PySONIC is a Python implementation of the multi-Scale Optimized Neuronal Intramembrane Cavitation (SONIC) model , a computationally efficient and interpretable model of neuronal intramembrane cavitation. It allows to simulate the responses of various neuron types to ultrasonic (and electrical) stimuli.
The package contains four model classes:
- Model defines the generic interface of a model, including mandatory attributes and methods for simulating it.
- BilayerSonophore defines the underlying biomechanical model of intramembrane cavitation.
- PointNeuron defines an abstract generic interface to conductance-based point-neuron electrical models. It is inherited by classes defining the different neuron types with specific membrane dynamics.
- NeuronalBilayerSonophore defines the full electromechanical model for any given neuron type. To do so, it inherits from BilayerSonophore and receives a specific PointNeuron object at initialization.
All model classes contain a simulate method to simulate the underlying model's behavior for a given set of stimulation and physiological parameters. The NeuronalBilayerSonophore.simulate method contains an additional method argument defining whether to perform a detailed (full), coarse-grained (sonic) or hybrid (hybrid) integration of the differential system.
Numerical integration routines are implemented outside the models, in separate Simulator classes:
- PeriodicSimulator integrates a differential system periodically until a stable periodic behavior is detected.
- PWSimulator integrates a differential system given a specific temporal stimulation pattern (pulse repetition frequency, stimulus duty cycle and post-stimulus offset), using different derivative functions for "ON" (with stimulus) and "OFF" (without stimulus) periods
- HybridSimulator inherits from both PeriodicSimulatorand PWSimulator. It integrates a differential system using a hybrid scheme inside each "ON" or "OFF" period:
- The full ODE system is integrated for a few cycles with a dense time granularity until a periodic stabilization detection
- The profiles of all variables over the last cycle are resampled to a far lower (i.e. sparse) sampling rate
- A subset of the ODE system is integrated with a sparse time granularity, while the remaining variables are periodically expanded from their last cycle profile, until the end of the period or that of an predefined update interval.
- The process is repeated from step 1
Several conductance-based point-neuron models are implemented that inherit from the PointNeuron generic interface:
- CorticalRS: cortical regular spiking (RS) neuron
- CorticalFS: cortical fast spiking (FS) neuron
- CorticalLTS: cortical low-threshold spiking (LTS) neuron
- CorticalIB: cortical intrinsically bursting (IB) neuron
- ThalamicRE: thalamic reticular (RE) neuron
- ThalamoCortical: thalamo-cortical (TC) neuron
- OstukaSTN: subthalamic nucleus (STN) neuron
- FrankenhaeuserHuxley: Xenopus myelinated fiber node (FH)
- batches: a generic interface to run simulation batches with or without multiprocessing
- parsers: command line parsing utilities
- plt: graphing utilities
- postpro: post-processing utilities (mostly signal features detection)
- constants: algorithmic constants used across modules and classes
- utils: generic utilities
- Python 3.6+
- Package dependencies (numpy, scipy, ...) are installed automatically upon installation of the package.
- Open a terminal.
- Activate a Python3 environment if needed, e.g. on the tnesrv5 machine:
source /opt/apps/anaconda3/bin activate
- Check that the appropriate version of pip is activated:
- Clone the repository and install the python package:
git clone https://c4science.ch/diffusion/4670/pysonic.git
pip install -e .
You can easily run simulations of any implemented point-neuron model under both electrical and ultrasonic stimuli, and visualize the simulation results, in just a few lines of code:
python import logging import matplotlib.pyplot as plt from PySONIC.core import NeuronalBilayerSonophore from PySONIC.neurons import getPointNeuron from PySONIC.utils import logger from PySONIC.plt import GroupedTimeSeries logger.setLevel(logging.INFO) # Stimulation parameters a = 32e-9 # m Fdrive = 500e3 # Hz Adrive = 100e3 # Pa Astim = 10. # mA/m2 tstim = 250e-3 # s toffset = 50e-3 # s PRF = 100. # Hz DC = 0.5 # - # Point-neuron model and corresponding neuronal intramembrane cavitation model pneuron = getPointNeuron('RS') nbls = NeuronalBilayerSonophore(a, pneuron) # Run simulation upon electrical stimulation, and plot results data, meta = pneuron.simulate(Astim, tstim, toffset, PRF, DC) fig1 = GroupedTimeSeries([(data, meta)]).render() # Run simulation upon ultrasonic stimulation, and plot results data, meta = nbls.simulate(Fdrive, Adrive, tstim, toffset, PRF, DC) fig2 = GroupedTimeSeries([(data, meta)]).render() plt.show()
You can easily run simulations of all 3 model types using the dedicated command line scripts. To do so, open a terminal in the scripts directory.
- Use run_mech.py for simulations of the mechanical model upon ultrasonic stimulation. For instance, for a 32 nm radius bilayer sonophore sonicated at 500 kHz and 100 kPa:
python run_mech.py -a 32 -f 500 -A 100 -p Z
- Use run_estim.py for simulations of point-neuron models upon intracellular electrical stimulation. For instance, a regular-spiking (RS) neuron injected with 10 mA/m2 intracellular current for 30 ms:
python run_estim.py -n RS -A 10 --tstim 30 -p Vm
- Use run_astim.py for simulations of point-neuron models upon ultrasonic stimulation. For instance, for a coarse-grained simulation of a 32 nm radius bilayer sonophore within a regular-spiking (RS) neuron membrane, sonicated at 500 kHz and 100 kPa for 150 ms:
python run_astim.py -n RS -a 32 -f 500 -A 100 --tstim 150 --method sonic -p Qm
Additionally, you can run batches of simulations by specifying more than one value for any given stimulation parameter (e.g. -A 100 200 for sonication with 100 and 200 kPa respectively). These batches can be parallelized using multiprocessing to optimize performance, with the extra argument --mpi.
