VS2N-MongoDB-schemas.md

@@ -91,9 +91,9 @@ 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 },
                 ...
              ] 
 }
@@ -114,8 +114,11 @@ In this collection, we collect every spike activity in the network during traini
   "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 it exists)
+    "i" : {                             // layer name : neuron id
+          "L": { "bsonType": "string" }, 
+          "N": { "bsonType": "int" }
+          },                 
+    "Input" : { "bsonType": "int" }  // label of current input data (if it exists)
   }
 }
 ```
@@ -127,7 +130,7 @@ In this collection, we collect every spike activity in the network during traini
   "_id" : ObjectId("6015ffc4dec986560a0709e5"), 
   "T" : NumberLong(30383000),
   "i" : { "L": "Layer1", "N": 92 },
-  "Input" : { "bsonType": "0" }
+  "Input" : 0
 }
 ```
 <div align="center">Example</div>