title: A simplified memory network model based on pattern formations.
abstract: Many experiments have evidenced the transition with different time scales from short-term memory (STM) to long-term memory (LTM) in mammalian brains, while its theoretical understanding is still under debate. To understand its underlying mechanism, it has recently been shown that it is possible to have a long-period rhythmic synchronous firing in a scale-free network, provided the existence of both the high-degree hubs and the loops formed by low-degree nodes. We here present a simplified memory network model to show that the self-sustained synchronous firing can be observed even without these two necessary conditions. This simplified network consists of two loops of coupled excitable neurons with different synaptic conductance and with one node being the sensory neuron to receive an external stimulus signal. This model can be further used to show how the diversity of firing patterns can be selectively formed by varying the signal frequency, duration of the stimulus and network topology, which corresponds to the patterns of STM and LTM with different time scales. A theoretical analysis is presented to explain the underlying mechanism of firing patterns.
authors.affiliation: Department of Physics, East China Normal University, Shanghai, 200062, China.
authors.forename: Kesheng
authors.initials: K
authors.lastname: Xu
authors.affiliation: Department of Physics, East China Normal University, Shanghai, 200062, China.
authors.forename: Xiyun
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Physics, East China Normal University, Shanghai, 200062, China.
authors.forename: Chaoqing
authors.initials: C
authors.lastname: Wang
authors.affiliation: Department of Physics, East China Normal University, Shanghai, 200062, China.
authors.forename: Zonghua
authors.initials: Z
authors.lastname: Liu
mesh_terms: D006801:Humans; D008568:Memory / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*