Developing a binary communication protocol between biological neural networks using virtual white matter
Year: 2026
Type: article
Abstract: Objective. Biological neural networks (BNNs) are composed of interconnected neurons, where a single neuron may connect to thousands of other neurons, and this interregional connectivity enables distributed computation across multiple nodes. Biocomputing exploits these inherent processing capabilities of neural tissue for computational tasks. However, most in vitro biocomputing systems rely on isolated neural cultures that may not be suitable for complex multi-network processing tasks. Building scalable biocomputing architectures requires reliable communication frameworks between physically separated biological processing units. Structured information exchange across distinct BNNs remains largely unexplored. Approach. virtual white matter (VWM) is a previously established platform that enables real-time functional connectivity between neural cultures in separate microelectrode array (MEA) dishes. In this study, we expanded the VWM platform to enable structured binary communication between dissociated cortical cells cultured in separate MEAs implemented on MED64 MEAs with Intan RHS stimulation/recording hardware and a custom real-time decoding pipeline. The system encodes 3‐bit data words using spatiotemporal electrical stimulation patterns, with designated electrodes representing binary states; evoked responses from selected output electrodes are decoded in real time using machine learning, and parity-based error correction is applied to enhance transmission fidelity. We validated the framework through unidirectional and bidirectional communication experiments across multiple independent neural preparation pairs. Main Results. The VWM system successfully transmitted 3-bit data packets between physically separate neural cultures, achieving individual bit decoding accuracies of 75%–90% and aggregate word accuracy exceeding 52%, well above the 12.5% chance level and an optimal-threshold spike-count baseline. Parity-based error correction with confidence-based bit toggling enhanced transmission fidelity, enabling reliable word-level communication. In bidirectional experiments, end-to-end round-trip accuracy reached approximately 20%, remaining above chance. Significance. This work demonstrates that physically separated neural culturescan reliably exchange structured information through engineered communication frameworks. By establishing reliable structured communication between living neural networks, VWM provides a foundational framework for scalable, distributed biocomputing architectures that may eventually support multi-network in vitro models of neural computation.
Source: Journal of Neural Engineering
Institutions: Thomas Jefferson University, Temple University
Language: English
Subfield: Cellular and Molecular Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,325
OpenAlex ID: W7166513323