A computational model of visual marking using an interconnected network of spiking neurons: The spiking Search over Time & Space model (sSOTS)

Eirini Mavritsaki, Dietmar Heinke, Glyn Humphreys, G Deco

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

In the real world, visual information is selected over time as well as space, when we prioritise new stimuli for attention. Watson and Humphreys [Watson, D., Humphreys, G.W., 1997. Visual marking: prioritizing selection for new objects by top-down attentional inhibition of old objects. Psychological Review 104, 90-122] presented evidence that new information in search tasks is prioritised by (amongst other processes) active ignoring of old items - a process they termed visual marking. In this paper we present, for the first time, an explicit computational model of visual marking using biologically plausible activation functions. The "spiking search over time and space" model (sSoTS) incorporates different synaptic components (NMDA, AMPA, GABA) and a frequency adaptation mechanism based on [Ca2+] sensitive K+ current. This frequency adaptation current can act as a mechanism that suppresses the previously attended items. We show that, when coupled with a process of active inhibition applied to old items, frequency adaptation leads to old items being de-prioritised (and new items prioritised) across time in search. Furthermore, the time course of these processes mimics the time course of the preview effect in human search. The results indicate that the sSoTS model can provide a biologically plausible account of human search over time as well as space. (c) 2006 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)110-124
Number of pages15
JournalJournal of Physiology (Paris)
Volume100
DOIs
Publication statusPublished - 1 Jan 2006

Fingerprint

Dive into the research topics of 'A computational model of visual marking using an interconnected network of spiking neurons: The spiking Search over Time & Space model (sSOTS)'. Together they form a unique fingerprint.

Cite this