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.

Chaotic itinerancy and its roles in cognitive neurodynamics

“…Indeed, to communicate, each person needs rapidly to construct and reconstruct history-dependent memory structures, which must not be disturbed by others' actions. One can construct coupled-neural networks to account for this situation [7], and extend them further to more realistic and biologically oriented neural networks consisting of a neuron model by Pinsky and Rinzel [8] for excitatory neurons, and another one by Wang and Buzsáki [9] for inhibitory neurons [10].…”

Exponential synchronization of memristive Cohen–Grossberg neural networks with mixed delays

“…Neural networks can be constructed by nonlinear circuits and have been extensively studied because of their immense potential applications in different areas such as pattern recognition, parallel computing, signal and image processing, and associative memory (Balasubramaniam et al 2011;Yang et al 2010;Zhu and Cao 2010;Chen and Song 2010;Tsukada et al 2013). Recently, dynamical behaviors of memristor-based neural networks have attracted increasing attention of researchers because this class of neural networks is a new model to emulate the human brain (Itoh and Chua 2009;Thomas 2013).…”

Computational model of visual hallucination in dementia with Lewy bodies

“…Phasic (transient) cholinergic (ACh) projections from the nucleus basalis of Meynert (NBM) (Kanamaru et al, 2013;Parikh, Kozak, Martinez, & Sarter, 2007) facilitate the transition of the IT network from a transitory (quasi-attractor) state to an attractor state. Top-down attention may facilitate the transition of the IT dynamics to the attractor regime while the index from the PFC contributes to the jump into the specified attractor (Kanamaru et al, 2013;Tsukada, Yamaguti, & Tsuda, 2013). If this is indeed the case, the postulated index could be a small part of the OSK network dynamics in the PFC.…”