From 15e07b765848dfddd566a1ea55b875f5e0c86007 Mon Sep 17 00:00:00 2001 From: Circle Ci Date: Thu, 12 Dec 2024 16:57:42 +0000 Subject: [PATCH] doc updates [skip ci] --- .../auto_examples_python.zip | Bin 90423 -> 91929 bytes .../plot_batch_simulate.zip | Bin 14225 -> 17319 bytes .../plot_batch_simulate.ipynb | 23 ++- .../plot_simulate_mpi_backend.zip | Bin 6332 -> 6332 bytes .../plot_simulate_gamma.zip | Bin 16310 -> 16310 bytes .../plot_simulate_beta.zip | Bin 27932 -> 27932 bytes .../plot_simulate_somato.zip | Bin 30364 -> 30364 bytes .../auto_examples_jupyter.zip | Bin 125450 -> 127038 bytes .../plot_simulate_alpha.zip | Bin 14222 -> 14222 bytes .../optimize_evoked.zip | Bin 20093 -> 20093 bytes .../plot_connectivity.zip | Bin 17079 -> 17079 bytes .../plot_batch_simulate.py | 66 +++++-- .../optimize_rhythmic.zip | Bin 11730 -> 11730 bytes .../plot_simulate_evoked.zip | Bin 19685 -> 19685 bytes .../plot_firing_pattern.zip | Bin 16790 -> 16790 bytes .../plot_hnn_animation.zip | Bin 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a/dev/_downloads/ad00e92f96773b3b036745547da3cddf/plot_batch_simulate.py +++ b/dev/_downloads/ad00e92f96773b3b036745547da3cddf/plot_batch_simulate.py @@ -24,13 +24,25 @@ from hnn_core import jones_2009_model # The number of cores may need modifying depending on your current machine. -n_jobs = 10 +n_jobs = 4 ############################################################################### +# 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 in the network drives. + Set parameters for the network drives. Parameters ---------- @@ -57,16 +69,16 @@ def set_params(param_values, net=None): synaptic_delays=synaptic_delays) ############################################################################### -# Define a parameter grid for the batch simulation. +# Next, we define a parameter grid for the batch simulation. param_grid = { - 'weight_basket': np.logspace(-4 - 1, 10), - 'weight_pyr': np.logspace(-4, -1, 10) + 'weight_basket': np.logspace(-4, -1, 20), + 'weight_pyr': np.logspace(-4, -1, 20) } ############################################################################### -# Define a function to calculate summary statistics +# We then define a function to calculate summary statistics. def summary_func(results): @@ -95,12 +107,8 @@ def summary_func(results): ############################################################################### # Run the batch simulation and collect the results. -# Comment off this code, if dask and distributed Python packages are installed -# from dask.distributed import Client -# client = Client(threads_per_worker=1, n_workers=5, processes=False) - -# Run the batch simulation and collect the results. +# 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, @@ -108,23 +116,45 @@ def summary_func(results): simulation_results = batch_simulation.run(param_grid, n_jobs=n_jobs, combinations=False, - backend='multiprocessing') -# backend='dask' if installed + backend='loky') + print("Simulation results:", simulation_results) ############################################################################### # This plot shows an overlay of all smoothed dipole waveforms from the -# batch simulation. Each line represents a different set of parameters, -# allowing us to visualize the range of responses across the parameter space. +# 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 = [] +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)) -for waveform in dpl_waveforms: - plt.plot(waveform, alpha=0.5, linewidth=3) +cmap = plt.get_cmap('viridis') +log_param_values = 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{'weight_basket': 0.09999999999999999, 'weight_pyr': 0.09999999999999999}, 'dpl': []}]]} -.. GENERATED FROM PYTHON SOURCE LINES 115-118 +.. GENERATED FROM PYTHON SOURCE LINES 123-142 This plot shows an overlay of all smoothed dipole waveforms from the -batch simulation. Each line represents a different set of parameters, -allowing us to visualize the range of responses across the parameter space. +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). -.. GENERATED FROM PYTHON SOURCE LINES 118-134 +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 142-164 .. code-block:: Python - dpl_waveforms = [] + 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)) - for waveform in dpl_waveforms: - plt.plot(waveform, alpha=0.5, linewidth=3) + 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)') @@ -287,13 +340,13 @@ allowing us to visualize the range of responses across the parameter space. -.. GENERATED FROM PYTHON SOURCE LINES 135-138 +.. GENERATED FROM PYTHON SOURCE LINES 165-168 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 138-159 +.. GENERATED FROM PYTHON SOURCE LINES 168-189 .. code-block:: Python @@ -333,7 +386,7 @@ dipole activity changes as we vary the synaptic strength parameter. