Package: cleosim
Cleo: the Closed-Loop, Electrophysiology, and Optophysiology experiment simulation testbedHello 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 theBrian 2spiking neural network simulator.
We hope users will find these components useful for prototyping experiments, innovating methods, and testing observations about a hypothesesin 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 byKyle Johnsenand Nathan Cruzado under the direction ofChris Rozellat Georgia Institute of Technology.
See the preprinthere.Closed Loop processingCleo 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 the stimulation to study the effects of computation delays.Electrode recordingCleo 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 byTeleńczuk et al., 2020.1P/2P optogenetic stimulationBy modeling light propagation and opsins, Cleo enables users to flexibly add photostimulation to their model.
Both a four-state Markov state model of opsin kinetics is available, as well as a minimal proportional current option for compatibility with simple neuron models.
Cleo also accounts for opsin action spectra to model the effects of multi-light/wavelength/opsin crosstalk and heterogeneous expression.
Parameters are for multiple opsins, and blue optic fiber (1P) and infrared spot (for 2P) illumination.2P imagingUsers can also inject a microscope into their model, selecting neurons on the specified plane of imaging or elsewhere, with signal and noise strength determined by indicator expression levels and position with respect to the focal plane.
The calcium indicator model ofSong et al., 2021is implemented, with parameters included for GCaMP6 variants.🚀 Getting startedJust use pip to install—the name on PyPI iscleosim:pipinstallcleosimThen head to theoverview section of the documentationfor a more detailed discussion of motivation, structure, and basic usage.📚 Related resourcesThose using Cleo to simulate closed-loop control experiments may be interested in software developed for the execution of real-time,in-vivoexperiments.
Developed by members ofChris Rozell's andGarrett Stanley's labs at Georgia Tech, theCLOCTools repositorycan serve these users in two ways:By providing utilities and interfaces with experimental platforms for moving from simulation to reality.By providing performant control and estimation algorithms for feedback control.
Although Cleo enables closed-loop manipulation of network simulations, it does not include any advanced control algorithms itself.
TheldsCtrlEstlibrary implements adaptive linear dynamical system-based control while thehmmlibrary can generate and decode systems with discrete latent states and observations.📃 PublicationsCleo: A testbed for bridging model and experiment by simulating closed-loop stimulation, electrode recording, and optophysiologyK.A. Johnsen, N.A. Cruzado, Z.C. Menard, A.A. Willats, A.S. Charles, and C.J. Rozell.bioRxiv, 2023.CLOC Tools: A Library of Tools for Closed-Loop NeuroscienceA.A. Willats, M.F. Bolus, K.A. Johnsen, G.B. Stanley, and C.J. Rozell.In prep, 2023.State-Aware Control of Switching Neural DynamicsA.A. Willats, M.F. Bolus, C.J. Whitmire, G.B. Stanley, and C.J. Rozell.In prep, 2023.Closed-Loop Identifiability in Neural CircuitsA. Willats, M. O'Shaughnessy, and C. Rozell.In prep, 2023.State-space optimal feedback control of optogenetically driven neural activityM.F. Bolus, A.A. Willats, C.J. Rozell and G.B. Stanley.Journal of Neural Engineering, 18(3), pp. 036006, March 2021.Design strategies for dynamic closed-loop optogenetic neurocontrol in vivoM.F. Bolus, A.A. Willats, C.J. Whitmire, C.J. Rozell and G.B. Stanley.Journal of Neural Engineering, 15(2), pp. 026011, January 2018.