05. Optimize simulated evoked response parameters

This example demonstrates how to optimize the parameters of the model simulation to match an experimental dipole waveform.

# Authors: Blake Caldwell <blake_caldwell@brown.edu>
#          Mainak Jas <mjas@mgh.harvard.edu>

import os.path as op

import numpy as np
import matplotlib.pyplot as plt

Let us import hnn_core

import hnn_core
from hnn_core import (MPIBackend, jones_2009_model, read_params,
                      simulate_dipole, read_dipole)


hnn_core_root = op.join(op.dirname(hnn_core.__file__))

# The number of cores may need modifying depending on your current machine.
n_procs = 10

First, we will load experimental data into Dipole object.

This is a different experiment than the one to which the base parameters were tuned. So, the initial RMSE will be large, giving the optimization procedure a lot to work with.

from urllib.request import urlretrieve

data_url = ('https://raw.githubusercontent.com/jonescompneurolab/hnn/master/'
            'data/MEG_detection_data/S1_SupraT.txt')
urlretrieve(data_url, 'S1_SupraT.txt')
exp_dpl = read_dipole('S1_SupraT.txt')

Read the base parameters from a file

params_fname = op.join(hnn_core_root, 'param', 'default.json')
params = read_params(params_fname)

Let’s first simulate the dipole with some initial parameters. The parameter definitions also contain the drives. Even though we could add drives explicitly through our API (see 01. Simulate Event Related Potentials (ERPs)), for conciseness, we add them automatically from the parameter files

scale_factor = 3000.
smooth_window_len = 30.
tstop = exp_dpl.times[-1]
net = jones_2009_model(params=params, add_drives_from_params=True)
with MPIBackend(n_procs=n_procs):
    print("Running simulation with initial parameters")
    initial_dpl = simulate_dipole(net, tstop=tstop, n_trials=1)[0]
    initial_dpl = initial_dpl.scale(scale_factor).smooth(smooth_window_len)

Out:

Running simulation with initial parameters
MPI will run 1 trial(s) sequentially by distributing network neurons over 10 processes.
numprocs=10
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/libnrnmech.so
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Now we start the optimization!

from hnn_core.optimization import optimize_evoked

with MPIBackend(n_procs=n_procs):
    net_opt = optimize_evoked(net, tstop=tstop, n_trials=1,
                              target_dpl=exp_dpl, initial_dpl=initial_dpl,
                              scale_factor=scale_factor,
                              smooth_window_len=smooth_window_len)

Out:

Initial RMSE: 2.99e+01
Starting optimization step 1/4
Optimizing from [13.450-115.025] ms
Optimization step 1, iteration 1
MPI will run 1 trial(s) sequentially by distributing network neurons over 10 processes.
numprocs=10
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weighted RMSE: 3.12e+01 over range [13.450-115.025] ms
Optimization step 1, iteration 2
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numprocs=10
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weighted RMSE: 3.12e+01 over range [13.450-115.025] ms
Optimization step 1, iteration 3
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numprocs=10
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weighted RMSE: 3.12e+01 over range [13.450-115.025] ms
Optimization step 1, iteration 4
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weighted RMSE: 3.13e+01 over range [13.450-115.025] ms
Optimization step 1, iteration 5
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numprocs=10
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weighted RMSE: 3.77e+01 over range [13.450-115.025] ms
Optimization step 1, iteration 6
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weighted RMSE: 3.12e+01 over range [13.450-115.025] ms
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weighted RMSE: 3.05e+01 over range [13.450-115.025] ms
Optimization step 1, iteration 8
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weighted RMSE: 4.08e+01 over range [13.450-115.025] ms
Optimization step 1, iteration 9
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weighted RMSE: 4.06e+01 over range [13.450-115.025] ms
Optimization step 1, iteration 10
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weighted RMSE: 3.09e+01 over range [13.450-115.025] ms
Optimization step 1, iteration 11
MPI will run 1 trial(s) sequentially by distributing network neurons over 10 processes.
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weighted RMSE: 2.79e+01 over range [13.450-115.025] ms
Optimization step 1, iteration 12
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Optimization step 2, iteration 1
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Optimization step 3, iteration 10
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Optimization step 3, iteration 12
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Starting optimization step 4/4
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Final RMSE: 2.23e+01

Now, let’s simulate the dipole with the optimized drive parameters.

with MPIBackend(n_procs=n_procs):
    best_dpl = simulate_dipole(net_opt, tstop=tstop, n_trials=1)[0]
    best_dpl = best_dpl.scale(scale_factor).smooth(smooth_window_len)

Out:

MPI will run 1 trial(s) sequentially by distributing network neurons over 10 processes.
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Finally, we can plot the pre- and post-optimization simulations alongside the experimental data. Upon visualizing the change in optimized versus initial dipole, you should consider exploring which parameters were changed to cause the improved dipole fit.

fig, axes = plt.subplots(2, 1, sharex=True, figsize=(6, 6))

exp_dpl.plot(ax=axes[0], layer='agg', show=False, color='tab:blue')
initial_dpl.plot(ax=axes[0], layer='agg', show=False, color='tab:orange')
best_dpl.plot(ax=axes[0], layer='agg', show=False, color='tab:green')
axes[0].legend(['experimental', 'initial', 'optimized'])
net_opt.cell_response.plot_spikes_hist(ax=axes[1])
Aggregate (L2 + L5)

Out:

<Figure size 600x600 with 2 Axes>

Total running time of the script: ( 34 minutes 31.713 seconds)

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