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Click here to download the full example code

Visualize NEURON model#

In this tutorial you will learn to visualize a compartment neuron model.

We will jump right in, so please make sure to have a look at the introductory NEURON tutorial first.

Setup the model#

The setup will be similar to the previous tutorial: use one of the example neurons to create a compartment model:

import navis
import neuron

import navis.interfaces.neuron as nrn

# Load one of the example neurons (a Drosophila projection neuron from the hemibrain connectome)
# Note the conversion to microns!
n = navis.example_neurons(1).convert_units("um")

# Here we manually corrected the soma
n.soma = 20

# Reroot to the soma
n.reroot(n.soma, inplace=True)

# Create the compartment model
cmp = nrn.CompartmentModel(n, res=10)

# Set the specific axial resistivity for the entire neuron in Ohm cm
cmp.Ra = 266.1

# Set the specific membran capacitance in mF / cm**2
cmp.cm = 0.8

# Add passive membran properties for the entire neuron
cmp.insert(
    "pas",
    g=1
    / 20800,  # specific leakage conductance = 1/Rm; Rm = specific membran resistance in Ohm cm**2
    e=-60,  # leakage reverse potential
)

# Label axon/dendrite
navis.split_axon_dendrite(n, label_only=True, cellbodyfiber="soma")

# Collect axon nodes
axon_nodes = n.nodes.loc[n.nodes.compartment.isin(["axon", "linker"]), "node_id"].values

# Get the sections for the given nodes
axon_secs = list(set(cmp.get_node_section(axon_nodes)))

# Insert HH mechanism at the given sections
cmp.insert("hh", subset=axon_secs)

Next, we will add a voltage recording at every single node of the neuron.

cmp.add_voltage_record(n.nodes.node_id.values)

Last but not least, we will add a synaptic input at some dendritic postsynapses of the neuron.

# Get dendritic postsynapses
post = n.postsynapses[n.postsynapses.compartment == "dendrite"]

# Add synaptic input to the first 10 postsynapses after 2 ms
cmp.add_synaptic_current(where=post.node_id.unique()[0:10], start=2, max_syn_cond=0.1, rev_pot=-10)

Now we can run our simulation for 100ms

# This is equivalent to neuron.h.finitialize + neuron.h.continuerun
cmp.run_simulation(100, v_init=-60)

Collect the data#

To visualize and animate, we will collect the results into a pandas DataFrame

import numpy as np
import pandas as pd

# Collect the voltage recordings at each node
records = pd.DataFrame(np.vstack([r.as_numpy() for r in cmp.records['v'].values()]), index=list(cmp.records['v'].keys()))

# Reindex to make sure it matches the node table
records = records.reindex(n.nodes.node_id)

records.head()
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 ... 3961 3962 3963 3964 3965 3966 3967 3968 3969 3970 3971 3972 3973 3974 3975 3976 3977 3978 3979 3980 3981 3982 3983 3984 3985 3986 3987 3988 3989 3990 3991 3992 3993 3994 3995 3996 3997 3998 3999 4000
node_id
1 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 ... -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0
2 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 ... -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0
3 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 ... -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0
4 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 ... -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0
5 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 ... -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0

5 rows × 4001 columns

Visualize#

Let's first visualize a single snapshot of the neuron at time t=5ms:

# The interval for each step is 0.025ms by default
print(neuron.h.dt)

Out:

0.025

Add a new column to the node table for time t=5ms

n.nodes['v'] = records.loc[:, int(5 / 0.025)].values

# Plot
fig, ax = navis.plot2d(
    n,
    method="2d",
    color_by="v",  # color by the voltage column
    palette="viridis",
    vmin = -70,
    vmax = 10,
    view=('x', '-y')
)

# Manually add a colorbar
import matplotlib.pyplot as plt
from matplotlib.cm import ScalarMappable
sm = ScalarMappable(norm=plt.Normalize(vmin=-70, vmax=10), cmap='viridis')
_ = fig.colorbar(sm, ax=ax, fraction=0.075, shrink=0.5, label="V")

tutorial interfaces 01 neuron2

Animate#

One option to animate the voltage recordings over time is to use matplotlib's animation functionality. For that we have to do a bit of setup:

# Convert our skeleton to a mesh for nicer visualization
mesh = navis.conversion.tree2meshneuron(n, warn_missing_radii=False)

# Plot the neuron
fig, ax = navis.plot2d(mesh, method='2d',color='k', view=('x','-y'))

sm = ScalarMappable(norm=plt.Normalize(vmin=-70, vmax=10), cmap='viridis')
_ = fig.colorbar(sm, ax=ax, fraction=0.075, shrink=0.5, label="V")

# Add a text in the top right for the timestamp
t = ax.text(0.02, 0.95, 'ms', ha='left', va='top', transform=ax.transAxes, color='r')

# Get the collection representing our neuron
c = ax.collections[0]
c.set_cmap('viridis')
c.set_norm(plt.Normalize(vmin=-70, vmax=10))

# This function updates the voltages according to the frame
def animate(i):
    # We need to map the voltages at individual nodes to faces in the mesh
    # First nodes to vertices
    vert_voltage = records[i].values[mesh.vertex_map]
    # Then vertices to faces
    face_voltage = vert_voltage[mesh.faces].mean(axis=1)
    # Set the values
    c.set_array(face_voltage)
    # Also update the timestamp
    t.set_text(f'{i * 0.025:.2f} ms')
    return (c, t)

import matplotlib.animation as animation
ani = animation.FuncAnimation(fig, animate, interval=40, blit=True, repeat=True, frames=400)

Total running time of the script: ( 3 minutes 44.773 seconds)

Download Python source code: tutorial_interfaces_01_neuron2.py

Download Jupyter notebook: tutorial_interfaces_01_neuron2.ipynb

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