Building AI Neuroscience: From Atoms to Bits

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Building AI Neuroscience: From Atoms to Bits<br>Neuroscience is slow. How can we make it faster?

Amaranth Foundation and Patrick Mineault<br>Jun 04, 2026

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Neuroscience, driven by small-scale labs in universities, proceeds meticulously; it is not uncommon to see projects carried heroically by undaunted postdocs and graduate students for a decade. This research could provide the seeds for treating neurodegenerative disorders or for understanding our intelligence. How can we accelerate neuroscience?<br>If we could use AI scientist agents — systems that can read literature, generate hypotheses, read data, write analysis code, and design experiments — to study Brains and Behavior — either directly or compiled as atlases and digital twins—, we could potentially vastly accelerate neuroscience. Indeed, in Machines of Loving Grace, Dario Amodei described how advanced AI, acting as a country of geniuses in a datacenter, could make rapid advances towards building a science of intelligence and curing all neuropsychiatric disease. While these are lofty goals, the essay doesn’t tell us how we might reach them. Here, I paint a picture of how to build AI neuroscience and what funders should prioritize.<br>Thanks for reading Amaranth Foundation Blog! Subscribe for free to receive new posts.

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A schematic of the AI neuroscientist<br>A working definition of AI scientists

AI neuroscientists are a special instance of AI scientist agents, which are already taking shape. The May 2026 issue of Nature featured three of these systems demonstrating their use for writing empirical software and testing hypotheses in the biomedical sciences.<br>In broad strokes, these systems are LLMs with agentic harnesses that manage context, memory, and access to skills. They are architected similarly to coding agents, like Claude Code or OpenAI Codex, with the caveat that the end product is not a piece of software or website, but rather insight derived from an analysis.<br>Several groups are building AI scientists across the sciences, and more specialized agents for the biosciences, such as the ones from FutureHouse. We can already see glimpses of the relevance of these agents to neuroscience. For example, Aygün et al. (2026) demonstrate an auto-research agent that can perform state-of-the-art neural activity prediction, a fundamental building block towards a science of intelligence. AI scientists iteratively designing better proteins and treatments is already part of the OpenAI Foundation’s theory of change for how AI can advance Alzheimer’s disease research.<br>Currently, AI scientists have limited autonomy, though we expect them to become more capable as LLM capabilities grow, similar to what we’ve seen in coding agents. A fundamental bottleneck will remain, however, in building skills that are highly specialized to neuroscience. We expect this will require data and software engineering that are beyond the scope of a single lab to match the throughput of the base model.<br>Studying Brains and Behavior with AI scientists

While AI scientists are relatively generic across scientific disciplines, they differ in the subjects that they interrogate. The subjects of conventional and AI neuroscientists are Brains and Behavior. Unlike verifiable domains like code and math, where AI agents can test hypotheses rapidly and cheaply, running experiments on brains and behavior is expensive .<br>To make progress, we have to move as much of our study of brains and behavior as possible from the world of atoms to the world of bits: collecting atlases, building digital twins, and closing the loop with hypothesis-driven experiments on real subjects.<br>The shape of the subject in AI neuroscience

Atlases

A subset of the MICrONS dataset.<br>For a static dataset to be useful for an AI neuroscientist, it has to rise to the level of an atlas: a high-coverage, high-entropy map of the brain that can answer many more questions than could have been anticipated by the original experiment designers. The Natural Scenes Dataset, the Allen Brain Cell Atlas, and FlyWire are recent examples of this idea across fMRI, transcriptomics, and connectomics, respectively. These datasets are collected with completeness in mind, containing a representative sample of their respective domain (or in FlyWire’s case, the complete domain). They are distributed according to FAIR principles: highly annotated, distributed on open platforms, with sample code and programmatic access.<br>Importantly, atlases must be generated in a virtuous cycle between experimental and computational teams: the best atlases are made in conjunction with computational teams that have clear ideas of how they will use the data. Tom Kalil, one of the architects of the BRAIN Initiative, says that researchers may not be able to immediately identify the right objective function that will result in an immediately useful model. “What is even more valuable...