## 2 The System
### The ANM System
#### 2.3.2 Manipulation Network  
As mentioned, the learning algorithm used in this study produces something similar to competitive learning by allowing each subnetwork to change its cytoskeletal structure. Changing the cytoskeletal structure shapes the ANM system with gradual structure/function switching properties. This feature helps generate paths in multi-dimensional spaces (please refer to Section 2.1), and the system thus has a relatively high chance of escaping from the regional optimal solution when searching. However, this kind of change is a gradual fine-tuning, which improves slowly and takes a long time. The difficulty is relatively high if the goal is to train a large group of neurons to complete a specified task. From another perspective, it may not be necessary to use this approach because it may be possible to train a small group of suitable neurons to perform the same task. To deal with this problem, this study uses another type of neuron, whose task is to select appropriate information-processing neurons to achieve it (that is, only the chosen neurons will participate in information processing and fitness evaluation). This type of neuron is called a control neuron (CN). This study assumes that control neurons have hierarchical control (selection) functions. This hierarchical control method controls a group of information-processing neurons (selected) to achieve the task. This way of selecting neurons is called orchestration. The current operation method uses two layers of control neurons, as shown in Figure 10, to find suitable neurons through the Darwinian variation-selection method.  
As mentioned, the current ANM system has 576 information-processing neurons (or 72 bundles of neurons). A lower-level control neuron controls each bundle. Therefore, there are a total of 72 low-level control neurons. This study utilizes another layer of neurons, high-level control neurons, to control the firing of lower-level control neurons. Learning of control neurons occurs only between higher and lower layers. In other words, each high-level control neuron can select different low-level control neurons and change along with the learning process. However, the information-processing neurons controlled by low-level control neurons do not change with the learning process. The entire evolutionary learning step is first to evaluate the performance of each high-level control neuron and select the better-performing high-level control neuron. Then, the better-performing higher-level control neurons are copied to the worse-performing higher-level control neurons. It is assumed that there are slight errors during the copying process, which results in the low-level control neurons controlled by the replicator and the copied being different (Figure 11).  
Figure 10: Two layers of control neurons control information-processing neurons.  
Evolutionary learning is implemented by allowing alternating learning between control and information-processing neurons. The current approach is to allow the system to learn at the control neuron level for a while and then allow the system to learn at the information-processing neuron level for another period. This cycle allows each level to learn sequentially until the system is stopped or the assigned task is completed.