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 17.916 seconds) + **Total running time of the script:** (0 minutes 15.054 seconds) .. _sphx_glr_download_auto_examples_howto_plot_batch_simulate.py: diff --git a/dev/_sources/auto_examples/howto/plot_connectivity.rst.txt b/dev/_sources/auto_examples/howto/plot_connectivity.rst.txt index 92e647864..194586bc6 100644 --- a/dev/_sources/auto_examples/howto/plot_connectivity.rst.txt +++ b/dev/_sources/auto_examples/howto/plot_connectivity.rst.txt @@ -478,7 +478,7 @@ the aggregate current dipole. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (1 minutes 31.708 seconds) + **Total running time of the script:** (1 minutes 43.813 seconds) .. _sphx_glr_download_auto_examples_howto_plot_connectivity.py: diff --git a/dev/_sources/auto_examples/howto/plot_firing_pattern.rst.txt b/dev/_sources/auto_examples/howto/plot_firing_pattern.rst.txt index 8949323b5..2f8cd1c5e 100644 --- a/dev/_sources/auto_examples/howto/plot_firing_pattern.rst.txt +++ b/dev/_sources/auto_examples/howto/plot_firing_pattern.rst.txt @@ -321,7 +321,7 @@ read spikes back in, we can use wildcard expressions. /home/circleci/project/examples/howto/plot_firing_pattern.py:111: DeprecationWarning: Writing cell response to txt files is deprecated and will be removed in future versions. Please save cell response along with network net.cell_response.write(op.join(tmp_dir_name, 'spk_%d.txt')) - Writing file /tmp/tmpnwin81z7/spk_0.txt + Writing file /tmp/tmpg3div2q0/spk_0.txt /home/circleci/project/examples/howto/plot_firing_pattern.py:112: DeprecationWarning: Reading cell response from txt files is deprecated and will be removed in future versions. Please load cell response along with simulated network cell_response = read_spikes(op.join(tmp_dir_name, 'spk_*.txt')) @@ -407,7 +407,7 @@ raster plots and histograms for the pyramidal cells. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 32.636 seconds) + **Total running time of the script:** (0 minutes 34.956 seconds) .. _sphx_glr_download_auto_examples_howto_plot_firing_pattern.py: diff --git a/dev/_sources/auto_examples/howto/plot_hnn_animation.rst.txt b/dev/_sources/auto_examples/howto/plot_hnn_animation.rst.txt index f971e2ee9..12a933294 100644 --- a/dev/_sources/auto_examples/howto/plot_hnn_animation.rst.txt +++ b/dev/_sources/auto_examples/howto/plot_hnn_animation.rst.txt @@ -110,7 +110,7 @@ the 3D structure of the network. .. code-block:: none - /home/circleci/miniconda/envs/testenv/lib/python3.8/site-packages/hnn_core/viz.py:1029: UserWarning: The figure layout has changed to tight + /home/circleci/miniconda/envs/testenv/lib/python3.8/site-packages/hnn_core/viz.py:1073: UserWarning: The figure layout has changed to tight plt.tight_layout()
    @@ -221,14 +221,14 @@ can adjust the xyz limits of the plot to better visualize the network. .. code-block:: none - + .. rst-class:: sphx-glr-timing - **Total running time of the script:** (1 minutes 4.384 seconds) + **Total running time of the script:** (1 minutes 7.526 seconds) .. _sphx_glr_download_auto_examples_howto_plot_hnn_animation.py: diff --git a/dev/_sources/auto_examples/howto/plot_record_extracellular_potentials.rst.txt b/dev/_sources/auto_examples/howto/plot_record_extracellular_potentials.rst.txt index 995eec04e..555422914 100644 --- a/dev/_sources/auto_examples/howto/plot_record_extracellular_potentials.rst.txt +++ b/dev/_sources/auto_examples/howto/plot_record_extracellular_potentials.rst.txt @@ -265,7 +265,7 @@ References .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 43.970 seconds) + **Total running time of the script:** (0 minutes 48.413 seconds) .. _sphx_glr_download_auto_examples_howto_plot_record_extracellular_potentials.py: diff --git a/dev/_sources/auto_examples/howto/plot_simulate_mpi_backend.rst.txt b/dev/_sources/auto_examples/howto/plot_simulate_mpi_backend.rst.txt index 7c9cfcbf6..d5160f5ad 100644 --- a/dev/_sources/auto_examples/howto/plot_simulate_mpi_backend.rst.txt +++ b/dev/_sources/auto_examples/howto/plot_simulate_mpi_backend.rst.txt @@ -172,7 +172,7 @@ start the simulation across the number of processors (cores) specified by .