The Physical and Computational Boundaries of Organoid Intelligence

Recent paradigms in Organoid Intelligence (OI) frequently characterize in vitro neural networks using cognitive metaphors. This manuscript rejects teleological metaphors, strictly framing human brain organoids as physical substrates undergoing local thermodynamic relaxation following external perturbations. By synthesizing electrophysiological limits, structural topological mapping, and algorithmic information theory, this research evaluates two full-lifecycle electrophysiological datasets (fs369 and fs437) from the FinalSpark platform. Analysis establishes a definitive Nyquist-limit temporal resolution floor of 33.3 microseconds for standard 30 kHz Multi-Electrode Arrays (MEAs), mathematically constraining the empirical observation of hypothesized sub-millisecond biological mechanisms. Furthermore, a custom Digital Signal Processing pipeline reveals a "Timescale Anomaly," where external electrical stimulation induces a scale-dependent phase inversion in algorithmic complexity, isolated primarily to high-frequency Beta and Gamma bands. Post-stimulus complexity decay heavily favors a Power-Law model, indicating scale-free, localized temporal relaxation rather than deliberate memory encoding.