Decomposition of neural circuits of human attention using a model based analysis: sSoTs model application to fMRI data
Mavritsaki, Eirini; Allen, Harriet A.; Humphreys, Glyn W.
Harriet A. Allen H.A.Allen@nottingham.ac.uk
Glyn W. Humphreys firstname.lastname@example.org
The complex neural circuits found in fMRI studies of human attention were decomposed using a model of spiking neurons. The model for visual search over time and space (sSoTS) incorporates different synaptic components (NMDA, AMPA, GABA) and a frequency adaptation mechanism based on IAHP current. This frequency adaptation current can act as a mechanism that suppresses the previously attended items. It has been shown  that when the passive process (frequency adaptation) is coupled with a process of active inhibition, new items can be successfully prioritized over time periods matching those found in psychological studies. In this study we use the model to decompose the neural regions mediating the processes of active attentional guidance, and the inhibition of distractors, in search. Activity related to excitatory guidance and inhibitory suppression was extracted from the model and related to different brain regions by using the synaptic activation from sSoTS’s maps as regressors for brain activity derived from standard imaging analysis techniques. The results show that sSoTS pulls-apart discrete brain areas mediating excitatory attentional guidance and active distractor inhibition.
|Journal Article Type||Article|
|Publication Date||Jul 16, 2008|
|Journal||Progress in Neural Processing|
|Peer Reviewed||Peer Reviewed|
|Institution Citation||Mavritsaki, E., Allen, H. A., & Humphreys, G. W. (2008). Decomposition of neural circuits of human attention using a model based analysis: sSoTs model application to fMRI data. doi:10.1142/9789812834232_0033|
|Copyright Statement||Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf|
|Additional Information||Electronic version of an article published as Progress in Neural Processing, Volume 18, 2009, p. 401-414, doi:10.1142/9789812834232_0033. © 2009 copyright World Scientific Publishing Company. http://www.worldscientific.com/series/pnp|
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf