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Updated Schemas documentation
authored
Apr 27, 2021
by
Elbez Hammouda
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VS2N-MongoDB-schemas.md
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...
...
@@ -24,8 +24,8 @@ A document schema is a JSON object that contains information about the shape, fi
```
json
{
"properties"
:
{
"_id"
:
{
"bsonType"
:
"objectId"
},
"name"
:
{
"bsonType"
:
"string"
}
"_id"
:
{
"bsonType"
:
"objectId"
},
"name"
:
{
"bsonType"
:
"string"
}
}
}
```
...
...
@@ -35,21 +35,23 @@ A document schema is a JSON object that contains information about the shape, fi
## VS2N documents schemas
## 1- General information schemas:
**Collection name**
: info
<br><br>
**Collection content**
:
<br>
In this collection, there are two schemas, the first one contains information about:
-
Used dataset
-
Date of simulation
-
Name of the simulation
-
Number of layers
-
Number of neurons per layer
-
Used dataset
.
-
Date of simulation
.
-
Name of the simulation
.
-
Number of layers
.
-
Number of neurons per layer
.
```
json
{
"properties"
:
{
"_id"
:
{
"bsonType"
:
"objectId"
},
//
id
"n"
:
{
"bsonType"
:
"string"
},
//
simulation
name
"L:N"
:
{
"<JSON>"
},
//
layer
name
:
neuron
s
number
"T"
:
{
"bsonType"
:
"string"
},
//
simulation
date
"D"
:
{
"bsonType"
:
"string"
}
//
dataset
name
"n"
:
{
"bsonType"
:
"string"
},
//
simulation
name
"L:N"
:
{
"<JSON>"
},
//
layer
name
:
neuron
id
"T"
:
{
"bsonType"
:
"string"
},
//
simulation
date
"D"
:
{
"bsonType"
:
"string"
}
//
dataset
name
}
}
```
...
...
@@ -60,7 +62,7 @@ In this collection, there are two schemas, the first one contains information ab
{
"_id"
:
ObjectId(
"603834c376e245cd78319032"
)
,
"n"
:
"My network"
,
"L:N"
:
{
"Input"
:
784
,
"layer1"
:
100
},
"L:N"
:
{
"Input"
:
784
,
"layer1"
:
100
},
"T"
:
"26-02-2021-00:37:39"
,
"D"
:
"Mnist"
}
...
...
@@ -75,8 +77,8 @@ The second one contains optional information, which is collected if there was a
{
"properties"
:
{
"_id"
:
{
"bsonType"
:
"objectId"
},
//
id
"MaxS"
:
{
"bsonType"
:
"string"
},
//
network
accuracy
(Max
Spike)
"NLabel"
:
{
"<Array of JSON>"
},
//
neurons
labels
"MaxS"
:
{
"bsonType"
:
"string"
},
//
network
accuracy
(Max
Spike)
"NLabel"
:
{
"<Array of JSON>"
},
//
neurons
labels
}
}
```
...
...
@@ -88,23 +90,120 @@ The second one contains optional information, which is collected if there was a
"_id"
:
ObjectId(
"60163a04dec986560a8d907a"
)
,
"MaxS"
:
83.51
,
"NLabel"
:
[
{
"L"
:
"Layer1"
,
"N"
:
"13"
,
"Label"
:
"2"
},
{
"L"
:
"Layer1"
,
"N"
:
"99"
,
"Label"
:
"1"
},
{
"L"
:
"Layer1"
,
"N"
:
"97"
,
"Label"
:
"0"
},
{
"L"
:
"Layer1"
,
"N"
:
"13"
,
"Label"
:
"2"
},
{
"L"
:
"Layer1"
,
"N"
:
"99"
,
"Label"
:
"1"
},
{
"L"
:
"Layer1"
,
"N"
:
"97"
,
"Label"
:
"0"
},
...
