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About this paper
David Hogan, Andrew Doherty, Boon Kien Khoo, Johnson Zhou, Richard Salib, James Stewart, Kiaran Lawson, Alon Loeffler and 1 more
The CL API provides Python with a closed-loop stimulation interface for biological neural networks, making timing, ordering, and admission behavior explicit. Its CL1 implementation exposes detected spikes to user code at up to 25 kHz, with a 40 μs frame and about 33–34 μs available per iteration; this demonstrates an interaction path, not improved neural function or jitter-free operation.
Key insights
- Defining a formal programming contract for BNN interactions establishes explicit guarantees for timing, ordering, and synchronization, enhancing reproducibility.
- A declarative Python interface can abstract hardware complexities, making precise, real-time electrophysiology accessible to a broader range of researchers.
- Achieving sub-millisecond, low-jitter performance in closed-loop BNN systems requires a tightly integrated software and hardware stack, including kernel-level drivers and FPGAs.
Summary by alphaXiv.
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