By default, simulation results are neither shown, nor saved.
To view results directly upon simulation completion, you can use the -p [xxx] option, where [xxx] can be all (to plot all resulting variables) or a given variable name (e.g. Z for membrane deflection, Vm for membrane potential, Qm for membrane charge density).
To save simulation results in binary .pkl files, you can use the -s option. You will be prompted to choose an output directory, unless you also specify it with the -o <output_directory> option. Output files are automatically named from model and simulation parameters to avoid ambiguity.
When running simulation batches, it is highly advised to specify the -s option in order to save results of each simulation. You can then visualize results at a later stage.
To visualize results, use the plot_timeseries.py script. You will be prompted to select the output files containing the simulation(s) results. By default, separate figures will be created for each simulation, showing the time profiles of all resulting variables. Here again, you can choose to show only a subset of variables using the -p [xxx] option. Moreover, if you select a subset of variables, you can visualize resulting profiles across simulations in comparative figures wih the --compare option.
Several more options are available. To view them, type in:
python <script_name> -h
You can easily add other neuron types into the package, providing their ion channel populations and underlying voltage-gated dynamics equations are known.
To add a new point-neuron model, follow this procedure:
- Create a new file, and save it in the neurons sub-folder, with an explicit name (e.g. my_neuron.py).
- Copy-paste the content of the template.py file (also located in the neurons sub-folder) into your file.
- In your file, change the class name from TemplateNeuron to something more explicit (e.g. MyNeuron), and change the neuron name accordingly (e.g. myneuron). This name is a keyword used to refer to the model from outside the class.
- Modify/add biophysical parameters of your model (resting parameters, reversal potentials, channel conductances, ionic concentrations, temperatures, diffusion constants, etc...) as class attributes. If some parameters are not fixed and must be computed, assign them to the class inside a __new__ method, taking the class (cls) as sole attribute.
- Specify a dictionary of names:descriptions of your different differential states (i.e. all the differential variables of your model, except for the membrane potential).
- Modify/add gating states kinetics (alphax and betax methods) that define the voltage-dependent activation and inactivation rates of the different ion channnels gates of your model. Those methods take the membrane potential Vm as input and return a rate in s-1. Alternatively, your can use steady-state open-probabilties (xinf) and adaptation time constants (taux) methods.
- Modify the derStates method that defines the derivatives of your different state variables. These derivatives are defined inside a dictionary, where each state key is paired to a lambda function that takes the membrane potential Vm and a states vector x as inputs, and returns the associated state derivative (in <state_unit>/s).
- Modify the steadyStates method that defines the steady-state values of your different state variables. These steady-states are defined inside a dictionary, where each state key is paired to a lambda function that takes the membrane potential Vm as only input, and returns the associated steady-state value (in <state_unit>). If some steady-states depend on the values of other-steady states, you can proceed as follows:
- define all independent steady-states functions in a dictionary called lambda_dict
- add dependent steady-state functions to the dictionary, calling lambda_dict[k](Vm) for each state k whose value is required.
- Modify/add membrane currents (iXX methods) of your model. Those methods take relevant gating states and the membrane potential Vm as inputs, and must return a current density in mA/m2. You also need to modify the docstring accordingly, as this information is used by the package.
- Modify the currents method that defines the membrane currents of your model. These currents are defined inside a dictionary, where each current key is paired to a lambda function that takes the membrane potential Vm and a states vector x as inputs, and returns the associated current (in mA/m2).
The derStates, steadyStates and currents methods are automatically parsed by the package to adapt neuron models to US stimulation. Hence, make sure to:
- keep them as class methods
- check that all calls to functions that depend solely on Vm appear directly in the methods' lambda expressions and are not hidden inside nested function calls.
- Add the neuron class to the package, by importing it in the __init__.py file of the neurons sub-folder:
python from .my_neuron import MyNeuron
- Verify your point-neuron model by running simulations under various electrical stimuli and comparing the output to the neurons's expected behavior. Implemented required corrections if any.
- Pre-compute lookup tables required to run coarse-grained simulations of the neuron model upon ultrasonic stimulation. To do so, go to the scripts directory and run the run_lookups.py script with the neuron's name as command line argument, e.g.:
python run_lookups.py -n myneuron --mpi
If possible, use the --mpi argument to enable multiprocessing, as lookups pre-computation greatly benefits from parallelization.
That's it! You can now run simulations of your point-neuron model upon ultrasonic stimulation.
Here is a list of future developments:
- Integration within the NEURON simulation environment
- Spatial expansion into nanoscale multicompartmental model
- Spatial expansion into morphological realistic fiber models
- Model validation against experimental data (leech neurons)
Code written and maintained by Theo Lemaire (firstname.lastname@example.org).
This project is licensed under the MIT License - see the LICENSE file for details.
 Lemaire, T., Neufeld, E., Kuster, N., and Micera, S. (2019). Understanding ultrasound neuromodulation using a computationally efficient and interpretable model of intramembrane cavitation. J. Neural Eng.