The modeling and simulation of visuospatial working memory

Abstract

Camperi and Wang (Comput Neurosci 5:383-405, 1998) presented a network model for working memory that combines intrinsic cellular bistability with the recurrent network architecture of the neocortex. While Fall and Rinzel (Comput Neurosci 20:97-107, 2006) replaced this intrinsic bistability with a biological mechanism-Ca(2+) release subsystem. In this study, we aim to further expand the above work. We integrate the traditional firing-rate network with Ca(2+) subsystem-induced bistability, amend the synaptic weights and suggest that Ca(2+) concentration only increase the efficacy of synaptic input but has nothing to do with the external input for the transient cue. We found that our network model maintained the persistent activity in response to a brief transient stimulus like that of the previous two models and the working memory performance was resistant to noise and distraction stimulus if Ca(2+) subsystem was tuned to be bistable.

Keywords: Bistability; Calcium signaling; Computational model; Working memory.

Fig. 1

Eye positions in the visually guided saccade task

Fig. 2

Space-time plots of the firing activity. A C-W model, B Fall and Rinzel’s model

Fig. 3

Cartoon diagram illustrating the IP3-modulated Ca²⁺ subsystem. Source Fall et al. (2004)

Fig. 4

Bistability resulting from the balance between the Ca²⁺ release and uptake by the ER

Fig. 5

Space-time plots of the firing activity and Ca concentration for low and elevated IP3

Fig. 6

Profiles of stable elevated states

Fig. 7

Noise-induced drift during delay period

Fig. 8

Distraction-induced drift during delay period