| Summary: | Computational modelling empowers scientists to test hypotheses that they could not have done so otherwise, mainly because of the complexity of the system to be tested. In order to investigate particular postulates, computational neuroscientists build quantitative models of the central nervous system in great detail, and in different levels of organisation. In this thesis, by simulating morphologically reconstructed neurons and composing networks consisting of these neurons, we investigate and aim to bridge the gap between neuroscientific hypotheses and levels of organisation. All of our investigations fall under, but are not limited to, the working memory concept, a theoretical system designed to address the information processing in order to achieve cognition. To begin with, we investigate macroscopic neural oscillations as observed during working memory tasks. We show the criticality of inter-regional delay coupling effect on synchronisation phase-shift, and that near zero-lag synchronisation can be achieved via the M3 structural motif. After showing that spiking neurons can successfully model macroscopic phenomena, we focus on the microscopic level, where we simulate a number of interconnecting neurons and proceed to scrutinise their complex behaviour and the information processing capabilities they exhibit. Specifically, we explore the effect of spiking network parameters on polychronization and the sustainability of spikes based on short-term synaptic dynamics. We show that the models are extremely sensitive to neuronal parameters including type of connectivity, axonal delays, density and topology. In the end we investigate two contradicting theories of forgetting in short term memory: the temporal and the non-temporal approaches. We show that the sustained representation of memory cues highly depend on the size of their neuronal counterparts, and that both the temporal and the non temporal approaches can have a role to play in sustaining information, and that they do not necessarily contradict each other.
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