organoid-oi v3: A Biologically Grounded Closed-Loop Framework for Organoid Intelligence Simulation

We present organoid-oi v3, an open-source Python framework for simulating closed-loop visual categorical learning in a synthetic brain organoid. The system implements Three-Factor Spike-Timing-Dependent Plasticity (STDP) with biologically realistic reward and penalty delivery: reward is a 10 Hz sub-threshold theta depolarization current producing natural long-term potentiation (LTP); penalty is a 200 Hz noise current producing long-term depression (LTD). A local Hebbian readout layer with weight decay decodes population activity. Every mechanism has a named biological counterpart — no backpropagation, no external weight forcing, no mathematical shortcuts. The framework passed 47/47 synthetic validation tests. A freeze-weights validation test confirms that STDP — not the decoder — is the source of learning. organoid-oi v3 is designed as the computational bridge between existing electrophysiology analysis (Axon pipeline) and future closed-loop hardware experiments on real organoid MEA platforms.