This paper considers a process for the creation and subsequent firing of sequences of neuronal patterns, as might be found in the human brain. The scale is one of larger patterns emerging from an ensemble mass, possibly through some type of energy equation and a reduction procedure. The links between the patterns can be formed naturally, as a residual effect of the pattern creation itself. If the process is valid, then the pattern creation can be relatively simplistic and automatic, where the neuron does not have to do anything particularly intelligent. The pattern interfaces become slightly abstract without firm boundaries and exact structure is determined more by averages or ratios . This paper follows-on closely from the earlier research, including two earlier papers in the series and uses the ideas of entropy and cohesion. With a small addition, it is possible to show how the inter-pattern links can be determined. A new compact Grid form of an earlier Counting Mechanism is also demonstrated. Finally, it is possible to explain how a very basic repeating structure can form the arbitrary patterns and activation sequences between them. This paper considers a process for the creation and subsequent firing of sequences of neuronal patterns, as might be found in the human brain. The scale is one of larger patterns emerging from an ensemble mass, possibly through some type of energy equation such as entropy and a reduction procedure. The links between the patterns can be formed naturally, as a residual effect of the pattern creation itself. If the process is valid, then the pattern creation can be relatively simplistic and automatic, where the neuron does not have to do anything particularly intelligent. The pattern interfaces become slightly abstract without 2 firm boundaries and exact structure is determined more by averages or ratios . If the process is based on an energy equation that reduces to a more stable state, it may require less intentional behaviour than, for example, creating clusters in the constructive way of deliberately linking between nodes. For a large-scale structure this would definitely be necessary. This paper follows-on closely from the earlier research, including two earlier papers in the series and uses the ideas of entropy and cohesion. An example of the reactiveproactive comparison can be carried out using an earlier Counting Mechanism [12] ; but a new compact Grid form is then suggested and may be another general mechanism. The rest of the paper is organised as follows: Section 2 describes the recent and new theory. Section 3 traces though an example to show how different classifiers can interpret the data. Section 4 compares the theory with some other research. Section 5 describes a repeating process that can explain how patterns may form and then activate each other. Finally, section 6 gives some conclusions on the work. A key point to the theory is that a lot of the neurons inside a pattern link with each other. It is not the case that there is a single path from one neuron to the next, but there are lots of links between all of the neurons. The test theory of [2] also used this principle and it is also assumed in the tests of [8] . While not every node needs to connect with every other node; if most nodes connect with each other, it is quite a good way of defining the pattern shape. It could be imagined that the nodes still have connections to other places, but when excited, the pattern group will reinforce itself sufficiently to make it significant, while other more sparsely connected sets would possibly die out. It is a well-known phenomenon, as described in the related work section. It would therefore also be the case that closer 'patterns' would prefer to connect with each other [8] . This would then also refer to more similar concepts, when the whole process can be more consistent with state transitions and such things. The paper [6] has already considered the general mechanisms and used energy and entropy as the measures to describe how the patterns might form. This paper extends those equations slightly to include the inter-pattern connections.
Then the ensemblehierarchy model has close relations to one suggested for the human brain in REF [10].
New Ideas for Brain Modelling 3
Cognitive Systems Research, Vol. 55, pp. 1-13, 2019, Elsevier
Computer Science