In this script features of different calss of the neuron are shown. The features are in the form of histogram, density or scalar values.



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import numpy as np
import McNeuron
import matplotlib.pyplot as plt
%matplotlib inline

Class1: Interneuron

An indivisual neuron



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#loc1 = "/Volumes/Arch/Projects/Computational Anatomy/neuron_nmo/poorthuis/CNG version/060110-LII-III.CNG.swc"
loc1 = "../Generative-Models-of-Neuron-Morphology/Data/Pyramidal/poorthuis/CNG version/060110-LV.CNG.swc"
loc2 = "../Generative-Models-of-Neuron-Morphology/Data/Interneuron/allen cell types/CNG version/Pvalb-IRES-Cre-Ai14-475465561.CNG.swc"
pyramidal = McNeuron.Neuron(file_format = 'swc', input_file=loc1)
inter = McNeuron.Neuron(file_format = 'swc', input_file=loc2)
a = pyramidal.subsample(20.)
McNeuron.visualize.plot_2D(a,show_radius=True)
print len(a.nodes_list)
a.show_features(15,17,30)



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btmorph3.visualize.plot_2D(inter,show_radius=False)



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len(inter.nodes_list)

Morphology of the neurons

The first one is pyramidal neuron and second is interneuron



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ax1 = McNeuron.visualize.plot_2D(pyramidal, show_radius=False)
ax2 = McNeuron.visualize.plot_2D(inter, show_radius=False)

Feature of interneuron



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inter.show_features(15,17,30)

Feature of Pyramidal



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pyramidal.show_features(15,17,50)

Sholl Diagram

For given real number of $r$, we can calculate how many times a sphere with the radius $r$ with the center of the soma intersects with the neuron. Sholl diagram shows this number for differnt values of $r$. For the pyramidal neuron it usually has two bumps, which represents the basal and apical dendrites, versus interneuron which usually has one.



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f,(ax1, ax2) = plt.subplots(1, 2)
ax1.plot(pyramidal.sholl_r,pyramidal.sholl_n,'g')
ax2.plot(inter.sholl_r,inter.sholl_n,'m')



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inter.features

histogram of diameters

The histogram of the diameters of all compartments in the neuron



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f, (ax1, ax2) = plt.subplots(1, 2)
a = pyramidal.diameter1
b = pyramidal.distance_from_root
c = ax1.hist(a[b>20],bins = 30,color = 'g')
ax1.set_xlabel('diameter (um3)')
ax1.set_ylabel('density')
#ax1.set_title('Histogram of the size of compartments of neuron')

a = inter.diameter
b = inter.distance_from_root
c = ax2.hist(a[b>20],bins = 15,color = 'm')
ax2.set_xlabel('diameter (um3)')
#ax2.set_ylabel('density')
#ax2.set_title('Histogram of the size of compartments of neuron')



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a = inter.diameter
b = inter.distance_from_root
c = plt.hist(a[b>20],bins = 15,color = 'm')
plt.xlabel('diameter (um3)')

Histogram of Slope of each segments

By looking at the conneceted compartments, we can calculate the slope of the segment by dividing the diffrernce of radius and difference of the location. For many of them the slope is zero and we ignor them.



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f, (ax1, ax2) = plt.subplots(1, 2)
e = pyramidal.slope
x = ax1.hist(e[e!=0],bins=40,color = 'g')
ax1.set_xlabel('Value of Slope')
ax1.set_ylabel('density')

e = inter.slope
x = ax2.hist(e[e!=0],bins=40,color = 'm')
ax2.set_xlabel('Value of Slope')
#ax2.set_ylabel('density')

Histogram of distance from soma

For each compartments on the neuron, the distance from the soma are calculated and histogram of them are shown



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f, (ax1, ax2) = plt.subplots(1, 2)
a = pyramidal.distance_from_root
b = ax1.hist(a[~np.isnan(a)],bins = 50,color = 'g')
ax1.set_xlabel('distance (um)')
ax1.set_ylabel('density')
#plt.title('Histogram of distance from soma for different compartments of neuron')

a = inter.distance_from_root
b = ax2.hist(a[~np.isnan(a)],bins = 50,color = 'm')
ax2.set_xlabel('distance (um)')
#ax2.set_ylabel('density')
#plt.title('Histogram of distance from soma for different compartments of neuron')



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a = inter.distance_from_root
b = plt.hist(a[~np.isnan(a)],bins = 50,color = 'm')
plt.xlabel('distance (um)')

Local Angle

Local angles are the angles between the vector with the starting point of one compartment and end point of its child, and the vector that connect it to $its$ $parent$.



