In recent years the focus has been on layers rather than the more biologically inspired individual nodes. As stated in the comment by thecomplexitytheorist you could use a computational graph, although then you have issues with distribution and you're limited to one framework.
I created something in my PhD about the same time as the thesis you reference that was a simulation of biological structure and connected in a way not dissimilar to figure b above using ML to determine the weights of all the connections. The control module of the network was distributed across several neurons, but allowed asymmetric connections between the inputs and outputs. This was a large effort, and similar results could now be achieved more quickly using the modern frameworks. This is, I believe, the main reason that biologically inspired neural networks are not as actively researched. I still believe that there are tricks in biology that we've not encapsulated and would love the head space to create a framework to test this hypothesis!
Multiple Neural Networks are the simplest way to go and I've created several of these over the years (sadly not open source). These can be distributed and use different frameworks - just wrap up each of your networks in an API that has common inputs and outputs and you can daisy chain them without worrying about the internals. This would give you the power and flexibility you need and allow you to swap in and out multiple models. I know I've made that sound simple but at this stage it's more a software engineering problem. You do need to be wary of compound errors between your networks if you're trickling down as it's basically a decision tree of NNs at that point.