arxiv: 2604.18384 · v1 · submitted 2026-04-20 · ⚛️ physics.optics

Recognition: unknown

NEMO: Neural Electro-Mechano-Optic Sensors for Multiplexed Neural Interfaces

Andrew Cochran (1) , Harshvardhan Gupta (1) , Vishal Jain (1 , 2) , Maysamreza Chamanzar (1 , Gianluca Piazza (1) ((1) Department of Electrical , Computer Engineering , Carnegie Mellon University

show 3 more authors

Pittsburgh USA. (2) Carnegie Mellon Neuroscience Institute USA.)

Authors on Pith no claims yet

Pith reviewed 2026-05-10 03:45 UTC · model grok-4.3

classification ⚛️ physics.optics

keywords neural sensorelectro-optomechanicNEMSsilicon photonicsneural recordingmultiplexingelectrophysiologyoptical readout

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The pith

A compact electro-optomechanic sensor detects neural signals down to 110 microvolts by modulating a photonic resonator and converts them to optical signals for transmission.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a new type of neural recording probe that uses a nano-electromechanical transducer to turn weak electrical signals from brain tissue directly into mechanical motion that modulates light in a silicon microdisk. This design targets two long-standing problems in neural interfaces: the difficulty of getting clean signals from very small electrodes and the artifacts that appear when stimulation is applied nearby. By reaching a detection limit of 110 microvolts, the sensor becomes sensitive enough to capture typical neural activity while the optical output allows the signal to leave the brain without electrical wires or bulky amplifiers attached to the subject. Benchtop tests and recordings from live brain slices confirm the basic function. A reader would care because the approach removes a major hardware barrier to recording from hundreds or thousands of sites at once in awake, behaving animals.

Core claim

The NEMO sensor integrates a miniaturized NEMS electrostatic transducer with a silicon photonic microdisk resonator so that electrophysiological voltages produce measurable shifts in the optical resonance; this conversion has been shown to operate with a limit of detection of 110 microvolts and has been used to record signals from ex-vivo neural tissue, removing the need for conventional headstage electronics.

What carries the argument

NEMS electrostatic transducer that modulates a silicon photonic microdisk resonator to convert electrical voltage into optical signal modulation

If this is right

Optical readout removes the need for bulky headstage electronics, allowing neural recordings from freely moving subjects.
The same optical channel can carry signals from many sensors without electrical crosstalk, supporting higher channel counts.
Direct conversion of voltage to light avoids intermediate amplification stages that often introduce stimulation artifacts.
Miniaturized form factor permits denser probe arrays than conventional electrode arrays of similar sensitivity.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If the optical link proves robust in vivo, the same architecture could be extended to bidirectional interfaces that both record and deliver light-based stimulation.
The absence of electrical headstages opens the possibility of fully wireless, chronically implanted high-density arrays that do not require percutaneous connectors.
Because the sensor is built on standard silicon photonic fabrication, arrays could be integrated with on-chip wavelength-division multiplexing to further increase channel density without additional wiring.
A direct comparison of artifact levels between NEMO sensors and conventional electrodes during simultaneous stimulation would quantify whether the optical approach truly reduces stimulation interference.

Load-bearing premise

Performance measured in ex-vivo tissue slices will remain stable and free of extra noise once the sensor is implanted inside an intact, moving animal.

What would settle it

An implanted device in a behaving animal that either fails to resolve signals below roughly 200 microvolts or shows motion-induced optical artifacts substantially larger than those observed in the ex-vivo tests.

Figures

Figures reproduced from arXiv: 2604.18384 by 2), Andrew Cochran (1), Carnegie Mellon University, Computer Engineering, Gianluca Piazza (1) ((1) Department of Electrical, Harshvardhan Gupta (1), Maysamreza Chamanzar (1, Pittsburgh, USA.), USA. (2) Carnegie Mellon Neuroscience Institute, Vishal Jain (1.

