Latching chains in K-nearest-neighbor and modular small-world networks

Abstract

Latching dynamics retrieve pattern sequences successively by neural adaption and pattern correlation. We have previously proposed a modular latching chain model in Song et al. (2014) to better accommodate the structured transitions in the brain. Different cortical areas have different network structures. To explore how structural parameters like rewiring probability, threshold, noise and feedback connections affect the latching dynamics, two different connection schemes, K-nearest-neighbor network and modular network both having modular structure are considered. Latching chains are measured using two proposed measures characterizing length of intra-modular latching chains and sequential inter-modular association transitions. Our main findings include: (1) With decreasing threshold coefficient and rewiring probability, both the K-nearest-neighbor network and the modular network experience quantitatively similar phase change processes. (2) The modular network exhibits selectively enhanced latching in the small-world range of connectivity. (3) The K-nearest-neighbor network is more robust to changes in rewiring probability, while the modular network is more robust to the presence of noise pattern pairs and to changes in the strength of feedback connections. According to our findings, the relationships between latching chains in K-nearest-neighbor and modular networks and different forms of cognition and information processing emerging in the brain are discussed.

• Associative retrieval
• latching chain
• modular structure
• Potts network
• sequential activity
• small-world