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 22.136 seconds) + **Total running time of the script:** (0 minutes 22.762 seconds) .. _sphx_glr_download_auto_examples_howto_plot_simulate_mpi_backend.py: diff --git a/dev/_sources/auto_examples/howto/sg_execution_times.rst.txt b/dev/_sources/auto_examples/howto/sg_execution_times.rst.txt index 33bbd335a..c71c40e50 100644 --- a/dev/_sources/auto_examples/howto/sg_execution_times.rst.txt +++ b/dev/_sources/auto_examples/howto/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**04:32.750** total execution time for 8 files **from auto_examples/howto**: +**04:52.525** total execution time for 8 files **from auto_examples/howto**: .. container:: @@ -33,22 +33,22 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_examples_howto_plot_connectivity.py` (``plot_connectivity.py``) - - 01:31.708 + - 01:43.813 - 0.0 * - :ref:`sphx_glr_auto_examples_howto_plot_hnn_animation.py` (``plot_hnn_animation.py``) - - 01:04.384 + - 01:07.526 - 0.0 * - :ref:`sphx_glr_auto_examples_howto_plot_record_extracellular_potentials.py` (``plot_record_extracellular_potentials.py``) - - 00:43.970 + - 00:48.413 - 0.0 * - :ref:`sphx_glr_auto_examples_howto_plot_firing_pattern.py` (``plot_firing_pattern.py``) - - 00:32.636 + - 00:34.956 - 0.0 * - :ref:`sphx_glr_auto_examples_howto_plot_simulate_mpi_backend.py` (``plot_simulate_mpi_backend.py``) - - 00:22.136 + - 00:22.762 - 0.0 * - :ref:`sphx_glr_auto_examples_howto_plot_batch_simulate.py` (``plot_batch_simulate.py``) - - 00:17.916 + - 00:15.054 - 0.0 * - :ref:`sphx_glr_auto_examples_howto_optimize_evoked.py` (``optimize_evoked.py``) - 00:00.000 diff --git a/dev/_sources/auto_examples/workflows/plot_simulate_alpha.rst.txt b/dev/_sources/auto_examples/workflows/plot_simulate_alpha.rst.txt index 7d0609b8a..4bf552a17 100644 --- a/dev/_sources/auto_examples/workflows/plot_simulate_alpha.rst.txt +++ b/dev/_sources/auto_examples/workflows/plot_simulate_alpha.rst.txt @@ -147,7 +147,7 @@ time with unique randomization. The drive is only connected to the Trial 1: 290.0 ms... Trial 1: 300.0 ms... - + @@ -326,7 +326,7 @@ References .. rst-class:: sphx-glr-timing - **Total running time of the script:** (1 minutes 41.573 seconds) + **Total running time of the script:** (1 minutes 56.244 seconds) .. _sphx_glr_download_auto_examples_workflows_plot_simulate_alpha.py: diff --git a/dev/_sources/auto_examples/workflows/plot_simulate_beta.rst.txt b/dev/_sources/auto_examples/workflows/plot_simulate_beta.rst.txt index 7a9262adc..53694c0eb 100644 --- a/dev/_sources/auto_examples/workflows/plot_simulate_beta.rst.txt +++ b/dev/_sources/auto_examples/workflows/plot_simulate_beta.rst.txt @@ -623,7 +623,7 @@ References .. rst-class:: sphx-glr-timing - **Total running time of the script:** (3 minutes 21.735 seconds) + **Total running time of the script:** (3 minutes 22.370 seconds) .. _sphx_glr_download_auto_examples_workflows_plot_simulate_beta.py: diff --git a/dev/_sources/auto_examples/workflows/plot_simulate_evoked.rst.txt b/dev/_sources/auto_examples/workflows/plot_simulate_evoked.rst.txt index 536d16b90..33c567800 100644 --- a/dev/_sources/auto_examples/workflows/plot_simulate_evoked.rst.txt +++ b/dev/_sources/auto_examples/workflows/plot_simulate_evoked.rst.txt @@ -453,7 +453,7 @@ References .. rst-class:: sphx-glr-timing - **Total running time of the script:** (1 minutes 4.572 seconds) + **Total running time of the script:** (1 minutes 15.802 seconds) .. _sphx_glr_download_auto_examples_workflows_plot_simulate_evoked.py: diff --git a/dev/_sources/auto_examples/workflows/plot_simulate_gamma.rst.txt b/dev/_sources/auto_examples/workflows/plot_simulate_gamma.rst.txt index e2e70e769..0012b78e4 100644 --- a/dev/_sources/auto_examples/workflows/plot_simulate_gamma.rst.txt +++ b/dev/_sources/auto_examples/workflows/plot_simulate_gamma.rst.txt @@ -478,7 +478,7 @@ References .. rst-class:: sphx-glr-timing - **Total running time of the script:** (2 minutes 10.143 seconds) + **Total running time of the script:** (2 minutes 23.489 seconds) .. _sphx_glr_download_auto_examples_workflows_plot_simulate_gamma.py: diff --git a/dev/_sources/auto_examples/workflows/plot_simulate_somato.rst.txt b/dev/_sources/auto_examples/workflows/plot_simulate_somato.rst.txt index a05e41188..6801e5fd8 100644 --- a/dev/_sources/auto_examples/workflows/plot_simulate_somato.rst.txt +++ b/dev/_sources/auto_examples/workflows/plot_simulate_somato.rst.txt @@ -104,11 +104,11 @@ Now we set the the path of the ``somato`` dataset for subject ``'01'``. Using default location ~/mne_data for somato... Creating ~/mne_data Downloading file 'MNE-somato-data.tar.gz' from 'https://osf.io/tp4sg/download?version=7' to '/home/circleci/mne_data'. - 0%| | 0.00/611M [00:00from hnn_core import jones_2009_model # The number of cores may need modifying depending on your current machine. -n_jobs = 10 +n_jobs = 4 +

