Adam Marblestone

"The first integrative overview of brain emulation since connectomics, brain activity mapping and machine learning really started to work."

Adam Marblestone, CEO Convergent Research
Anders Sandberg

"The most useful quantitative update I've seen on the whole brain emulation roadmap. A treasure trove of useful information."

Anders Sandberg, Institute of Futures Studies
Prof. Jianfeng Feng

"This is the material for anyone seeking to develop a computational approach integrating microscopic (neurotransmitters), mesoscopic (neuronal activity), and macroscopic (imaging, behavioral, and environmental) data."

Prof. Jianfeng Feng, Warwick & Fudan Universities

Research Report DOI: 10.5281/zenodo.18377594

State of
Brain Emulation
2025

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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 from all of it. Such brain models would be a one-of-a-kind scientific tool; a digital way to study how neurological diseases arise, or how cognition forms, or even to reverse-engineer evolution's solutions to hard computational problems.

No other tool offers this combination of biological realism and experimental control, including AI.

Key Findings

Data is the bottleneck, not hardware or algorithms

The main barrier to better brain emulation models is more and higher-quality experimental data. No organism’s full brain has been recorded at single-neuron resolution.

Small-organism brain emulation is within reach

For organisms under 1 million neurons — fruit flies, small fish, bees — capturing all aspects of the brain faithfully is increasingly plausible, potentially within the decade, at a cost in the low $100Ms.

Scale is an enormous challenge

A mouse brain has 500x more neurons than a fruit fly; a human brain has about a million times more. Mapping a mouse brain at the needed resolution is comparable in scale to a high-resolution reconstruction of Earth.

The field is tiny and underfunded

Everyone worldwide focused specifically on brain emulation could fit in a single workshop room. Total global funding for basic neuroscience has been roughly $0.5B/year — about 1% of the NIH’s annual budget.

Outsized impact is possible

Any individual or funder entering this field can have outsized impact given its small size and early stage.

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Introductory Materials
Asimov Press Asimov Press Building brains on a computer Coming Soon Guide 2h Guide to Brain Emulation
International
Coming Soon Translations Translations

Authors

Niccolo Zanichelli, Maximilian Schons, Isaak Freeman, Philip K. Shiu, Anton Arkhipov

With contributions from:

Adam Glaser, Adam Marblestone, Anders Sandberg, Andrew Payne, Andy McKenzie, Anshul Kashyap, Camille Mitchell, Christian Larsen, Claire Wang, Connor Flexman, Daniel Leible, Davi Bock, Davy Deng, Ed Boyden, Florian Engert, Glenn Clayton, James Lin, Jianfeng Feng, Jordan Matelsky, Ken Hayworth, Kevin Esvelt, Konrad Kording, Lei Ma, Logan Thrasher Collins, Michael Andregg, Michael Skuhersky, Michał Januszewski, Nicolas Patzlaff, Niko McCarty, Oliver Evans, Ons M'Saad, Patrick Mineault, Quilee Simeon, Richie Kohman, Srinivas Turaga, Tomaso Poggio, Viren Jain, Yangning Lu, Zeguan Wang

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Executive Summary

Brain emulation pipeline overview

Accurate brain emulations would occupy a unique position in science: combining the experimental control of computational models with the biological fidelity needed to study how neural activity gives rise to cognition, disease, and perhaps consciousness.

Building a brain emulation requires three core capabilities: 1) recording brain activity, 2) reconstructing brain wiring, and 3) digitally modelling brains with respective data. In this report, we explain how all three capabilities have advanced substantially over the past two decades, to the point where neuroscientists are collecting enough data to emulate the brains of sub-million neuron organisms, such as zebrafish larvae and fruit flies.

Technical Overview

Neural Dynamics — Recording Brain Activity

Neural recording capabilities across organisms

Despite impressive progress in neuron recording capabilities, neuroscience has not yet achieved whole-brain recording (≥ 95% of neurons and brain volume) at single-neuron resolution in any organism. The closest achievements include larval zebrafish with approximately 80% brain coverage and C. elegans with roughly 50% of nervous system neurons recorded at single-cell resolution.

Even these figures come with substantial limitations: temporal resolution is typically well below neuronal firing rates (often 1-30 Hz for calcium imaging), recording durations remain short (minutes to hours), and the need for head-fixation severely constrains behavior repertoires.

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Connectomics — Reconstructing Brain Wiring

Cost per quality-controlled reconstructed neuron

Complete connectomes at synaptic resolution currently exist only for small organisms. C. elegans has multiple whole-nervous-system reconstructions from individual specimens, with approximately ten datasets available. Adult Drosophila has fully proofread connectomes for both the male central nervous system and the female brain.

For larger organisms, progress remains at the proof-of-concept stage. In mice, the largest densely reconstructed volume is a cubic millimeter of visual cortex, containing approximately 120,000 neurons and 523 million automatically detected synapses.

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Computational Neuroscience — Modelling Brains Faithfully

Neural recording capabilities heatmap

Meaningful progress toward whole-brain emulation is currently confined to small organisms where comprehensive datasets are becoming available. In C. elegans, multi-scale, closed-loop simulations now reproduce basic behaviors by integrating neural dynamics, body mechanics, and environmental interaction.

For Drosophila, the adult connectome has enabled models spanning the entire brain, successfully predicting neural responses and circuit functions for behaviors like feeding and grooming.

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Part 1: Foundations

Dimensions of Brain Representations
Connectivity Cell types Plasticity Neuromodulation Temporal resolution Behavior Learning Neurons Accuracy Personality Distribution
What this section covers

The section establishes the key distinction between simulation (matching outputs) and emulation (reproducing the causal machinery), and defines what we consider the minimum threshold for a model to qualify as an emulation at all.

Part 2: State of Brain Emulation across Organisms

C. elegans connectome C. elegans ~300 neurons
Zebrafish larval brain Zebrafish ~100K neurons
Drosophila brain Fruit Fly ~140K neurons
Mouse brain Mouse ~70M neurons
Human brain Human ~86B neurons
What this section covers

A systematic tour through five model organisms, from the 300-neuron worm to the 86-billion-neuron human brain. For each, we assess the current state of neural recording, connectomics, and computational modeling, then identify the key gaps blocking progress toward faithful emulation.

Part 3: Methods for Brain Emulation

Neural recording modalities comparison chart
What this section covers

A technical deep-dive into the three pillars of brain emulation: recording neural activity (from patch clamps to calcium imaging), reconstructing structure (electron microscopy, expansion microscopy, barcoding), and simulating it all in silico (neuron models, synapse dynamics, hardware requirements). We cover what each method can and cannot do, what it costs, and where the bottlenecks lie.

Part 4: Appendix

What this section covers

You can find all of the data the report was built upon on our website. Explore our complete bibliography, figure library with downloadable graphics, and public data repository.

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Cite this report: DOI: 10.5281/zenodo.18377594 All citation formats →