Building Brains on a Computer

I first heard people seriously discussing the prospect of “running” a brain in silico back in 2023. Their aim was to emulate, or replicate, all the biological processes of a human brain entirely on a computer.

In that same year, the Wellcome Trust released a report on what it would take to map the mouse connectome: all 70 million neurons. They estimated that imaging would cost $200-300 million and that human proofreading, or ensuring that automated traces between neurons were correct, would cost an additional $7-21 billion. Collecting the images would require 20 electron microscopes running continuously, in parallel, for about five years and occupy about 500 petabytes. The report estimated that mapping the full mouse connectome would take up to 17 years of work.

Given this projection — not to mention the added complexity of scaling this to human brains — I remember finding the idea of brain emulation absurd. Without a map of how neurons in the brain connect with each other, any effort to emulate a brain computationally would prove impossible. But after spending the past year researching the possibility (and writing a 175-page report about it), I’ve updated my views.

Three recent breakthroughs have provided a path toward mapping the full mouse brain in about five years for $100 million. First, thanks to advances in expansion microscopy, we can now “enlarge” the brain to twenty times its normal size using a swellable polymer. This makes it far simpler to image neurons and trace their connections using light rather than electron microscopes. Second, E11 Bio (a nonprofit research organization) recently developed protein barcodes, stained with colorful antibodies that, when delivered into brain tissue, cause each neuron to light up in a distinct color. This makes tracing them much easier. And third, Google Research released PATHFINDER last May, an AI-based, neuron-tracing tool that can proofread about 67,200 cubic microns of brain tissue per hour, with very high accuracy.

These technical advances are just one part of the “brain emulation pipeline,” and scaling these methods to human brains may still prove a challenge. But given these breakthroughs and other trendlines, I now find it plausible that readers of this essay will live to see the first human brain running on a computer; not in the next few years, but likely in the next few decades. This computational brain emulation won’t just be an abstract