NSF AI grant $2M: growing "brain organoids" from human-derived neurons for computing — fusing AI and neuroscience (EFRI)
The NSF awarded about $2M to foundational research that grows "brain organoids" (3D neural tissue in vitro) from human-derived neurons and uses them for low-power computing, learning, and memory. Against the high power use and limited flexibility of silicon AI chips, it aims to replicate the brain's energy-efficient, flexible information processing in biology — with ethics embedded throughout.
Grant overview (primary data)
- Award amount$1,998,582
- RecipientUniversity of Texas at San Antonio（TX）
- ProgramEFRI Research Projects
- Period2025-10-01 〜 2029-09-30
- FunderU.S. National Science Foundation (NSF) / NSF
Key points
- Grows brain organoids from human-derived neurons for low-power computing, learning, and memory (biocomputing)
- Targets the high power use and limited flexibility of silicon AI chips by replicating brain-like processing in biology
- Blood → hiPSCs → neurons; builds neocortex-like organoids of excitatory/inhibitory neurons
- Compares against brain-mimicking chips; studies ethical/social implications in parallel
- About $2M; EFRI; Texas; from 2025 — a new field across CS, neuroscience, and neuroengineering
The NSF awarded about $1,998,582 to a project integrating human-derived neural networks and AI for information processing in brain organoids (NSF Award 2515404; program: EFRI Research Projects [EFRI BEGIN OI]; Texas; starting October 2025).
Per the abstract, AI is poised to dramatically alter our world, but two of the biggest obstacles are the high energy consumption of the hardware and a lack of flexibility for adapting to new environments or problems. By contrast, the human nervous system is flexible, nimble, and energy-efficient. The award supports developing biotechnology to grow cellular brain organoids that replicate the network and activity observed in the brain. These organoids are integrated into an engineered system and, using insights from cognitive science, programmed to solve increasingly complex associative problems. The team evaluates how this bioengineering compares to state-of-the-art brain-mimicking chips, and also studies the social and ethical implications of using biological tissue to address AI challenges — aiming to foster a new field at the interface of computer science, neuroscience, and neuroengineering.
Technically, there is growing interest in engineering systems incorporating organoids — self-organized 3D cellular structures in vitro that resemble organs. Amid the need for low-energy ML/AI on increasingly complex tasks, brain organoids may recapitulate fundamental computational learning and memory functions not achievable with silicon. The work uses cellular reprogramming to convert blood cells into hiPSCs and then neurons, building organoids that integrate excitatory and inhibitory neurons like the neocortex. The motivating hypothesis is that the self-organization of these cortical-like organoids can be harnessed for computation, learning, and memory. For viability in ML/AI, the project must show organoids can be reliably produced and "programmed" using biological mechanisms of learning and memory; it establishes a framework to characterize, validate, and program them, with ethical considerations embedded throughout.
Why it matters
A frontier case tackling AI's core problems of energy use and flexibility with biological neurons. For those tracking neuromorphic computing, organoid intelligence, and AI ethics, a useful read on early-stage U.S. research investment and ethics-embedded research design.
FAQ
What is a brain organoid?
Why use biological neurons for AI?
Sources (primary)
Source: NSF Award Search (U.S. National Science Foundation, public domain). Amounts are the obligated amount. For privacy, we do not handle principal investigator names.
- NSF Award (original, official)
- NSF Award ID: 2515404