Multiscale representations of community structures in attractor neural networks.

Our cognition relies on the ability of the brain to segment hierarchically structured events on multiple scales. Recent evidence suggests that the brain performs this event segmentation based on the structure of state-transition graphs behind sequential experiences. However, the underlying circuit mechanisms are poorly understood. In this paper we propose an extended attractor network model for graph-based hierarchical computation which we call the Laplacian associative memory. This model generates multiscale representations for communities (clusters) of associative links between memory items, and the scale is regulated by the heterogenous modulation of inhibitory circuits. We analytically and numerically show that these representations correspond to graph Laplacian eigenvectors, a popular method for graph segmentation and dimensionality reduction. Finally, we demonstrate that our model exhibits chunked sequential activity patterns resembling hippocampal theta sequences. Our model connects graph theory and attractor dynamics to provide a biologically plausible mechanism for abstraction in the brain. Author summary: Our experiences are often hierarchically organized, so is our knowledge. Identifying meaningful segments in hierarchically structured information is crucial for many cognitive functions including visual, auditory, motor, memory, language processing, and reasoning. Herein, we show that the attractor dynamics of recurrent neural circuits offer a biologically plausible way for hierarchical segmentation. We found that an extended model of associative memory autonomously performs segmentation by finding groups of tightly linked memories. We proved that the neural dynamics of our model mathematically coincide with optimal graph segmentation in graph theory and are consistent with the experimentally observed nature of human behaviors and neural activities. Our model established a previously unexpected relationship between attractor neural networks and the graph-theoretic processing of knowledge structures. Our model also provides experimentally testable predictions, particularly regarding the role of inhibitory circuits in controlling representational granularity. [ABSTRACT FROM AUTHOR]