--- title: "Executive Materials" document_id: sobe-2025-main-ai document_type: report_section parent_document: ../report.md section_id: main-section-03-executive-materials section_order: 3 section_count: 17 language: en license: CC-BY-4.0 --- # Executive Materials > Selective-retrieval section 3 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-03-executive-materials` - **Order:** 3 of 17 - **Words:** 750 - **Previous:** [Preface](02-preface.md) - **Next:** [Technical Overview](04-technical-overview.md) ------------------------------------------------------------------------ ## Executive Summary 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. A brain emulation is a computational model that aims to match a brain’s biological components and internal, causal dynamics at a chosen level of biophysical detail. Building one requires three core capabilities: recording brain activity, reconstructing brain wiring, and digitally modelling brains with the resulting data. All three 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. The first capability is **neural dynamics**. Functional optical imaging has moved from a nascent technology to large-scale recordings: calcium imaging now captures approximately one million cortical neurons in mice, though without resolving individual spikes, while voltage imaging resolves individual spikes in tens of thousands of neurons in larval zebrafish. Considering neuron count and sampling rate together, effective recording bandwidth improved by about two orders of magnitude in the past two decades. Causal perturbation methods such as optogenetics have also improved. It is now feasible to propose systematic reverse-engineering of neuron-level input-output relationships across entire small nervous systems. Neural recording still faces trade-offs among spatial coverage, temporal resolution, duration, invasiveness, signal quality, and behavior. Recording modulatory molecules such as hormones and neuropeptides is harder still. Defining “whole-brain” as simultaneous capture of more than 95 percent of neurons across more than 95 percent of brain volume, no experiment has yet delivered that scale with single-neuron, single-spike resolution during any behavior. It seems plausible that this barrier will be overcome for sub-million-neuron organisms in the coming years. The second capability is **connectomics**, reconstructing wiring diagrams for all neurons in a brain. The field has moved beyond *C. elegans* maps to two fully reconstructed adult fruit-fly connectomes—roughly three orders of magnitude more neurons. Scans for other organisms, including larval zebrafish, have also been acquired and are being processed. Dataset sizes increasingly reach petabyte scale, challenging storage, backup, sharing, and collaboration. Faster electron microscopy, automated tissue handling, and algorithmic image processing have pushed estimated reconstruction cost from about $16,500 per neuron in the original *C. elegans* connectome to roughly $100 in recent larval-zebrafish projects. Manual proofreading remains the dominant time and cost factor, particularly for large, complex mammalian neurons. Machine learning may reduce this bottleneck. Current reconstructions mostly trace contours and wiring while lacking molecular annotations of key proteins. Future connectomes may be much cheaper using expansion microscopy combined with molecular annotation and protein barcoding. The third capability is **computational neuroscience**, or modeling brains faithfully. Richer datasets and improved software and hardware have enabled connectome-constrained and embodied *C. elegans* models that reproduce specific behaviors, whole-brain fruit-fly models that recapitulate known circuit dynamics, and simplified feasibility studies approaching human-brain scale on large GPU clusters. The hardware bottleneck for mammalian-scale simulation is increasingly memory capacity and interconnect bandwidth, not raw processing power. Software improvements include automatically differentiable data-driven parameter fitting, more efficient simulation, and more rigorous evaluation. Yet many biological mechanisms, including neuromodulation, remain omitted. Models are still severely data-constrained: structural and functional data from the same individual are rare and lack sufficient detail, while passive recordings alone often cannot uniquely identify model parameters, reinforcing the need for causal perturbations. **Conclusion.** Two challenges will shape the next phase of research. First, the field must determine which biological features—from gap junctions to glia and neuromodulators—are necessary for faithful emulation, using evaluation criteria that include neural-activity prediction, embodied behavior, and controlled perturbations. Second, there is a widening gap between the scale of reconstructed connectomes and the ability to record neural activity across them. Better methods are needed to infer functional properties of neurons and synapses from structural and molecular data. For both challenges, sub-million-neuron organisms—where whole-brain recording is already feasible—are a compelling target. Comprehensive functional, structural, and molecular data can be collected at scale, and aligned data from multiple individuals can ground methods that predict function from structure. This evidence will define what is necessary for emulation and help justify larger mammalian projects. In short, faithful emulation of small brains is the necessary first step toward emulating larger ones. Mammalian projects also require parallel progress in cost-effective connectomics. The end-to-end nature of the work calls for integrated organizational models alongside the vital contributions of university and research-campus labs.