brian2modelfitting.fitter module¶

class
brian2modelfitting.fitter.
Fitter
(dt, model, input, output, input_var, output_var, n_samples, threshold, reset, refractory, method, param_init, penalty, use_units=True)[source]¶ Bases:
object
Base Fitter class for model fitting applications.
Creates an interface for model fitting of traces with parameters draw by gradientfree algorithms (through ask/tell interfaces).
Initiates n_neurons = num input traces * num samples, to which drawn parameters get assigned and evaluates them in parallel.
Parameters:  dt (
Quantity
) – The size of the time step.  model (
Equations
or str) – The equations describing the model.  input (
ndarray
orQuantity
) – A 2D array of shape(n_traces, time steps)
given the input that will be fed into the model.  output (
Quantity
or list) – Recorded output of the model that the model should reproduce. Should be a 2D array of the same shape as the input when fitting traces withTraceFitter
, a list of spike times when fitting spike trains withSpikeFitter
.  input_var (str) – The name of the input variable in the model. Note that this variable
should be used in the model (e.g. a variable
I
that is added as a current in the membrane potential equation), but not defined.  output_var (str) – The name of the output variable in the model. Only needed when fitting
traces with
TraceFitter
.  n_samples (int) – Number of parameter samples to be optimized over in a single iteration.
 threshold (
str
, optional) – The condition which produces spikes. Should be a boolean expression as a string.  reset (
str
, optional) – The (possibly multiline) string with the code to execute on reset.  refractory (
str
orQuantity
, optional) – Either the length of the refractory period (e.g. 2*ms), a string expression that evaluates to the length of the refractory period after each spike (e.g. ‘(1 + rand())*ms’), or a string expression evaluating to a boolean value, given the condition under which the neuron stays refractory after a spike (e.g. ‘v > 20*mV’)  method (
str
, optional) – Integration method  penalty (str, optional) – The name of a variable or subexpression in the model that will be added to the error at the end of each iteration. Note that only the term at the end of the simulation is taken into account, so this term should not be varying in time.
 param_init (
dict
, optional) – Dictionary of variables to be initialized with respective values

best_error
¶

best_params
¶

calc_errors
(metric)[source]¶ Abstract method required in all Fitter classes, used for calculating errors
Parameters: metric ( Metric
children) – Child of Metric class, specifies optimization metric

fit
(optimizer, metric=None, n_rounds=1, callback='text', restart=False, online_error=False, start_iteration=None, penalty=None, level=0, **params)[source]¶ Run the optimization algorithm for given amount of rounds with given number of samples drawn. Return best set of parameters and corresponding error.
Parameters:  optimizer (
Optimizer
children) – Child of Optimizer class, specific for each library.  metric (
Metric
children) – Child of Metric class, specifies optimization metric  n_rounds (int) – Number of rounds to optimize over (feedback provided over each round).
 callback (
str
orCallable
) – Either the name of a provided callback function (text
orprogressbar
), or a custom feedback functionfunc(parameters, errors, best_parameters, best_error, index)
. If this function returnsTrue
the fitting execution is interrupted.  restart (bool) – Flag that reinitializes the Fitter to reset the optimization. With restart True user is allowed to change optimizer/metric.
 online_error (bool, optional) – Whether to calculate the squared error between target trace and
simulated trace online. Defaults to
False
.  start_iteration (int, optional) – A value for the
iteration
variable at the first iteration. If not given, will use 0 for the first call offit
(and for later calls whenrestart
is specified). Later calls will continue to increase the value from the previous calls.  penalty (str, optional) – The name of a variable or subexpression in the model that will be
added to the error at the end of each iteration. Note that only
the term at the end of the simulation is taken into account, so
this term should not be varying in time. If not given, will reuse
the value specified during
Fitter
initialization.  level (
int
, optional) – How much farther to go down in the stack to find the namespace.  **params – bounds for each parameter
Returns:  best_results (dict) – dictionary with best parameter set
 error (float) – error value for best parameter set
 optimizer (

