I'm implementing a cyclic neural network. At time t, each neuron will update its output based on the outputs (at 
time t-1) of the neurons that it's connected to. It will also update the
 weights for its connections according to some learning rule, and it may
 destroy a connection or create a new one. <br>
<br>So far, the best way I can think of to do this is to have a master 
list of neurons, and each neuron would know the indices for the other 
neurons it connects to. I&#39;d write a function to update the neuron 
(actually returning a &quot;new&quot; neuron), and then do a &quot;map&quot; over the list 
with that function.<br>
<br>That seems OK, but I wonder if there&#39;s a better way. I suspect the 
State monad could be used for this, but I can&#39;t figure out how to put 
the pieces of the puzzle together. Here&#39;s what I was thinking:<br>- A 
connection could be a State monad, where the state is the source neuron 
and the current weight, and the result would be the weighted input.<br>
- A neuron could also be a State Monad, where the state is a list of connections, and the result is the neuron&#39;s output.<br><br>I&#39;ve
 read dozens of monad tutorials, but I can&#39;t figure out how to trigger 
all the neurons to update themselves. Is the State Monad appropriate for
 this problem?<br>