VS2N-MongoDB-schemas.md

 # VS2N MongoDB schemas
 
-In VS2N, we use MongoDB to store all the collected data from SNN simulations. This makes it possible to deploy VS2N on multiple nodes, for a faster and better user experience especially when working with large networks.
+In VS2N, we use MongoDB to store all the collected data from SNN simulations. This makes it possible to deploy VS2N on multiple nodes for a faster and better user experience, especially when working with large networks.
 
 
-Each type of collected data is stored in one collection (the equivalent of a table in a relational database system), and all the collections on one simulation are stored in one MongoDB database.
+Each type of collected data is stored in one collection (the equivalent of a table in a relational database system). All the collections on one simulation are stored in one MongoDB database.
 
 In VS2N, the types of collected data from each SNN simulation (which are stored as a collection each) are:
 
@@ -16,10 +16,10 @@ In VS2N, the types of collected data from each SNN simulation (which are stored
 **Optional**
 - Labels of each input to the network (if possible).
 
-Each collection contains documents (the equivalent of a row in a table of a relational database system), each document has a specific schema that needs to be respected by any simulator, in order for VS2N to work properly.
+Each collection contains documents (the equivalent of a row in a table of a relational database system). Each document has a specific schema that needs to be respected by any simulator in order for VS2N to work correctly.
 
 ## Document schemas
-A document schema is a JSON object that contains information about the shape, fields, and type of data stored in that document.
+A *document schema* is a JSON object that contains information about the shape, fields, and type of data stored in that document.
 
 ```json
 {
@@ -37,7 +37,7 @@ A document schema is a JSON object that contains information about the shape, fi
 ## 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:
+In this collection, there are two schemas. The first one contains information about:
 - Used dataset.
 - Date of simulation.
 - Name of the simulation.
@@ -69,6 +69,7 @@ In this collection, there are two schemas, the first one contains information ab
 ```
 <div align="center">Example</div>
 <br>
+
 The second one contains optional information, which is collected if there was a test phase after training:
 - Network accuracy
 - final neuron classes (for non supervised learning)
@@ -105,7 +106,7 @@ The second one contains optional information, which is collected if there was a
 **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).
+- Label of current input data (if it exists).
 - Layer name and neuron id.
 
 ```json
@@ -114,7 +115,7 @@ In this collection, we collect every spike activity in the network during traini
     "_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)
+    "Input" : { "bsonType": "string" }  // label of current input data (if it exists)
   }
 }
 ```
@@ -211,7 +212,7 @@ In this collection, we collect every synapse weight activity in the network duri
 ## 5- Labels activity schemas (optional):
 **Collection name** : labels<br><br>
 **Collection content** :<br>
-In this collection, we collect every input label in the network during training (if exist). The collection contains information about:
+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).
 - Label value.
 - Value of total inputs already introduced.