Model-free method for inferring synaptic interactions from spike train recordings.

By mapping spike timing data in event spaces (spanned by inter-spike and cross-spike intervals),
we identify synaptic interactions in networks of spiking neurons through Event Space Linearisations (ESL) .
Here, we provide implementations of network simulations and reconstructions as described in:
Casadiego*, Jose, Maoutsa*, Dimitra, Timme, Marc, Inferring network connectivity from event timing patterns, Physical Review Letters 2018
For further information refer to the article and the supplementary info. (can be found here and here as pdf) .

1. Generate input data

   • Either extract provided data

     cd Connectivity_from_event_timing_patterns/simulate_network
     tar -xzvf Data.tar.gz
     

   • Or simulate network (requires [NEST simulator] (http://www.nest-simulator.org/) )

     python Connectivity_from_event_timing_patterns/simulate_network/simulate_network.py
     

2. Reconstruct

   python Connectivity_from_event_timing_patterns/reconstruct_network/inferring_connections_from_spikes.py
   

   Caution: Input data files should be stored in folder simulate_network/Data/

For questions please contact: Dimitra Maoutsa [ dimitra.maoutsa <-at-> tu-berlin.de ]

@article{ESL18,
  title = {Inferring Network Connectivity from Event Timing Patterns},
  author = {Casadiego, Jose and Maoutsa, Dimitra and Timme, Marc},
  journal = {Phys. Rev. Lett.},
  volume = {121},
  issue = {5},
  pages = {054101},
  numpages = {6},
  year = {2018},
  month = {Aug},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevLett.121.054101},
  url = {https://link.aps.org/doi/10.1103/PhysRevLett.121.054101}
}