generate
(output_var=None, params=None, param_init=None, iteration=1000000000.0, level=0)[source]¶ Generates traces for best fit of parameters and all inputs. If provided with other parameters provides those.
Parameters:  output_var (str or sequence of str) – Name of the output variable to be monitored, or the special name
spikes
to record spikes. Can also be a sequence of names to record multiple variables.  params (dict) – Dictionary of parameters to generate fits for.
 param_init (dict) – Dictionary of initial values for the model.
 iteration (int, optional) – Value for the
iteration
variable provided to the simulation. Defaults to a high value (1e9). This is based on the assumption that the model implements some coupling of the fitted variable to the target variable, and that this coupling inversely depends on the iteration number. In this case, one would usually want to switch off the coupling when generating traces/spikes for given parameters.  level (
int
, optional) – How much farther to go down in the stack to find the namespace.
Returns: Either a 2D
Quantity
with the recorded output variable over time, with shape <number of input traces> × <number of time steps>, or a list of spike times for each input trace. If several names were given asoutput_var
, then the result is a dictionary with the names of the variable as the key.Return type: fit
 output_var (str or sequence of str) – Name of the output variable to be monitored, or the special name

optimization_iter
(optimizer, metric, penalty)[source]¶ Function performs all operations required for one iteration of optimization. Drawing parameters, setting them to simulator and calulating the error.
Parameters:  optimizer (
Optimizer
) –  metric (
Metric
) –  penalty (str, optional) – The name of a variable or subexpression in the model that will be added to the error at the end of each iteration. Note that only the term at the end of the simulation is taken into account, so this term should not be varying in time.
Returns:  results (list) – recommended parameters
 parameters (list of list) – drawn parameters
 errors (list) – calculated errors
 optimizer (

results
(format='list', use_units=None)[source]¶ Returns all of the gathered results (parameters and errors). In one of the 3 formats: ‘dataframe’, ‘list’, ‘dict’.
Parameters:  format (str) – The desired output format. Currently supported:
dataframe
,list
, ordict
.  use_units (bool, optional) – Whether to use units in the results. If not specified, defaults to
Tracefitter.use_units
, i.e. the value that was specified when theTracefitter
object was created (True
by default).
Returns: ‘dataframe’: returns pandas
DataFrame
without units ‘list’: list of dictionaries ‘dict’: dictionary of listsReturn type:  format (str) – The desired output format. Currently supported:

setup_neuron_group
(n_neurons, namespace, calc_gradient=False, optimize=True, online_error=False, name='neurons')[source]¶ Setup neuron group, initialize required number of neurons, create namespace and initialize the parameters.
Parameters:  n_neurons (int) – number of required neurons
 **namespace – arguments to be added to NeuronGroup namespace
Returns: neurons – group of neurons
Return type:
 dt (

class
brian2modelfitting.fitter.
OnlineTraceFitter
(model, input_var, input, output_var, output, dt, n_samples=30, method=None, reset=None, refractory=False, threshold=None, param_init=None, t_start=0. * second, penalty=None)[source]¶ Bases:
brian2modelfitting.fitter.Fitter

best_error
¶

best_params
¶

fit
(optimizer, n_rounds=1, callback='text', restart=False, start_iteration=None, penalty=None, level=0, **params)[source]¶ Run the optimization algorithm for given amount of rounds with given number of samples drawn. Return best set of parameters and corresponding error.
Parameters:  optimizer (
Optimizer
children) – Child of Optimizer class, specific for each library.  metric (
Metric
children) – Child of Metric class, specifies optimization metric  n_rounds (int) – Number of rounds to optimize over (feedback provided over each round).
 callback (
str
orCallable
) – Either the name of a provided callback function (text
orprogressbar
), or a custom feedback functionfunc(parameters, errors, best_parameters, best_error, index)
. If this function returnsTrue
the fitting execution is interrupted.  restart (bool) – Flag that reinitializes the Fitter to reset the optimization. With restart True user is allowed to change optimizer/metric.
 online_error (bool, optional) – Whether to calculate the squared error between target trace and
simulated trace online. Defaults to
False
.  start_iteration (int, optional) – A value for the
iteration
variable at the first iteration. If not given, will use 0 for the first call offit
(and for later calls whenrestart
is specified). Later calls will continue to increase the value from the previous calls.  penalty (str, optional) – The name of a variable or subexpression in the model that will be
added to the error at the end of each iteration. Note that only
the term at the end of the simulation is taken into account, so
this term should not be varying in time. If not given, will reuse
the value specified during
Fitter
initialization.  level (
int
, optional) – How much farther to go down in the stack to find the namespace.  **params – bounds for each parameter
Returns:  best_results (dict) – dictionary with best parameter set
 error (float) – error value for best parameter set
 optimizer (

