EI-clustered circuit model

This is PyNEST implementation of the EI-clustered circuit model described by Rostami et al. [1].

Figure 45 Schematic of the EI-clustered circuit model. The network consists of n_clusters with one excitatory and one inhibitory population each.

Citing this code

If you use this code, we ask you to cite the paper by Rostami et al. [1] and the NEST release on Zenodo.

File structure

• run_simulation.py: an example script to try out the EI-clustered circuit model

• network.py: the main Network class with functions to build and simulate the network

• helper.py: helper functions for calculation of synaptic weights and currents and plot function for raster plots

• network_params.py: network and neuron parameters

• stimulus_params.py: parameters for optional external stimulation

• sim_params.py: simulation parameters

Running the simulation

python run_simulation.py

A raster plot of the network activity is saved as clustered_ei_raster.png.

The code can be parallelized by using multiple threads during the NEST simulation. This can be done by setting the parameter n_vp in the run_simulation.py script.

Contributions to this PyNEST model implementation

2023: initial version of code and documentation by Felix J. Schmitt, Vahid Rostami and Martin Nawrot.

Acknowledgments

Funding for the study by Rostami et al. [1]: This work was supported by the German Research Foundation (DFG), in parts through the Collaborative Research Center ’Motor Control in Health and Disease’ (DFG-SFB 1451, Project-ID 431549029) and under the Institutional Strategy of the University of Cologne within the German Excellence Initiative (DFG-ZUK 81/1) and in parts through the DFG graduate school ’Neural Circuit Analysis’ (DFG-RTG 1960, ID 365082554) and through the European Union’s Horizon 2020 Framework Programme for Research and Innovation under grant agreement number 945539 (Human Brain Project SGA3). The figure is created with BioRender.com.

Other implementations of the EI-clustered model

A GeNN version by Felix J. Schmitt, Vahid Rostami and Martin Nawrot [2].

References

[1] (1,2,3)

Rostami V, Rost T, Riehle A, van Albada SJ and Nawrot MP. 2020. Excitatory and inhibitory motor cortical clusters account for balance, variability, and task performance. bioRxiv 2020.02.27.968339. DOI: 10.1101/2020.02.27.968339.

[2]

Schmitt FJ, Rostami V and Nawrot MP. 2023. Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST. Front. Neuroinform. 17:941696. DOI: 10.3389/fninf.2023.941696.

PyNEST EI-clustered network: Simulation Parameters

PyNEST EI-clustered network: Simulation Parameters

PyNEST EI-clustered network: Stimulus Parameters

PyNEST EI-clustered network: Stimulus Parameters

PyNEST EI-clustered network: Run Simulation

PyNEST EI-clustered network: Run Simulation

PyNEST EI-clustered network: Network Parameters

PyNEST EI-clustered network: Network Parameters

PyNEST EI-clustered network: Helper Functions

PyNEST EI-clustered network: Helper Functions

PyNEST EI-clustered network: Network Class

PyNEST EI-clustered network: Network Class