Parameters and distributions¶
This page describes the format of the expected parameters and distributions used by NeuroTS.
Parameters¶
Schema¶
TMD parameters¶
The parameters used to synthesize new cells |
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type |
object |
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properties |
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|
Specific parameters for apicals |
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oneOf |
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|
Specific parameters for axons |
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oneOf |
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|
Specific parameters for basals |
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oneOf |
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|
Parameters used to generate the diameters |
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type |
object |
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oneOf |
properties |
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|
The method used to synthesize the diameters |
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type |
string |
||||
enum |
external, default, uniform |
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additionalProperties |
True |
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properties |
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|
The method used to synthesize the diameters |
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type |
string |
||||
not |
enum |
external, default, uniform |
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additionalProperties |
False |
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|
Select which tree types will be generated |
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type |
array |
||||
items |
type |
string |
|||
enum |
basal_dendrite, apical_dendrite, axon |
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|
Pia direction |
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|
Defines the center of the soma, from which the growth starts |
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Empty object¶
An object without any property |
|
type |
object |
additionalProperties |
False |
Null object¶
A null object |
|
type |
null |
Neurite¶
The properties of a neurite |
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type |
object |
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properties |
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|
The apical distance (NEVER USED) |
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type |
number |
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|
The branches that are major inherit a bias towards the target orientation of the tree. Bias defines the weight. If bias = 0 the growth is isotropic, and if bias = 1 it goes straight along orientation. |
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type |
number |
||||||||
maximum |
1 |
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minimum |
0 |
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|
The branches that are major inherit a bias towards the target orientation of the tree. Bias_length defines which branches are major and therefore will have a bias. Only branches of size greater than (bias_length * barcode length) are biased. |
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type |
number |
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minimum |
0 |
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|
Defines the method that will be used for the bifurcation angles |
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type |
string |
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|
Defines the method that will be used for the growth |
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type |
string |
||||||||
enum |
tmd, tmd_gradient, tmd_apical, axon_trunk, trunk |
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|
Termination length of major branches, to use mostly for main axon to connect to long-range axon |
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type |
number |
||||||||
minimum |
0 |
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|
Select True if the tree is an apical and is expected to have a tuft |
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type |
boolean |
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|
Defines the metric for the growth (only path distance is currently available) |
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type |
string |
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|
A modification function to be applied to a TMD (barcode) before synthesis starts |
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oneOf |
type |
object |
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|
Number of segments (used when an alternative to TMD, basic growth algorithm is chosen) |
||||||||
type |
integer |
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minimum |
0 |
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|
Defines the target orientation of a tree, as it emanates from the soma |
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anyOf |
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type |
array |
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items |
The orientation vector |
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type |
object |
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properties |
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|
The orientation mode to use |
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type |