generate
(output_var=None, params=None, param_init=None, iteration=1000000000.0, level=0)¶ Generates traces for best fit of parameters and all inputs. If provided with other parameters provides those.
Parameters:  output_var (str or sequence of str) – Name of the output variable to be monitored, or the special name
spikes
to record spikes. Can also be a sequence of names to record multiple variables.  params (dict) – Dictionary of parameters to generate fits for.
 param_init (dict) – Dictionary of initial values for the model.
 iteration (int, optional) – Value for the
iteration
variable provided to the simulation. Defaults to a high value (1e9). This is based on the assumption that the model implements some coupling of the fitted variable to the target variable, and that this coupling inversely depends on the iteration number. In this case, one would usually want to switch off the coupling when generating traces/spikes for given parameters.  level (
int
, optional) – How much farther to go down in the stack to find the namespace.
Returns: Either a 2D
Quantity
with the recorded output variable over time, with shape <number of input traces> × <number of time steps>, or a list of spike times for each input trace. If several names were given asoutput_var
, then the result is a dictionary with the names of the variable as the key.Return type: fit
 output_var (str or sequence of str) – Name of the output variable to be monitored, or the special name

generate_traces
(params=None, param_init=None, level=0)[source]¶ Generates traces for best fit of parameters and all inputs

optimization_iter
(optimizer, metric, penalty)¶ Function performs all operations required for one iteration of optimization. Drawing parameters, setting them to simulator and calulating the error.
Parameters:  optimizer (
Optimizer
) –  metric (
Metric
) –  penalty (str, optional) – The name of a variable or subexpression in the model that will be added to the error at the end of each iteration. Note that only the term at the end of the simulation is taken into account, so this term should not be varying in time.
Returns:  results (list) – recommended parameters
 parameters (list of list) – drawn parameters
 errors (list) – calculated errors
 optimizer (

results
(format='list', use_units=None)¶ Returns all of the gathered results (parameters and errors). In one of the 3 formats: ‘dataframe’, ‘list’, ‘dict’.
Parameters:  format (str) – The desired output format. Currently supported:
dataframe
,list
, ordict
.  use_units (bool, optional) – Whether to use units in the results. If not specified, defaults to
Tracefitter.use_units
, i.e. the value that was specified when theTracefitter
object was created (True
by default).
Returns: ‘dataframe’: returns pandas
DataFrame
without units ‘list’: list of dictionaries ‘dict’: dictionary of listsReturn type:  format (str) – The desired output format. Currently supported:

setup_neuron_group
(n_neurons, namespace, calc_gradient=False, optimize=True, online_error=False, name='neurons')¶ Setup neuron group, initialize required number of neurons, create namespace and initialize the parameters.
Parameters:  n_neurons (int) – number of required neurons
 **namespace – arguments to be added to NeuronGroup namespace
Returns: neurons – group of neurons
Return type:

setup_simulator
(network_name, n_neurons, output_var, param_init, calc_gradient=False, optimize=True, online_error=False, level=1)¶


class
brian2modelfitting.fitter.
SpikeFitter
(model, input, output, dt, reset, threshold, input_var='I', refractory=False, n_samples=30, method=None, param_init=None, penalty=None, use_units=True)[source]¶ Bases:
brian2modelfitting.fitter.Fitter

best_error
¶

best_params
¶

calc_errors
(metric)[source]¶ Returns errors after simulation with SpikeMonitor. To be used inside optim_iter.

