CL API: Real-Time Closed-Loop Interactions with Biological Neural Networks

• URL: http://arxiv.org/abs/2602.11632v1
• Date: Thu, 12 Feb 2026 06:29:01 GMT
• Title: CL API: Real-Time Closed-Loop Interactions with Biological Neural Networks
• Authors: David Hogan, Andrew Doherty, Boon Kien Khoo, Johnson Zhou, Richard Salib, James Stewart, Kiaran Lawson, Alon Loeffler, Brett Kagan,
• Abstract summary: Biological networks (BNNs) can only function as reliable information-processing systems if inputs are delivered in a temporally and structurally consistent manner.<n>Existing approaches to interacting with BNNs face a fundamental trade-off: they either depend on low-level hardware mechanisms, imposing prohibitive complexity for rapid iteration.<n>The Cortical Labs Application Programming Interface (CL API) enables real-time, sub-millisecond closed-loop interactions with BNNs.
• Score: 0.7223361655030193
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Biological neural networks (BNNs) are increasingly explored for their rich dynamics, parallelism, and adaptive behavior. Beyond understanding their function as a scientific endeavour, a key focus has been using these biological systems as a novel computing substrate. However, BNNs can only function as reliable information-processing systems if inputs are delivered in a temporally and structurally consistent manner. In practice, this requires stimulation with precisely controlled structure, microsecond-scale timing, multi-channel synchronization, and the ability to observe and respond to neural activity in real-time. Existing approaches to interacting with BNNs face a fundamental trade-off: they either depend on low-level hardware mechanisms, imposing prohibitive complexity for rapid iteration, or they sacrifice temporal and structural control, undermining consistency and reproducibility - particularly in closed-loop experiments. The Cortical Labs Application Programming Interface (CL API) enables real-time, sub-millisecond closed-loop interactions with BNNs. Taking a contract-based API design approach, the CL API provides users with precise stimulation semantics, transactional admission, deterministic ordering, and explicit synchronization guarantees. This contract is presented through a declarative Python interface, enabling non-expert programmers to express complex stimulation and closed-loop behavior without managing low-level scheduling or hardware details. Ultimately, the CL API provides an accessible and reproducible foundation for real-time experimentation with BNNs, supporting both fundamental biological research and emerging neurocomputing applications.

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