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.. only:: html
.. note::
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.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_howto_plot_batch_simulate.py:
====================
07. 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.
.. GENERATED FROM PYTHON SOURCE LINES 12-15
Note that batch simulation requires you to install HNN-core with Joblib
parallel support, which you can do by installing it with
``pip install "hnn_core[parallel]"``
.. GENERATED FROM PYTHON SOURCE LINES 15-24
.. code-block:: Python
# 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.
.. GENERATED FROM PYTHON SOURCE LINES 25-26
Let us import ``hnn_core``.
.. GENERATED FROM PYTHON SOURCE LINES 26-35
.. code-block:: Python
import matplotlib.pyplot as plt
import numpy as np
from hnn_core.batch_simulate import BatchSimulate
from hnn_core import jones_2009_model
# The number of cores may need modifying depending on your current machine.
n_jobs = 4
.. GENERATED FROM PYTHON SOURCE LINES 36-48
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.
.. GENERATED FROM PYTHON SOURCE LINES 48-78
.. code-block:: Python
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)
.. GENERATED FROM PYTHON SOURCE LINES 79-80
Next, we define a parameter grid for the batch simulation.
.. GENERATED FROM PYTHON SOURCE LINES 80-87
.. code-block:: Python
param_grid = {
'weight_basket': np.logspace(-4, -1, 20),
'weight_pyr': np.logspace(-4, -1, 20)
}
.. GENERATED FROM PYTHON SOURCE LINES 88-89
We then define a function to calculate summary statistics.
.. GENERATED FROM PYTHON SOURCE LINES 89-114
.. code-block:: Python
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
.. GENERATED FROM PYTHON SOURCE LINES 115-116
Run the batch simulation and collect the results.
.. GENERATED FROM PYTHON SOURCE LINES 116-129
.. code-block:: Python
# Initialize the network model and run the batch simulation.
net = jones_2009_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)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=4)]: Done 1 tasks | elapsed: 1.2s
[Parallel(n_jobs=4)]: Done 2 tasks | elapsed: 1.2s
[Parallel(n_jobs=4)]: Done 3 tasks | elapsed: 1.3s
[Parallel(n_jobs=4)]: Done 4 tasks | elapsed: 1.3s
[Parallel(n_jobs=4)]: Done 5 tasks | elapsed: 1.9s
[Parallel(n_jobs=4)]: Done 6 tasks | elapsed: 1.9s
[Parallel(n_jobs=4)]: Done 7 tasks | elapsed: 1.9s
[Parallel(n_jobs=4)]: Done 8 tasks | elapsed: 2.0s
[Parallel(n_jobs=4)]: Done 9 tasks | elapsed: 2.6s
[Parallel(n_jobs=4)]: Done 10 tasks | elapsed: 2.6s
[Parallel(n_jobs=4)]: Done 11 tasks | elapsed: 2.6s
[Parallel(n_jobs=4)]: Done 12 tasks | elapsed: 2.6s
[Parallel(n_jobs=4)]: Done 13 tasks | elapsed: 3.3s
[Parallel(n_jobs=4)]: Done 14 out of 20 | elapsed: 3.3s remaining: 1.4s
[Parallel(n_jobs=4)]: Done 15 out of 20 | elapsed: 3.3s remaining: 1.1s
[Parallel(n_jobs=4)]: Done 16 out of 20 | elapsed: 3.3s remaining: 0.8s
[Parallel(n_jobs=4)]: Done 17 out of 20 | elapsed: 4.0s remaining: 0.7s
[Parallel(n_jobs=4)]: Done 18 out of 20 | elapsed: 4.0s remaining: 0.4s
[Parallel(n_jobs=4)]: Done 20 out of 20 | elapsed: 4.0s finished
Simulation results: {'summary_statistics': [[{'min_peak': -1.9487233699163027e-05, 'max_peak': 2.438299811172476e-05}, {'min_peak': -1.9487233699163027e-05, 'max_peak': 3.5625970074068226e-05}, {'min_peak': -1.9487233699163027e-05, 'max_peak': 5.187123572695806e-05}, {'min_peak': -1.9487233699163027e-05, 'max_peak': 7.53973769021332e-05}, {'min_peak': -1.9487233699163027e-05, 'max_peak': 0.00010962271639761108}, {'min_peak': -0.000818709814074504, 'max_peak': 0.0011853731897260855}, {'min_peak': -0.0006900911098816319, 'max_peak': 0.001453236614203411}, {'min_peak': -7.443630674152589e-05, 'max_peak': 0.0006303483688791772}, {'min_peak': -0.0003831247250500106, 'max_peak': 0.0015588783545865915}, {'min_peak': -0.0005827286517535978, 'max_peak': 0.0014794795458269567}, {'min_peak': -0.0005759203533290788, 'max_peak': 0.0014539235295249035}, {'min_peak': -1.9487233699163027e-05, 'max_peak': 0.0013262182394243836}, {'min_peak': -1.9487233699163027e-05, 'max_peak': 0.001328331309960044}, {'min_peak': -1.9487233699163027e-05, 'max_peak': 0.0011853462053579697}, {'min_peak': -1.9487233699163027e-05, 'max_peak': 0.001164963332945789}, {'min_peak': -1.9487233699163027e-05, 'max_peak': 0.001184585414815396}, {'min_peak': -1.9487233699163027e-05, 'max_peak': 0.001231155285096099}, {'min_peak': -1.9487233699163027e-05, 'max_peak': 0.0013115554222262646}, {'min_peak': -1.9487233699163027e-05, 