fit
(optimizer, metric=None, n_rounds=1, callback='text', restart=False, start_iteration=None, penalty=None, level=0, **params)[source]¶ Run the optimization algorithm for given amount of rounds with given number of samples drawn. Return best set of parameters and corresponding error.
Parameters:  optimizer (
Optimizer
children) – Child of Optimizer class, specific for each library.  metric (
Metric
children) – Child of Metric class, specifies optimization metric  n_rounds (int) – Number of rounds to optimize over (feedback provided over each round).
 callback (
str
orCallable
) – Either the name of a provided callback function (text
orprogressbar
), or a custom feedback functionfunc(parameters, errors, best_parameters, best_error, index)
. If this function returnsTrue
the fitting execution is interrupted.  restart (bool) – Flag that reinitializes the Fitter to reset the optimization. With restart True user is allowed to change optimizer/metric.
 online_error (bool, optional) – Whether to calculate the squared error between target trace and
simulated trace online. Defaults to
False
.  start_iteration (int, optional) – A value for the
iteration
variable at the first iteration. If not given, will use 0 for the first call offit
(and for later calls whenrestart
is specified). Later calls will continue to increase the value from the previous calls.  penalty (str, optional) – The name of a variable or subexpression in the model that will be
added to the error at the end of each iteration. Note that only
the term at the end of the simulation is taken into account, so
this term should not be varying in time. If not given, will reuse
the value specified during
Fitter
initialization.  level (
int
, optional) – How much farther to go down in the stack to find the namespace.  **params – bounds for each parameter
Returns:  best_results (dict) – dictionary with best parameter set
 error (float) – error value for best parameter set
 optimizer (

generate
(output_var=None, params=None, param_init=None, iteration=1000000000.0, level=0)¶ Generates traces for best fit of parameters and all inputs. If provided with other parameters provides those.
Parameters:  output_var (str or sequence of str) – Name of the output variable to be monitored, or the special name
spikes
to record spikes. Can also be a sequence of names to record multiple variables.  params (dict) – Dictionary of parameters to generate fits for.
 param_init (dict) – Dictionary of initial values for the model.
 iteration (int, optional) – Value for the
iteration
variable provided to the simulation. Defaults to a high value (1e9). This is based on the assumption that the model implements some coupling of the fitted variable to the target variable, and that this coupling inversely depends on the iteration number. In this case, one would usually want to switch off the coupling when generating traces/spikes for given parameters.  level (
int
, optional) – How much farther to go down in the stack to find the namespace.
Returns: Either a 2D
Quantity
with the recorded output variable over time, with shape <number of input traces> × <number of time steps>, or a list of spike times for each input trace. If several names were given asoutput_var
, then the result is a dictionary with the names of the variable as the key.Return type: fit
 output_var (str or sequence of str) – Name of the output variable to be monitored, or the special name

generate_spikes
(params=None, param_init=None, iteration=1000000000.0, level=0)[source]¶ Generates traces for best fit of parameters and all inputs

optimization_iter
(optimizer, metric, penalty)¶ Function performs all operations required for one iteration of optimization. Drawing parameters, setting them to simulator and calulating the error.
Parameters:  optimizer (
Optimizer
) –  metric (
Metric
) –  penalty (str, optional) – The name of a variable or subexpression in the model that will be added to the error at the end of each iteration. Note that only the term at the end of the simulation is taken into account, so this term should not be varying in time.
Returns:  results (list) – recommended parameters
 parameters (list of list) – drawn parameters
 errors (list) – calculated errors
 optimizer (

