SpikES

Companion code to Confavreux, Agnes, Zenke, Sprekeler and Vogels, arXiv 2023, Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks.

Required packages

• python part: numpy, pytorch, matplotlib
• C++ part: Auryn

Installing Auryn:

All spiking network simulations in this repo use Auryn, a fast, C++ simulator developped by Friedemann Zenke. To install, please refer to https://fzenke.net/auryn/doku.php?id=start
Note that installing Auryn with MPI support is not required for the tutorial.

Compile Auryn simulations:

• Compile the auryn simulation sim_innerloop_bg_TIF_IE_6pPol.cpp located in Innerloop/cpp_innerloops/. First, edit the Makefile in the same directory, you should only need to change AURYNPATH there.
  For troubleshooting, refer to https://fzenke.net/auryn/doku.php?id=manual:compileandrunaurynsimulations
• Go to tasks_configs/ and update auryn_sim_dir and workdir inside the 2 yaml files (these variables control where Auryn will write output spike trains).