**Figure 1.** Figure 1: Dimensions of state-of-the-art neural probes. While the probe shank is very narrow (~ 100 μm) which minimizes tissue damage, the backend connectors and headstages (needed for fanout, digitization, and data transmission) are large. This makes use of multiple probes impractical, especially in chronic applications. The NEMO sensor is a multiplexor and encodes the neural signal optically allowing the signals f… view at source ↗

**Figure 6.** Figure 6: Cortex recordings: (a) Full recording of stimulation and evoked response from both the NEMO sensor and conventional neural amplifier. (b) Stimulation artifact recorded by the NEMO sensor shows a smooth RC response that follows the stimulation artifact. The neural amplifier recording (Intan) has significant distortion and non-linearities due to the small dynamic range of the amplifier needed to achieve larg… view at source ↗

read the original abstract

We introduce a novel electro-optomechanic neural sensor for realizing ultra-compact neural recording probes that can detect and relay electrophysiology signals from within neural tissue. This technology addresses outstanding challenges faced by existing neural recording technologies, including the resolution trade-off with signal-to-noise-ratio (SNR) due to the high impedances of small electrodes, and lingering stimulation artifacts. The sensor employs a highly miniaturized NEMS (nano-electromechanical systems) electrostatic transducer that modulates a silicon photonic microdisk resonator to convert electrical signals to an optical signal modulation. We have been able to achieve a limit of detection down to 110 microvolts, making the sensor sensitive enough to detect neural signals. This sensitive electro-optomechanic sensor directly detects electrophysiology signals and converts them to optomechanic modulation for effective transmission to outside the brain, which provides the unique potential for massive multiplexing of neural recordings. This design eliminates the need for bulky backend headstages that limit neural recording on awake free-roaming subjects. The ability of the device to record electrophysiological signals has been demonstrated using benchtop characterization and ex-vivo recordings from live neural tissue.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces NEMO, a novel electro-optomechanic neural sensor that integrates a nano-electromechanical systems (NEMS) electrostatic transducer with a silicon photonic microdisk resonator to convert electrophysiological signals directly into optical modulations. It reports achieving a limit of detection of 110 μV and demonstrates functionality via benchtop characterization and ex-vivo recordings from live neural tissue, positioning the device as enabling massive multiplexing of neural interfaces without bulky headstages or electrical wiring limitations.

Significance. If the performance metrics and transduction chain are robustly validated, the work could advance implantable neural recording by combining high sensitivity with optical readout for improved multiplexing and reduced artifacts, addressing longstanding trade-offs in electrode-based systems. The experimental device approach offers a concrete path toward compact probes suitable for behaving animals, though its significance is currently constrained by the preliminary nature of the supporting data.

major comments (2)

Abstract: The central claim of a 110 μV limit of detection (sufficient for neural signals) and successful ex-vivo recordings is stated without accompanying quantitative data, error bars, baseline comparisons to conventional electrodes, or detailed characterization methods (e.g., noise floor measurements or SNR calculations), leaving the performance assertions only partially supported.
Results/Discussion: The extrapolation to 'massive multiplexing' and in-vivo applicability rests on the assumption that ex-vivo performance is invariant to tissue environment, yet no measurements address mechanical damping, refractive index shifts from gliosis, insertion losses, or microdisk Q-factor degradation under biological loading, which directly impacts the noise floor and multiplexing feasibility.

minor comments (1)

The abstract and text use 'electro-optomechanic' and 'optomechanic' inconsistently; standard terminology is 'electro-optomechanical'.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful review and constructive comments on our manuscript. We address each major point below, providing clarifications and indicating revisions where the manuscript can be strengthened without overstating the current data.

read point-by-point responses

Referee: Abstract: The central claim of a 110 μV limit of detection (sufficient for neural signals) and successful ex-vivo recordings is stated without accompanying quantitative data, error bars, baseline comparisons to conventional electrodes, or detailed characterization methods (e.g., noise floor measurements or SNR calculations), leaving the performance assertions only partially supported.

