7.5: Batch Simulation

This example shows how to do batch simulations in HNN-core, allowing users to efficiently run multiple simulations with different parameters for comprehensive analysis.

Authors:

Abdul Samad Siddiqui ( abdulsamadsid1@gmail.com )
Nick Tolley ( nicholas_tolley@brown.edu )
Ryan Thorpe ( ryan_thorpe@brown.edu )
Mainak Jas ( mjas@mgh.harvard.edu )

This project was supported by Google Summer of Code (GSoC) 2024.

1. Import Parallel dependencies

Please note that in order to run this tutorial you MUST have installed the "parallel" dependencies for HNN-Core. Normally, these are optional for general HNN-Core use, but they are required for this tutorial. In the next cell, we will check to make sure they are installed, and if they are not, then please follow the directions provided.


                        try:
    from hnn_core.batch_simulate import BatchSimulate
except ModuleNotFoundError:
    raise ModuleNotFoundError("""
The joblib module required for BatchSimulate is not available. Please install the hnn-core package using

    pip install "hnn-core[parallel]"

""")

If you installed hnn_core using the Python or Conda package (i.e. not from source), then you can uncomment and run the following code cell to easily install the optimization dependences into your current Python environment. If you do so, please restart your Jupyter kernel before running the notebook again.


        # %pip install -q "hnn-core[parallel]"

2. Import remaining libraries

Next, let's import the other hnn_core modules and external libraries required for this tutorial.


                        import matplotlib.pyplot as plt
import numpy as np
import psutil  # installed via "hnn-core[parallel]" above

from hnn_core import neymotin_2020_model

# This uses the total number of CPU cores on your machine minus one:
n_jobs = psutil.cpu_count(logical=False) - 1

The add_evoked_drive function simulates external input to the network, mimicking sensory stimulation or other external events.

evprox indicates a proximal drive, targeting dendrites near the cell bodies.
mu=40 and sigma=5 define the timing (mean and spread) of the input.
weights_ampa and synaptic_delays control the strength and timing of the input.

This evoked drive causes the initial positive deflection in the dipole signal, triggering a cascade of activity through the network and resulting in the complex waveforms observed.


                    def set_params(param_values, net=None):
"""
Set parameters for the network drives.

Parameters
----------
param_values : dict
    Dictionary of parameter values.
net : instance of Network, optional
    If None, a new network is created using the specified model type.
"""
weights_ampa = {'L2_basket': param_values['weight_basket'],
                'L2_pyramidal': param_values['weight_pyr'],
                'L5_basket': param_values['weight_basket'],
                'L5_pyramidal': param_values['weight_pyr']}

synaptic_delays = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,
                   'L5_basket': 1., 'L5_pyramidal': 1.}

# Add an evoked drive to the network.
net.add_evoked_drive('evprox',
                     mu=40,
                     sigma=5,
                     numspikes=1,
                     location='proximal',
                     weights_ampa=weights_ampa,
                     synaptic_delays=synaptic_delays)

Next, we define a parameter grid for the batch simulation.


                        param_grid = {
    'weight_basket': np.logspace(-4, -1, 20),
    'weight_pyr': np.logspace(-4, -1, 20)
}

We then define a function to calculate summary statistics.


                    def summary_func(results):
"""
Calculate the min and max dipole peak for each simulation result.

Parameters
----------
results : list
    List of dictionaries containing simulation results.

Returns
-------
summary_stats : list
    Summary statistics for each simulation result.
"""
summary_stats = []
for result in results:
    dpl_smooth = result['dpl'][0].copy().smooth(window_len=30)
    dpl_data = dpl_smooth.data['agg']
    min_peak = np.min(dpl_data)
    max_peak = np.max(dpl_data)
    summary_stats.append({'min_peak': min_peak, 'max_peak': max_peak})
return summary_stats

Run the batch simulation and collect the results.


