Email updates

Keep up to date with the latest news and content from BMC Neuroscience and BioMed Central.

This article is part of the supplement: Nineteenth Annual Computational Neuroscience Meeting: CNS*2010

Open Access Poster Presentation

A mathematical model of human inhibitory control

Kyle Lyman1*, Joaquin Anguera2, Adam Gazzaley2 and David Terman1

Author Affiliations

1 Department of Mathematics, Ohio State University, Columbus, OH 43201, USA

2 Department of Neurology and Physiology, University of California San Francisco, San Francisco, California, USA

For all author emails, please log on.

BMC Neuroscience 2010, 11(Suppl 1):P82  doi:10.1186/1471-2202-11-S1-P82


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/11/S1/P82


Published:20 July 2010

© 2010 Lyman et al; licensee BioMed Central Ltd.

Poster Presentation

The Stop-Signal paradigm has generated a great deal of research interest among cognitive neuroscientists because of its ability to probe response inhibition [1]. In the task, subjects make a speeded button press when prompted, except on a fraction of trials where a second stimulus (the stop signal) instructs the subject to withold the prepotent response. The success or failure of the subject on trial 'n' is known to influence the following 'n+1' trial, and is thought to index top-down control [1-3]. In the present study, we examined the putative networks involved in action monitoring [4] and top down control [5] with the goal of understanding the neural mechanisms underlying this stop signal after-effect [6]. We utilize a dynamical systems approach based on a recent conductance-based model of prefrontal cortex activity [7]. The behavior of the model is dictated by the amount of inhibitory control utilized during trial 'n', with successful inhibition requiring the most inhibitory resources and go trials (no stop signal) the least, modeled here as recurrent activity within the prefrontal cortex. The use of a biophysically realistic model facilitated testing the hypothesis that a prefrontal top-down signal drives the extent of response inhibition on subsequent trials.

A single trial in the model begins with the presentation of a 'GO' stimulus to a 'GO' neural group. During a non-stop signal trial, activity within the GO group increases until it reaches a fixed threshold and we define the model to have 'gone'. During a stop signal trial, an 'Inhibition' neural group (as in [5]) begins to generate activity. Depending on the stop signal delay, the model either goes (failed inhibition) or successfully inhibits. Activation of these inhibitory groups also activates another neural group that represents how the anterior cingulate cortex (ACC, as in [4]) monitors conflict with respect to the different time courses of the 'Inhibition' and 'GO' groups. As suggested in [3-5], ACC activity is passed onto the prefrontal cortex (modeled per [7]) where recurrent activity is maintained to influence the following trials. The model exhibits realistic patterns of reaction times, with successful inhibition trials eliciting the most post-trial slowing, then failed inhibition trials, then no stop signal trials. These findings suggest that inhibitory after-effects may be driven by ACC and prefrontal influences, with the proposed mechanism potentially having implications for situations where motor inhibition is impaired, such as schizophrenia and aging.

References

  1. Logan GD, Cowan WB: On the ability to inhibit thought and action - A theory of an act of control.

    Psychological Review 1984, 91:295-327. Publisher Full Text OpenURL

  2. Rieger M, Gauggel S: Inhibitory after-effects in the stop signal paradigm.

    British Journal of Psychology 1999, 90:509-518. Publisher Full Text OpenURL

  3. Aron A: The neural basis of inhibition in cognitive control.

    Neuroscientist 2007, 13(3):214-228. PubMed Abstract | Publisher Full Text OpenURL

  4. Botvinick MM, Braver TS, Carter CS, Barch DM, Cohen JD: Conflict monitoring and cognitive control.

    Psychological Review 2001, 108(3):624-652. PubMed Abstract | Publisher Full Text OpenURL

  5. Lo CC, Boucher L, Paré M, Schall JD, Wang X-J: Proactive inhibitory control and attractor dynamics in countermanding action: a spiking neural circuit model.

    J. Neurosci 2009, 29:9059-9071. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  6. Anguera JA, Lyman K, Gazzaley A: Neural correlates of response inhibition after-effects. Published Human Brain Mapping Conference; 2010.

    Barcelona Spain

  7. Lyman K, McDougal R, Myers B, Tien J, Zeki M, Fall C, Terman D: A working memory model based on excitatory-inhibitory interactions and calcium dynamics. Published abstract, Presented at this conference