Limit cycle representation of spatial locations using self-organizing maps

We use the term “neurocognitive architecture” here to refer to any artificially intelligent agent where cognitive functions are implemented using brain-inspired neurocomputational methods. Creating and studying neurocognitive architectures is a very active and increasing focus of research efforts. We have recently been exploring the use of neural activity limit cycles as representations of perceived external information in self-organizing maps (SOMs). Specifically, we have been examining limit cycle representations in terms of their compatibility with self-organizing map formation and as working memory encodings for cognitively-relevant stimuli (e.g., for images of objects and their corresponding names expressed as phoneme sequences [1]). Here we evaluate the use of limit cycle representations in a new context of relevance to any cognitive agent: representing a spatial location. We find that, following repeated exposure to external 2D coordinate input values, robust limit cycles occur in a network´s map region, the limit cycles representing nearby locations in external space are close to one another in activity state space, and the limit cycles representing widely separated external locations are very different from one another. Further, and in spite of the continually varying activity patterns in the network (instead of the fixed activity patterns used in most SOM work), map formation based on the learned limit cycles still occurs. We believe that these results, along with those in our earlier work, make limit cycle representations potentially useful for encoding information in the working memory of neurocognitive architectures.