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Updated: Apr 4, 2026

Optrode Array for Simultaneous Optogenetic Modulation and Electrical Neural Recording
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A Bidirectional Neural Interface With Direct On-Device Neuromorphic Decoding for Closed-Loop Optogenetics.

G Bilodeau1,2, A Miao2, G Gagnon-Turcotte1

  • 1Dept. of Electrical and Computer Eng., Université Laval, Quebec, QC G1V 0A6, Canada.

Biorxiv : the Preprint Server for Biology
|April 3, 2026
PubMed
Summary
This summary is machine-generated.

This study presents a compact, wireless system for closed-loop (CL) neuromodulation, integrating neural decoding onto an FPGA for real-time control of optogenetic stimulation in freely moving rodents.

Keywords:
FPGANeural DecoderNeural InterfaceNeuromorphicOptogeneticsReal-TimeWireless

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Embedded Systems

Background:

  • Closed-loop (CL) neuromodulation requires bidirectional interfaces for simultaneous neural monitoring and responsive stimulation.
  • Implementing computationally intensive neural decoders in compact, wireless systems for freely moving animals is a significant challenge.
  • Existing platforms often rely on tethered setups and external processors, limiting experimental freedom.

Purpose of the Study:

  • To design and optimize a neural decoder integrated into a bidirectional wireless system for CL optogenetic experiments.
  • To develop a resource-efficient platform capable of real-time neural decoding and stimulation control.
  • To enable untethered, real-time neuroscience research in freely moving subjects.

Main Methods:

  • Integrated a 32-channel electrophysiological recording system with neuromorphic feature extraction and dimensionality reduction (PCA) on a Spartan-6 FPGA.
  • Implemented a nonlinear support vector machine (NL-SVM) decoder using spike-count features and leaky integrators, with k-means clustering for model size reduction.
  • Validated decoder performance using non-human primate and rat motor cortex datasets, comparing accuracy against CNNs and Wiener filters.
  • Demonstrated the system *in vivo* with wireless closed-loop optogenetic stimulation in rats.

Main Results:

  • The integrated NL-SVM decoder achieved high accuracy (R²=0.85), comparable to CNNs (R²=0.87) and superior to Wiener filters (R²=0.81).
  • The system demonstrated sub-millisecond inference with minimal memory and power requirements due to efficient feature extraction and dimensionality reduction.
  • *In vivo* experiments showed successful wireless closed-loop optogenetic stimulation in rats with a variance accounted for (VAF) of 0.9148.
  • The platform is fully self-contained, versatile, and resource-efficient for real-time applications.

Conclusions:

  • This work introduces a novel, resource-efficient, and fully self-contained wireless platform for real-time closed-loop neuromodulation.
  • The integrated neural decoder overcomes limitations of tethered systems, enabling greater experimental flexibility for neuroscience research.
  • The developed system facilitates advanced untethered studies in freely moving animals, paving the way for more complex brain-machine interfaces.