Authors: We agree that the abstract would benefit from additional quantitative context to better support the stated performance claims. In the revised manuscript, we have updated the abstract to explicitly reference the noise floor measurements, SNR calculations, and error bars presented in the main text (Figures 3 and 4) and methods section. We have also added a brief mention of baseline comparisons to conventional electrodes, which are detailed in the supplementary information. These changes ensure the abstract more accurately reflects the supporting data without altering the core claims. revision: yes
Referee: Results/Discussion: The extrapolation to 'massive multiplexing' and in-vivo applicability rests on the assumption that ex-vivo performance is invariant to tissue environment, yet no measurements address mechanical damping, refractive index shifts from gliosis, insertion losses, or microdisk Q-factor degradation under biological loading, which directly impacts the noise floor and multiplexing feasibility.

Authors: We acknowledge that the manuscript's discussion of multiplexing potential and in-vivo applicability relies on ex-vivo validation and does not include direct experimental measurements of tissue-induced effects such as mechanical damping, gliosis-related refractive index changes, insertion losses, or Q-factor degradation. In the revised version, we have expanded the discussion section to include a theoretical analysis of these factors based on established models of photonic resonators in biological media, along with estimates of their potential impact on the noise floor. We have also clarified that full in-vivo characterization remains future work. The ex-vivo recordings provide initial evidence of functionality in neural tissue, but we do not claim invariance to all biological conditions. revision: partial

Circularity Check

0 steps flagged

No circularity: experimental device report with performance claims grounded in direct measurements

full rationale

The manuscript is a device fabrication and characterization paper. Claims rest on benchtop electrical/optical measurements and ex-vivo tissue recordings that yield the reported 110 μV LOD. No equations, fitted parameters, or derivation steps are presented that reduce to prior results by construction. Self-citations (if any) are not load-bearing for any central claim, and the transduction principle is described as a physical mechanism rather than a mathematical identity. The extrapolation to in-vivo multiplexing is an untested assumption but does not constitute circularity in the derivation sense.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on successful microfabrication and testing of a new integrated device; it draws on standard silicon photonics and NEMS processes but introduces no new physical constants or fitted parameters.

axioms (1)

domain assumption Standard microfabrication processes for NEMS and silicon photonics can be combined at the required scale without unacceptable yield or performance loss.
The sensor design assumes these existing techniques transfer directly to the neural probe geometry.

invented entities (1)

NEMO sensor no independent evidence
purpose: Ultra-compact neural recording probe with optical multiplexing
New device concept that integrates NEMS transducer and photonic resonator for electrophysiology-to-light conversion.

pith-pipeline@v0.9.0 · 5553 in / 1383 out tokens · 35834 ms · 2026-05-10T03:45:14.540915+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

61 extracted references · 7 canonical work pages

[1]

A., Kauvar, I

Machado, T. A., Kauvar, I. v. & Deisseroth, K. Multiregion neuronal activity: the forest and the trees. Nat Rev Neurosci 23, 683–704 (2022)

2022
[2]

& Olcese, U

Montelisciani, L., Dijkema, E. & Olcese, U. Single-Neuron and Population Methods to Study the Circuit-Level Cortical Mechanisms of Multisensory Processing. in 1–37 (2025). doi:10.1007/978-1-0716-4208-5_1

work page doi:10.1007/978-1-0716-4208-5_1 2025
[3]

& Murayama, M

Saito, Y., Osako, Y. & Murayama, M. Unraveling the neural code: analysis of large- scale two-photon microscopy data. Microscopy 74, 146–163 (2025)

2025
[4]

Teichert, T. et al. Volumetric mesoscopic electrophysiology: a new imaging modality for the nonhuman primate. J Neurophysiol 133, 1034–1053 (2025)

2025
[5]

& Fang, Y

Chen, H. & Fang, Y. Recent developments in implantable neural probe technologies. MRS Bull 48, 484–494 (2023)

2023
[6]

N., Berdondini, L

Perna, A., Angotzi, G. N., Berdondini, L. & Ribeiro, J. F. Advancing the interfacing performances of chronically implantable neural probes in the era of CMOS neuroelectronics. Front Neurosci 17, (2023)