    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 in the network drives.
    +    Set parameters for the network drives.
     
         Parameters
         ----------
    @@ -197,14 +209,14 @@
                              synaptic_delays=synaptic_delays)
     
    -

    Define a parameter grid for the batch simulation.

    +

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

    param_grid = {
    -    'weight_basket': np.logspace(-4 - 1, 10),
    -    'weight_pyr': np.logspace(-4, -1, 10)
    +    'weight_basket': np.logspace(-4, -1, 20),
    +    'weight_pyr': np.logspace(-4, -1, 20)
     }
     
    -

    Define a function to calculate summary statistics

    +

    We then define a function to calculate summary statistics.

    def summary_func(results):
         """
         Calculate the min and max dipole peak for each simulation result.
    @@ -230,12 +242,7 @@
     

    Run the batch simulation and collect the results.

    -
    # Comment off this code, if dask and distributed Python packages are installed
    -# from dask.distributed import Client
    -# client = Client(threads_per_worker=1, n_workers=5, processes=False)
    -
    -
    -# Run the batch simulation and collect the results.
    +
    # 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,
    @@ -243,66 +250,105 @@
     simulation_results = batch_simulation.run(param_grid,
                                               n_jobs=n_jobs,
                                               combinations=False,
    -                                          backend='multiprocessing')
    -# backend='dask' if installed
    +                                          backend='loky')
    +
     print("Simulation results:", simulation_results)
     