                        # Initialize the network model and run the batch simulation.
net = neymotin_2020_model(mesh_shape=(3, 3))
batch_simulation = BatchSimulate(net=net,
                                 set_params=set_params,
                                 summary_func=summary_func)
simulation_results = batch_simulation.run(param_grid,
                                          n_jobs=n_jobs,
                                          combinations=False,
                                          backend='loky')

print("Simulation results:", simulation_results)

Out:

Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Loading custom mechanism files from /usr/share/miniconda/envs/textbook-stable-env/lib/python3.12/site-packages/hnn_core/mod/x86_64/libnrnmech.so Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs. Building the NEURON model [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Simulation results: {'summary_statistics': [[{'min_peak': np.float64(-1.948723369916257e-05), 'max_peak': np.float64(2.438299811172486e-05)}, {'min_peak': np.float64(-1.948723369916257e-05), 'max_peak': np.float64(3.5625970074068165e-05)}, {'min_peak': np.float64(-1.948723369916257e-05), 'max_peak': np.float64(5.187123572695802e-05)}, {'min_peak': np.float64(-1.948723369916257e-05), 'max_peak': np.float64(7.539737690213312e-05)}, {'min_peak': np.float64(-1.948723369916257e-05), 'max_peak': np.float64(0.0001096227163976112)}, {'min_peak': np.float64(-0.0008187098140745243), 'max_peak': np.float64(0.0011853731897260916)}, {'min_peak': np.float64(-0.0006900911098816527), 'max_peak': np.float64(0.0014532366142034129)}, {'min_peak': np.float64(-7.443630674152272e-05), 'max_peak': np.float64(0.0006303483688791759)}, {'min_peak': np.float64(-0.00038312472505001556), 'max_peak': np.float64(0.0015588783545866054)}, {'min_peak': np.float64(-0.0005827286517535943), 'max_peak': np.float64(0.0014794795458269719)}, {'min_peak': np.float64(-0.0005759203533290843), 'max_peak': np.float64(0.001453923529524903)}, {'min_peak': np.float64(-1.948723369916257e-05), 'max_peak': np.float64(0.0013262182394243893)}, {'min_peak': np.float64(-1.948723369916257e-05), 'max_peak': np.float64(0.001328331309960038)}, {'min_peak': np.float64(-1.948723369916257e-05), 'max_peak': np.float64(0.0011853462053579675)}, {'min_peak': np.float64(-1.948723369916257e-05), 'max_peak': np.float64(0.0011649633329457551)}, {'min_peak': np.float64(-1.948723369916257e-05), 'max_peak': np.float64(0.001184585414815373)}, {'min_peak': np.float64(-1.948723369916257e-05), 'max_peak': np.float64(0.0012311552850961124)}, {'min_peak': np.float64(-1.948723369916257e-05), 'max_peak': np.float64(0.0013115554222262733)}, {'min_peak': np.float64(-1.948723369916257e-05), 'max_peak': np.float64(0.001431431523222179)}, {'min_peak': np.float64(-1.948723369916257e-05), 'max_peak': np.float64(0.0015123789975719434)}]], 'simulated_data': [[{'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.0001), 'weight_pyr': np.float64(0.0001)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd1c619db80>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.0001438449888287663), 'weight_pyr': np.float64(0.0001438449888287663)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd1c630d8e0>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.00020691380811147902), 'weight_pyr': np.float64(0.00020691380811147902)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd1c612eb10>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.00029763514416313193), 'weight_pyr': np.float64(0.00029763514416313193)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd1c60ee300>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.00042813323987193956), 'weight_pyr': np.float64(0.00042813323987193956)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd1c60c1b20>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.0006158482110660267), 'weight_pyr': np.float64(0.0006158482110660267)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd1c6259d00>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.0008858667904100822), 'weight_pyr': np.float64(0.0008858667904100822)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd1c7b87a10>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.0012742749857031334), 'weight_pyr': np.float64(0.0012742749857031334)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd1c60eb9b0>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.0018329807108324356), 'weight_pyr': np.float64(0.0018329807108324356)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd1c612d7f0>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.0026366508987303583), 'weight_pyr': np.float64(0.0026366508987303583)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd210b9b170>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.00379269019073225), 'weight_pyr': np.float64(0.00379269019073225)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd1c60c09e0>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.005455594781168515), 'weight_pyr': np.float64(0.005455594781168515)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd1c60a0e00>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.007847599703514606), 'weight_pyr': np.float64(0.007847599703514606)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd1c5ed2210>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.011288378916846883), 'weight_pyr': np.float64(0.011288378916846883)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd1c60a0ef0>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.01623776739188721), 'weight_pyr': np.float64(0.01623776739188721)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd1c5ef9fd0>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.023357214690901212), 'weight_pyr': np.float64(0.023357214690901212)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd1c5efa780>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.03359818286283781), 'weight_pyr': np.float64(0.03359818286283781)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd210b9abd0>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.04832930238571752), 'weight_pyr': np.float64(0.04832930238571752)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd1c5efb050>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.06951927961775606), 'weight_pyr': np.float64(0.06951927961775606)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd1c7a21c10>]}, {'net': <Network | 3 L2_basket cells 9 L2_pyramidal cells 3 L5_basket cells 9 L5_pyramidal cells>, 'param_values': {'weight_basket': np.float64(0.1), 'weight_pyr': np.float64(0.1)}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7fd1c5efb6b0>]}]]}

