Brian 2 simulation of memory consolidation in recurrent spiking neural networks based on synaptic tagging and capture

This package serves to simulate recurrent spiking neural networks, consisting of leaky integrate-and-fire neurons connected via current-based plastic synapses, with the Brian 2 simulator. The long-term plasticity model that is employed features a calcium-based early phase and a late phase that is based on synaptic tagging-and-capture mechanisms. The underlying model has been described in detail in Luboeinski and Tetzlaff (2021) and has previously been implemented with a stand-alone simulator and with the Arbor simulator. The code provided here can serve to reproduce the previous results. However, note that a fast-forward approximation for running the network on long biological timescales (minutes to hours), which is available in the other implementations, has not been implemented here yet.