networkinference / ESL

Network inference via Event Space Linearization (ESL)

ESL -- Event Space Linearization

The Event Space Linearization (ESL) is the framework proposed in the article Inferring network connectivity from event timing patterns, PRL.

Relying only on spike timing data, ESL reveals the interaction topology of networks of spiking neurons without assuming specific neuron models to be known in advance.

In this repository, you will find simple example codes for simulating and reconstructing networks of spiking neurons in Python. Nest Simulator is required for simulations.

Optimized codes for reconstruction may also be found in Connectivity_from_event_timing_patterns

Citation

@article{PhysRevLett.121.054101,
 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}
}