Code for recurrent spiking neural network model, to simulate effect of noradrenaline on learning a cognitive map
Parthasarathy P, Koolschijn RS, Vogels TP, Barron HC
Description
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. Please cite this dataset by title, creators and DOI.
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Year Published
2025
DOI
10.60964/BNDU-9B3H-A961
Metadata
Funders
UKRI (MR/W008939/1)
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Terms
Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
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