add default values to weight andconnection_rule

_literate/blox_connections.jl

@@ -45,17 +45,17 @@ equations(lif)
 # The first one will print only the connection equations 
 @named ifn = IFNeuron() ## create an Integrate-and-Fire neuron, simpler than the `LIFNeuron`
 
-connection_equations(lif, ifn)
+connection_equations(lif, ifn, weight=1, connection_rule="basic")
 # While the second function prints out all fields that take part in the connection rule
-connection_rule(lif, ifn)
-# The output of both functions now seems very similar. However `connection_rule` will be more useful later on when we start using more complex Bloxs and connection rules.
+connection_rule(lif, ifn, weight=1, connection_rule="psp")
+# The `weight` and `connection_rule` are keyword arguments that can be ommitted. If we ommit them then we will get a message informing us about the default values that they take. The `connection_rule` argument applies to connections between `Neuron` types; `"basic"` is a simple weighted connection and `"psp"` applies a postsynaptic potential type of connection. The output of both `connection_equations` and `connection_rule` functions now seems very similar. However `connection_rule` will be more useful later on when we start using more complex Bloxs and connection rules that do more than just adding an equation and a symbolic weight.
 
 # ## Simulating connected Bloxs
 # We are now ready to define a couple of Bloxs, connect them and simulate the final model.
 # Every Neuroblox model starts off as a graph. Every vertex of the graph is a Blox and every edge is a connection between two Bloxs.
 # Let's build a simple circuit by using the two neurons we created above; `lif` connects to `ifn`.
 g = MetaDiGraph()
-add_edge!(g, lif => ifn)
+add_edge!(g, lif => ifn, weight=1) ## add connection with specific weight value 
 
 @named sys = system_from_graph(g)
 prob = ODEProblem(sys, [], (0, 200.0))
@@ -106,8 +106,8 @@ end
 # One benefit of assigning `IzhNeuron <: Neuron` is now apparent. Without defining a new connection equation for it, `IzhNeuron` can connect to `LIFNeuron` and to any other neuron type using a generic connection equation in Neuroblox.
 # We can see what this equation looks like by running 
 
-connection_equations(izh, lif) ## connection from izh to lif
-connection_equations(lif, izh) ## connection from lif to izh
+connection_equations(izh, lif, weight=1, connection_rule="basic") ## connection from izh to lif
+connection_equations(lif, izh, weight=1, connection_rule="basic") ## connection from lif to izh
 
 # We even get a warning saying that the connection rule is not specified so Neuroblox defaults to this basic weighted connection.
 
@@ -127,7 +127,7 @@ end
 # Internally Neuroblox will call the function dispatch with the most specific combination of its input arguments. Before defining the function above there was no dispatch that had `IzhNeuron` in its signature so Neuroblox defaulted to the connection we saw above.
 # Now that we have defined a specialized equation, we can connect the same two Bloxs in a new way.
 
-connection_equations(lif, izh)
+connection_equations(lif, izh, weight=1, connection_rule="basic")
 
 # Notice how the equation has changed compared to above and it is equal to our latest `connection_equations` dispatch.
 # > **_NOTE_:** When we define a new `connection_equations` dispatch we need to include three positional arguments, the source Blox, the destination Blox and a symbolic weight parameter that is generated internally in Neuroblox and assigned to a specific connection.