    -
    [Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers.
    -[Parallel(n_jobs=10)]: Done   1 tasks      | elapsed:    1.7s
    -[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers.
    -[Parallel(n_jobs=10)]: Done   1 tasks      | elapsed:    1.7s
    -[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers.
    -[Parallel(n_jobs=10)]: Done   1 tasks      | elapsed:    1.7s
    -[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers.
    -[Parallel(n_jobs=10)]: Done   1 tasks      | elapsed:    1.7s
    -[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers.
    -[Parallel(n_jobs=10)]: Done   1 tasks      | elapsed:    1.7s
    -[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers.
    -[Parallel(n_jobs=10)]: Done   1 tasks      | elapsed:    1.7s
    -[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers.
    -[Parallel(n_jobs=10)]: Done   1 tasks      | elapsed:    1.7s
    -[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers.
    -[Parallel(n_jobs=10)]: Done   1 tasks      | elapsed:    1.7s
    -[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers.
    -[Parallel(n_jobs=10)]: Done   1 tasks      | elapsed:    1.7s
    -[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers.
    -[Parallel(n_jobs=10)]: Done   1 tasks      | elapsed:    1.7s
    -Simulation results: {'summary_statistics': [[{'min_peak': -1.9487233699162363e-05, 'max_peak': 2.438299811172486e-05}], [{'min_peak': -1.9487233699162363e-05, 'max_peak': 5.406961258004377e-05}], [{'min_peak': -1.9487233699162363e-05, 'max_peak': 0.00011922352353898099}], [{'min_peak': -0.0006636604554164317, 'max_peak': 0.0011404645089040637}], [{'min_peak': -1.9487233699162363e-05, 'max_peak': 0.0009097461915339956}], [{'min_peak': -1.9487233699162363e-05, 'max_peak': 0.0011362095936085991}], [{'min_peak': -1.9487233699162363e-05, 'max_peak': 0.001168458230350182}], [{'min_peak': -1.9487233699162363e-05, 'max_peak': 0.0012737618628301784}], [{'min_peak': -1.9487233699162363e-05, 'max_peak': 0.0014391544369764205}], [{'min_peak': -1.9487233699162363e-05, 'max_peak': 0.001616939552849071}]], 'simulated_data': [[{'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
    +
    [Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.
    +[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:    3.3s
    +[Parallel(n_jobs=4)]: Done   2 tasks      | elapsed:    3.4s
    +[Parallel(n_jobs=4)]: Done   3 tasks      | elapsed:    3.4s
    +[Parallel(n_jobs=4)]: Done   4 tasks      | elapsed:    3.4s
    +[Parallel(n_jobs=4)]: Done   5 tasks      | elapsed:    6.1s
    +[Parallel(n_jobs=4)]: Done   6 tasks      | elapsed:    6.1s
    +[Parallel(n_jobs=4)]: Done   7 tasks      | elapsed:    6.1s
    +[Parallel(n_jobs=4)]: Done   8 tasks      | elapsed:    6.1s
    +[Parallel(n_jobs=4)]: Done   9 tasks      | elapsed:    8.8s
    +[Parallel(n_jobs=4)]: Done  10 tasks      | elapsed:    8.9s
    +[Parallel(n_jobs=4)]: Done  11 tasks      | elapsed:    8.9s
    +[Parallel(n_jobs=4)]: Done  12 tasks      | elapsed:    8.9s
    +[Parallel(n_jobs=4)]: Done  13 tasks      | elapsed:   11.6s
    +[Parallel(n_jobs=4)]: Done  14 out of  20 | elapsed:   11.6s remaining:    5.0s
    +[Parallel(n_jobs=4)]: Done  15 out of  20 | elapsed:   11.7s remaining:    3.9s
    +[Parallel(n_jobs=4)]: Done  16 out of  20 | elapsed:   11.7s remaining:    2.9s
    +[Parallel(n_jobs=4)]: Done  17 out of  20 | elapsed:   14.3s remaining:    2.5s
    +[Parallel(n_jobs=4)]: Done  18 out of  20 | elapsed:   14.4s remaining:    1.6s
    +[Parallel(n_jobs=4)]: Done  20 out of  20 | elapsed:   14.5s finished
    +Simulation results: {'summary_statistics': [[{'min_peak': -1.9487233699162363e-05, 'max_peak': 2.438299811172486e-05}, {'min_peak': -1.9487233699162363e-05, 'max_peak': 3.5625970074068246e-05}, {'min_peak': -1.9487233699162363e-05, 'max_peak': 5.187123572695809e-05}, {'min_peak': -1.9487233699162363e-05, 'max_peak': 7.53973769021331e-05}, {'min_peak': -1.9487233699162363e-05, 'max_peak': 0.00010962271639761121}, {'min_peak': -0.0008187098140745109, 'max_peak': 0.0011853731897260896}, {'min_peak': -0.0006900911098816427, 'max_peak': 0.001453236614203405}, {'min_peak': -7.443630674152117e-05, 'max_peak': 0.0006303483688791793}, {'min_peak': -0.0003831247250500206, 'max_peak': 0.0015588783545865902}, {'min_peak': -0.0005827286517536018, 'max_peak': 0.0014794795458269632}, {'min_peak': -0.0005759203533290847, 'max_peak': 0.0014539235295249087}, {'min_peak': -1.9487233699162363e-05, 'max_peak': 0.001326218239424397}, {'min_peak': -1.9487233699162363e-05, 'max_peak': 0.0013283313099600618}, {'min_peak': -1.9487233699162363e-05, 'max_peak': 0.0011853462053579688}, {'min_peak': -1.9487233699162363e-05, 'max_peak': 0.0011649633329457493}, {'min_peak': -1.9487233699162363e-05, 'max_peak': 0.0011845854148153823}, {'min_peak': -1.9487233699162363e-05, 'max_peak': 0.001231155285096076}, {'min_peak': -1.9487233699162363e-05, 'max_peak': 0.0013115554222262672}, {'min_peak': -1.9487233699162363e-05, 'max_peak': 0.0014314315232221652}, {'min_peak': -1.9487233699162363e-05, 'max_peak': 0.001512378997571943}]], 'simulated_data': [[{'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
