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Cerebral Cortex Advance Access published online on June 1, 2006
Cerebral Cortex, doi:10.1093/cercor/bhk044
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org

Article

Dynamical Basis of Irregular Spiking in NMDA-Driven Prefrontal Cortex Neurons

Daniel Durstewitz 1 * and Thomas Gabriel 1

1 Centre for Theoretical and Computational Neuroscience, University of Plymouth, Plymouth, UK

* To whom correspondence should be addressed.
Daniel Durstewitz, E-mail: daniel.durstewitz{at}plymouth.ac.uk


  Abstract

Slow N-Methyl-D-aspartic acid (NMDA) synaptic currents are assumed to strongly contribute to the persistently elevated firing rates observed in prefrontal cortex (PFC) during working memory. During persistent activity, spiking of many neurons is highly irregular. Here we report that highly irregular firing can be induced through a combination of NMDA- and dopamine D1 receptor agonists applied to adult PFC neurons in vitro. The highest interspike-interval (ISI) variability occurred in a transition regime where the subthreshold membrane potential distribution shifts from mono- to bimodality, while neurons with clearly mono- or bimodal distributions fired much more regularly. Predictability within irregular ISI series was significantly higher than expected from a noise-driven linear process, indicating that it might best be described through complex (potentially chaotic) nonlinear deterministic processes. Accordingly, the phenomena observed in vitro could be reproduced in purely deterministic biophysical model neurons. High spiking irregularity in these models emerged within a chaotic, close-to-bifurcation regime characterized by a shift of the membrane potential distribution from mono- to bimodality and by similar ISI return maps as observed in vitro. The nonlinearity of NMDA conductances was crucial for inducing this regime. NMDA-induced irregular dynamics may have important implications for computational processes during working memory and neural coding.

Keywords: chaos; neural coding; computation; bursting; slice electrophysiology; biophysical model.
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