This plot shows an overlay of all smoothed dipole waveforms from the batch simulation. Each line represents a different set of synaptic strength parameters (weight_basket), allowing us to visualize the range of responses across the parameter space. The colormap represents synaptic strengths, from weaker (purple) to stronger (yellow).

As drive strength increases, dipole responses show progressively larger amplitudes and more distinct features, reflecting heightened network activity. Weak drives (purple lines) produce smaller amplitude signals with simpler waveforms, while stronger drives (yellow lines) generate larger responses with more pronounced oscillatory features, indicating more robust network activity.

The y-axis represents dipole amplitude in nAm (nanoAmpere-meters), which is the product of current flow and distance in the neural tissue.

Stronger synaptic connections (yellow lines) generally show larger amplitude responses and more pronounced features throughout the simulation.


                        dpl_waveforms, param_values = [], []
for data_list in simulation_results['simulated_data']:
    for data in data_list:
        dpl_smooth = data['dpl'][0].copy().smooth(window_len=30)
        dpl_waveforms.append(dpl_smooth.data['agg'])
        param_values.append(data['param_values']['weight_basket'])

plt.figure(figsize=(10, 6))
cmap = plt.get_cmap('viridis')
log_param_values = np.log10(param_values)
norm = plt.Normalize(log_param_values.min(), log_param_values.max())

for waveform, log_param in zip(dpl_waveforms, log_param_values):
    color = cmap(norm(log_param))
    plt.plot(waveform, color=color, alpha=0.7, linewidth=2)
plt.title('Overlay of Dipole Waveforms')
plt.xlabel('Time (ms)')
plt.ylabel('Dipole Amplitude (nAm)')
plt.grid(True)
plt.tight_layout()
plt.show()

Out:

This plot displays the minimum and maximum dipole peaks across different synaptic strengths. This allows us to see how the range of dipole activity changes as we vary the synaptic strength parameter.


                        min_peaks, max_peaks, param_values = [], [], []
for summary_list, data_list in zip(simulation_results['summary_statistics'],
                                   simulation_results['simulated_data']):
    for summary, data in zip(summary_list, data_list):
        min_peaks.append(summary['min_peak'])
        max_peaks.append(summary['max_peak'])
        param_values.append(data['param_values']['weight_basket'])

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(param_values, min_peaks, label='Min Dipole Peak')
plt.plot(param_values, max_peaks, label='Max Dipole Peak')
plt.xlabel('Synaptic Strength (nS)')
plt.ylabel('Dipole Peak Magnitude')
plt.title('Min and Max Dipole Peaks across Simulations')
plt.legend()
plt.grid(True)
plt.xscale('log')
plt.tight_layout()
plt.show()

Out: