Noise-Resilient Detection of Neuronal Spikes by a Hopf-Bifurcation Device
Abstract
Detecting weak transient signals in noise is a persistent challenge in sensing, communication, and electrophysiology. Here, we demonstrate a physical weak-signal detector based on a semiconductor negative differential resistance (NDR) device operated near a Hopf bifurcation. A coherent input that persists over the response time of the dynamical system can drive a transition between quiescent and oscillatory states, whereas faster stochastic fluctuations are largely suppressed. This nonlinear transformation converts a weak analog threshold crossing into all-or-none voltage spikes and therefore provides asynchronous signal detection without a reference clock. Using a modulated photovoltaic signal, we detect a weak frequency component of 100 Hz as a demonstration, at an input signal-to-noise amplitude ratio as low as 1/500, and reliably recover it in the output spectrum. We then apply the same principle to neuronal multisite extracellular recordings. After standard band-pass filtering, the raw microelectrode signal is transformed by the NDR dynamics, enhancing the distinction between neuronal spikes and background fluctuations. The resulting detected spike times agree closely with those obtained using a traditional spike-detection pipeline. These results establish bifurcation-engineered NDR dynamics as a compact hardware approach to noise-resilient signal discrimination and event-based analog-to-digital conversion that will be useful for neuroprosthetic devices.
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BibTeX
@misc{kumar2026noiseresilient,
title = {{Noise-Resilient Detection of Neuronal Spikes by a Hopf-Bifurcation Device}},
author = {Jitendra Kumar and Roberto Fenollosa and Gonzalo Rivera-Sierra and So-Yeon Kim and Adam Armada-Moreira and Juan Bisquert and Michele Giugliano},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.04949},
eprint = {2609.04949},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.04949},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Noise-Resilient Detection of Neuronal Spikes by a Hopf-Bifurcation Device
AU - Jitendra Kumar
AU - Roberto Fenollosa
AU - Gonzalo Rivera-Sierra
AU - So-Yeon Kim
AU - Adam Armada-Moreira
AU - Juan Bisquert
AU - Michele Giugliano
PY - 2026
DA - 2026/09/04
PB - arXiv
DO - 10.48550/arxiv.2609.04949
UR - https://arxiv.org/abs/2609.04949
AB - Detecting weak transient signals in noise is a persistent challenge in sensing, communication, and electrophysiology. Here, we demonstrate a physical weak-signal detector based on a semiconductor negative differential resistance (NDR) device operated near a Hopf bifurcation. A coherent input that persists over the response time of the dynamical system can drive a transition between quiescent and oscillatory states, whereas faster stochastic fluctuations are largely suppressed. This nonlinear transformation converts a weak analog threshold crossing into all-or-none voltage spikes and therefore provides asynchronous signal detection without a reference clock. Using a modulated photovoltaic signal, we detect a weak frequency component of 100 Hz as a demonstration, at an input signal-to-noise amplitude ratio as low as 1/500, and reliably recover it in the output spectrum. We then apply the same principle to neuronal multisite extracellular recordings. After standard band-pass filtering, the raw microelectrode signal is transformed by the NDR dynamics, enhancing the distinction between neuronal spikes and background fluctuations. The resulting detected spike times agree closely with those obtained using a traditional spike-detection pipeline. These results establish bifurcation-engineered NDR dynamics as a compact hardware approach to noise-resilient signal discrimination and event-based analog-to-digital conversion that will be useful for neuroprosthetic devices.
ER -