Abstract

The immense computational and adaptive power of biological neural systems emerges from the collective dynamics of large populations of interacting neurons. Thus, for theoretical investigations, optimal strategies for modeling biophysically faithful neuronal dynamics are required. Here, we propose an extension of the classical leaky integrate-and-fire neuronal model, the gIF model. It incorporates various aspects of high-conductance state dynamics typically seen in cortical neurons in vivo, as well as activity-dependent modulation of synaptic weights. The analytic description of the resulting neuronal models allows their use together with the event-driven simulation strategy. The latter provides an efficient tool for exact simulations of large-scale neuronal networks.