Neural Network based Formation of Cognitive Maps of Semantic Spaces and the Emergence of Abstract Concepts
This work addresses how abstract concepts emerge in AI by modeling hippocampal mechanisms, offering a potential building block for incorporating prior knowledge in machine learning, though it is incremental as it applies known methods to a new domain.
The authors tackled the problem of forming cognitive maps of semantic spaces using neural networks based on successor representations, achieving around 30% accuracy in learning similarities between animal species and up to 95% accuracy in representing new or incomplete inputs through interpolation.
The hippocampal-entorhinal complex plays a major role in the organization of memory and thought. The formation of and navigation in cognitive maps of arbitrary mental spaces via place and grid cells can serve as a representation of memories and experiences and their relations to each other. The multi-scale successor representation is proposed to be the mathematical principle underlying place and grid cell computations. Here, we present a neural network, which learns a cognitive map of a semantic space based on 32 different animal species encoded as feature vectors. The neural network successfully learns the similarities between different animal species, and constructs a cognitive map of 'animal space' based on the principle of successor representations with an accuracy of around 30% which is near to the theoretical maximum regarding the fact that all animal species have more than one possible successor, i.e. nearest neighbor in feature space. Furthermore, a hierarchical structure, i.e. different scales of cognitive maps, can be modeled based on multi-scale successor representations. We find that, in fine-grained cognitive maps, the animal vectors are evenly distributed in feature space. In contrast, in coarse-grained maps, animal vectors are highly clustered according to their biological class, i.e. amphibians, mammals and insects. This could be a possible mechanism explaining the emergence of new abstract semantic concepts. Finally, even completely new or incomplete input can be represented by interpolation of the representations from the cognitive map with remarkable high accuracy of up to 95%. We conclude that the successor representation can serve as a weighted pointer to past memories and experiences, and may therefore be a crucial building block for future machine learning to include prior knowledge, and to derive context knowledge from novel input.