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:

MPI will run over 10 processes
Running simulation with initial parameters
Running 1 trials...
numprocs=10
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
Building the NEURON model
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[Done]
running trial 1 on 10 cores
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Now we start the optimization!

from hnn_core.optimization import optimize_evoked

with MPIBackend(n_procs=n_procs):
    params_optim = optimize_evoked(params, exp_dpl, initial_dpl,
                                   scale_factor=scale_factor,
                                   smooth_window_len=smooth_window_len)

Out:

MPI will run over 10 processes
Initial RMSE: 31.05
Starting optimization step 1/4
Optimizing from [13.475-114.625] ms
Optimization step 1, iteration 1
Running 1 trials...
numprocs=10
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
Building the NEURON model
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 32.66 over range [13.475-114.625] ms
Optimization step 1, iteration 2
Running 1 trials...
numprocs=10
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Building the NEURON model
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 32.74 over range [13.475-114.625] ms
Optimization step 1, iteration 3
Running 1 trials...
numprocs=10
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
Building the NEURON model
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 32.20 over range [13.475-114.625] ms
Optimization step 1, iteration 4
Running 1 trials...
numprocs=10
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
Building the NEURON model
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 33.02 over range [13.475-114.625] ms
Optimization step 1, iteration 5
Running 1 trials...
numprocs=10
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
Building the NEURON model
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 38.32 over range [13.475-114.625] ms
Optimization step 1, iteration 6
Running 1 trials...
numprocs=10
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
Building the NEURON model
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 44.63 over range [13.475-114.625] ms
Optimization step 1, iteration 7
Running 1 trials...
numprocs=10
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Building the NEURON model
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 41.38 over range [13.475-114.625] ms
Optimization step 1, iteration 8
Running 1 trials...
numprocs=10
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 32.52 over range [13.475-114.625] ms
Optimization step 1, iteration 9
Running 1 trials...
numprocs=10
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
Building the NEURON model
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 32.20 over range [13.475-114.625] ms
Optimization step 1, iteration 10
Running 1 trials...
numprocs=10
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Building the NEURON model
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 32.24 over range [13.475-114.625] ms
Optimization step 1, iteration 11
Running 1 trials...
numprocs=10
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Building the NEURON model
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 29.47 over range [13.475-114.625] ms
Optimization step 1, iteration 12
Running 1 trials...
numprocs=10
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Building the NEURON model
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 29.45 over range [13.475-114.625] ms
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Running 1 trials...
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 29.29 over range [13.475-114.625] ms
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 29.16 over range [13.475-114.625] ms
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 29.02 over range [13.475-114.625] ms
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 28.93 over range [13.475-114.625] ms
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 29.60 over range [13.475-114.625] ms
Optimization step 1, iteration 42
Running 1 trials...
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 29.54 over range [13.475-114.625] ms
Optimization step 1, iteration 43
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 29.46 over range [13.475-114.625] ms
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 29.42 over range [13.475-114.625] ms
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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Running 1 trials...
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 28.91 over range [13.475-114.625] ms
Optimization step 1, iteration 50
Running 1 trials...
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 28.55 over range [13.475-114.625] ms
Starting optimization step 2/4
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Optimization step 2, iteration 1
Running 1 trials...
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Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 32.03 over range [43.025-170.000] ms
Optimization step 2, iteration 2
Running 1 trials...
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Building the NEURON model
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 32.67 over range [43.025-170.000] ms
Optimization step 2, iteration 3
Running 1 trials...
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Building the NEURON model
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 32.08 over range [43.025-170.000] ms
Optimization step 2, iteration 4
Running 1 trials...
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Building the NEURON model
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 32.15 over range [43.025-170.000] ms
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 36.19 over range [43.025-170.000] ms
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 27.96 over range [43.025-170.000] ms
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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Running 1 trials...
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[Done]
running trial 1 on 10 cores
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Running 1 trials...
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[Done]
running trial 1 on 10 cores
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Running 1 trials...
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[Done]
running trial 1 on 10 cores
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Running 1 trials...
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[Done]
running trial 1 on 10 cores
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Running 1 trials...
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
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[Done]
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[Done]
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[Done]
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 28.67 over range [43.025-170.000] ms
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 28.84 over range [43.025-170.000] ms
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 28.85 over range [43.025-170.000] ms
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 29.10 over range [43.025-170.000] ms
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 29.13 over range [43.025-170.000] ms
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 29.11 over range [43.025-170.000] ms
Starting optimization step 3/4
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Optimization step 3, iteration 1
Running 1 trials...