]
}
```
<div
align=
"center"
>
Example
</div>
<br>
## 2- Spikes activity schemas:
In this collection, there are two schemas, the first one contains information about:
-
Used dataset
**Collection name**
: spikes
<br><br>
**Collection content**
:
<br>
In this collection, we collect every spike activity in the network during training. The collection contains information about:
-
Time of spike (μs).
-
Label of current input data (if exists).
-
Layer name and neuron id.
```
json
{
"properties"
:
{
"_id"
:
{
"bsonType"
:
"objectId"
},
//
id
"T"
:
{
"bsonType"
:
"NumberLong"
},
//
time
of
spike
"i"
:
{
"<JSON>"
},
//
layer
name
:
neuron
id
"Input"
:
{
"bsonType"
:
"string"
}
//
label
of
current
input
data
(if
exists)
}
}
```
<div
align=
"center"
>
Spikes activity schema
</div>
<br>
```
json
{
"_id"
:
ObjectId(
"6015ffc4dec986560a0709e5"
)
,
"T"
:
NumberLong(
30383000
)
,
"i"
:
{
"L"
:
"Layer1"
,
"N"
:
92
},
"Input"
:
{
"bsonType"
:
"0"
}
}
```
<div
align=
"center"
>
Example
</div>
<br>
## 3- Neurons membrane potential schemas:
In this collection, there are two schemas, the first one contains information about:
-
Used dataset
**Collection name**
: potential
<br><br>
**Collection content**
:
<br>
In this collection, we collect every neuron membrane potential activity in the network during training. The collection contains information about:
-
Time of update (μs).
-
Layer name.
-
Neuron id.
-
The new potential value.
```
json
{
"properties"
:
{
"_id"
:
{
"bsonType"
:
"objectId"
},
//
id
"T"
:
{
"bsonType"
:
"NumberLong"
},
//
time
of
update
"L"
:
{
"bsonType"
:
"string"
},
//
layer
name
"N"
:
{
"bsonType"
:
"int"
},
//
neuron
id
"V"
:
{
"bsonType"
:
"double"
}
//
the
new
potential
value
}
}
```
<div
align=
"center"
>
Neurons membrane potential schemas
</div>
<br>
```
json
{
"_id"
:
ObjectId(
"6015ffb7dec986560a06e29d"
)
,
"T"
:
NumberLong(
18027000
)
,
"L"
:
"Layer1"
,
"N"
:
19
,
"V"
:
5.675836
}
```
<div
align=
"center"
>
Example
</div>
<br>
## 4- Synapses weight activity schemas:
In this collection, there are two schemas, the first one contains information about:
-
Used dataset
\ No newline at end of file
**Collection name**
: synapseWeight
<br><br>
**Collection content**
:
<br>
In this collection, we collect every synapse weight activity in the network during training. The collection contains information about:
-
Time of synapse update (μs).
-
Source neuron id.
-
Destination neuron id.
-
Synapse weight value.
-
Synapse index coordinates (for heatmap construction).
```
json
{
"properties"
:
{
"_id"
:
{
"bsonType"
:
"objectId"
},
//
id
"T"
:
{
"bsonType"
:
"NumberLong"
},
//
time
of
synapse
update
"C"
:
{
"bsonType"
:
"int"
},
//
destination
neuron
id
"To"
:
{
"bsonType"
:
"int"
},
//
source
neuron
id
"V"
:
{
"bsonType"
:
"double"
},
//
weight
value
"index"
:
{
//
index
coordinates
"x"
:
{
"bsonType"
:
"int"
},
"y"
:
{
"bsonType"
:
"int"
}}
}
}
```
<div
align=
"center"
>
Synapses weight activity schema
</div>
<br>
```
json
{
"_id"
:
ObjectId(
"6015ffefdec986560a078796"
)
,
"T"
:
NumberLong(
66847000
)
,
"C"
:
333
,
"To"
:
0
,
"V"
:
0.649092
,
"index"
:
{
"x"
:
0
,
"y"
:
0
}
}
```
<div
align=
"center"
>
Example
</div>
<br>
\ No newline at end of file
