Spiking neural networks · Python, Numba · Research
NeuroFlux
A spiking neural network that strips powerline hum from ECG with no labels, no reference channel and no idea of the frequency.
● Livewaiting for telemetry…
“No labeled data, no reference noise channel, no prior knowledge of the interference frequency required.”
Two mechanisms work together. The passive architecture — LIF membrane filtering, Poisson rate encoding, population decoding — keeps clinical R-peaks intact at every noise level. STDP learning adds amplitude-domain SNR at low input SNR and pays for it in landmark fidelity. Phase 3 ran the whole thing on the MIT-BIH Arrhythmia Database and pinned down exactly where it wins.
Python · Numba @njit · uv · MIT-BIH · SQLite sweeps
- synthetic ECG configs significant at p < 0.01
- 192/192
- on real MIT-BIH ECG, R-peak integrity 0.985
- +2.26 dB
- runs in the synthetic sweep, 576 configs × 10 seeds
- 5,760
- test coverage on the core engine
- 97%
Hic sunt erratae. STDP buys 3–4 dB but costs R-peak fidelity, and on EEG and pure tones it loses (−3.4 dB, −2.0 dB). The Phase 4 baselines are not built yet.