Update index.rst (#2316)

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doc/concepts/index.rst

@@ -3,8 +3,8 @@
 Concepts overview
 =================
 
-Arbor is a library that lets you to model neural networks with morphologically
-detailed cells; which it then executes the resulting simulation on a variety of
+Arbor is a library that lets you model neural networks with morphologically
+detailed cells, and then execute the resulting simulation on a variety of
 hardware. The execution can optionally be configured in high detail but comes
 with sensible defaults.
 
@@ -23,9 +23,9 @@ To be able to simulate a model, three basic steps need to be considered:
 2. Define the computational resources available to execute the model;
 3. Initiate and execute a simulation of the recipe on the chosen hardware resources.
 
-The Python front-end further abstracts away some of these steps for single cell models, where users only need to
-describe the cell and simulation; and the details of the recipe and computational resources construction are
-handled under the hood. Generally speaking though, these 3 steps are the building blocks of an Arbor application.
+The Python front-end further abstracts away some of these steps for single-cell models, where users only need to
+describe the cell and simulation, and the details of the recipe and computational resources construction are
+handled under the hood. Generally speaking, though, these 3 steps are the building blocks of an Arbor application.
 
 .. raw:: html
    :file: index-diag-2.html
@@ -40,14 +40,14 @@ of work distributed across processes. Arbor has built-in support for different
 :ref:`cell types <modelcells>`, which can be extended by adding new cell types
 to the C++ cell group interface.
 
-:ref:`modelsimulation` manage the instantiation of the model and the scheduling
+:ref:`modelsimulation` manages the instantiation of the model and the scheduling
 of spike exchange as well as the integration for each cell group. A cell group
 represents a collection of cells of the same type computed together on the GPU
 or CPU. The partitioning into cell groups is provided by :ref:`modeldomdec`
 which describes the distribution of the model over the locally available
 computational resources.
 
-In order to visualize the result of detected spikes a spike recorder can be
+In order to visualize the result of detected spikes, a spike recorder can be
 used, and to analyse Arbor's performance a meter manager is available.
 
 :ref:`probesample` shows how to extract data from simulations.