biophysics_fitting ❭ simulator ❭ Simulator

Simulator¶

class biophysics_fitting.simulator.Simulator¶

This class can be used to transform a parameter vector into simulated voltage traces.

This is typically done in two steps:

Set up a cell with biophysics from a parameter vector. See Simulator_Setup
Apply a variety of stimuli to the cell and extract voltage traces.

An examplary specification of such a program flow can be found in the module get_Simulator().

The usual application is to specify biophysical parameters in a parameter vector and simulate current injection responses depending on these parameters.

Example

>>> def stim_setup_fun(cell, params):
>>>     # set up some stimulus
>>>     return cell

>>> def stim_run_fun(cell, params):
>>>     # run the simulation
>>>     return cell

>>> def stim_response_measure_fun(cell, params)
>>>     # extract voltage traces from the cell
>>>     # Extract ionic currents from the cell?
>>>     return result

>>> params = pd.Series({"param1": 1, "param2": 2})

>>> s = Simulator()
>>> cell, params = s.setup.get(params)
>>> s.setup.stim_setup_funs.append(
    ['stim_1.setup', stim_setup_fun])
>>> s.setup.stim_run_funs.append(
    ['stim_1.run', stim_run_fun])
>>> s.setup.stim_response_measure_funs.append(
    ['stim_1.measure', stim_response_measure_fun])

Often, it is easier to write functions that accept keyword arguments instead of full parameter vectors This can be done by using params_to_kwargs().

Example

>>> def stim_setup_function(cell, recSite = None):
>>>    # I dont need the :paramref:`params` argument, but I ask recSite directly
>>>    # set up current injection at soma distance recSite
>>>    return cell

>>> s.setup.stim_setup_funs.append([
        BAC.stim_setup,
        params_to_kwargs(stim_setup_function)
    ])

Notable methods:

>>> s.run(params): returns a dictionary with the specified voltage traces for all stimuli
{'stim_1': {'tVec': ..., 'vList': [[...], ...]}, 'stim_2': ...}

>>> s.get_simulated_cell(params, stim)
cell, params  # cell has simulation data

>>> s.setup.get(params):
cell  # with bipohysics set up

>>> s.setup.get_cell_params(params)
cell_params

>>> s.setup.get_cell_params_with_default_sim_prams(params, ...)
neuron_parameter_file

Note

The names for stim_setup_funs, stim_run_funs and stim_response_measure_funs need to start: with the name of the simulus followed by a dot. For each stimulus, each of the three functions needs to be defined exactly once.

Attributes:¶

setup¶

A Simulator_Setup object that keeps track of the simulation setup.

Type:¶: Simulator_Setup

Methods:¶

`get_simulated_cell`(params, stim, simulate)	Get the simulated cell.
`run`(params, stims)	Simulates all stimuli for a given parameter vector.