    +3 L2 basket cells
    +3 L5 basket cells>, 'param_values': {'weight_basket': 9.999999999999999e-05, 'weight_pyr': 9.999999999999999e-05}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f8455afe7c0>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
    +3 L2 basket cells
    +3 L5 basket cells>, 'param_values': {'weight_basket': 0.00014384498882876628, 'weight_pyr': 0.00014384498882876628}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f8455ceed60>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
    +3 L2 basket cells
    +3 L5 basket cells>, 'param_values': {'weight_basket': 0.000206913808111479, 'weight_pyr': 0.000206913808111479}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f845a3c3460>]}, {'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 0x7f845b6e1310>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
     3 L2 basket cells
    -3 L5 basket cells>, 'param_values': {'weight_basket': 9.999999999999999e-06, 'weight_pyr': 9.999999999999999e-05}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f8c19741b50>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
    +3 L5 basket cells>, 'param_values': {'weight_basket': 0.00042813323987193956, 'weight_pyr': 0.00042813323987193956}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f8457219f70>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
     3 L2 basket cells
    -3 L5 basket cells>, 'param_values': {'weight_basket': 2.0235896477251556e-05, 'weight_pyr': 0.00021544346900318845}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f8c2136de20>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
    +3 L5 basket cells>, 'param_values': {'weight_basket': 0.0006158482110660266, 'weight_pyr': 0.0006158482110660266}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f844df62550>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
     3 L2 basket cells
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     3 L2 basket cells
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     3 L2 basket cells
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     3 L2 basket cells
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     3 L2 basket cells
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    +3 L5 basket cells>, 'param_values': {'weight_basket': 0.007847599703514606, 'weight_pyr': 0.007847599703514606}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f8455ea7e50>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
     3 L2 basket cells
    -3 L5 basket cells>, 'param_values': {'weight_basket': 0.005689866029018299, 'weight_pyr': 0.09999999999999999}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f8c1944a610>]}]]}
    +3 L5 basket cells>, 'param_values': {'weight_basket': 0.011288378916846885, 'weight_pyr': 0.011288378916846885}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f845a215160>]}, {'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 0x7f845a3c6d30>]}, {'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 0x7f845a6925b0>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
    +3 L2 basket cells
    +3 L5 basket cells>, 'param_values': {'weight_basket': 0.033598182862837805, 'weight_pyr': 0.033598182862837805}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f846051bfd0>]}, {'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 0x7f84554c8790>]}, {'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 0x7f8460730160>]}, {'net': <Network | 3 x 3 Pyramidal cells (L2, L5)
    +3 L2 basket cells
    +3 L5 basket cells>, 'param_values': {'weight_basket': 0.09999999999999999, 'weight_pyr': 0.09999999999999999}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f844df494c0>]}]]}
     

    This plot shows an overlay of all smoothed dipole waveforms from the -batch simulation. Each line represents a different set of parameters, -allowing us to visualize the range of responses across the parameter space.

    -
    dpl_waveforms = []
    +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))
    -for waveform in dpl_waveforms:
    -    plt.plot(waveform, alpha=0.5, linewidth=3)
    +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)')
    @@ -336,7 +382,7 @@
     plt.show()
     
    -Min and Max Dipole Peaks across Simulations

    Total running time of the script: (0 minutes 17.916 seconds)

    +Min and Max Dipole Peaks across Simulations

    Total running time of the script: (0 minutes 15.054 seconds)