1988.0 8.0 Aiple and Kruger Ephys 200.0 5800.0 https://doi.org/10.1007/BF00248509 1996.0 1.0 Gothard et al. Ephys 200.0 4600.0 https://doi.org/10.1523/JNEUROSCI.16-02-00823.1996 1993.0 3.0 Nicolelis et al. Ephys 200.0 4600.0 https://doi.org/10.1073/pnas.90.6.2212 1988.0 7.0 Eckhorn et al. Ephys 200.0 3800.0 https://doi.org/10.1007/BF00202899 1985.0 7.0 Kuperstein and Eichenbaum Ephys 200.0 3800.0 https://doi.org/10.1016/0306-4522(85)90072-7 1981.0 1.0 Kruger and Bach Ephys 200.0 3600.0 https://doi.org/10.1007/BF00236609 1995.0 2.0 Vaadia et al. Ephys 200.0 3200.0 https://doi.org/10.1038/373515a0 1988.0 5.0 Espinosa and Gerstein Ephys 200.0 3000.0 https://doi.org/10.1016/0006-8993(88)91542-9 1989.0 6.0 Buzsaki et al. Ephys 200.0 2800.0 https://doi.org/10.1016/0165-0270(89)90038-1 1986.0 1.0 Kuperstein et al. Ephys 200.0 2800.0 https://doi.org/10.1007/BF00239532 1976.0 9.0 Schmidt et al. Ephys 200.0 2400.0 https://doi.org/10.1016/0014-4886(76)90220-X 1990.0 11.0 Ahissar and Vaadia Ephys 200.0 2200.0 https://doi.org/10.1073/pnas.87.22.8935 1991.0 1.0 Mountcastle et al. Ephys 200.0 2000.0 https://doi.org/10.1016/0165-0270(91)90140-U 1984.0 1.0 Kubie Ephys 200.0 2000.0 https://doi.org/10.1016/0031-9384(84)90080-5 1989.0 4.0 Lindsey et al. Ephys 200.0 1800.0 https://doi.org/10.1016/0006-8993(89)90183-2 1988.0 6.0 Mioche and Singer Ephys 200.0 1600.0 https://doi.org/10.1016/0165-0270(88)90131-8 1988.0 9.0 Drake et al. Ephys 200.0 1600.0 https://doi.org/10.1109/10.7273 1987.0 4.0 Lindsey et al. Ephys 200.0 1600.0 https://doi.org/10.1152/jn.1987.57.4.1101 1970.0 11.0 Humphrey, Schmidt, and Thompson Ephys 200.0 1600.0 https://doi.org/10.1126/science.170.3959.758 1985.0 4.0 Legendy and Salcman Ephys 200.0 1400.0 https://doi.org/10.1152/jn.1985.53.4.926 1982.0 6.0 Reitboeck and Werner Ephys 200.0 1400.0 https://doi.org/10.1007/BF01955338 1978.0 7.0 Durelli et al. Ephys 200.0 1400.0 https://doi.org/10.1016/0014-4886(78)90270-4 1983.0 8.0 Frostig et al. Ephys 200.0 1200.0 https://doi.org/10.1016/0006-8993(83)90567-X 1982.0 5.0 Kruger Ephys 200.0 1200.0 https://doi.org/10.1016/0165-0270(82)90035-8 1977.0 5.0 Abeles and Goldstein Ephys 200.0 1200.0 https://doi.org/10.1109/PROC.1977.10559 1983.0 6.0 Michalski et al. Ephys 200.0 1000.0 https://doi.org/10.1007/BF00236807 1982.0 12.0 Wheeler and Heetderks Ephys 200.0 1000.0 https://doi.org/10.1109/TBME.1982.324870 1981.0 3.0 Kuperstein and Whittington Ephys 200.0 1000.0 https://doi.org/10.1109/TBME.1981.324702 1975.0 7.0 Voronin et al. Ephys 200.0 1000.0 https://doi.org/10.1016/0006-8993(75)90324-8 1970.0 12.0 Humphrey Ephys 200.0 1000.0 https://doi.org/10.1016/0013-4694(70)90105-7 1976.0 9.0 Schmidt et al. Ephys 200.0 800.0 https://doi.org/10.1016/0014-4886(76)90217-X 1974.0 1.0 Dickson and Gerstein Ephys 200.0 600.0 https://doi.org/10.1152/jn.1974.37.6.1239 1972.0 1.0 Gerstein and Perkel Ephys 200.0 600.0 https://doi.org/10.1016/S0006-3495(72)86097-1 1970.0 9.0 Kristan and Gerstein Ephys 200.0 600.0 https://doi.org/10.1126/science.169.3952.1336 1964.0 3.0 Gerstein and Clark Ephys 200.0 600.0 https://doi.org/10.1126/science.143.3612.1325 1962.0 1.0 Hubel and Wiesel Ephys 200.0 600.0 https://doi.org/10.1113/jphysiol.1962.sp006837 1959.0 3.0 Amassian et al. Ephys 200.0 600.0 https://doi.org/10.1111/j.2164-0947.1959.tb01676.x 1969.0 3.0 O'Keefe and Bouma Ephys 200.0 400.0 https://doi.org/10.1016/0014-4886(69)90086-7 1967.0 9.0 Rodieck Ephys 200.0 400.0 https://doi.org/10.1152/jn.1967.30.5.1043 1965.0 2.0 Oikawa et al. Ephys 200.0 400.0 https://www.ncbi.nlm.nih.gov/pubmed/14287709 1965.0 3.0 Evarts Ephys 200.0 400.0 https://doi.org/10.1152/jn.1965.28.2.216 1964.0 10.0 Braitenberg et al. Ephys 200.0 400.0 https://doi.org/10.1007/BF00306415 1963.0 8.0 Griffith and Horn Ephys 200.0 400.0 https://doi.org/10.1038/199876a0 1961.0 1.0 Amassian et al. Ephys 200.0 400.0 https://doi.org/10.1111/j.1749-6632.1961.tb20184.x 1957.0 8.0 Baumgarten and Schaefer Ephys 200.0 400.0 https://doi.org/10.1007/BF00595651 ``` #### Figure-generation source map (valid Python; not standalone plotting code) ```python GENERATOR_PATH = "code/run_all_figures.py" SOURCE_FUNCTION = 'generate_neuro_recordings_compact' SELECTED_SOURCE_LINES = ( "info_df = info_df[info_df['calculated_information_rate'].notna()]", "info_df = info_df[info_df['calculated_information_rate'] > 0]", "min_year = info_df['Year'].min()", "info_df_display, x='Year', y='calculated_information_rate', hue='Method',", ) ``` Information rates improved dramatically from 1960–2020, but whole-brain coverage is only approaching the scale of small organisms such as *C. elegans* and fruit flies. ## 4. Scale across organisms ![Neuron counts across organisms](assets/glance/glance-fig-04-publication.png) ### Machine-readable figure record: `glance-fig-04` ```yaml id: glance-fig-04 document_id: sobe-2025-glance-ai document_path: at-a-glance.md anchor: glance-fig-04 canonical_url: https://brainemulation.mxschons.com/ai/at-a-glance.md#glance-fig-04 figure: "Neuron counts across organisms" assets: - "assets/glance/glance-fig-04-publication.png" data_status: publication_raster_with_contextual_current_sources data_sources: - "data/current-repository/organisms/organisms.tsv" publication_export_members: - "images/image6.png" note: "The publication illustration is preserved; the five depicted organism rows from the current repository are contextual." ``` #### Inline contextual data (not an exact publication input): organism counts ```tsv id name neurons volume_mm3 synapses source c_elegans C. elegans 302 0.001 7500 WormAtlas drosophila Drosophila (fruit fly) 135000 0.5 50000000 FlyWire zebrafish_larva Zebrafish larva 100000 0.1 10000000 Literature mouse Mouse 70000000 500 700000000000 Literature human Human 86000000000 1200000 1.5e+14 Literature ``` The challenge spans approximately 300 neurons in *C. elegans* to about 86 billion in humans. A mouse brain has roughly 500 times more neurons and 10,000 times the volume of a fruit-fly brain; the human brain has roughly one million times more neurons and 30 million times the volume. Capturing many physical dimensions simultaneously creates trade-offs: faster recording reduces field of view; greater coverage reduces resolution. For organisms below one million neurons—fruit flies, small fish, bees, or mosquitoes—capturing all necessary dimensions is increasingly plausible. A sub-million-neuron emulation effort could cost in the low hundreds of millions of dollars and answer which biological details and data quality actually improve emulation fidelity. At mammalian scale, physical limits may require inference from partial recordings, while ethical constraints become more important. The dedicated brain-emulation community remains small enough to fit in one workshop room, so new researchers and funders can have outsized impact. ## Project opportunities by funding scale | Funding | Fieldbuilding | Data collection | Technology R&D | Infrastructure | Finance and contests | |---|---|---|---|---|---| | ~$100K+ | Roadmaps, workshops, online lectures | Organism-specific public-data catalog; gap analysis | AI literature mining; small pilots; metrics and benchmarks | Compute credits; reporting checklist; OpenWorm IT | Prizes; conference tracks; economic-impact analysis | | ~$1M+ | Coordinating organization; trend observatory; lobbying; technical roadmap | Multimodal collection; electrophysiology gap filling; molecular annotation | Miniaturized microscopy; viral barcoding; voltage imaging; AI proofreading | Port OpenWorm to new organisms | Continuous public competitions; larger prizes; seed funding; PhD grants | | ~$10M+ | Focused research organizations; university institute | 10 mm³ mouse reconstruction; integrated structure/function/behavior data; interindividual insect datasets; end-to-end *C. elegans* | Industrial prototypes; X-ray tomography; plasticity/identity/glia methods; scaling laws | Synchrotron beamline; integrated compute/data platform | X-Prize-style challenge | | ~$100M+ | Insect-scale model effort; brain-emulation institute | First mouse connectome; structure-to-function studies | Dedicated microscopy/simulation chips; synchrotron or X-ray center | Centralized BrainFab prototype | Venture fund; DARPA-style program; philanthropic endowment | | ~$1B+ | Mouse brain-emulation model effort | Comprehensive multimodal mouse datasets; partial human connectomes | — | Robotic BrainFab with advanced scanning | BRAIN Initiative v2 or large philanthropic consortium | | ~$10B+ | Human Genome/CERN-scale coalition | Cross-sector human-scale program | Cross-sector human-scale program | Cross-sector human-scale program | Government, philanthropy, and industry coalition | Explore the [full report](report.md), [data and figures](../data-repository.html), and [budget guesstimator](../guesstimator/).