Robbins, A. (ECE) – How to train your organoid: goal-directed learning in biological neural networks

Artificial neural networks can now learn to play games, control robots, generate language, and solve complicated reasoning tasks, yet we still lack a clear understanding of how to directly guide learning in biological neural networks. We show that brain organoids can learn to solve a fundamental control task, balancing an inverted pendulum, through closed-loop electrophysiology. Cortical organoids interfaced with high-density microelectrode arrays received sensory input about the pole’s angle and produced motor output through their neural activity. Training signals selected by a reinforcement learning algorithm significantly outperformed random stimulation and no-stimulation controls. Blocking glutamatergic transmission abolished the learning and washout restored it, confirming the adaptation depends on synaptic plasticity. To support this work and future experiments, we developed BrainDance, an open-source framework for running reproducible biological learning experiments, and contributed to RT-Sort, a real-time spike sorting algorithm. This dissertation presents the tools, experiments, and findings from pursuing goal-directed learning in biological neural networks. BrainDance makes these experiments easy-to-create, reproducible and shareable, letting any lab with compatible hardware start training their own organoids.

Event Host: Ash Robbins, Ph.D. Candidate, Electrical and Computer Engineering 

Advisor: Mircea Teodorescu

Zoom- https://ucsc.zoom.us/j/95839863615?pwd=EmqTWPN9RRBYZRW7rcpoaT9kqacfRP.1

Passcode- 069118