2023
[7]

Melin, M. D. et al. Large scale, simultaneous, chronic neural recordings from multiple brain areas. Preprint at https://doi.org/10.1101/2023.12.22.572441 (2023)

work page doi:10.1101/2023.12.22.572441 2023
[8]

A., Zatka-Haas, P., Carandini, M

Steinmetz, N. A., Zatka-Haas, P., Carandini, M. & Harris, K. D. Distributed coding of choice, action and engagement across the mouse brain. Nature 576, 266–273 (2019)

2019
[9]

Stringer, C. et al. Spontaneous behaviors drive multidimensional, brainwide activity. Science (1979) 364, (2019)

1979
[10]

Durand, S. et al. Acute head-fixed recordings in awake mice with multiple Neuropixels probes. Nat Protoc 18, 424–457 (2023)

2023
[11]

van Daal, R. J. J. et al. Implantation of Neuropixels probes for chronic recording of neuronal activity in freely behaving mice and rats. Nat Protoc 16, 3322–3347 (2021). 31

2021
[12]

Ghestem, A. et al. Long-term near-continuous recording with Neuropixels probes in healthy and epileptic rats. J Neural Eng 20, 046003 (2023)

2023
[13]

Bimbard, C. et al. An adaptable, reusable, and light implant for chronic Neuropixels probes. Elife 13, (2025)

2025
[14]

Ferreira-Fernandes, E. et al. In vivo recordings in freely behaving mice using independent silicon probes targeting multiple brain regions. Front Neural Circuits 17, (2023)

2023
[15]

Horan, M. et al. Repix: reliable, reusable, versatile chronic Neuropixels implants using minimal components. Preprint at https://doi.org/10.7554/eLife.98977.1 (2024)

work page doi:10.7554/elife.98977.1 2024
[16]

Product Catalog

Cambridge NeuroTech. Product Catalog. (2025)

2025
[17]

N-Form Multi-Electrode Array

Plexon. N-Form Multi-Electrode Array. (2025)

2025
[18]

& Cicoira, F

Rossetti, N., Hagler, J., Kateb, P. & Cicoira, F. Neural and electromyography PEDOT electrodes for invasive stimulation and recording. J Mater Chem C Mater 9, 7243– 7263 (2021)

2021
[19]

Malekoshoaraie, M. H. et al. Fully flexible implantable neural probes for electrophysiology recording and controlled neurochemical modulation. Microsyst Nanoeng 10, 91 (2024)

2024
[20]

& Robinson, J

Fan, B., Wolfrum, B. & Robinson, J. T. Impedance scaling for gold and platinum microelectrodes. J Neural Eng 18, 056025 (2021)

2021
[21]

P., Hynd, M

Frampton, J. P., Hynd, M. R., Shuler, M. L. & Shain, W. Effects of Glial Cells on Electrode Impedance Recorded from Neural Prosthetic Devices In Vitro. Ann Biomed Eng 38, 1031–1047 (2010)

2010
[22]

W., Ludwig, K

Salatino, J. W., Ludwig, K. A., Kozai, T. D. Y. & Purcell, E. K. Glial responses to implanted electrodes in the brain. Nat Biomed Eng 1, 862–877 (2017). 32

2017
[23]

Sillay, K. A. et al. Long-Term Surface Electrode Impedance Recordings Associated with Gliosis for a Closed-Loop Neurostimulation Device. Ann Neurosci 25, 289–298 (2018)

2018
[24]

& Malliaras, G

Oldroyd, P., Gurke, J. & Malliaras, G. G. Stability of Thin Film Neuromodulation Electrodes under Accelerated Aging Conditions. Adv Funct Mater 33, (2023)

2023
[25]

M., Flesher, S

Weiss, J. M., Flesher, S. N., Franklin, R., Collinger, J. L. & Gaunt, R. A. Artifact-free recordings in human bidirectional brain–computer interfaces. J Neural Eng 16, 016002 (2019)

2019
[26]

