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

2026-02-12

AI Summary

This study addresses the limited reproducibility of closed-loop experiments with biological neural networks (BNNs), which stems from the absence of an interaction framework that simultaneously ensures real-time performance, precise temporal control, and ease of use. To overcome this challenge, the authors propose the first contract-based API for BNN interaction—CL API—which integrates microsecond-precision multi-channel synchronization, deterministic event ordering, and explicit transaction control through a declarative Python interface. By combining a real-time scheduling mechanism with a hardware abstraction layer, the design guarantees sub-millisecond closed-loop response latency while substantially lowering the barrier to entry. Experimental results demonstrate that CL API significantly enhances the consistency, reproducibility, and accessibility of BNN experiments, offering a robust platform for both neuromorphic computing and fundamental neuroscience research.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Bio-inspired Learning
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• biological neural networks
• closed-loop interaction
• real-time stimulation
• temporal precision
• reproducibility

Innovation

• closed-loop interaction
• biological neural networks
• real-time stimulation
• contract-based API
• neurocomputing