How we solve complexity in our brain

Our brain is fantastic: after presumably an evolution of several thousands of years we have a brain with about 100 billion neurons, each one connected to about 10.000 other neurons using 1015 synapses to exchange information, performing 1000 trillion operations per second! And the energy consumption is only 20 Watts! We are still not able to make something similar till now, despite all our knowledge and experience…

It is even worse, when looking at how we organize, we seem to have learned nothing from our own brain.

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The brain uses a heterarchy structure: several groups of neurons, networks, are running parallel, but at the same time there can be a hierarchical relation between the networks. When we are walking around and see a dog, our mission is to walk, but our eyes see the dog and will send signals to different parts of our brain; we have to recognize the animal as a dog and we have to make a decision whether the dog is dangerous. The processing is done parallel based on what we sense and on our memory. Decisions are sent to other parts in the brain controlling the nerve and muscle system; we either run away, stop or pay attention to the dog. The organization of all of this is described as a heterarchy minimizing the complexity and the amount of relations between the neurons.

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How do we organize? Normally as a hierarchy. We draw an organization using the cascading M-structure: someone managing several others, who manage several others etcetera. So instead of building networks in a heterarchical structure, we build a process structure and link people to processes based on functions, i.e., what is needed to manage that process (knowledge, skills, experience). This gets us into problems because all the processes become interdependent. The result is a complex system.

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In nature parallel processing is normal, in fact it is used more than sequential processing; for instance all types of cells including bacteria are working parallel to get all the nutrients out of our food. When looking at the structure for the processing we can distinguish clearly separated networks, one for sensing, one for pain experiences, one for muscle control etc.

On the other hand those networks are steered by the central networks in the brain to control the sensing, pain and movements. We have evolved to a heterarchical structure in our bodies, but not in the organizations we build for ourselves.

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We propose to use another approach for organizing based on field theory using the Network Field Model (Toward a General Theory of Organizing, London 2022). The reason is that using field theory allows us to describe large networks with many relations between the different objects plus parallel processing. When using a heterarchical structure it is possible to minimize complexity but still optimize with respect to the goal of the network. One of the results of using the Network Field Model is the possibility to represent the network as a Graphical Neural Network. In that way it is possible to calculate the optimum capital distribution in the network.