Goal-directed learning in cortical organoids
Experimental neuroscience techniques are advancing rapidly, with developments in high-density electrophysiology and targeted electrical stimulation enabling single-cell-resolution recording and stimulation. Cortical organoids derived from pluripotent stem cells show great promise as in vitro models of brain development, function, and disease. In this work, we demonstrate goal-directed learning in brain organoids through feedback-driven neural plasticity. We developed a closed-loop electrophysiology framework to embody mouse cortical organoids into a pole-balancing task ("cartpole") and evaluated performance improvements when delivering high-frequency training signals. We found that, for most organoids, training signals chosen by artificial reinforcement learning yield better performance than randomly chosen training signals or no training signal, yet improvements do not persist after the 45-min rest period. We further show that training-induced plasticity requires intact glutamatergic transmission, as pharmacological blockade of AMPA and NMDA receptors abolished performance improvements. This systematic approach to studying goal-directed neural plasticity mechanisms in vitro opens new possibilities for neural rehabilitation and biological computation.
Authors
- Tjitse van der Molen (ORCID: https://orcid.org/0000-0001-6147-542X)
- Mohammed A. Mostajo-Radji (ORCID: https://orcid.org/0000-0002-1634-7514)
- Alex Spaeth (ORCID: https://orcid.org/0000-0003-0702-3945)
- H. Schweiger (ORCID: https://orcid.org/0000-0003-0399-9481)
- Kenneth S. Kosik
- Isabel Cline
- Tal Sharf
- Ash Robbins
- Sofie R. Salama
- Sebastian Hernandez
- Mircea Teodorescu
- David F. Parks
- Jinghui Geng
- David Haussler
Institutions
- University of California, Santa Barbara (US)
- University of California, Santa Cruz (US)
Publication Details
- Journal
- Cell Reports
- Published
- 2026-02-01
- DOI
- https://doi.org/10.1016/j.celrep.2026.116984
- Citations
- 1
- Primary Topic
- Neuroscience and Neural Engineering
- Type
- article
- Field-Weighted Citation Impact
- 23.21