Attractor networks have shownmuch promise as a neural network architecture that can describe many aspects of brain function. Much of the field of study around these networks has coalesced around pioneering work done by John Hoprield, and therefore many approaches have been strongly linked to the field of statistical physics. In this thesis I use existing theoretical and statistical notions of attractor networks, and introduce several biologically inspired extensions to an attractor network for which a mean-field solution has been previously derived. This attractor network is a computational neuroscience model that accounts for decision-making in the situation of two competing stimuli. By basing our simulation studies on such a network, we are able to study situations where mean- field solutions have been derived, and use these as the starting case, which we then extend with large scale integrate-and-fire attractor network simulations. The simulations are large enough to provide evidence that the results apply to networks of the size found in the brain. One factor that has been highlighted by previous research to be very important to brain function is that of noise. Spiking-related noise is seen to be a factor that influences processes such as decision-making, signal detection, short-term memory, and memory recall even with the quite large networks found in the cerebral cortex, and this thesis aims to measure the effects of noise on biologically plausible attractor networks. Our results are obtained using a spiking neural network made up of integrate-and-fire neurons, and we focus our results on the stochastic transition that this network undergoes. In this thesis we examine two such processes that are biologically relevant, but for which no mean-field solutions yet exist: graded firing rates, and diluted connectivity. Representations in the cortex are often graded, and we find that noise in these networks may be larger than with binary representations. In further investigations it was shown that diluted connectivity reduces the effects of noise in the situation where the number of synapses onto each neuron is held constant. In this thesis we also use the same attractor network framework to investigate the Communication through Coherence hypothesis. The Communication through Coherence hypothesis states that synchronous oscillations, especially in the gamma range, can facilitate communication between neural systems. It is shown that information transfer from one network to a second network occurs for a much lower strength of synaptic coupling between the networks than is required to produce coherence. Thus, information transmission can occur before any coherence is produced. This indicates that coherence is not needed for information transmission between coupled networks. This raises a major question about the Communication through Coherence hypothesis. Overall, the results provide substantial contributions towards understanding operation of attractor neuronal networks in the brain.