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# Description
`PySONIC` is a Python implementation of the **multi-Scale Optimized Neuronal Intramembrane Cavitation (SONIC) model [1]**, 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.
## Content of repository
### Core model classes
The package contains three core model classes:
- `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 three 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.
### Simulators
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 `PeriodicSimulator`and `PWSimulator`. It integrates a differential system using a hybrid scheme inside each "ON" or "OFF" period:
1. The full ODE system is integrated for a few cycles with a dense time granularity until a periodic stabilization detection
2. The profiles of all variables over the last cycle are resampled to a far lower (i.e. sparse) sampling rate
3. 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.
4. The process is repeated from step 1
### Neurons
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
### Other modules
- `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
# Requirements
- Python 3.6 or more
# Installation
- 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:
```$ pip --version```
- Go to the package directory (where the setup.py file is located):
```$ cd <path_to_directory>```
- Insall the package and all its dependencies:
```$ pip install -e .```
# Usage
## Python scripts
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
elec_args = (Astim, tstim, toffset, PRF, DC)
data, tcomp = pneuron.simulate(*elec_args)
logger.info('completed in %.0f ms', tcomp * 1e3)
scheme_plot = GroupedTimeSeries([(data, pneuron.meta(*elec_args))])
fig1 = scheme_plot.render()
# Run simulation upon ultrasonic stimulation, and plot results
US_int_method = 'sonic' # Integration method ('sonic', 'full' or 'hybrid')
US_args = (Fdrive, Adrive, tstim, toffset, PRF, DC, US_int_method)
data, tcomp = nbls.simulate(*US_args)
logger.info('completed in %.0f ms', tcomp * 1e3)
scheme_plot = GroupedTimeSeries([(data, nbls.meta(*US_args))])
fig2 = scheme_plot.render()
plt.show()
```
## From the command line
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```
The simulation results are saved in `.pkl` files. To view these results directly upon simulation completion, you can use the `-p [xxx]` option, where `[xxx]` can be `all` or a given variable name (e.g. `Z` for membrane deflection, `Vm` for membrane potential, `Qm` for membrane charge density).
You can also easily 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`.
Several more options are available. To view them, type in:
```$ python <script_name> -h```
# Extend the package
## Add other neuron types
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:
1. Create a new file, and save it in the `neurons` sub-folder, with an explicit name (e.g. `my_neuron.py`).
2. Copy-paste the content of the `template.py` file (also located in the `neurons` sub-folder) into your file.
3. 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.
4. Modify/add **biophysical parameters** of your model (resting parameters, reversal potentials, channel conductances, ionic concentrations, temperatures, diffusion constants, etc...) as class attributes.
5. 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).
6. 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.
7. Modify the `derStates` method defining a dictionary of lambda functions that take the membrane potential `Vm` and a states vector `x` as inputs, and return a dictionary of lambda function returning the derivatives of your different state variables (in `<state_unit>/s`). **This method is automatically parsed to generate the equivalent `derEffStates` method used in coarse-grained US simulations. Hence, make sure that all internal calls to functions that depend solely on `Vm` appear directly in these lambda expressions and are not hidden inside nested function calls.**
8. Modify the `steadyStates` method defining a dictionary of lambda functions that take a membrane potential value `Vm` as input, and return the steady-state values of your different state variables (in `<state_unit>`).
9. 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**.
10. Modify the `currents` method defining a dictionary of lambda functions that take a membrane potential value Vm and a states vector `x` as inputs, and return the different membrane currents of your model (in `mA/m2`).
11. Add the neuron class to the package, by importing it in the `__init__.py` file of the `neurons` sub-folder:
```from .my_neuron import MyNeuron```
11. 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.
12. 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.
13. That's it! You can now run simulations of your point-neuron model upon ultrasonic stimulation.
## Future developments
Here is a list of future developments:
- [x] Integration within the [NEURON simulation environment](https://www.neuron.yale.edu/neuron/)
- [x] Spatial expansion into nanoscale multicompartmental model
- [ ] Spatial expansion into morphological realistic fiber models
- [ ] Model validation against experimental data (leech neurons)
# Authors
Code written and maintained by Theo Lemaire (theo.lemaire@epfl.ch).
# License
This project is licensed under the MIT License - see the LICENSE file for details.
# References
[1] 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.

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