Cleo: the Closed-Loop, Electrophysiology, and Optogenetics experiment simulation testbed#

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Cleo: the Closed-Loop, Electrophysiology, and Optogenetics experiment simulation testbed

Hello there! Cleo has the goal of bridging theory and experiment for mesoscale neuroscience, facilitating electrode recording, optogenetic stimulation, and closed-loop experiments (e.g., real-time input and output processing) with the Brian 2 spiking neural network simulator. We hope users will find these components useful for prototyping experiments, innovating methods, and testing observations about a hypotheses in silico, incorporating into spiking neural network models laboratory techniques ranging from passive observation to complex model-based feedback control. Cleo also serves as an extensible, modular base for developing additional recording and stimulation modules for Brian simulations.

This package was developed by Kyle Johnsen and Nathan Cruzado under the direction of Chris Rozell at Georgia Institute of Technology.


CL icon Closed Loop processing#

Cleo allows for flexible I/O processing in real time, enabling the simulation of closed-loop experiments such as event-triggered or feedback control. The user can also add latency to closed-loop stimulation to study the effects of computation delays.

CL icon Electrode recording#

Cleo provides functions for configuring electrode arrays and placing them in arbitrary locations in the simulation. The user can then specify parameters for probabilistic spike detection or a spike-based LFP approximation developed by Teleńczuk et al., 2020.

CL icon Optogenetic stimulation#

By providing an optic fiber-light propagation model, Cleo enables users to flexibly add photostimulation to their model. Both a four-state Markov state model of opsin dynamics is available, as well as a minimal proportional current option for compatibility with simple neuron models. Parameters are provided for the common blue light/ChR2 setup.

Getting started#

Just use pip to install—the name on PyPI is cleosim:

pip install cleosim

Then head to the overview section of the documentation for a more detailed discussion of motivation, structure, and basic usage.

Documentation contents#

Indices and tables#