results
(format='list', use_units=None)¶ Returns all of the gathered results (parameters and errors). In one of the 3 formats: ‘dataframe’, ‘list’, ‘dict’.
Parameters:  format (str) – The desired output format. Currently supported:
dataframe
,list
, ordict
.  use_units (bool, optional) – Whether to use units in the results. If not specified, defaults to
Tracefitter.use_units
, i.e. the value that was specified when theTracefitter
object was created (True
by default).
Returns: ‘dataframe’: returns pandas
DataFrame
without units ‘list’: list of dictionaries ‘dict’: dictionary of listsReturn type:  format (str) – The desired output format. Currently supported:

setup_neuron_group
(n_neurons, namespace, calc_gradient=False, optimize=True, online_error=False, name='neurons')¶ Setup neuron group, initialize required number of neurons, create namespace and initialize the parameters.
Parameters:  n_neurons (int) – number of required neurons
 **namespace – arguments to be added to NeuronGroup namespace
Returns: neurons – group of neurons
Return type:

setup_simulator
(network_name, n_neurons, output_var, param_init, calc_gradient=False, optimize=True, online_error=False, level=1)¶


class
brian2modelfitting.fitter.
TraceFitter
(model, input_var, input, output_var, output, dt, n_samples=30, method=None, reset=None, refractory=False, threshold=None, param_init=None, penalty=None, use_units=True)[source]¶ Bases:
brian2modelfitting.fitter.Fitter
A
Fitter
for fitting recorded traces (e.g. of the membrane potential).Parameters:  model –
 input_var –
 input –
 output_var –
 output –
 dt –
 n_samples –
 method –
 reset –
 refractory –
 threshold –
 param_init –
 use_units (bool, optional) – Whether to use units in all userfacing interfaces, e.g. in the callback
arguments or in the returned parameter dictionary and errors. Defaults
to
True
.

best_error
¶

best_params
¶

calc_errors
(metric)[source]¶ Returns errors after simulation with StateMonitor. To be used inside
optim_iter
.

fit
(optimizer, metric=None, n_rounds=1, callback='text', restart=False, start_iteration=None, penalty=None, level=0, **params)[source]¶ Run the optimization algorithm for given amount of rounds with given number of samples drawn. Return best set of parameters and corresponding error.
Parameters:  optimizer (
Optimizer
children) – Child of Optimizer class, specific for each library.  metric (
Metric
children) – Child of Metric class, specifies optimization metric  n_rounds (int) – Number of rounds to optimize over (feedback provided over each round).
 callback (
str
orCallable
) – Either the name of a provided callback function (text
orprogressbar
), or a custom feedback functionfunc(parameters, errors, best_parameters, best_error, index)
. If this function returnsTrue
the fitting execution is interrupted.  restart (bool) – Flag that reinitializes the Fitter to reset the optimization. With restart True user is allowed to change optimizer/metric.
 online_error (bool, optional) – Whether to calculate the squared error between target trace and
simulated trace online. Defaults to
False
.  start_iteration (int, optional) – A value for the
iteration
variable at the first iteration. If not given, will use 0 for the first call offit
(and for later calls whenrestart
is specified). Later calls will continue to increase the value from the previous calls.  penalty (str, optional) – The name of a variable or subexpression in the model that will be
added to the error at the end of each iteration. Note that only
the term at the end of the simulation is taken into account, so
this term should not be varying in time. If not given, will reuse
the value specified during
Fitter
initialization.  level (
int
, optional) – How much farther to go down in the stack to find the namespace.  **params – bounds for each parameter
Returns:  best_results (dict) – dictionary with best parameter set
 error (float) – error value for best parameter set
 optimizer (

generate
(output_var=None, params=None, param_init=None, iteration=1000000000.0, level=0)¶ Generates traces for best fit of parameters and all inputs. If provided with other parameters provides those.
Parameters:  output_var (str or sequence of str) – Name of the output variable to be monitored, or the special name
spikes
to record spikes. Can also be a sequence of names to record multiple variables.  params (dict) – Dictionary of parameters to generate fits for.
 param_init (dict) – Dictionary of initial values for the model.
 iteration (int, optional) – Value for the
iteration
variable provided to the simulation. Defaults to a high value (1e9). This is based on the assumption that the model implements some coupling of the fitted variable to the target variable, and that this coupling inversely depends on the iteration number. In this case, one would usually want to switch off the coupling when generating traces/spikes for given parameters.  level (
int
, optional) – How much farther to go down in the stack to find the namespace.
Returns: Either a 2D
Quantity
with the recorded output variable over time, with shape <number of input traces> × <number of time steps>, or a list of spike times for each input trace. If several names were given asoutput_var
, then the result is a dictionary with the names of the variable as the key.Return type: fit
 output_var (str or sequence of str) – Name of the output variable to be monitored, or the special name

