title: Transitory memory retrieval in a biologically plausible neural network model.
abstract: A number of memory models have been proposed. These all have the basic structure that excitatory neurons are reciprocally connected by recurrent connections together with the connections with inhibitory neurons, which yields associative memory (i.e., pattern completion) and successive retrieval of memory. In most of the models, a simple mathematical model for a neuron in the form of a discrete map is adopted. It has not, however, been clarified whether behaviors like associative memory and successive retrieval of memory appear when a biologically plausible neuron model is used. In this paper, we propose a network model for associative memory and successive retrieval of memory based on Pinsky-Rinzel neurons. The state of pattern completion in associative memory can be observed with an appropriate balance of excitatory and inhibitory connection strengths. Increasing of the connection strength of inhibitory interneurons changes the state of memory retrieval from associative memory to successive retrieval of memory. We investigate this transition.
authors.affiliation: Department of Mathematics, Graduate School of Science, Hokkaido University, Sapporo, 060-0810 Japan.
authors.forename: Hiromichi
authors.initials: H
authors.lastname: Tsukada
authors.affiliation: Research Institute for Electronic Science, Hokkaido University, Sapporo, 060-0812 Japan ; Research Center for Integrative Mathematics (RCIM), Hokkaido University, Sapporo, 060-0812 Japan.
authors.forename: Yutaka
authors.initials: Y
authors.lastname: Yamaguti
authors.affiliation: Research Institute for Electronic Science, Hokkaido University, Sapporo, 060-0812 Japan ; Research Center for Integrative Mathematics (RCIM), Hokkaido University, Sapporo, 060-0812 Japan.
authors.forename: Ichiro
authors.initials: I
authors.lastname: Tsuda
keywords: Associative memory; Recurrent network; Successive retrieval of memory; Transitory dynamics