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 30.75 over range [92.775-170.000] ms
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Running 1 trials...
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 30.52 over range [92.775-170.000] ms
Optimization step 3, iteration 3
Running 1 trials...
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Building the NEURON model
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 30.56 over range [92.775-170.000] ms
Optimization step 3, iteration 4
Running 1 trials...
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Building the NEURON model
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 30.49 over range [92.775-170.000] ms
Optimization step 3, iteration 5
Running 1 trials...
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 30.42 over range [92.775-170.000] ms
Optimization step 3, iteration 6
Running 1 trials...
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Building the NEURON model
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 30.13 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 29.08 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 27.84 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 30.61 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 27.46 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 27.25 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 27.11 over range [92.775-170.000] ms
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Running 1 trials...
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 28.76 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 27.28 over range [92.775-170.000] ms
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Running 1 trials...
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Building the NEURON model
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 28.76 over range [92.775-170.000] ms
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Running 1 trials...
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 27.15 over range [92.775-170.000] ms
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[Done]
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weighted RMSE: 28.76 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 26.29 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 25.14 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 25.05 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 24.90 over range [92.775-170.000] ms
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[Done]
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 24.56 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 24.53 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 24.15 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 23.61 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 23.34 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 23.24 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 22.49 over range [92.775-170.000] ms
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Building the NEURON model
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 24.26 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 24.37 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 23.03 over range [92.775-170.000] ms
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 22.45 over range [92.775-170.000] ms
Starting optimization step 4/4
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 23.58 over range [0.000-170.000] ms
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Running 1 trials...
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 24.41 over range [0.000-170.000] ms
Optimization step 4, iteration 3
Running 1 trials...
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 24.89 over range [0.000-170.000] ms
Optimization step 4, iteration 4
Running 1 trials...
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 22.31 over range [0.000-170.000] ms
Optimization step 4, iteration 5
Running 1 trials...
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 24.73 over range [0.000-170.000] ms
Optimization step 4, iteration 6
Running 1 trials...
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Building the NEURON model
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 24.02 over range [0.000-170.000] ms
Optimization step 4, iteration 7
Running 1 trials...
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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Running 1 trials...
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[Done]
running trial 1 on 10 cores
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Running 1 trials...
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[Done]
running trial 1 on 10 cores
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Running 1 trials...
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[Done]
running trial 1 on 10 cores
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Running 1 trials...
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[Done]
running trial 1 on 10 cores
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Running 1 trials...
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[Done]
running trial 1 on 10 cores
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[Done]
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 21.32 over range [0.000-170.000] ms
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 21.26 over range [0.000-170.000] ms
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[Done]
running trial 1 on 10 cores
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[best] weighted RMSE: 21.25 over range [0.000-170.000] ms
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[Done]
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[Done]
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[Done]
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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[Done]
running trial 1 on 10 cores
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weighted RMSE: 21.09 over range [0.000-170.000] ms
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weighted RMSE: 21.33 over range [0.000-170.000] ms
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[best] weighted RMSE: 18.88 over range [0.000-170.000] ms
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[best] weighted RMSE: 18.59 over range [0.000-170.000] ms
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running trial 1 on 10 cores
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weighted RMSE: 18.63 over range [0.000-170.000] ms
Final RMSE: 18.59

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

net = jones_2009_model(params=params_optim, add_drives_from_params=True)
with MPIBackend(n_procs=n_procs):
    best_dpl = simulate_dipole(net, tstop=tstop, n_trials=1)[0]
    best_dpl = best_dpl.scale(scale_factor).smooth(smooth_window_len)

Out:

MPI will run over 10 processes
Running 1 trials...
numprocs=10
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
Building the NEURON model
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
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[Done]
running trial 1 on 10 cores
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Finally, we can plot the results against experimental data along with the input histograms: 1. Initial dipole 2. Optimized dipole fit

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)
initial_dpl.plot(ax=axes[0], layer='agg', show=False)
best_dpl.plot(ax=axes[0], layer='agg', show=False)
axes[0].legend(['experimental', 'initial', 'optimized'])
net.cell_response.plot_spikes_hist(ax=axes[1])

Out:

<Figure size 600x600 with 2 Axes>