string |
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|
oneOf |
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type |
object |
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properties |
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|
type |
array |
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items |
The orientation vector |
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additionalProperties |
False |
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type |
object |
||||||||
properties |
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|
The primary orientation vector |
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additionalProperties |
False |
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type |
object |
||||||||
properties |
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|
False |
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|
type |
object |
|||||||
properties |
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|
False |
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|
oneOf |
type |
number |
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maximum |
3.1416 |
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minimum |
0 |
||||||||
type |
array |
||||||||
items |
type |
number |
|||||||
maximum |
3.1416 |
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minimum |
0 |
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|
oneOf |
type |
number |
||||||
minimum |
0 |
||||||||
type |
array |
||||||||
items |
type |
number |
|||||||
minimum |
0 |
||||||||
additionalProperties |
False |
||||||||
type |
object |
||||||||
properties |
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|
Form of the 3d angle fit |
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type |
string |
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|
type |
array |
|||||||
items |
The fit parameters |
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additionalProperties |
False |
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additionalProperties |
False |
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|
[Deprecated] Defines the radius of the tree. If default diameter is chosen, diameter will be constant. If external diametrization is used, the radius value is not important |
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type |
number |
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minimum |
0 |
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|
Controls the percentage of randomness within a section of a neuron. 0.0: the path is straight, 1.0: the path is a random walk. Randomness + targeting + history should be normalized to 1. |
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type |
number |
||||||||
maximum |
1 |
||||||||
minimum |
0 |
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|
Defines the distribution from which the step size is sampled |
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type |
object |
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properties |
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|
The normal distribution used |
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type |
object |
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properties |
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|
The mean of the distribution |
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type |
number |
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minimum |
0 |
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|
The std of the distribution |
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type |
number |
||||||||
minimum |
0 |
||||||||
additionalProperties |
False |
||||||||
additionalProperties |
False |
||||||||
|
Controls the percentage of targeting (complementary to the randomness). 1.0: the path is straight, 0.0: the path is a random walk. Randomness + targeting + history should be normalized to 1. |
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type |
number |
||||||||
maximum |
1 |
||||||||
minimum |
0 |
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|
Defines the tree index that will be written to the file |
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type |
integer |
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|
Orientation of trunks emerging from the soma (on x-y plane). |
||||||||
type |
boolean |
||||||||
additionalProperties |
False |
neurite_type¶
Type of neurite |
|
type |
string |
enum |
basal_dendrite, apical_dendrite, axon |
Point¶
A point with 3 coordinates |
|
type |
array |
maxItems |
3 |
minItems |
3 |
Example¶
1{"apical_dendrite": {"branching_method": "directional",
2 "growth_method": "tmd",
3 "has_apical_tuft": true,
4 "metric": "path_distances",
5 "modify": null,
6 "orientation": {"mode": "use_predefined", "values": {"orientations": [[0.0, 1.0, 0.0]]}},
7 "randomness": 0.32,
8 "targeting": 0.24,
9 "tree_type": 4,
10 "step_size": {"norm":{"mean":1.0, "std":0.2}}},
11"axon": {},
12"basal_dendrite": {"branching_method": "bio_oriented",
13 "growth_method": "tmd",
14 "metric": "path_distances",
15 "modify": null,
16 "orientation": {"mode": "sample_pairwise_angles", "values": null},
17 "randomness": 0.32,
18 "targeting": 0.24,
19 "tree_type": 3,
20 "step_size": {"norm":{"mean":1.0, "std":0.2}}},
21"grow_types": ["basal_dendrite", "apical_dendrite"],
22"origin": [0.0,
23 0.0,
24 0.0],
25"diameter_params": {"method": "uniform",
26 "basal_dendrite": 0.6,
27 "apical_dendrite": 0.6,
28 "axon": 0.6}}
Distributions¶
Schema¶
TMD distributions¶
type |
object |
||||
properties |
|||||
|
Specific distributions for apicals |