generate_traces
(params=None, param_init=None, iteration=1000000000.0, level=0)[source]¶ Generates traces for best fit of parameters and all inputs

optimization_iter
(optimizer, metric, penalty)¶ Function performs all operations required for one iteration of optimization. Drawing parameters, setting them to simulator and calulating the error.
Parameters:  optimizer (
Optimizer
) –  metric (
Metric
) –  penalty (str, optional) – The name of a variable or subexpression in the model that will be added to the error at the end of each iteration. Note that only the term at the end of the simulation is taken into account, so this term should not be varying in time.
Returns:  results (list) – recommended parameters
 parameters (list of list) – drawn parameters
 errors (list) – calculated errors
 optimizer (

refine
(params=None, t_start=None, t_weights=None, normalization=None, callback='text', calc_gradient=False, optimize=True, iteration=1000000000.0, level=0, **kwds)[source]¶ Refine the fitting results with a sequentially operating minimization algorithm. Uses the lmfit package which itself makes use of scipy.optimize. Has to be called after
fit
, but a call withn_rounds=0
is enough.Parameters:  params (dict, optional) – A dictionary with the parameters to use as a starting point for the
refinement. If not given, the best parameters found so far by
fit
will be used.  t_start (
Quantity
, optional) – Start of time window considered for calculating the fit error. If not set, will reuse thet_start
value from the previously used metric.  t_weights (
ndarray
, optional) – A 1dimensional array of weights for each time point. This array has to have the same size as the input/output traces that are used for fitting. A value of 0 means that data points are ignored. The weight values will be normalized so only the relative values matter. For example, an array containing 1s, and 2s, will weigh the regions with 2s twice as high (with respect to the squared error) as the regions with 1s. Using instead values of 0.5 and 1 would have the same effect. Cannot be combined witht_start
. If not set, will reuse thet_weights
value from the previously used metric.  normalization (float, optional) – A normalization term that will be used rescale results before
handing them to the optimization algorithm. Can be useful if the
algorithm makes assumptions about the scale of errors, e.g. if the
size of steps in the parameter space depends on the absolute value
of the error. The difference between simulated and target traces
will be divided by this value. If not set, will reuse the
normalization
value from the previously used metric.  callback (
str
orCallable
) – Either the name of a provided callback function (text
orprogressbar
), or a custom feedback functionfunc(parameters, errors, best_parameters, best_error, index)
. If this function returnsTrue
the fitting execution is interrupted.  calc_gradient (bool, optional) – Whether to add “sensitivity variables” to the equation that track
the sensitivity of the equation variables to the parameters. This
information will be used to pass the local gradient of the error
with respect to the parameters to the optimization function. This
can lead to much faster convergence than with an estimated gradient
but comes at the expense of additional computation. Defaults to
False
.  optimize (bool, optional) – Whether to remove sensitivity variables from the equations that do
not evolve if initialized to zero (e.g.
dS_x_y/dt = S_x_y/tau
would be removed). This avoids unnecessary computation but will fail in the rare case that such a sensitivity variable needs to be initialized to a nonzero value. Only taken into account ifcalc_gradient
isTrue
. Defaults toTrue
.  iteration (int, optional) – Value for the
iteration
variable provided to the simulation. Defaults to a high value (1e9). This is based on the assumption that the model implements some coupling of the fitted variable to the target variable, and that this coupling inversely depends on the iteration number. In this case, one would usually want to switch off the coupling when refining the solution.  level (int, optional) – How much farther to go down in the stack to find the namespace.
 kwds – Additional arguments can overwrite the bounds for individual
parameters (if not given, the bounds previously specified in the
call to
fit
will be used). All other arguments will be passed on tolmfit.minimize
and can be used to e.g. change the method, or to specify methodspecific arguments.
Returns:  parameters (dict) – The parameters at the end of the optimization process as a dictionary.
 result (
lmfit.MinimizerResult
) – The result of the optimization process.
Notes
The default method used by
lmfit
is leastsquares minimization using a LevenbergMarquardt method. Note that there is no support for specifying aMetric
, the given output trace(s) will be subtracted from the simulated trace(s) and passed on to the minimization algorithm.This method always uses the runtime mode, independent of the selection of the current device.
 params (dict, optional) – A dictionary with the parameters to use as a starting point for the
refinement. If not given, the best parameters found so far by

