Long term memory and the densest K-subgraph problem

R. Legenstein, W. Maass, C. H. Papadimitriou, and S. S. Vempala

Abstract:

In a recent experiment [9], a cell in the human medial temporal lobe (MTL) encoding one sensory stimulus starts to also respond to a second stimulus following a combined experience associating the two. We develop a theoretical model predicting that an assembly of cells with exceptionally high synaptic intraconnectivity can emerge, in response to a particular sensory experience, to encode and abstract that experience. We also show that two such assemblies are modified to increase their intersection after a sensory event that associates the two corresponding stimuli. The main technical tools employed are random graph theory, and Bernoulli approximations. Assembly creation must overcome a computational challenge akin to the Densest K-Subgraph problem, namely selecting, from a large population of randomly and sparsely interconnected cells, a subset with exceptionally high density of interconnections. We identify three mechanisms that help achieve this feat in our model: (1) a simple two-stage randomized algorithm, and (2) the "triangle completion bias" in synaptic connectivity [14] and a "birthday paradox", while (3) the strength of these connections is enhanced through Hebbian plasticity.

Reference: R. Legenstein, W. Maass, C. H. Papadimitriou, and S. S. Vempala. Long term memory and the densest K-subgraph problem. In Proc. of Innovations in Theoretical Computer Science (ITCS), 2018.