cubnm.sim.rwwex

rWWExSimGroup

Group of reduced Wong Wang (excitatory) simulations that are executed in parallel.
class cubnm.sim.rwwex.rWWExSimGroup(*args, **kwargs)

Bases: cubnm.sim.base.SimGroup

Group of reduced Wong Wang (excitatory) simulations that are executed in parallel.

Parameters

*args, **kwargs:

see cubnm.sim.SimGroup for details

Attributes

param_lists: dict of np.ndarray
dictionary of parameter lists, including

  • 'G': global coupling strength. Shape: (N_SIMS,)

  • 'w': local excitatory recurrence. Shape: (N_SIMS, nodes)

  • 'I0': external input current. Shape: (N_SIMS, nodes)

  • 'sigma': noise amplitude. Shape: (N_SIMS, nodes)

  • 'v': conduction velocity. Shape: (N_SIMS,)

Equations

\[\begin{split}\begin{gather} x_i = w_i J^N S_i + G J^N \sum_j C_{ij} S_j + I_{0_{i}} \\ r_i = \frac{a x_i - b}{1 - e^{-d(a x_i - b)}} \\ \dot{S_i} = -\frac{S_i}{\tau_s} + (1 - S_i) \gamma r_i + {\sigma}_i \epsilon_i \\ \end{gather}\end{split}\]

References

  • Deco et al. 2013 Journal of Neuroscience (10.1523/JNEUROSCI.1091-13.2013)

model_name = 'rWWEx'
global_param_names = ['G']
regional_param_names = ['w', 'I0', 'sigma']
state_names = ['x', 'r', 'S']
sel_state_var = 'r'
n_noise = 1
default_params
post_init()

Post-initilaization hook that normally does nothing, but can be overridden in derived classes to add custom post-initialization steps.

_check_dt()

Check if integrations steps are valid

_set_default_params(missing=True)

Set default parameters for the simulations.

Parameters

missing: bool, optional

If True (default), only sets parameters that are currently None or have an incorrect length in self.param_lists. If False, always overwrites all listed defaults.

get_config(include_N=False, for_reinit=False)

Get the configuration of the simulation group

Parameters

include_N: bool

include N in the output config is ignored when for_reinit is True

for_reinit: bool

include the parameters that need reinitialization of the simulation core session if changed

Returns

config: dict

dictionary of simulation group configuration

run(force_reinit=False)

Run the simulations in parallel (as possible) on GPU/CPU through the cubnm._core.run_simulations() function which runs compiled C++/CUDA code.

Parameters

force_reinit: bool

force reinitialization of the session. At the beginning of each session (when cubnm is imported) some variables are initialized on CPU/GPU and reused in every run. Set this to True if you want to reinitialize these variables. This is rarely needed.

_process_out(out)

Assigns model outputs (as arrays) to object attributes with correct shapes, names and types.

Parameters

out: tuple

output of cubnm._core.run_simulations function

Notes

The simulation outputs are assigned to the following object attributes:

  • init_time: float

    initialization time of the simulations

  • run_time: float

    run time of the simulations

  • sim_bold: np.ndarray

    simulated BOLD time series. Shape: (N_SIMS, duration/TR, nodes)

  • sim_states: dict of np.ndarray

    simulated state variables with keys as state names and values as arrays with the shape (N_SIMS, nodes) when states_ts is False, and (N_SIMS, duration/TR, nodes) when states_ts is True

If do_fc is True, additionally includes:

  • sim_fc_trils: np.ndarray

    simulated FC lower triangle. Shape: (N_SIMS, n_pairs)

If do_fcd is True, additionally includes:

  • sim_fcd_trils: np.ndarray

    simulated FCD lower triangle. Shape: (N_SIMS, n_window_pairs)

get_state_averages()

Get the averages of state variables across time and nodes for each simulation.

Returns

state_averages: pd.DataFrame

DataFrame of state averages with columns as state names and rows as simulations

get_noise()

Get the (recreated) noise time series. This requires recreation of the noise array if noise segmenting is on. Noise will be recreated based on shuffling indices of nodes and time steps, similar to how it is done in the core code.