'max_peak': 0.0014314315232221647}, {'min_peak': -1.9487233699163027e-05, 'max_peak': 0.001512378997571946}]], 'simulated_data': [[{'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.0001, 'weight_pyr': 0.0001}, 'dpl': [<hnn_core.dipole.Dipole object at 0x147ed3230>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.0001438449888287663, 'weight_pyr': 0.0001438449888287663}, 'dpl': [<hnn_core.dipole.Dipole object at 0x134de92b0>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.00020691380811147902, 'weight_pyr': 0.00020691380811147902}, 'dpl': [<hnn_core.dipole.Dipole object at 0x1370f1ee0>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.00029763514416313193, 'weight_pyr': 0.00029763514416313193}, 'dpl': [<hnn_core.dipole.Dipole object at 0x147ed3b60>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.00042813323987193956, 'weight_pyr': 0.00042813323987193956}, 'dpl': [<hnn_core.dipole.Dipole object at 0x1373d87a0>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.0006158482110660267, 'weight_pyr': 0.0006158482110660267}, 'dpl': [<hnn_core.dipole.Dipole object at 0x1370f0e60>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.0008858667904100823, 'weight_pyr': 0.0008858667904100823}, 'dpl': [<hnn_core.dipole.Dipole object at 0x1571dd880>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.0012742749857031334, 'weight_pyr': 0.0012742749857031334}, 'dpl': [<hnn_core.dipole.Dipole object at 0x16b76c8f0>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.0018329807108324356, 'weight_pyr': 0.0018329807108324356}, 'dpl': [<hnn_core.dipole.Dipole object at 0x1571df4a0>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.0026366508987303583, 'weight_pyr': 0.0026366508987303583}, 'dpl': [<hnn_core.dipole.Dipole object at 0x1373d9af0>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.00379269019073225, 'weight_pyr': 0.00379269019073225}, 'dpl': [<hnn_core.dipole.Dipole object at 0x1361d2450>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.005455594781168515, 'weight_pyr': 0.005455594781168515}, 'dpl': [<hnn_core.dipole.Dipole object at 0x1373daf00>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.007847599703514606, 'weight_pyr': 0.007847599703514606}, 'dpl': [<hnn_core.dipole.Dipole object at 0x1470db4d0>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.011288378916846883, 'weight_pyr': 0.011288378916846883}, 'dpl': [<hnn_core.dipole.Dipole object at 0x1365cdcd0>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.01623776739188721, 'weight_pyr': 0.01623776739188721}, 'dpl': [<hnn_core.dipole.Dipole object at 0x1571afa10>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.023357214690901212, 'weight_pyr': 0.023357214690901212}, 'dpl': [<hnn_core.dipole.Dipole object at 0x16b76c530>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.03359818286283781, 'weight_pyr': 0.03359818286283781}, 'dpl': [<hnn_core.dipole.Dipole object at 0x1412b9dc0>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.04832930238571752, 'weight_pyr': 0.04832930238571752}, 'dpl': [<hnn_core.dipole.Dipole object at 0x147eb0740>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.06951927961775606, 'weight_pyr': 0.06951927961775606}, 'dpl': [<hnn_core.dipole.Dipole object at 0x1421f9d00>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
3 L2 basket cells
3 L5 basket cells>, 'param_values': {'weight_basket': 0.1, 'weight_pyr': 0.1}, 'dpl': [<hnn_core.dipole.Dipole object at 0x1412b8f50>]}]]}
.. GENERATED FROM PYTHON SOURCE LINES 130-149
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.
.. GENERATED FROM PYTHON SOURCE LINES 149-171
.. code-block:: Python
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()
.. image-sg:: /auto_examples/howto/images/sphx_glr_plot_batch_simulate_001.png
:alt: Overlay of Dipole Waveforms
:srcset: /auto_examples/howto/images/sphx_glr_plot_batch_simulate_001.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 172-175
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.
.. GENERATED FROM PYTHON SOURCE LINES 175-196
.. code-block:: Python
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()
.. image-sg:: /auto_examples/howto/images/sphx_glr_plot_batch_simulate_002.png
:alt: Min and Max Dipole Peaks across Simulations
:srcset: /auto_examples/howto/images/sphx_glr_plot_batch_simulate_002.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-timing
**Total running time of the script:** (0 minutes 4.478 seconds)
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