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f, (ax1, ax2) = plt.subplots(1, 2)
a = pyramidal.local_angle
b = ax1.hist(a[~np.isnan(a)],bins = 50,color = 'g')
ax1.set_xlabel('angle (radian)')
ax1.set_ylabel('density')
#plt.title('Histogram of local angles')
a = inter.local_angle
b = ax2.hist(a[~np.isnan(a)],bins = 50,color = 'm')
ax2.set_xlabel('angle (radian)')
#ax2.set_ylabel('density')



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a = inter.local_angle
b = plt.hist(a[~np.isnan(a)],bins = 50,color = 'm')
plt.xlabel('angle (radian)')

Global Angle

Global angles are the angles between the vector with the starting point of one compartment and end point of its child, and the vector that connect it to $soma$.



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f, (ax1, ax2) = plt.subplots(1, 2)
a = pyramidal.angle_global
b = ax1.hist(a[~np.isnan(a)],bins = 50,color = 'g')
ax1.set_xlabel('angle (radian)')
ax1.set_ylabel('density')
#plt.title('Histogram of global angles')

a = inter.angle_global
b = ax2.hist(a[~np.isnan(a)],bins = 50,color = 'm')
ax2.set_xlabel('angle (radian)')



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a = inter.angle_global
b = plt.hist(a[~np.isnan(a)],bins = 50,color = 'm')
plt.xlabel('angle (radian)')

Angle at the branching point

At each branching points in the neuron, we can cauculate the angles between two outward segments. Here we plot the histogram of them for different branching points.



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f, (ax1, ax2) = plt.subplots(1, 2)
a = pyramidal.angle_branch[0,:]
b = ax1.hist(a[~np.isnan(a)],bins = 20,color = 'g')
a = inter.angle_branch[0,:]
b = ax2.hist(a[~np.isnan(a)],bins = 20,color = 'm')



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a = inter.angle_branch[0,:]
b = plt.hist(a[~np.isnan(a)],bins = 10,color = 'm')

Rall Ratio

The Rall ratio is defined by taking the ratio of the diameter^2/3 parent in branching point divided by sum of diameter^2/3 of its children.



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f, (ax1, ax2) = plt.subplots(1, 2)
a = pyramidal.rall_ratio
b = ax1.hist(a[~np.isnan(a)],bins = 20,color = 'g')

a = inter.rall_ratio
b = ax2.hist(a[~np.isnan(a)],bins = 20,color = 'm')

Slope



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f, (ax1, ax2) = plt.subplots(1, 2)
a = pyramidal.slope
b = ax1.hist(a[~np.isnan(a)],bins = 40,color = 'g')

a = inter.slope
b = ax2.hist(a[~np.isnan(a)],bins = 40,color = 'm')



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a = inter.slope
b = plt.hist(a[~np.isnan(a)],bins = 40,color = 'm')

distance from parent



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f, (ax1, ax2) = plt.subplots(1, 2)
a = pyramidal.length_to_parent
b = ax1.hist(a[~np.isnan(a)],bins = 40,color = 'g')

a = inter.length_to_parent
b = ax2.hist(a[~np.isnan(a)],bins = 40,color = 'm')



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a = inter.length_to_parent
b = a[~np.isnan(a)]
c = plt.hist(b[np.absolute(b)<4],bins = 70,color = 'm')



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np.absolute(b)<3

Ratio of neuronal distance over Euclidian distance from root



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f, (ax1, ax2) = plt.subplots(1, 2)
ax1.hist(inter.overall_connectivity_matrix.sum(axis = 1)/inter.distance_from_root,bins = 40,color = 'g')
ax2.hist(pyramidal.overall_connectivity_matrix.sum(axis = 1)/pyramidal.distance_from_root,bins = 40,color = 'g')



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plt.hist(inter.features['ratio_euclidian_neuronal'],bins = 40,color = 'g')

Connectivity matrix



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import matplotlib.pyplot as plt
%matplotlib inline
plt.imshow(inter.connection)
plt.show()



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