Mechanisms influencing learning in neural networks are usually investigated on either a local or a global scale. The former relates to synaptic processes, the latter to unspecific modulatory systems. Here we study the interaction of a local learning rule that evaluates coincidences of pre- and postsynaptic action potentials and a global modulatory mechanism, such as the action of the basal forebrain onto cortical neurons. The simulations demonstrate that the interaction of these mechanisms leads to a learning rule supporting fast learning rates, stability, and flexibility. Furthermore, the simulations generate two experimentally testable predictions on the dependence of backpropagating action potential on basal forebrain activity and the relative timing of the activity of inhibitory and excitatory neurons in the neocortex.

Neuroscience is progressing vigorously, and knowledge at different levels of description is rapidly accumulating. To establish relationships between results found at these different levels is one of the central challenges. In this simulation study, we demonstrate how microscopic cellular properties, taking the example of the action of modulatory substances onto the membrane leakage current, can provide the basis for the perceptual functions reflected in the macroscopic behavior of a cortical network. In the first part, the action of the modulatory system on cortical dynamics is investigated. First, it is demonstrated that the inclusion of these biophysical properties in a model of the primary visual cortex leads to the dynamic formation of synchronously active neuronal assemblies reflecting a context-dependent binding and segmentation of image components. Second, it is shown that the differential regulation of the leakage current can be used to bias the interactions of multiple cortical modules. This allows the flexible use of different feature domains for scene segmentation. Third, we demonstrate how, within the proposed architecture, the mapping of a moving stimulus onto the spatial dimension of the network results in an increased speed of synchronization. In the second part, we demonstrate how the differential regulation of neuromodulatory activity can be achieved in a self-consistent system. Three different mechanisms are described and investigated. This study thus demonstrates how a modulatory system, affecting the biophysical properties of single cells, can be used to achieve context-dependent processing at the system level.