Handwritten Digit classification with neural cultures is influenced by neural architecture, network dynamics, and decoding methods
As silicon-based computing approaches fundamental physical limits, neurocomputing offers an energy-efficient alternative by leveraging the intrinsic non-linear dynamics of biological systems. To harness these dynamics, it is vital to understand the structure-function relationship governing how neural cultures process complex spatio-temporal information and how to appropriately decode the resulting neural electrophysiological activity. We investigated this utilizing a closed-loop electrophysiology platform, the CL1, to implement reservoir computing in human iPSC-derived neuronal networks. To systematically evaluate the variables driving neurocomputational capacity, we explored how cellular composition (cortical vs. hippocampal lineages), and the physical architecture (unstructured monolayers, 3D neural organoids, and modular networks confined by microfluidic devices) influenced electrophysiological properties and interacted with different decoding methodologies. Using a spatio-temporal version of a handwritten digit pattern recognition task (MNIST), we analyzed how these biological and analytical factors influenced classification accuracy. To ensure robust interpretation this required us to first demonstrated that reservoir computing decoding methods require strict artifact control and trial-based cross-validation to distinguish network computation from artifactual signal separability or temporal data leakage. Applying this validated frequency-domain pipeline, we suggest a clear functional hierarchy where structural modularity acts as a vital functional regularizer. Modular cortical cultures significantly outperformed unconstrained monolayers and organoids on MNIST. Furthermore, decoding frequency information from raw signals proved superior to typical time-bin decoding implementations. These findings establish that maximizing the computational potential of Synthetic Biological Intelligence, while avoiding false positives, requires a synergistic optimization of cellular identity, structural governance, and rigorous decoding logic. In doing so, this work provides a critical base establishing the criteria under which to evaluate neurocomputing implementations.