New Ideas for Brain Modelling 5
This work addresses brain modeling for cognitive science, but it appears incremental as it builds on existing ideas of neuronal networks and pattern matching without clear empirical validation.
The paper tackles the problem of modeling cognitive processes by proposing a formal language for defining pattern-based sequences and transitions, enabling the construction of static memory and dynamic process hierarchies as tree structures.
This paper describes a process for combining patterns and features, to guide a search process and make predictions. It is based on the functionality that a human brain might have, which is a highly distributed network of simple neuronal components that can apply some level of matching and cross-referencing over retrieved patterns. The process uses memory in a dynamic way and it is directed through the pattern matching. The paper firstly describes the mechanisms for neuronal search, memory and prediction. The paper then presents a formal language for defining cognitive processes, that is, pattern-based sequences and transitions. The language can define an outer framework for concept sets that are linked to perform the cognitive act. The language also has a mathematical basis, allowing for the rule construction to be consistent. Now, both static memory and dynamic process hierarchies can be built as tree structures. The new information can also be used to further integrate the cognitive model and the ensemble-hierarchy structure becomes an essential part. A theory about linking can suggest that nodes in different regions link together when generally they represent the same thing.