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Related Experiment Video

Updated: Apr 11, 2026

Evaluation and Manipulation of Neural Activity Using Two-Photon Holographic Microscopy

Evaluation and Manipulation of Neural Activity Using Two-Photon Holographic Microscopy

Published on: September 16, 2022

Modeling neural dynamics with optical nonlinearity: From classical models to optogenetic approaches.

Svetlana A Gerasimova^1,2, Alexander N Pisarchik³

¹Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky University, Nizhny Novgorod, Russia.

Mathematical Biosciences and Engineering : MBE

|April 10, 2026

Summary

Photonic neuromorphic systems offer ultrafast, energy-efficient optical interfaces for real-time neural circuit interrogation. This approach enhances optogenetic experiments by enabling adaptive feedback and precise control of neural activity.

Keywords:

Optical neural networks bifurcation and nonlinear dynamics dynamical systems in neuroscience integrated photonic circuits neuromorphic computing nonlinear photonics optogenetics and bio-inspired modeling spiking photonic neurons temporal coding and signal processing

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Last Updated: Apr 11, 2026

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Published on: September 16, 2022

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Engineering and Characterization of an Optogenetic Model of the Human Neuromuscular Junction

Engineering and Characterization of an Optogenetic Model of the Human Neuromuscular Junction

Published on: April 14, 2022

Area of Science:

Neuroscience
Photonics
Computational Science

Background:

Optogenetic experiments in vivo are limited by real-time optical stimulation and signal processing.
Current methods constrain the efficiency and adaptability of neural circuit interrogation.

Purpose of the Study:

To establish how photonic neuromorphic systems can overcome limitations in optogenetics.
To present models for neural dynamics in optical neuromorphic devices.
To provide a roadmap for next-generation neural interfaces.

Main Methods:

Analysis of mathematical and computational models for neural dynamics in optical neuromorphic devices.
Bridging classical neuronal frameworks with optogenetic control paradigms.
Utilizing nonlinear dynamical modeling and data-driven approaches for photonic neuron and synapse design.

Main Results:

Photonic neuromorphic systems enable closed-loop, optically driven interfaces with neural tissue.
Integration with tapered fibers, fiber photometry, and computational optical control systems is demonstrated.
Real-time signal processing and adaptive feedback enhance precision and bidirectionality in neural circuit interactions.

Conclusions:

Photonic neuromorphic computing offers a pathway to overcome optogenetic limitations.
This technology advances intelligent neuroengineering and biomedical diagnostics.
Converging theory, hardware, and neurobiology is key for future neural interfaces.