Scaling Factor Grid Search

Functions for optimizing the scaling factor (lambda) used in rate-to-spike conversion. The scaling factor is crucial for maintaining task performance when converting from continuous rate dynamics to discrete spiking dynamics.

Main Functions

spiking.lambda_grid_search.lambda_grid_search(model_path, task_name, n_trials, scaling_factors, task_settings: Dict[str, Any] | None = None)

Perform grid search over scaling factors for spiking network evaluation.

Parameters:

• model_path – Path to the trained model .mat file

• task_name – Name of the task (‘go_nogo’, ‘xor’, ‘mante’). Hyphens accepted (e.g. ‘go-nogo’).

• n_trials – Number of trials to run for each scaling factor

• scaling_factors – List of scaling factors to test

• task_settings – Optional custom task settings. If None, uses default settings. Can include: T, stim_on, stim_dur, delay, eval_amp_thresh

Returns:

Optimal scaling factor for the model

spiking.lambda_grid_search.evaluate_single_trial(task_name: str, model_path: str, scaling_factor: float, settings: Dict[str, Any] | None = None) → int

Evaluate a single trial for a given task using the appropriate evaluator class.

Parameters:

• task_name – Name of the task (‘go_nogo’, ‘xor’, ‘mante’)

• model_path – Path to the model .mat file

• scaling_factor – Scaling factor for the model

• settings – Optional custom settings. If None, uses default settings.

Returns:

1 if trial is correct, 0 if incorrect

Return type:

int

Grid Search Parameters

The main grid search function accepts:

• model_path (str): Path to trained rate RNN model .mat file (required)

• task_name (str): Task type (‘go_nogo’, ‘xor’, or ‘mante’). Hyphens are accepted and normalized internally (e.g. ‘go-nogo’ becomes ‘go_nogo’). (required)

• n_trials (int): Number of trials to evaluate each scaling factor (required)

• scaling_factors (list): List of scaling factors to test (required)

• task_settings (dict, optional): Custom task settings (T, stim_on, stim_dur, delay, eval_amp_thresh). If None, uses task-specific defaults.

Single Trial Evaluation

The evaluate_single_trial function tests a specific scaling factor:

• task_name (str): Name of the task to evaluate (‘go_nogo’, ‘xor’, ‘mante’)

• model_path (str): Full path to model .mat file

• scaling_factor (float): Scaling factor to test

• settings (dict, optional): Custom task settings. If None, uses task-specific defaults.

Returns 1 if the trial is correct, 0 if incorrect.

Example Usage

from spiking import lambda_grid_search

# Grid search with custom parameters
lambda_grid_search(
    model_path='models/go-nogo/model.mat',
    task_name='go_nogo',
    n_trials=50,
    scaling_factors=list(range(30, 81, 5)),
)

Optimization Process

The grid search follows these steps:

1. Load trained rate model from the specified .mat file

2. Generate test stimuli appropriate for the task type

3. Iterate through scaling factors in the specified range

4. Convert to spiking network for each scaling factor

5. Evaluate performance using task-specific metrics

6. Select optimal scaling factor based on performance

7. Save optimal value to model file for future use

Performance Metrics

Different metrics are used depending on the task:

Go-NoGo Task:

• Response accuracy for Go trials

• Response inhibition accuracy for NoGo trials

• Combined accuracy score

XOR Task:

• Output accuracy for all input combinations

• Temporal precision of responses

Mante Task:

• Context-dependent decision accuracy

• Sensory integration performance

Output Files

The function modifies the input .mat file to include:

• opt_scaling_factor: The optimal scaling factor found

• all_perfs: Performance scores for all tested scaling factors

• scaling_factors: List of all scaling factors that were tested

Output

The function outputs:

• Progress updates during optimization

• Performance scores for each scaling factor tested

• Optimal scaling factor and its performance

• Updated model file with optimal scaling factor saved