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Updated: Apr 9, 2026

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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
Published on: June 24, 2015
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Bridging Model and Experiment in Systems Neuroscience with Cleo: The Closed-Loop, Electrophysiology, and
Kyle A Johnsen1, Nathanael A Cruzado2, Zachary C Menard3
1Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia 30332.
Summary
Cleo, a new Python package, simulates complex neuroscience experiments, including optogenetics and imaging. This tool aids in designing and validating experiments, improving neural circuit research.
Area of Science:
- Systems Neuroscience
- Computational Neuroscience
Background:
- Advances in neural recording and stimulation tools have increased experimental complexity.
- Designing effective neuroscience experiments requires sophisticated simulation capabilities.
Purpose of the Study:
- To develop an integrated simulation testbed for designing and validating neuroscience experiments.
- To address the lack of tools integrating optogenetics, imaging, and electrophysiology simulation with neural network models.
Main Methods:
- Developed Cleo, a Python package for simulating closed-loop electrophysiology and optophysiology experiments.
- Integrated virtual recording/stimulation devices and realistic latency into Brian spiking neural network models.
- Enabled simulation of two-photon and multi-opsin/wavelength optogenetics.
Main Results:
- Cleo successfully simulates integrated neuroscience experimental paradigms.
- The tool provides a low-cost platform for experiment design, model validation, and methods engineering.
- Demonstrated utility for advancing optogenetic techniques.
Conclusions:
- Cleo is a unique, open-source tool for simulating complex neuroscience experiments.
- Facilitates the design and optimization of experiments involving neural circuit interrogation.
- Aims to advance systems neuroscience research through accessible and robust simulation.
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