A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity

PLoS Comput Biol. 2016 May 20;12(5):e1004930. doi: 10.1371/journal.pcbi.1004930. eCollection 2016 May.


The prefrontal cortex is centrally involved in a wide range of cognitive functions and their impairment in psychiatric disorders. Yet, the computational principles that govern the dynamics of prefrontal neural networks, and link their physiological, biochemical and anatomical properties to cognitive functions, are not well understood. Computational models can help to bridge the gap between these different levels of description, provided they are sufficiently constrained by experimental data and capable of predicting key properties of the intact cortex. Here, we present a detailed network model of the prefrontal cortex, based on a simple computationally efficient single neuron model (simpAdEx), with all parameters derived from in vitro electrophysiological and anatomical data. Without additional tuning, this model could be shown to quantitatively reproduce a wide range of measures from in vivo electrophysiological recordings, to a degree where simulated and experimentally observed activities were statistically indistinguishable. These measures include spike train statistics, membrane potential fluctuations, local field potentials, and the transmission of transient stimulus information across layers. We further demonstrate that model predictions are robust against moderate changes in key parameters, and that synaptic heterogeneity is a crucial ingredient to the quantitative reproduction of in vivo-like electrophysiological behavior. Thus, we have produced a physiologically highly valid, in a quantitative sense, yet computationally efficient PFC network model, which helped to identify key properties underlying spike time dynamics as observed in vivo, and can be harvested for in-depth investigation of the links between physiology and cognition.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Action Potentials / physiology
  • Animals
  • Cognition / physiology
  • Computational Biology
  • Computer Simulation
  • Electrophysiological Phenomena
  • Humans
  • Mice
  • Models, Neurological*
  • Models, Psychological
  • Nerve Net / physiology*
  • Neural Networks, Computer
  • Neuronal Plasticity / physiology
  • Neurons / physiology
  • Prefrontal Cortex / physiology*
  • Rats

Grant support

This work was funded by grants from the German ministry for education and research (BMBF) through the Bernstein Center for Computational Neuroscience (01GQ1003B) and the e:Med program (01ZX1314G), and the Deutsche Forschungsgemeinschaft to DD (Du354/6-1 & 7-2). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.