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
type	object
properties
apical	Specific parameters for apicals
	oneOf	Empty object
		Neurite
axon	Specific parameters for axons
	oneOf	Empty object
		Neurite
basal	Specific parameters for basals
	oneOf	Empty object
		Neurite
diameter_params	Parameters used to generate the diameters
	type	object
	oneOf	properties
		method	The method used to synthesize the diameters
			type	string
			enum	external
		additionalProperties	True
		properties
		method	The method used to synthesize the diameters
			type	string
			not	enum	external
		additionalProperties	False
grow_types	Select which tree types will be generated
	type	array
	items	type	string
		enum	basal, apical, axon
origin	Defines the center of the soma, from which the growth starts
	Point

Empty object

An object without any property
type	object
additionalProperties	False

Neurite

The properties of a neurite
type	object
properties
apical_distance	The apical distance (NEVER USED)
	type	number
bias	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.
	type	number
	maximum	1
	minimum	0
bias_length	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.
	type	number
	minimum	0
branching_method	Defines the method that will be used for the bifurcation angles
	type	string
growth_method	Defines the method that will be used for the growth
	type	string
	enum	tmd, tmd_gradient, tmd_apical, axon_trunk, trunk
has_apical_tuft	Select True if the tree is an apical and is expected to have a tuft
	type	boolean
metric	Defines the metric for the growth (only path distance is currently available)
	type	string
modify	A modification function to be applied to a TMD (barcode) before synthesis starts
	oneOf	type	object
		type	null
num_seg	Number of segments (used when an alternative to TMD, basic growth algorithm is chosen)
	type	integer
	minimum	0
orientation	Defines the target orientation of a tree, as it emanates from the soma
	anyOf	type	null
		type	array
		items	The orientation vector
			Point
		type	object
		properties
		mode	The orientation mode to use
			type	string
		values	oneOf	type	object
				properties
				orientations	type	array
					items	The orientation vector
						Point
				additionalProperties	False
				type	object
				properties
				primary_orientation	type	array
					items	The primary orientation vector
						Point
				additionalProperties	False
radius	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
	type	number
	minimum	0
randomness	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.
	type	number
	maximum	1
	minimum	0
step_size	Defines the distribution from which the step size is sampled
	type	object
	properties
	norm	The normal distribution used
		type	object
		properties
		mean	The mean of the distribution
			type	number
		std	The std of the distribution
			type	number
targeting	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.
	type	number
	maximum	1
	minimum	0
tree_type	Defines the tree index that will be written to the file
	type	integer
trunk_absolute_orientation	Orientation of trunks emerging from the soma (on x-y plane).
	type	boolean
additionalProperties	False

neurite_type

Type of neurite
type	string
enum	basal, apical, axon

Point

A point with 3 coordinates
type	array
maxItems	3
minItems	3

Example

 1{"apical": {"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            "radius": 0.3,
 8            "randomness": 0.32,
 9            "targeting": 0.24,
10            "tree_type": 4,
11            "step_size": {"norm":{"mean":1.0, "std":0.2}}},
12"axon": {},
13"basal": {"branching_method": "bio_oriented",
14          "growth_method": "tmd",
15          "metric": "path_distances",
16          "modify": null,
17          "orientation": {"mode": "sample_pairwise_angles", "values": {}},
18          "radius": 0.3,
19          "randomness": 0.32,
20          "targeting": 0.24,
21          "tree_type": 3,
22          "step_size": {"norm":{"mean":1.0, "std":0.2}}},
23"grow_types": ["basal", "apical"],
24"origin": [0.0,
25           0.0,
26           0.0],
27"diameter_params": {"method": "default"}}

Distributions

Schema

TMD distributions

type	object
properties
apical	Specific distributions for apicals
	oneOf	Empty distribution
		Neurite distribution
axon	Specific distributions for axons
	oneOf	Empty distribution
		Neurite distribution
basal	Specific distributions for basals
	Neurite distribution
diameter	The diameter specifications
	type	object
	additionalProperties	True
	oneOf	properties
		method	type	string
			enum	external
		additionalProperties	True
		properties
		method	type	string
			not	enum	external
		apical	Neurite diameter distribution
		basal	Neurite diameter distribution
		axon	Neurite diameter distribution
		additionalProperties	False
soma	Specific distributions for the somas
	type	object
	properties
	size	The distribution of soma sizes
		type	object
		properties
		norm	type	object
			properties
			mean	The mean of the distribution
				type	number
			std	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
data	The data used for the distribution
	type	object
	properties
	bins	The bins
		type	array
		items	type	number
	weights	The weights
		type	array
		items	type	number
	additionalProperties	False
additionalProperties	False

