# 8. Learning

Since dana stands for Distributed Asynchronous Numerical & Adaptive computation, it is time for us to discover how to make a model adaptive (and yes, we also need to talk about this asynchronous thing...).

A model can be made adaptive by specifying a differential equation for any connection that tell dana how to update connection weights. At this stage, it is quite important to distinguish between the connection output and the connection weights. A connection output is referred within a group equation by the name of the group field that receives the connection output while the actual weight matrix within a connection differential equation is referred with the W letter and the equation is thus an equation of the form dW/dt = A where A is a valid python expression.

## 8.1. Basic concepts

Let us consider the simple example below:

```>>> G = ones(1, '''V1 = I1; I1
V2 = I2; I2''')
>>> C1 = DenseConnection(G('V1'), G('I1'), np.ones(1) )
>>> C2 = DenseConnection(G('V2'), G('I2'), np.ones(1), 'dW/dt = 1')
```

G is a group with four fields (V₁, V₂, I₁ and I₂) and both I₁ and I₂ receives the output respectively from C₁ and C₂ connections.

Weights from the C₂ connection possess an equation and are consequently updated at each time step with a constant increase of 1 (from their definition). C₁ connection does not have an equation and consequently, weights will remain constant during a simulation. Now, let’s run the group for a few iterations with dt=1:

```>>> run(n=3)
>>> print G.V1, G.V2
[ 1.] [ 6.]
>>> print C1.weights, C2.weights
[[ 1.]] [[ 4.]]
```

We can observe (as expected) that both final V₁ and V₂ values are different as well as weights from C₁ and C₂ connection. If we run manually the simulation, we can check those are the expected values:

```t=0: V₁(0) = 1
V₂(0) = 1
W₁(0) = 1
W₂(0) = 1

t=1: V₁(1) = W₁(0)*V₁(0) = 1
V₂(1) = W₂(0)*V₂(0) = 1
W₁(1) = 1
W₂(1) = W₂(0)+dt*1 = 2

t=2: V₁(2) = W₁(1)*V₁(1) = 1
V₂(2) = W₂(1)*V₂(1) = 2
W₁(2) = 1
W₂(2) = W₂(1)+dt*1 = 3

t=3: V₁(3) = W₁(2)*V₁(2) = 1
V₂(3) = W₂(2)*V₂(2) = 6
W₁(3) = 1
W₂(3) = W₂(2)+dt*1 = 4```

## 8.2. Pre-synaptic and post-synaptic activities

As explained in the previous chapter, a connection is made between a source group and a target group and the differential equation governing weights activity over time may used activities from either source or target group. Now, consider the following situation:

```>>> source = Group(10, 'V')
>>> target = Group(10, 'V;I')
>>> C = DenseConnection(source('V'), target('I'), np.ones(1),
'dW/dt = V')
```

Does the V value relates to the source or to the target group ? To disambiguate this kind of situation, dana provides the pre and post keyword for the definition of the equation of a connection. The pre relates to the source and the post relates to the target. We an now re-write the equation withtou any ambiguities:

```>>> C = DenseConnection(source('V'), target('I'), np.ones(1),
'dW/dt = post.V')
```