Code for recurrent spiking neural network model, to simulate effect of noradrenaline on learning a cognitive map

A 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: https://github.com/p-rakriti/koolschijn_et_al/tree/v1.0 Please cite this dataset by title, creators and DOI.