VS2N-MongoDB-schemas.md

@@ -113,7 +113,7 @@ The second one contains optional information, which is collected if there was a
 **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).
+- Time of spike (s).
 - Label of current input data (if it exists).
 - Layer name and neuron id.
 
@@ -121,7 +121,7 @@ In this collection, we collect every spike activity in the network during traini
 { 
   "properties" : {
     "_id" : { "bsonType": "objectId" }, // id
-    "T" : { "bsonType": "NumberLong" }, // time of spike
+    "T" : { "bsonType": "double" }, // time of spike
     "i" : {                             // layer name : neuron id
           "L": { "bsonType": "string" }, 
           "N": { "bsonType": "int"    }
@@ -136,7 +136,7 @@ In this collection, we collect every spike activity in the network during traini
 ```json
 { 
   "_id" : ObjectId("6015ffc4dec986560a0709e5"), 
-  "T" : NumberLong(30383000),
+  "T" : 1.0,
   "i" : { "L": "Layer1", "N": 92 },
   "Input" : 0
 }
@@ -148,7 +148,7 @@ In this collection, we collect every spike activity in the network during traini
 **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).
+- Time of update (s).
 - Layer name.
 - Neuron id.
 - The new potential value.
@@ -157,7 +157,7 @@ In this collection, we collect every neuron membrane potential activity in the n
 { 
   "properties" : {
     "_id" : { "bsonType": "objectId" }, // id
-    "T" : { "bsonType": "NumberLong" }, // time of update
+    "T" : { "bsonType": "double" }, // time of update
     "L" : { "bsonType": "string" },     // layer name
     "N" : { "bsonType": "int"    },     // neuron id
     "V" : { "bsonType": "double" }      // the new potential value
@@ -171,7 +171,7 @@ In this collection, we collect every neuron membrane potential activity in the n
 ```json
 { 
   "_id" : ObjectId("6015ffb7dec986560a06e29d"), 
-  "T" : NumberLong(18027000),
+  "T" : 17.0,
   "L" : "Layer1",
   "N" : 19,
   "V" : 5.675836
@@ -184,7 +184,7 @@ In this collection, we collect every neuron membrane potential activity in the n
 **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).
+- Time of synapse update (s).
 - Source neuron id.
 - Destination neuron id.
 - Synapse weight value.
@@ -194,7 +194,7 @@ In this collection, we collect every synapse weight activity in the network duri
 { 
   "properties" : {
     "_id" : { "bsonType": "objectId" }, // id
-    "T" : { "bsonType": "NumberLong" }, // time of synapse update
+    "T" : { "bsonType": "double" }, // time of synapse update
     "C" : { "bsonType": "int"    },     // destination neuron id
     "To" : { "bsonType": "int"   },     // source neuron id
     "V" : { "bsonType": "double" },     // weight value
@@ -211,7 +211,7 @@ In this collection, we collect every synapse weight activity in the network duri
 ```json
 { 
   "_id" : ObjectId("6015ffefdec986560a078796"), 
-  "T" : NumberLong(66847000),
+  "T" : 0.0,
   "C" : 333,
   "To" : 0,
   "V" : 0.649092,
@@ -225,7 +225,7 @@ In this collection, we collect every synapse weight activity in the network duri
 **Collection name** : labels<br><br>
 **Collection content** :<br>
 In this collection, we collect every input label in the network during training (if it exists). The collection contains information about:
-- Time of input introduction to the network (μs).
+- Time of input introduction to the network (s).
 - Label value.
 - Value of total inputs already introduced.
 
@@ -233,7 +233,7 @@ In this collection, we collect every input label in the network during training
 { 
   "properties" : {
     "_id" : { "bsonType": "objectId" }, // id
-    "T" : { "bsonType": "NumberLong" }, // time of input introduction to the network
+    "T" : { "bsonType": "double" }, // time of input introduction to the network
     "L" : { "bsonType": "int" },        // label value
     "G" : { "bsonType": "int" },        // value of total inputs already introduced
 }
@@ -244,7 +244,7 @@ In this collection, we collect every input label in the network during training
 ```json
 { 
   "_id" : ObjectId("6015bb74ef223d473e4ec000"), 
-  "T" : NumberLong(62250000),
+  "T" : 8.0,
   "L" : 3,
   "G" : 11
 }