Simulation details

By default, this implementation is based on the LIF neuron, iaf_psc_alpha and the stdp_pl_synapse_hom synapse models provided in NEST. With pars['neuron_model']='ignore_and_fire', the model is configured into a truly scalable mode where the integrate-and-fire dynamics are replaced by an ignore_and_fire dynamics, while the plasticity dynamics remains intact.

The network is connected according to the fixed_indegree connection rule in NEST.

The neuron dynamics is propagated in time using exact integration based on Rotter and Diesmann [1] with a simulation step size \(\Delta{}t\). The synapse dynamics is updated in an event-based manner as described by Morrison et al. [2].

Simulation parameters

Name	Value	Description
\(T\)	\(1000\,\text{ms}\)	total simulation time
\(\Delta{}t\)	\(2^{-3}=0.125\,\text{ms}\)	duration pof simulation step
tics_per_step	\(2^7=128\)	number of tics per simulation step \(\Delta{t}\) (time resolution)

References

[1]

Rotter & Diesmann (1999). Exact digital simulation of time-invariant linear systems with applications to neuronal modeling. Biological Cybernetics 81(5-6):381-402. doi:10.1007/s004220050570 https://doi.org/10.1007/s004220050570

[2]

Morrison et al. (2007). Spike-timing dependent plasticity in balanced random networks. Neural Computation 19:1437-1467 10.1162/neco.2007.19.6.1437