Empty distribution

An object without any property
type	object
additionalProperties	False

Neurite distribution

The distributions of a type of neurite
type	object
properties
filtration_metric	type	string
num_trees	The data distribution used for the number of trees to synthesize
	Data distribution
persistence_diagram	The persistence diagram
	type	array
	items	type	array
		items	type	array
			items	type	number
trunk	The distributions used to synthesize the trunk
	type	object
	properties
	absolute_elevation_deviation	The data distribution used for the absolute elevation deviation
		Data distribution
	azimuth	The uniform distribution used for the azimuth
		type	object
		properties
		uniform	The properties of the uniform distribution
			type	object
			properties
			max	type	number
			min	type	number
			additionalProperties	False
		additionalProperties	False
	orientation_deviation	The data distribution used for the orientation deviation
		Data distribution
	additionalProperties	False
additionalProperties	False

Neurite diameter distribution

The diameter distributions of a type of neurite
type	object
properties
Rall_ratio	type	number
siblings_ratio	type	number
taper	type	array
	items	type	number
term	type	array
	items	type	number
trunk	type	array
	items	type	number
trunk_taper	type	array
	items	type	number
additionalProperties	False

Example

  1{
  2    "apical": {
  3        "filtration_metric": "path_distances",
  4        "num_trees": {
  5            "data": {
  6                "bins": [
  7                    1
  8                ],
  9                "weights": [
 10                    1
 11                ]
 12            }
 13        },
 14        "persistence_diagram": [
 15            [
 16                [
 17                    290,
 18                    95,
 19                    0,
 20                    0,
 21                    1.0471975511965976,
 22                    0
 23                ],
 24                [
 25                    150,
 26                    100,
 27                    0,
 28                    0,
 29                    1.0471975511965976,
 30                    0
 31                ],
 32                [
 33                    170,
 34                    50,
 35                    0,
 36                    0,
 37                    1.0471975511965976,
 38                    0
 39                ],
 40                [
 41                    145,
 42                    29,
 43                    0,
 44                    0,
 45                    1.0471975511965976,
 46                    0
 47                ],
 48                [
 49                    300,
 50                    0,
 51                    NaN,
 52                    NaN,
 53                    NaN,
 54                    NaN
 55                ]
 56            ]
 57        ],
 58        "trunk": {
 59            "azimuth": {
 60                "uniform": {
 61                    "max": 0.0,
 62                    "min": 3.141592653589793
 63                }
 64            },
 65            "orientation_deviation": {
 66                "data": {
 67                    "bins": [
 68                        0.016666666666666663
 69                    ],
 70                    "weights": [
 71                        1
 72                    ]
 73                }
 74            },
 75            "absolute_elevation_deviation": {
 76                "data": {
 77                    "bins": [
 78                        0
 79                    ],
 80                    "weights": [
 81                        1
 82                    ]
 83                }
 84            }
 85        }
 86    },
 87    "axon": {},
 88    "basal": {
 89        "filtration_metric": "path_distances",
 90        "num_trees": {
 91            "data": {
 92                "bins": [
 93                    4
 94                ],
 95                "weights": [
 96                    1
 97                ]
 98            }
 99        },
100        "persistence_diagram": [
101            [
102                [
103                    224.08653783450782,
104                    10.891041859252676,
105                    0.06392198549840433,
106                    -0.27205559604985874,
107                    -0.5365480370168811,
108                    0.8312206407245757
109                ],
110                [
111                    196.61363089528587,
112                    2.674821865134806,
113                    -0.5220708314792457,
114                    -0.7471831099566546,
115                    3.6071418547490937,
116                    0.589636578046099
117                ],
118                [
119                    265.9921913001744,
120                    0,
121                    NaN,
122                    NaN,
123                    NaN,
124                    NaN
125                ]
126            ]
127        ],
128        "trunk": {
129            "azimuth": {
130                "uniform": {
131                    "max": 0.0,
132                    "min": 3.141592653589793
133                }
134            },
135            "orientation_deviation": {
136                "data": {
137                    "bins": [
138                        0.09586683335089932,
139                        0.39001954178950027,
140                        1.174426764292436,
141                        1.566630375543904,
142                        2.9393430149240416
143                    ],
144                    "weights": [
145                        4,
146                        3,
147                        1,
148                        1,
149                        2
150                    ]
151                }
152            },
153            "absolute_elevation_deviation": {
154                "data": {
155                    "bins": [
156                        0
157                    ],
158                    "weights": [
159                        1
160                    ]
161                }
162            }
163        }
164    },
165    "soma": {
166        "size": {
167            "norm": {
168                "mean": 9.024144162609812,
169                "std": 3.5462697985669935
170            }
171        }
172    },
173    "diameter": {
174        "method": "default"
175    }
176}