10.60964/BNDU-9B3H-A961Parthasarathy PPrakritiParthasarathyKoolschijn RSRenée SKoolschijnhttps://orcid.org/0000-0001-9553-4213Vogels TPT PVogelsBarron HCH CBarronhttps://orcid.org/0000-0002-4575-6472Code for recurrent spiking neural network model, to simulate effect of noradrenaline on learning a cognitive map20252025enCreative Commons Attribution Share Alike 4.0 InternationalA proof-of-concept spiking neural network model used to provide insight into the neural mechanisms that causes a spread of association in memory maps when learning occurs under elevated noradrenaline. In the model, a memory map with six assemblies is embedded. A co-dependent plasticity rule is applied to both excitatory and inhibitory synapses. Elevated noradrenaline is simulated by transiently reducing the strength of network inhibition during learning, leading to graded co-activity, graded synaptic plasticity and consequently graded assembly overlap across the memory map. Code includes: C++ code using Auryn (http://www.fzenke.net/auryn) to simulate the spiking neural network model.The code is available as a release on GitHub. Please cite this dataset by title, creators and DOI.UKRIMR/W008939/1