@@ -69,13 +69,13 @@ technical parameters such as the number of parallel MPI processes and
threads. The simulation uses the network simulator NEST
(https://www.nest-simulator.org). For the simulations in [2, 3], we
used NEST version 2.8.0. The code in this repository runs with a
later release of NEST, version 2.14.0.
later release of NEST, version 2.14.0.
`Theory`
This class can be initialized by `MultiAreaModel` or as standalone and
takes simulation parameters as input. It provides two main features:
- predict the stable fixed point of the system using mean-field theory and characterize them (for instance by computing the gain matrix).
- predict the stable fixed points of the system using mean-field theory and characterize them (for instance by computing the gain matrix).
- via the script `stabilize.py`, one can execute the stabilization method described in [2] on a network instance. Please see `figures/SchueckerSchmidt2017/stabilization.py` for an example of running the stabilization.
`Analysis`
...
...
@@ -168,7 +168,7 @@ The multi-area model can be run in different modes.
This can be achieved by setting the parameters `N_scaling` and `K_scaling` in `network_params`
to values smaller than 1. In general, this will affect the dynamics of the network.
To approximately preserve the population-averaged spike rates, one can specify a set of target rates
that is used to scale synaptic weights and apply an additional external DC current.
that is used to scale synaptic weights and apply an additional external DC input.