Zhou, A., Johnson, B. C. & Muller, R. Toward true closed-loop neuromodulation: artifact-free recording during stimulation. Curr Opin Neurobiol 50, 119–127 (2018)

2018
[27]

Chen, J. et al. Recent Trends and Future Prospects of Neural Recording Circuits and Systems: A Tutorial Brief. IEEE Transactions on Circuits and Systems II: Express Briefs 69, 2654–2660 (2022)

2022
[28]

A., Koch, C., Harris, K

Steinmetz, N. A., Koch, C., Harris, K. D. & Carandini, M. Challenges and opportunities for large-scale electrophysiology with Neuropixels probes. Curr Opin Neurobiol 50, 92–100 (2018)

2018
[29]

Steinmetz, N. A. et al. Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings. Science (1979) 372, (2021)

1979
[30]

Angotzi, G. N. et al. SiNAPS: An implantable active pixel sensor CMOS-probe for simultaneous large-scale neural recordings. Biosens Bioelectron 126, 355–364 (2019)

2019
[31]

Raducanu, B. C. et al. Time multiplexed active neural probe with 678 parallel recording sites. in 2016 46th European Solid-State Device Research Conference (ESSDERC) 385–388 (IEEE, 2016). doi:10.1109/ESSDERC.2016.7599667

work page doi:10.1109/essderc.2016.7599667 2016
[32]

Guha, K. K. S. S. I. J. Micro and Nanoelectronics Devices, Circuits and Systems. vol. 1382 (Springer Nature, Singapore, 2025). 33

2025
[33]

Kinget, P. R. Scaling analog circuits into deep nanoscale CMOS: Obstacles and ways to overcome them. in 2015 IEEE Custom Integrated Circuits Conference (CICC) 1–8 (IEEE, 2015). doi:10.1109/CICC.2015.7338394

work page doi:10.1109/cicc.2015.7338394 2015
[34]

& Nelson, D

Minkenberg, C., Krishnaswamy, R., Zilkie, A. & Nelson, D. Co‐packaged datacenter optics: Opportunities and challenges. IET Optoelectronics 15, 77–91 (2021)

2021
[35]

A., Janaszek, B

Butt, M. A., Janaszek, B. & Piramidowicz, R. Lighting the way forward: The bright future of photonic integrated circuits. Sensors International 6, 100326 (2025)

2025
[36]

Kim, M. et al. O-Band Silicon Ring Modulators With Highly Efficient Electro- and Thermo-Optic Modulation. Journal of Lightwave Technology 43, 1328–1334 (2025)

2025
[37]

H., Eftekhar, A

Atabaki, A. H., Eftekhar, A. A., Yegnanarayanan, S. & Adibi, A. Sub-100-nanosecond thermal reconfiguration of silicon photonic devices. Opt Express 21, 15706 (2013)

2013
[38]

Edinger, P. et al. Vacuum-sealed silicon photonic MEMS tunable ring resonator with an independent control over coupling and phase. Opt Express 31, 6540 (2023)

2023
[39]

Errando-Herranz, C. et al. MEMS for Photonic Integrated Circuits. IEEE Journal of Selected Topics in Quantum Electronics 26, 1–16 (2020)

2020
[40]

RHD2000 Series Digital Electrophysiology Interface Chips. (2012)

2012
[41]

Neuro Amp EX. (2024)

2024
[42]

Ye, Z. et al. Ultra-high density electrodes improve detection, yield, and cell type identification in neuronal recordings. Preprint at https://doi.org/10.1101/2023.08.23.554527 (2023)

work page doi:10.1101/2023.08.23.554527 2023
[43]

Beau, M. et al. A deep learning strategy to identify cell types across species from high-density extracellular recordings. Cell 188, 2218-2234.e22 (2025)

2025
[44]

Tian, Y. et al. Reconfigurable Electro-optic Logic Circuits Using Microring Resonator-Based Optical Switch Array. IEEE Photonics J 8, 1–8 (2016). 34

2016
[45]

