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.

翻译：生物神经网络因其丰富的动力学特性、并行处理能力和自适应行为而日益受到研究关注。除了作为科学探索以理解其功能外，一个关键研究方向是将这些生物系统用作新型计算载体。然而，生物神经网络要成为可靠的信息处理系统，必须保证输入在时间和结构上具有一致性。实践中，这需要具备精确可控的结构、微秒级时序精度、多通道同步能力，以及实时观测并响应神经活动的手段。现有与生物神经网络交互的方法面临一个根本性权衡：要么依赖底层硬件机制（导致快速迭代的复杂度极高），要么牺牲时间和结构控制（从而损害一致性和可重复性——在闭环实验中尤为突出）。Cortical Labs应用程序接口（CL API）实现了与生物神经网络的亚毫秒级实时闭环交互。采用基于契约的API设计方法，CL API为用户提供精确的刺激语义、事务化准入机制、确定性排序和显式同步保证。该契约通过声明式Python接口呈现，使非专业程序员能够表达复杂的刺激模式和闭环行为，而无需管理底层调度或硬件细节。最终，CL API为生物神经网络的实时实验提供了易用且可复现的基础框架，既支持基础生物学研究，也适用于新兴的神经计算应用。