results
(format='list', use_units=None)¶ Returns all of the gathered results (parameters and errors). In one of the 3 formats: ‘dataframe’, ‘list’, ‘dict’.
Parameters:  format (str) – The desired output format. Currently supported:
dataframe
,list
, ordict
.  use_units (bool, optional) – Whether to use units in the results. If not specified, defaults to
Tracefitter.use_units
, i.e. the value that was specified when theTracefitter
object was created (True
by default).
Returns: ‘dataframe’: returns pandas
DataFrame
without units ‘list’: list of dictionaries ‘dict’: dictionary of listsReturn type:  format (str) – The desired output format. Currently supported:

setup_neuron_group
(n_neurons, namespace, calc_gradient=False, optimize=True, online_error=False, name='neurons')¶ Setup neuron group, initialize required number of neurons, create namespace and initialize the parameters.
Parameters:  n_neurons (int) – number of required neurons
 **namespace – arguments to be added to NeuronGroup namespace
Returns: neurons – group of neurons
Return type:

setup_simulator
(network_name, n_neurons, output_var, param_init, calc_gradient=False, optimize=True, online_error=False, level=1)¶

brian2modelfitting.fitter.
get_param_dic
(params, param_names, n_traces, n_samples)[source]¶ Transform parameters into a dictionary of appropiate size

brian2modelfitting.fitter.
get_sensitivity_equations
(group, parameters, namespace=None, level=1, optimize=True)[source]¶ Get equations for sensitivity variables.
Parameters:  group (
NeuronGroup
) – The group of neurons that will be simulated.  parameters (list of str) – Names of the parameters that are fit.
 namespace (dict, optional) – The namespace to use.
 level (
int
, optional) – How much farther to go down in the stack to find the namespace.  optimize (bool, optional) – Whether to remove sensitivity variables from the equations that do
not evolve if initialized to zero (e.g.
dS_x_y/dt = S_x_y/tau
would be removed). This avoids unnecessary computation but will fail in the rare case that such a sensitivity variable needs to be initialized to a nonzero value. Defaults toTrue
.
Returns: sensitivity_eqs – The equations for the sensitivity variables.
Return type: Equations
 group (

brian2modelfitting.fitter.
get_sensitivity_init
(group, parameters, param_init)[source]¶ Calculate the initial values for the sensitivity parameters (necessary if initial values are functions of parameters).
Parameters:  group (
NeuronGroup
) – The group of neurons that will be simulated.  parameters (list of str) – Names of the parameters that are fit.
 param_init (dict) – The dictionary with expressions to initialize the model variables.
Returns: sensitivity_init – Dictionary of expressions to initialize the sensitivity parameters.
Return type:  group (

brian2modelfitting.fitter.
get_spikes
(monitor, n_samples, n_traces)[source]¶ Get spikes from spike monitor change format from dict to a list, remove units.

brian2modelfitting.fitter.
setup_fit
()[source]¶ Function sets up simulator in one of the two available modes: runtime or standalone. The
Simulator
that will be used depends on the currently setDevice
. In the case ofCPPStandaloneDevice
, the device will also be reset if it has already run a simulation.Returns: simulator Return type: Simulator