& Takenaka, M

Ohno, S., Tang, R., Toprasertpong, K., Takagi, S. & Takenaka, M. Si Microring Resonator Crossbar Array for On-Chip Inference and Training of the Optical Neural Network. ACS Photonics 9, 2614–2622 (2022)

2022
[46]

Posadas, A. B. et al. Electro-Optic Barium Titanate Modulators on Silicon Photonics Platform. in 2023 IEEE Silicon Photonics Conference (SiPhotonics) 1–2 (IEEE, 2023). doi:10.1109/SiPhotonics55903.2023.10141930

work page doi:10.1109/siphotonics55903.2023.10141930 2023
[47]

& Wang, A

Hsu, W.-C., Zhen, C. & Wang, A. X. Electrically Tunable High-Quality Factor Silicon Microring Resonator Gated by High Mobility Conductive Oxide. ACS Photonics 8, 1933–1936 (2021)

1933
[48]

Chu, H. M. & Hane, K. A Wide-Tuning Silicon Ring-Resonator Composed of Coupled Freestanding Waveguides. IEEE Photonics Technology Letters 26, 1411– 1413 (2014)

2014
[49]

& Hane, K

Ikeda, T. & Hane, K. A microelectromechanically tunable microring resonator composed of freestanding silicon photonic waveguide couplers. Appl Phys Lett 102, (2013)

2013
[50]

& Hane, K

Takahashi, K., Kanamori, Y., Kokubun, Y. & Hane, K. A wavelength-selective add- drop switch using silicon microring resonator with a submicron-comb electrostatic actuator. Opt Express 16, 14421 (2008)

2008
[51]

Sattari, H. et al. Silicon photonic microelectromechanical systems add-drop ring resonator in a foundry process. Journal of Optical Microsystems 2, (2022)

2022
[52]

& Sorger, V

Liu, K., Sun, S., Majumdar, A. & Sorger, V. J. Fundamental Scaling Laws in Nanophotonics. Sci Rep 6, 37419 (2016)

2016
[53]

Purcell, E. M. Spontaneous Emission Probabilities at Radio Frequencies. Physics Reviews 69, 681 (1995). 35

1995
[54]

H., Soltani, M

Shah Hosseini, E., Yegnanarayanan, S., Atabaki, A. H., Soltani, M. & Adibi, A. Systematic design and fabrication of high-Q single-mode pulley-coupled planar silicon nitride microdisk resonators at visible wavelengths. Opt Express 18, 2127 (2010)

2010
[55]

H., Shen, H

Xiao, S., Khan, M. H., Shen, H. & Qi, M. Compact silicon microring resonators with ultra-low propagation loss in the C band. Opt Express 15, 14467 (2007)

2007
[56]

& Kanamori, Y

Okatani, T., Sato, Y., Imai, K., Hane, K. & Kanamori, Y. Improvement of silicon microdisk resonators with movable waveguides by hydrogen annealing treatment. Journal of Vacuum Science & Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phenomena 39, (2021)

2021
[57]

& Lipson, M

Roberts, S., Ji, X., Cardenas, J., Corato‐Zanarella, M. & Lipson, M. Measurements and Modeling of Atomic‐Scale Sidewall Roughness and Losses in Integrated Photonic Devices. Adv Opt Mater 10, (2022)

2022
[58]

The Hippocampus Book

Andersen, Per. The Hippocampus Book. (Oxford University Press, 2007)

2007
[59]

Traub, R. D. & Wong, R. K. S. Cellular Mechanism of Neuronal Synchronization in Epilepsy. Science (1979) 216, 745–747 (1982)

1979
[60]

& Draguhn, A

Buzsáki, G. & Draguhn, A. Neuronal Oscillations in Cortical Networks. Science (1979) 304, 1926–1929 (2004)

1979
[61]

Buzsáki, G., Anastassiou, C. A. & Koch, C. The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes. Nat Rev Neurosci 13, 407–420 (2012). Acknowledgements The authors would like to thank Zabir Ahmed for his assistance in development of the photonics fabrication process and experimental setup. This work has received funding from the Nati...

2012