A major challenge in computational neuroscience is to derive neuronal network models that are both physiologically realistic, and computationally tractable. Towards this goal, we constructed a computational network model of the prefrontal cortex (PFC) based on simple single neuron elements, yet equipped with highly realistic anatomy and close fitting of cellular and synaptic parameter distributions derived from an extensive data base of prefrontal in vitro and in vivo recordings.
Even without any tuning, the network model manages to reproduce a number of statistics extracted from in vivo data from awake behaving rats. We tested to which extend this result is affected by the parameters of the model, namely the constant background currents driving the network activity, the synaptic peak conductances and time constants, the reversal potential of the GABA current, and the distributions of neuron parameters extracted from the in vitro data. The network showed robustness against small changes in all tested parameters, but the comparison with the in vivo data also allowed to constrain the range of admissible values for several of them.
In summary, we have generated a PFC network model which is biologically highly valid, yet still reasonably fast to simulate and fit, and can now be exploited to probe the impact of pharmacological or genetic conditions on network dynamics, and to model performance in PFC-dependent tasks.
CIMH Mannheim, Therapy Building, big lecture hall, 15:00-16:00