||||
oneOf |
|||||
|
Specific distributions for axons |
||||
oneOf |
|||||
|
Specific distributions for basals |
||||
|
The diameter specifications |
||||
type |
object |
||||
additionalProperties |
True |
||||
oneOf |
properties |
||||
|
type |
string |
|||
enum |
external |
||||
additionalProperties |
True |
||||
properties |
|||||
|
type |
string |
|||
enum |
default |
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|
type |
array |
|||
items |
type |
number |
|||
|
type |
array |
|||
items |
type |
number |
|||
|
type |
array |
|||
items |
type |
number |
|||
additionalProperties |
False |
||||
properties |
|||||
|
|||||
|
|||||
|
|||||
|
type |
string |
|||
not |
enum |
external, default |
|||
|
|||||
|
|||||
additionalProperties |
False |
||||
|
Specific distributions for the somas |
||||
type |
object |
||||
properties |
|||||
|
The distribution of soma sizes |
||||
type |
object |
||||
properties |
|||||
|
type |
object |
|||
properties |
|||||
|
The mean of the distribution |
||||
type |
number |
||||
|
The std of the distribution |
||||
type |
number |
||||
additionalProperties |
False |
||||
additionalProperties |
False |
||||
additionalProperties |
False |
||||
additionalProperties |
False |
Data distribution¶
A distribution based on data bins and weights |
||||
type |
object |
|||
properties |
||||
|
The data used for the distribution |
|||
type |
object |
|||
properties |
||||
|
The bins |
|||
type |
array |
|||
items |
type |
number |
||
|
The weights |
|||
type |
array |
|||
items |
type |
number |
||
additionalProperties |
False |
|||
additionalProperties |
False |
Empty distribution¶
An object without any property |
|
type |
object |
additionalProperties |
False |
Neurite diameter distribution¶
The diameter distributions of a type of neurite |
|||
type |
object |
||
properties |
|||
|
type |
number |
|
|
type |
number |
|
|
type |
array |
|
items |
type |
number |
|
|
type |
array |
|
items |
type |
number |
|
|
type |
array |
|
items |
type |
number |
|
|
type |
array |
|
items |
type |
number |
|
additionalProperties |
False |
Neurite distribution¶
The distributions of a type of neurite |
||||||
type |
object |
|||||
properties |
||||||
|
type |
string |
||||
|
The data distribution used for the number of trees to synthesize |
|||||
|
The minimum bar length, used for checking input parameters consistency |
|||||
type |
number |
|||||
minimum |
0 |
|||||
|
The persistence diagram |
|||||
type |
array |
|||||
items |
type |
array |
||||
items |
type |
array |
||||
items |
type |
number |
||||
|
oneOf |
No trunk distribution provided |
||||
type |
null |
|||||
The distributions used to synthesize the trunk using orientation deviation |
||||||
type |
object |
|||||
properties |
||||||
|
The data distribution used for the absolute elevation deviation |
|||||
|
The uniform distribution used for the azimuth |
|||||
type |
object |
|||||
properties |
||||||
|
The properties of the uniform distribution |
|||||
type |
object |
|||||
properties |
||||||
|
type |
number |
||||
|
type |
number |
||||
additionalProperties |
False |
|||||
additionalProperties |
False |
|||||
|
The data distribution used for the orientation deviation |
|||||
|
The data distribution used for the 3d angles wrt pia direction |
|||||
|
The data distribution used for the 3d angles wrt apical direction |
|||||
additionalProperties |
False |
|||||
additionalProperties |
False |
Example¶
1{
2 "apical_dendrite": {
3 "filtration_metric": "path_distances",
4 "min_bar_length": 1.0,
5 "num_trees": {
6 "data": {
7 "bins": [
8 1
9 ],
10 "weights": [
11 1
12 ]
13 }
14 },
15 "persistence_diagram": [
16 [
17 [
18 290,
19 95,
20 0,
21 0,
22 1.0471975511965976,
23 0
24 ],
25 [
26 150,
27 100,
28 0,
29 0,
30 1.0471975511965976,
31 0
32 ],
33 [
34 170,
35 50,
36 0,
37 0,
38 1.0471975511965976,
39 0
40 ],
41 [
42 145,
43 29,
44 0,
45 0,
46 1.0471975511965976,
47 0
48 ],
49 [
50 300,
51 0,
52 NaN,
53 NaN,
54 NaN,
55 NaN
56 ]
57 ]
58 ],
59 "trunk": {
60 "azimuth": {
61 "uniform": {
62 "max": 0.0,
63 "min": 3.141592653589793
64 }
65 },
66 "orientation_deviation": {
67 "data": {
68 "bins": [
69 0.016666666666666663
70 ],
71 "weights": [
72 1
73 ]
74 }
75 },
76 "absolute_elevation_deviation": {
77 "data": {
78 "bins": [
79 0
80 ],
81 "weights": [
82 1
83 ]
84 }
85 }
86 }
87 },
88 "axon": {},
89 "basal_dendrite": {
90 "filtration_metric": "path_distances",
91 "min_bar_length": 1.0,
92 "num_trees": {
93 "data": {
94 "bins": [
95 4
96 ],
97 "weights": [
98 1
99 ]
100 }
101 },
102 "persistence_diagram": [
103 [
104 [
105 224.08653783450782,
106 10.891041859252676,
107 0.06392198549840433,
108 -0.27205559604985874,
109 -0.5365480370168811,
110 0.8312206407245757
111 ],
112 [
113 196.61363089528587,
114 2.674821865134806,
115 -0.5220708314792457,
116 -0.7471831099566546,
117 3.6071418547490937,
118 0.589636578046099
119 ],
120 [
121 265.9921913001744,
122 0,
123 NaN,
124 NaN,
125 NaN,
126 NaN
127 ]
128 ]
129 ],
130 "trunk": {
131 "azimuth": {
132 "uniform": {
133 "max": 0.0,
134 "min": 3.141592653589793
135 }
136 },
137 "orientation_deviation": {
138 "data": {
139 "bins": [
140 0.09586683335089932,
141 0.39001954178950027,
142 1.174426764292436,
143 1.566630375543904,
144 2.9393430149240416
145 ],
146 "weights": [
147 4,
148 3,
149 1,
150 1,
151 2
152 ]
153 }
154 },
155 "absolute_elevation_deviation": {
156 "data": {
157 "bins": [
158 0
159 ],
160 "weights": [
161 1
162 ]
163 }
164 }
165 }
166 },
167 "soma": {
168 "size": {
169 "norm": {
170 "mean": 9.024144162609812,
171 "std": 3.5462697985669935
172 }
173 }
174 },
175 "diameter": {
176 "method": "uniform"
177 }
178}