Returns

noise: np.ndarray

Noise segment array. Shape: (n_noise, nodes, bw_it, inner_it)

get_sim_fc(idx)

Get the simulated FC of a given simulation idx as a square matrix.

Parameters

idx: int

index of the simulation to get the FC for

Returns

fc: np.ndarray

simulated FC matrix of shape (nodes, nodes) for the simulation with index idx

get_sim_fcd(idx)

Get the simulated FCD of a given simulation idx as a square matrix.

Parameters

idx: int

index of the simulation to get the FC for

Returns

fcd: np.ndarray

simulated FC matrix of shape (n_windows, n_windows) for the simulation with index idx

slice(key, inplace=False)

Slice the simulation group to a single simulation

Parameters

key: int

index of the simulation to slice

inplace: bool

the object will be sliced in place and therefore the data of other simulations will be removed. Otherwise a new object copied from the current object will be returned.

Returns

obj: cubnm.sim.SimGroup

sliced simulation group

clear()

Clear the simulation outputs

_problem_init(problem)

Extends BNMProblem initialization if needed. By default it doesn’t do anything.

Parameters

problem: cubnm.optimize.BNMProblem

optimization problem object

_problem_evaluate(problem, X, out, *args, **kwargs)

Extends BNMProblem evaluation if needed. By default it doesn’t do anything.

Parameters

X: np.ndarray

the normalized parameters of current population in range [0, 1]. Shape: (N, ndim)

out: dict

the output dictionary to store the results with keys ‘F’ and ‘G’. Currently only ‘F’ (cost) is used.

*args, **kwargs

score(emp_fc_tril=None, emp_fcd_tril=None, emp_bold=None, force_cpu=False, usable_mem=None)

Calcualates individual goodness-of-fit terms and aggregates them.

Note

If emp_bold is provided, emp_fc_tril and emp_fcd_tril will be ignored.

Note

For each measure, if the value is NaN, it will be set to the “worst” possible value. NaNs may occur in simulated FCD or FC. For example, in the rWWEx model, when excitation is too high and noise is low, S and in turn BOLD in some areas may become saturated and show no variability. This can result in correlations of their BOLD signals with other nodes (within certain dynamic windows) being NaN.

Parameters

emp_fc_tril: np.ndarray or None

1D array of empirical FC lower triangle. Shape: (edges,)

emp_fcd_tril: np.ndarray or None

1D array of empirical FCD lower triangle. Shape: (window_pairs,)

emp_bold: np.ndarray or None

cleaned and parcellated empirical BOLD time series. Shape: (nodes, volumes) Motion outliers should either be excluded (not recommended as it disrupts the temporal structure) or replaced with zeros. If provided emp_fc_tril and emp_fcd_tril will be ignored.

force_cpu: bool

force CPU for the calculations. Otherwise if GPU is available some of the scores (including “+fc_corr”, “-fcd_ks”) will be calculated on GPU.

usable_mem: int

amount of available GPU memory to be used in bytes. If None, 80% of the free memory will be used.

Returns

scores: pd.DataFrame

The goodness of fit measures (columns) of each simulation (rows)

_get_save_data()

Get the simulation outputs and parameters to be saved to disk

Returns

out_data: dict

dictionary of simulation outputs and parameters

save()

Save simulation outputs to disk as an npz file.

classmethod _get_test_configs(cpu_gpu_identity=False)

Get configs for testing the simulations

Parameters

cpu_gpu_identity: bool

indicates whether configs are for CPU/GPU identity tests in which case force_cpu is not included in the configs since tests will be done on both CPU and GPU

Returns

configs: dict of list

classmethod _get_test_instance(opts)

Initializes an instance that is used in tests

Parameters

opts: dict

dictionary of test options

Returns

sim_group: cubnm.sim.SimGroup

simulation group object of the test simulation which is not run yet