Introduction Research on integrated neural-symbolic systems has made significant progress in the recent past. In particular the understanding of ways to deal with symbolic knowledge within connectionist systems (also called artificial neural networks) has reached a critical mass which enables the community to strive for applicable implementations and use cases. Recent work has covered a great variety of logics used in artificial intelligence and provides a multitude of techniques for dealing with them within the context of artificial neural networks. Already in the pioneering days of computational models of neural cognition, the question was raised how symbolic knowledge can be represented and dealt with within neural networks. The landmark paper [McCulloch and Pitts, 1943] provides fundamental insights how propositional logic can be processed using simple artificial neural networks. Within the following decades, however, the topic did not receive much attention as research in arti

Abstract The brain processes underlying cognitive tasks must be very robust. Disruptions such as the destruction of large numbers of neurons, or the impact of alcohol and lack of sleep do not have negative effects except when they occur in an extreme form. This robustness implies that the parameters determining the functioning of networks of individual neurons must have large ranges or there must exist stabilizing mechanisms that keep the functioning of a network within narrow bounds. The simulation of a minimal neuronal architecture necessary to study cognitive tasks is described, which consists of a loop of three cellassemblies. A crucial factor in this architecture is the critical threshold of a cell-assembly. When activated at a level above the critical threshold, the activation in a cellassembly is subject to autonomous growth, which leads to an oscillation in the loop. When activated below the critical threshold, excitation gradually extinguishes. In order to circumvent the large parameter space of spiking neurons, a rate-dependent model of neuronal firing was chosen. The resulting parameter space of 12 parameters was explored by means of a genetic algorithm. The ranges of the parameters for which the architecture produced the required oscillations and extinctions, turned out to be relatively narrow. These ranges remained narrow when a stabilizing mechanism, controlling the total amount of activation, was introduced. The architecture thus shows chaotic behaviour. Given the overall stability of the operation of the brain, it can be concluded that there must exist other mechanisms that make the network robust. Three candidate mechanisms are discussed: synaptic scaling, synaptic homeostasis, and the synchronization of neural spikes.